54_practice pandas_1 lecture.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 269,
   "id": "de31a700",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in c:\\users\\sunny\\anaconda3\\lib\\site-packages (1.3.4)\n",
      "Requirement already satisfied: pytz>=2017.3 in c:\\users\\sunny\\anaconda3\\lib\\site-packages (from pandas) (2021.3)\n",
      "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\sunny\\anaconda3\\lib\\site-packages (from pandas) (1.20.3)\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in c:\\users\\sunny\\anaconda3\\lib\\site-packages (from pandas) (2.8.2)\n",
      "Requirement already satisfied: six>=1.5 in c:\\users\\sunny\\anaconda3\\lib\\site-packages (from python-dateutil>=2.7.3->pandas) (1.16.0)\n",
      "Note: you may need to restart the kernel to use updated packages.\n"
     ]
    }
   ],
   "source": [
    "pip install pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "77af95ef",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Downloading...\n",
      "From: https://drive.google.com/uc?id=1E3bwvYGf1ig32RmcYiWc0IXPN-mD_bI_\n",
      "To: C:\\Users\\sunny\\DSML Advance\\gapminder.csv\n",
      "\n",
      "  0%|          | 0.00/83.8k [00:00<?, ?B/s]\n",
      "100%|##########| 83.8k/83.8k [00:00<00:00, 698kB/s]\n",
      "100%|##########| 83.8k/83.8k [00:00<00:00, 698kB/s]\n"
     ]
    }
   ],
   "source": [
    "!gdown 1E3bwvYGf1ig32RmcYiWc0IXPN-mD_bI_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "eaf5fe57",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "601fe5f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"gapminder.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "247530b2",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "      <th>life_exp</th>\n",
       "      <th>gdp_cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>9240934</td>\n",
       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083</td>\n",
       "      <td>Asia</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1987</td>\n",
       "      <td>9216418</td>\n",
       "      <td>Africa</td>\n",
       "      <td>62.351</td>\n",
       "      <td>706.157306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1992</td>\n",
       "      <td>10704340</td>\n",
       "      <td>Africa</td>\n",
       "      <td>60.377</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1997</td>\n",
       "      <td>11404948</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.809</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year  population continent  life_exp     gdp_cap\n",
       "0     Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
       "1     Afghanistan  1957     9240934      Asia    30.332  820.853030\n",
       "2     Afghanistan  1962    10267083      Asia    31.997  853.100710\n",
       "3     Afghanistan  1967    11537966      Asia    34.020  836.197138\n",
       "4     Afghanistan  1972    13079460      Asia    36.088  739.981106\n",
       "...           ...   ...         ...       ...       ...         ...\n",
       "1699     Zimbabwe  1987     9216418    Africa    62.351  706.157306\n",
       "1700     Zimbabwe  1992    10704340    Africa    60.377  693.420786\n",
       "1701     Zimbabwe  1997    11404948    Africa    46.809  792.449960\n",
       "1702     Zimbabwe  2002    11926563    Africa    39.989  672.038623\n",
       "1703     Zimbabwe  2007    12311143    Africa    43.487  469.709298\n",
       "\n",
       "[1704 rows x 6 columns]"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3ba5d3cd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Afghanistan\n",
       "1       Afghanistan\n",
       "2       Afghanistan\n",
       "3       Afghanistan\n",
       "4       Afghanistan\n",
       "           ...     \n",
       "1699       Zimbabwe\n",
       "1700       Zimbabwe\n",
       "1701       Zimbabwe\n",
       "1702       Zimbabwe\n",
       "1703       Zimbabwe\n",
       "Name: country, Length: 1704, dtype: object"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"country\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "35e1acd4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df[\"country\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f058b5e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method DataFrame.info of           country  year  population continent  life_exp     gdp_cap\n",
       "0     Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
       "1     Afghanistan  1957     9240934      Asia    30.332  820.853030\n",
       "2     Afghanistan  1962    10267083      Asia    31.997  853.100710\n",
       "3     Afghanistan  1967    11537966      Asia    34.020  836.197138\n",
       "4     Afghanistan  1972    13079460      Asia    36.088  739.981106\n",
       "...           ...   ...         ...       ...       ...         ...\n",
       "1699     Zimbabwe  1987     9216418    Africa    62.351  706.157306\n",
       "1700     Zimbabwe  1992    10704340    Africa    60.377  693.420786\n",
       "1701     Zimbabwe  1997    11404948    Africa    46.809  792.449960\n",
       "1702     Zimbabwe  2002    11926563    Africa    39.989  672.038623\n",
       "1703     Zimbabwe  2007    12311143    Africa    43.487  469.709298\n",
       "\n",
       "[1704 rows x 6 columns]>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.info"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7f4b53c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 1704 entries, 0 to 1703\n",
      "Data columns (total 6 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   country     1704 non-null   object \n",
      " 1   year        1704 non-null   int64  \n",
      " 2   population  1704 non-null   int64  \n",
      " 3   continent   1704 non-null   object \n",
      " 4   life_exp    1704 non-null   float64\n",
      " 5   gdp_cap     1704 non-null   float64\n",
      "dtypes: float64(2), int64(2), object(2)\n",
      "memory usage: 80.0+ KB\n"
     ]
    }
   ],
   "source": [
    "df.info()     # summery of data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4785857a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "6f63ed44",
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   "outputs": [
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       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
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       "      <td>10267083</td>\n",
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       "      <td>31.997</td>\n",
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       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
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       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       country  year  population continent  life_exp     gdp_cap\n",
       "0  Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
       "1  Afghanistan  1957     9240934      Asia    30.332  820.853030\n",
       "2  Afghanistan  1962    10267083      Asia    31.997  853.100710\n",
       "3  Afghanistan  1967    11537966      Asia    34.020  836.197138\n",
       "4  Afghanistan  1972    13079460      Asia    36.088  739.981106"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "33b26fef",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Afghanistan</td>\n",
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       "      <td>28.801</td>\n",
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       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>9240934</td>\n",
       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
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       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
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       "      <td>31.997</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1977</td>\n",
       "      <td>14880372</td>\n",
       "      <td>Asia</td>\n",
       "      <td>38.438</td>\n",
       "      <td>786.113360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1982</td>\n",
       "      <td>12881816</td>\n",
       "      <td>Asia</td>\n",
       "      <td>39.854</td>\n",
       "      <td>978.011439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1987</td>\n",
       "      <td>13867957</td>\n",
       "      <td>Asia</td>\n",
       "      <td>40.822</td>\n",
       "      <td>852.395945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1992</td>\n",
       "      <td>16317921</td>\n",
       "      <td>Asia</td>\n",
       "      <td>41.674</td>\n",
       "      <td>649.341395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1997</td>\n",
       "      <td>22227415</td>\n",
       "      <td>Asia</td>\n",
       "      <td>41.763</td>\n",
       "      <td>635.341351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2002</td>\n",
       "      <td>25268405</td>\n",
       "      <td>Asia</td>\n",
       "      <td>42.129</td>\n",
       "      <td>726.734055</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country  year  population continent  life_exp     gdp_cap\n",
       "0   Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
       "1   Afghanistan  1957     9240934      Asia    30.332  820.853030\n",
       "2   Afghanistan  1962    10267083      Asia    31.997  853.100710\n",
       "3   Afghanistan  1967    11537966      Asia    34.020  836.197138\n",
       "4   Afghanistan  1972    13079460      Asia    36.088  739.981106\n",
       "5   Afghanistan  1977    14880372      Asia    38.438  786.113360\n",
       "6   Afghanistan  1982    12881816      Asia    39.854  978.011439\n",
       "7   Afghanistan  1987    13867957      Asia    40.822  852.395945\n",
       "8   Afghanistan  1992    16317921      Asia    41.674  649.341395\n",
       "9   Afghanistan  1997    22227415      Asia    41.763  635.341351\n",
       "10  Afghanistan  2002    25268405      Asia    42.129  726.734055"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd5b2a61",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "c67e4c2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "      <th>life_exp</th>\n",
       "      <th>gdp_cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1698</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1982</td>\n",
       "      <td>7636524</td>\n",
       "      <td>Africa</td>\n",
       "      <td>60.363</td>\n",
       "      <td>788.855041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1987</td>\n",
       "      <td>9216418</td>\n",
       "      <td>Africa</td>\n",
       "      <td>62.351</td>\n",
       "      <td>706.157306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1992</td>\n",
       "      <td>10704340</td>\n",
       "      <td>Africa</td>\n",
       "      <td>60.377</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1997</td>\n",
       "      <td>11404948</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.809</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year  population continent  life_exp     gdp_cap\n",
       "1698  Zimbabwe  1982     7636524    Africa    60.363  788.855041\n",
       "1699  Zimbabwe  1987     9216418    Africa    62.351  706.157306\n",
       "1700  Zimbabwe  1992    10704340    Africa    60.377  693.420786\n",
       "1701  Zimbabwe  1997    11404948    Africa    46.809  792.449960\n",
       "1702  Zimbabwe  2002    11926563    Africa    39.989  672.038623\n",
       "1703  Zimbabwe  2007    12311143    Africa    43.487  469.709298"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7ab45f2b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d8904833",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1704, 6)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "836f14ac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "304635bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>life_exp</th>\n",
       "      <th>gdp_cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1704.00000</td>\n",
       "      <td>1.704000e+03</td>\n",
       "      <td>1704.000000</td>\n",
       "      <td>1704.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1979.50000</td>\n",
       "      <td>2.960121e+07</td>\n",
       "      <td>59.474439</td>\n",
       "      <td>7215.327081</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>17.26533</td>\n",
       "      <td>1.061579e+08</td>\n",
       "      <td>12.917107</td>\n",
       "      <td>9857.454543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1952.00000</td>\n",
       "      <td>6.001100e+04</td>\n",
       "      <td>23.599000</td>\n",
       "      <td>241.165876</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1965.75000</td>\n",
       "      <td>2.793664e+06</td>\n",
       "      <td>48.198000</td>\n",
       "      <td>1202.060309</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1979.50000</td>\n",
       "      <td>7.023596e+06</td>\n",
       "      <td>60.712500</td>\n",
       "      <td>3531.846988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1993.25000</td>\n",
       "      <td>1.958522e+07</td>\n",
       "      <td>70.845500</td>\n",
       "      <td>9325.462346</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>2007.00000</td>\n",
       "      <td>1.318683e+09</td>\n",
       "      <td>82.603000</td>\n",
       "      <td>113523.132900</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "             year    population     life_exp        gdp_cap\n",
       "count  1704.00000  1.704000e+03  1704.000000    1704.000000\n",
       "mean   1979.50000  2.960121e+07    59.474439    7215.327081\n",
       "std      17.26533  1.061579e+08    12.917107    9857.454543\n",
       "min    1952.00000  6.001100e+04    23.599000     241.165876\n",
       "25%    1965.75000  2.793664e+06    48.198000    1202.060309\n",
       "50%    1979.50000  7.023596e+06    60.712500    3531.846988\n",
       "75%    1993.25000  1.958522e+07    70.845500    9325.462346\n",
       "max    2007.00000  1.318683e+09    82.603000  113523.132900"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "ff53bce9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1704</td>\n",
       "      <td>1704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>142</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>12</td>\n",
       "      <td>624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            country continent\n",
       "count          1704      1704\n",
       "unique          142         5\n",
       "top     Afghanistan    Africa\n",
       "freq             12       624"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include=\"object\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea88bbb9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "1e9d45ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "4bc0cf97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(np.unique(df[\"country\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "cd91cb2a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Afghanistan', 'Albania', 'Algeria', 'Angola', 'Argentina',\n",
       "       'Australia', 'Austria', 'Bahrain', 'Bangladesh', 'Belgium',\n",
       "       'Benin', 'Bolivia', 'Bosnia and Herzegovina', 'Botswana', 'Brazil',\n",
       "       'Bulgaria', 'Burkina Faso', 'Burundi', 'Cambodia', 'Cameroon',\n",
       "       'Canada', 'Central African Republic', 'Chad', 'Chile', 'China',\n",
       "       'Colombia', 'Comoros', 'Congo, Dem. Rep.', 'Congo, Rep.',\n",
       "       'Costa Rica', \"Cote d'Ivoire\", 'Croatia', 'Cuba', 'Czech Republic',\n",
       "       'Denmark', 'Djibouti', 'Dominican Republic', 'Ecuador', 'Egypt',\n",
       "       'El Salvador', 'Equatorial Guinea', 'Eritrea', 'Ethiopia',\n",
       "       'Finland', 'France', 'Gabon', 'Gambia', 'Germany', 'Ghana',\n",
       "       'Greece', 'Guatemala', 'Guinea', 'Guinea-Bissau', 'Haiti',\n",
       "       'Honduras', 'Hong Kong, China', 'Hungary', 'Iceland', 'India',\n",
       "       'Indonesia', 'Iran', 'Iraq', 'Ireland', 'Israel', 'Italy',\n",
       "       'Jamaica', 'Japan', 'Jordan', 'Kenya', 'Korea, Dem. Rep.',\n",
       "       'Korea, Rep.', 'Kuwait', 'Lebanon', 'Lesotho', 'Liberia', 'Libya',\n",
       "       'Madagascar', 'Malawi', 'Malaysia', 'Mali', 'Mauritania',\n",
       "       'Mauritius', 'Mexico', 'Mongolia', 'Montenegro', 'Morocco',\n",
       "       'Mozambique', 'Myanmar', 'Namibia', 'Nepal', 'Netherlands',\n",
       "       'New Zealand', 'Nicaragua', 'Niger', 'Nigeria', 'Norway', 'Oman',\n",
       "       'Pakistan', 'Panama', 'Paraguay', 'Peru', 'Philippines', 'Poland',\n",
       "       'Portugal', 'Puerto Rico', 'Reunion', 'Romania', 'Rwanda',\n",
       "       'Sao Tome and Principe', 'Saudi Arabia', 'Senegal', 'Serbia',\n",
       "       'Sierra Leone', 'Singapore', 'Slovak Republic', 'Slovenia',\n",
       "       'Somalia', 'South Africa', 'Spain', 'Sri Lanka', 'Sudan',\n",
       "       'Swaziland', 'Sweden', 'Switzerland', 'Syria', 'Taiwan',\n",
       "       'Tanzania', 'Thailand', 'Togo', 'Trinidad and Tobago', 'Tunisia',\n",
       "       'Turkey', 'Uganda', 'United Kingdom', 'United States', 'Uruguay',\n",
       "       'Venezuela', 'Vietnam', 'West Bank and Gaza', 'Yemen, Rep.',\n",
       "       'Zambia', 'Zimbabwe'], dtype=object)"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"country\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "e81c17d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[\"country\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3843aabe",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc3acd36",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "83c1de2f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['country', 'year', 'population', 'continent', 'life_exp', 'gdp_cap'], dtype='object')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f387b50b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b14c6762",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<bound method NDFrame.keys of           country  year  population continent  life_exp     gdp_cap\n",
       "0     Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
       "1     Afghanistan  1957     9240934      Asia    30.332  820.853030\n",
       "2     Afghanistan  1962    10267083      Asia    31.997  853.100710\n",
       "3     Afghanistan  1967    11537966      Asia    34.020  836.197138\n",
       "4     Afghanistan  1972    13079460      Asia    36.088  739.981106\n",
       "...           ...   ...         ...       ...       ...         ...\n",
       "1699     Zimbabwe  1987     9216418    Africa    62.351  706.157306\n",
       "1700     Zimbabwe  1992    10704340    Africa    60.377  693.420786\n",
       "1701     Zimbabwe  1997    11404948    Africa    46.809  792.449960\n",
       "1702     Zimbabwe  2002    11926563    Africa    39.989  672.038623\n",
       "1703     Zimbabwe  2007    12311143    Africa    43.487  469.709298\n",
       "\n",
       "[1704 rows x 6 columns]>"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "c02f2a66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['country', 'year', 'population', 'continent', 'life_exp', 'gdp_cap'], dtype='object')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a52dff6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "143cdd02",
   "metadata": {},
   "outputs": [],
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "a1b7ef78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>life_exp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>28.801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>30.332</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>31.997</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>34.020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>36.088</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1987</td>\n",
       "      <td>62.351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1992</td>\n",
       "      <td>60.377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1997</td>\n",
       "      <td>46.809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>39.989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>43.487</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year  life_exp\n",
       "0     Afghanistan  1952    28.801\n",
       "1     Afghanistan  1957    30.332\n",
       "2     Afghanistan  1962    31.997\n",
       "3     Afghanistan  1967    34.020\n",
       "4     Afghanistan  1972    36.088\n",
       "...           ...   ...       ...\n",
       "1699     Zimbabwe  1987    62.351\n",
       "1700     Zimbabwe  1992    60.377\n",
       "1701     Zimbabwe  1997    46.809\n",
       "1702     Zimbabwe  2002    39.989\n",
       "1703     Zimbabwe  2007    43.487\n",
       "\n",
       "[1704 rows x 3 columns]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[[\"country\",\"year\",\"life_exp\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "87159a8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Afghanistan', 'Albania', 'Algeria', 'Angola', 'Argentina',\n",
       "       'Australia', 'Austria', 'Bahrain', 'Bangladesh', 'Belgium',\n",
       "       'Benin', 'Bolivia', 'Bosnia and Herzegovina', 'Botswana', 'Brazil',\n",
       "       'Bulgaria', 'Burkina Faso', 'Burundi', 'Cambodia', 'Cameroon',\n",
       "       'Canada', 'Central African Republic', 'Chad', 'Chile', 'China',\n",
       "       'Colombia', 'Comoros', 'Congo, Dem. Rep.', 'Congo, Rep.',\n",
       "       'Costa Rica', \"Cote d'Ivoire\", 'Croatia', 'Cuba', 'Czech Republic',\n",
       "       'Denmark', 'Djibouti', 'Dominican Republic', 'Ecuador', 'Egypt',\n",
       "       'El Salvador', 'Equatorial Guinea', 'Eritrea', 'Ethiopia',\n",
       "       'Finland', 'France', 'Gabon', 'Gambia', 'Germany', 'Ghana',\n",
       "       'Greece', 'Guatemala', 'Guinea', 'Guinea-Bissau', 'Haiti',\n",
       "       'Honduras', 'Hong Kong, China', 'Hungary', 'Iceland', 'India',\n",
       "       'Indonesia', 'Iran', 'Iraq', 'Ireland', 'Israel', 'Italy',\n",
       "       'Jamaica', 'Japan', 'Jordan', 'Kenya', 'Korea, Dem. Rep.',\n",
       "       'Korea, Rep.', 'Kuwait', 'Lebanon', 'Lesotho', 'Liberia', 'Libya',\n",
       "       'Madagascar', 'Malawi', 'Malaysia', 'Mali', 'Mauritania',\n",
       "       'Mauritius', 'Mexico', 'Mongolia', 'Montenegro', 'Morocco',\n",
       "       'Mozambique', 'Myanmar', 'Namibia', 'Nepal', 'Netherlands',\n",
       "       'New Zealand', 'Nicaragua', 'Niger', 'Nigeria', 'Norway', 'Oman',\n",
       "       'Pakistan', 'Panama', 'Paraguay', 'Peru', 'Philippines', 'Poland',\n",
       "       'Portugal', 'Puerto Rico', 'Reunion', 'Romania', 'Rwanda',\n",
       "       'Sao Tome and Principe', 'Saudi Arabia', 'Senegal', 'Serbia',\n",
       "       'Sierra Leone', 'Singapore', 'Slovak Republic', 'Slovenia',\n",
       "       'Somalia', 'South Africa', 'Spain', 'Sri Lanka', 'Sudan',\n",
       "       'Swaziland', 'Sweden', 'Switzerland', 'Syria', 'Taiwan',\n",
       "       'Tanzania', 'Thailand', 'Togo', 'Trinidad and Tobago', 'Tunisia',\n",
       "       'Turkey', 'Uganda', 'United Kingdom', 'United States', 'Uruguay',\n",
       "       'Venezuela', 'Vietnam', 'West Bank and Gaza', 'Yemen, Rep.',\n",
       "       'Zambia', 'Zimbabwe'], dtype=object)"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"country\"].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "dbbcff06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[\"country\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5289f7ac",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Afghanistan          12\n",
       "Pakistan             12\n",
       "New Zealand          12\n",
       "Nicaragua            12\n",
       "Niger                12\n",
       "                     ..\n",
       "Eritrea              12\n",
       "Equatorial Guinea    12\n",
       "El Salvador          12\n",
       "Egypt                12\n",
       "Zimbabwe             12\n",
       "Name: country, Length: 142, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"country\"].value_counts()   # this is a series of value count data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad9d0b99",
   "metadata": {},
   "outputs": [],
   "source": [
    "# its type : series..... first col text data is its index "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "40b52b8c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df[\"country\"].value_counts())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "142acf55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Afghanistan', 'Pakistan', 'New Zealand', 'Nicaragua', 'Niger',\n",
       "       'Nigeria', 'Norway', 'Oman', 'Panama', 'Nepal',\n",
       "       ...\n",
       "       'Gambia', 'Gabon', 'France', 'Finland', 'Ethiopia', 'Eritrea',\n",
       "       'Equatorial Guinea', 'El Salvador', 'Egypt', 'Zimbabwe'],\n",
       "      dtype='object', length=142)"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"country\"].value_counts().index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "64ddcc6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[\"country\"].value_counts().index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "98a44c3a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b5c88356",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "764054e2",
   "metadata": {},
   "outputs": [],
   "source": [
    "# rename a column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "45314071",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Country</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "      <th>life_exp</th>\n",
       "      <th>gdp_cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>9240934</td>\n",
       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083</td>\n",
       "      <td>Asia</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1987</td>\n",
       "      <td>9216418</td>\n",
       "      <td>Africa</td>\n",
       "      <td>62.351</td>\n",
       "      <td>706.157306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1992</td>\n",
       "      <td>10704340</td>\n",
       "      <td>Africa</td>\n",
       "      <td>60.377</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1997</td>\n",
       "      <td>11404948</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.809</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country  year  population continent  life_exp     gdp_cap\n",
       "0     Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
       "1     Afghanistan  1957     9240934      Asia    30.332  820.853030\n",
       "2     Afghanistan  1962    10267083      Asia    31.997  853.100710\n",
       "3     Afghanistan  1967    11537966      Asia    34.020  836.197138\n",
       "4     Afghanistan  1972    13079460      Asia    36.088  739.981106\n",
       "...           ...   ...         ...       ...       ...         ...\n",
       "1699     Zimbabwe  1987     9216418    Africa    62.351  706.157306\n",
       "1700     Zimbabwe  1992    10704340    Africa    60.377  693.420786\n",
       "1701     Zimbabwe  1997    11404948    Africa    46.809  792.449960\n",
       "1702     Zimbabwe  2002    11926563    Africa    39.989  672.038623\n",
       "1703     Zimbabwe  2007    12311143    Africa    43.487  469.709298\n",
       "\n",
       "[1704 rows x 6 columns]"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.rename({\"country\":\"Country\"},axis = 1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "3ef1b10a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>8425333</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
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      "text/plain": [
       "       country  year  population continent  life_exp     gdp_cap\n",
       "0  Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
       "1  Afghanistan  1957     9240934      Asia    30.332  820.853030"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "dc4a3bed",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename({\"country\":\"Country\"},axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "d90a615c",
   "metadata": {},
   "outputs": [
    {
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      "text/plain": [
       "       Country  year  population continent  life_exp     gdp_cap\n",
       "0  Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
       "1  Afghanistan  1957     9240934      Asia    30.332  820.853030"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "88f9043c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.rename({\"Country\":\"country\"},axis = 1, inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "7c7a4bea",
   "metadata": {},
   "outputs": [
    {
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       "      <td>Afghanistan</td>\n",
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      "text/plain": [
       "       country  year  population continent  life_exp     gdp_cap\n",
       "0  Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
       "1  Afghanistan  1957     9240934      Asia    30.332  820.853030"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "58fe44a0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc4d5f77",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "753910e8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c211e17",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9234a518",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49f2fb80",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5e5f5218",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "27700b50",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c07dd75",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4af14f52",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66c83be7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "f8def3ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Afghanistan\n",
       "1       Afghanistan\n",
       "2       Afghanistan\n",
       "3       Afghanistan\n",
       "4       Afghanistan\n",
       "           ...     \n",
       "1699       Zimbabwe\n",
       "1700       Zimbabwe\n",
       "1701       Zimbabwe\n",
       "1702       Zimbabwe\n",
       "1703       Zimbabwe\n",
       "Name: country, Length: 1704, dtype: object"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"country\"]  # dictionary way of accessing a column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "988f17b5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Afghanistan\n",
       "1       Afghanistan\n",
       "2       Afghanistan\n",
       "3       Afghanistan\n",
       "4       Afghanistan\n",
       "           ...     \n",
       "1699       Zimbabwe\n",
       "1700       Zimbabwe\n",
       "1701       Zimbabwe\n",
       "1702       Zimbabwe\n",
       "1703       Zimbabwe\n",
       "Name: country, Length: 1704, dtype: object"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# not recommended \n",
    "\n",
    "df.country   # attribute way of accessing a column\n",
    "             # dont work if col name has space init\n",
    "             # col name could be same as some attibute name : like \"shape\"\n",
    "             # what if col name is a numerical value or special character "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "6f9a70de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.country)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a6c37d37",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd886599",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3aea54fc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "4c167fa0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>life_exp</th>\n",
       "      <th>gdp_cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>9240934</td>\n",
       "      <td>30.332</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1987</td>\n",
       "      <td>9216418</td>\n",
       "      <td>62.351</td>\n",
       "      <td>706.157306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1992</td>\n",
       "      <td>10704340</td>\n",
       "      <td>60.377</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1997</td>\n",
       "      <td>11404948</td>\n",
       "      <td>46.809</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year  population  life_exp     gdp_cap\n",
       "0     Afghanistan  1952     8425333    28.801  779.445314\n",
       "1     Afghanistan  1957     9240934    30.332  820.853030\n",
       "2     Afghanistan  1962    10267083    31.997  853.100710\n",
       "3     Afghanistan  1967    11537966    34.020  836.197138\n",
       "4     Afghanistan  1972    13079460    36.088  739.981106\n",
       "...           ...   ...         ...       ...         ...\n",
       "1699     Zimbabwe  1987     9216418    62.351  706.157306\n",
       "1700     Zimbabwe  1992    10704340    60.377  693.420786\n",
       "1701     Zimbabwe  1997    11404948    46.809  792.449960\n",
       "1702     Zimbabwe  2002    11926563    39.989  672.038623\n",
       "1703     Zimbabwe  2007    12311143    43.487  469.709298\n",
       "\n",
       "[1704 rows x 5 columns]"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop a column\n",
    "\n",
    "# providing axis is necessary \n",
    "df.drop(\"continent\",axis = 1) # inplace = True  for implicit changes "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "ffd3a9d4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "      <th>life_exp</th>\n",
       "      <th>gdp_cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year  population continent  life_exp     gdp_cap\n",
       "0  Afghanistan  1952     8425333      Asia    28.801  779.445314"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "66451483",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3d2fff36",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "be2f04dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a feature \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "5b75d890",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"total_gdp\"] = df[\"population\"] * df[\"gdp_cap\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "3b1164ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
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       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
       "      <td>820.853030</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083</td>\n",
       "      <td>Asia</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "      <td>8.758856e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966</td>\n",
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       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "      <td>9.648014e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "      <td>9.678553e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1987</td>\n",
       "      <td>9216418</td>\n",
       "      <td>Africa</td>\n",
       "      <td>62.351</td>\n",
       "      <td>706.157306</td>\n",
       "      <td>6.508241e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1992</td>\n",
       "      <td>10704340</td>\n",
       "      <td>Africa</td>\n",
       "      <td>60.377</td>\n",
       "      <td>693.420786</td>\n",
       "      <td>7.422612e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1997</td>\n",
       "      <td>11404948</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.809</td>\n",
       "      <td>792.449960</td>\n",
       "      <td>9.037851e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "      <td>8.015111e+09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "      <td>5.782658e+09</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year  population continent  life_exp     gdp_cap  \\\n",
       "0     Afghanistan  1952     8425333      Asia    28.801  779.445314   \n",
       "1     Afghanistan  1957     9240934      Asia    30.332  820.853030   \n",
       "2     Afghanistan  1962    10267083      Asia    31.997  853.100710   \n",
       "3     Afghanistan  1967    11537966      Asia    34.020  836.197138   \n",
       "4     Afghanistan  1972    13079460      Asia    36.088  739.981106   \n",
       "...           ...   ...         ...       ...       ...         ...   \n",
       "1699     Zimbabwe  1987     9216418    Africa    62.351  706.157306   \n",
       "1700     Zimbabwe  1992    10704340    Africa    60.377  693.420786   \n",
       "1701     Zimbabwe  1997    11404948    Africa    46.809  792.449960   \n",
       "1702     Zimbabwe  2002    11926563    Africa    39.989  672.038623   \n",
       "1703     Zimbabwe  2007    12311143    Africa    43.487  469.709298   \n",
       "\n",
       "         total_gdp  \n",
       "0     6.567086e+09  \n",
       "1     7.585449e+09  \n",
       "2     8.758856e+09  \n",
       "3     9.648014e+09  \n",
       "4     9.678553e+09  \n",
       "...            ...  \n",
       "1699  6.508241e+09  \n",
       "1700  7.422612e+09  \n",
       "1701  9.037851e+09  \n",
       "1702  8.015111e+09  \n",
       "1703  5.782658e+09  \n",
       "\n",
       "[1704 rows x 7 columns]"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f8c8b0f0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5bc4ec2d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add a feature which is non related to the original data \n",
    "# .... create from scratch "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "67d6db34",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a list / array \n",
    "\n",
    "df[\"custom_column\"] = [i for i in range(len(df))]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "6a1ebf26",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>population</th>\n",
       "      <th>continent</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
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       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "      <td>6.567086e+09</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>9240934</td>\n",
       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
       "      <td>820.853030</td>\n",
       "      <td>7.585449e+09</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083</td>\n",
       "      <td>Asia</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "      <td>8.758856e+09</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "      <td>9.648014e+09</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "      <td>9.678553e+09</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1987</td>\n",
       "      <td>9216418</td>\n",
       "      <td>Africa</td>\n",
       "      <td>62.351</td>\n",
       "      <td>706.157306</td>\n",
       "      <td>6.508241e+09</td>\n",
       "      <td>1699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1992</td>\n",
       "      <td>10704340</td>\n",
       "      <td>Africa</td>\n",
       "      <td>60.377</td>\n",
       "      <td>693.420786</td>\n",
       "      <td>7.422612e+09</td>\n",
       "      <td>1700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1997</td>\n",
       "      <td>11404948</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.809</td>\n",
       "      <td>792.449960</td>\n",
       "      <td>9.037851e+09</td>\n",
       "      <td>1701</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "      <td>8.015111e+09</td>\n",
       "      <td>1702</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "      <td>5.782658e+09</td>\n",
       "      <td>1703</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year  population continent  life_exp     gdp_cap  \\\n",
       "0     Afghanistan  1952     8425333      Asia    28.801  779.445314   \n",
       "1     Afghanistan  1957     9240934      Asia    30.332  820.853030   \n",
       "2     Afghanistan  1962    10267083      Asia    31.997  853.100710   \n",
       "3     Afghanistan  1967    11537966      Asia    34.020  836.197138   \n",
       "4     Afghanistan  1972    13079460      Asia    36.088  739.981106   \n",
       "...           ...   ...         ...       ...       ...         ...   \n",
       "1699     Zimbabwe  1987     9216418    Africa    62.351  706.157306   \n",
       "1700     Zimbabwe  1992    10704340    Africa    60.377  693.420786   \n",
       "1701     Zimbabwe  1997    11404948    Africa    46.809  792.449960   \n",
       "1702     Zimbabwe  2002    11926563    Africa    39.989  672.038623   \n",
       "1703     Zimbabwe  2007    12311143    Africa    43.487  469.709298   \n",
       "\n",
       "         total_gdp  custom_column  \n",
       "0     6.567086e+09              0  \n",
       "1     7.585449e+09              1  \n",
       "2     8.758856e+09              2  \n",
       "3     9.648014e+09              3  \n",
       "4     9.678553e+09              4  \n",
       "...            ...            ...  \n",
       "1699  6.508241e+09           1699  \n",
       "1700  7.422612e+09           1700  \n",
       "1701  9.037851e+09           1701  \n",
       "1702  8.015111e+09           1702  \n",
       "1703  5.782658e+09           1703  \n",
       "\n",
       "[1704 rows x 8 columns]"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "0769f2e1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop([\"custom_column\",\"total_gdp\"],axis = 1, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "d243df5b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "      <th>life_exp</th>\n",
       "      <th>gdp_cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>9240934</td>\n",
       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083</td>\n",
       "      <td>Asia</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1987</td>\n",
       "      <td>9216418</td>\n",
       "      <td>Africa</td>\n",
       "      <td>62.351</td>\n",
       "      <td>706.157306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1992</td>\n",
       "      <td>10704340</td>\n",
       "      <td>Africa</td>\n",
       "      <td>60.377</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1997</td>\n",
       "      <td>11404948</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.809</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year  population continent  life_exp     gdp_cap\n",
       "0     Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
       "1     Afghanistan  1957     9240934      Asia    30.332  820.853030\n",
       "2     Afghanistan  1962    10267083      Asia    31.997  853.100710\n",
       "3     Afghanistan  1967    11537966      Asia    34.020  836.197138\n",
       "4     Afghanistan  1972    13079460      Asia    36.088  739.981106\n",
       "...           ...   ...         ...       ...       ...         ...\n",
       "1699     Zimbabwe  1987     9216418    Africa    62.351  706.157306\n",
       "1700     Zimbabwe  1992    10704340    Africa    60.377  693.420786\n",
       "1701     Zimbabwe  1997    11404948    Africa    46.809  792.449960\n",
       "1702     Zimbabwe  2002    11926563    Africa    39.989  672.038623\n",
       "1703     Zimbabwe  2007    12311143    Africa    43.487  469.709298\n",
       "\n",
       "[1704 rows x 6 columns]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57306186",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "95143b99",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6bf68a8f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49944bbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "# working with rows\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "c43e3466",
   "metadata": {},
   "outputs": [],
   "source": [
    "series1 = df[\"country\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "0d0e9108",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       Afghanistan\n",
       "1       Afghanistan\n",
       "2       Afghanistan\n",
       "3       Afghanistan\n",
       "4       Afghanistan\n",
       "           ...     \n",
       "1699       Zimbabwe\n",
       "1700       Zimbabwe\n",
       "1701       Zimbabwe\n",
       "1702       Zimbabwe\n",
       "1703       Zimbabwe\n",
       "Name: country, Length: 1704, dtype: object"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "af71d3ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Afghanistan'"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series1[5]  # accessing 6th row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "e4baa23a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4     Afghanistan\n",
       "5     Afghanistan\n",
       "6     Afghanistan\n",
       "7     Afghanistan\n",
       "8     Afghanistan\n",
       "9     Afghanistan\n",
       "10    Afghanistan\n",
       "11    Afghanistan\n",
       "12        Albania\n",
       "13        Albania\n",
       "14        Albania\n",
       "15        Albania\n",
       "Name: country, dtype: object"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 4th to 15th row\n",
    "\n",
    "series1[4:16]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d7b1aff",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea7c12ef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "844324d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=1704, step=1)"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# lets change indexing starts from 1\n",
    "\n",
    "series1.index\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "87331366",
   "metadata": {},
   "outputs": [],
   "source": [
    "series1.index = np.arange(1,len(series1)+1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "73bc6d04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([   1,    2,    3,    4,    5,    6,    7,    8,    9,   10,\n",
       "            ...\n",
       "            1695, 1696, 1697, 1698, 1699, 1700, 1701, 1702, 1703, 1704],\n",
       "           dtype='int64', length=1704)"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series1.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "33948a83",
   "metadata": {},
   "outputs": [],
   "source": [
    "# series1.index = np.arange(1,df.shape[0]+1,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "e1bcf159",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1       Afghanistan\n",
       "2       Afghanistan\n",
       "3       Afghanistan\n",
       "4       Afghanistan\n",
       "5       Afghanistan\n",
       "           ...     \n",
       "1700       Zimbabwe\n",
       "1701       Zimbabwe\n",
       "1702       Zimbabwe\n",
       "1703       Zimbabwe\n",
       "1704       Zimbabwe\n",
       "Name: country, Length: 1704, dtype: object"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "064118e6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1992bab",
   "metadata": {},
   "outputs": [],
   "source": [
    "# implicit and explicit indices\n",
    "\n",
    "# 0 - n is called positional indexes (implicit indexing)\n",
    "# what we give maually ffor ex 1 to n-1 is called explicit indeces (row labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fff3dd94",
   "metadata": {},
   "outputs": [],
   "source": [
    "# explicit indexes can be string or number also "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "97ec975e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "series1.index[0]  #will return explicit index . "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "99e506c6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "665727cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     a\n",
       "9     b\n",
       "16    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([\"a\",\"b\",\"c\"],index = [ 1,9,16])\n",
    "data\n",
    "# labels # data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "bf96078e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a'"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4eb28d9d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "6d229aa1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     a\n",
       "9     b\n",
       "16    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "55516d9d",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "501818f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "x    a\n",
       "y    b\n",
       "z    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([\"a\",\"b\",\"c\"],index = [ \"x\",\"y\",\"z\"])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "07145daa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('a', 'a', 'b')"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[\"x\"],data[0],data[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3081c4b7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "debbce27",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "494b2d5a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec627b75",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78cf2d26",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c0f1ef7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# to avoid confusion  pandas has special indexes\n",
    "\n",
    "# 1: loc - works with explicit indexes [ labels ]\n",
    "# 2: iloc - works with implicit indexes [ positional indexes ]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "6a4102a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1    a\n",
       "2    b\n",
       "2    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series([\"a\",\"b\",\"c\"],index = [ 1,2,2])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "0a729bd7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    b\n",
       "2    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[2]   # labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "id": "29efa83e",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "0",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3360\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3361\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3362\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine._get_loc_duplicates\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 0",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_3368/1743741225.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    940\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    941\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mkey_is_scalar\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 942\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    943\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    944\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mis_hashable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m_get_value\u001b[1;34m(self, label, takeable)\u001b[0m\n\u001b[0;32m   1049\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1050\u001b[0m         \u001b[1;31m# Similar to Index.get_value, but we do not fall back to positional\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1051\u001b[1;33m         \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1052\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_values_for_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1053\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3361\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3362\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3363\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3364\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3365\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 0"
     ]
    }
   ],
   "source": [
    "data[0]  # label 0 not exist in dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "78f581ec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "0762fcca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'b'"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "b57b0e37",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'c'"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "51b9b923",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'a'"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.iloc[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "07d30dbb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    b\n",
       "2    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.loc[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7550adb7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d08cff04",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "dc45c23a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "      <th>life_exp</th>\n",
       "      <th>gdp_cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>9240934</td>\n",
       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083</td>\n",
       "      <td>Asia</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1987</td>\n",
       "      <td>9216418</td>\n",
       "      <td>Africa</td>\n",
       "      <td>62.351</td>\n",
       "      <td>706.157306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1992</td>\n",
       "      <td>10704340</td>\n",
       "      <td>Africa</td>\n",
       "      <td>60.377</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1997</td>\n",
       "      <td>11404948</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.809</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year  population continent  life_exp     gdp_cap\n",
       "0     Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
       "1     Afghanistan  1957     9240934      Asia    30.332  820.853030\n",
       "2     Afghanistan  1962    10267083      Asia    31.997  853.100710\n",
       "3     Afghanistan  1967    11537966      Asia    34.020  836.197138\n",
       "4     Afghanistan  1972    13079460      Asia    36.088  739.981106\n",
       "...           ...   ...         ...       ...       ...         ...\n",
       "1699     Zimbabwe  1987     9216418    Africa    62.351  706.157306\n",
       "1700     Zimbabwe  1992    10704340    Africa    60.377  693.420786\n",
       "1701     Zimbabwe  1997    11404948    Africa    46.809  792.449960\n",
       "1702     Zimbabwe  2002    11926563    Africa    39.989  672.038623\n",
       "1703     Zimbabwe  2007    12311143    Africa    43.487  469.709298\n",
       "\n",
       "[1704 rows x 6 columns]"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "id": "13b670ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
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       "        text-align: right;\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "      <th>life_exp</th>\n",
       "      <th>gdp_cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1987</td>\n",
       "      <td>13867957</td>\n",
       "      <td>Asia</td>\n",
       "      <td>40.822</td>\n",
       "      <td>852.395945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1992</td>\n",
       "      <td>16317921</td>\n",
       "      <td>Asia</td>\n",
       "      <td>41.674</td>\n",
       "      <td>649.341395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1997</td>\n",
       "      <td>22227415</td>\n",
       "      <td>Asia</td>\n",
       "      <td>41.763</td>\n",
       "      <td>635.341351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2002</td>\n",
       "      <td>25268405</td>\n",
       "      <td>Asia</td>\n",
       "      <td>42.129</td>\n",
       "      <td>726.734055</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country  year  population continent  life_exp     gdp_cap\n",
       "7   Afghanistan  1987    13867957      Asia    40.822  852.395945\n",
       "8   Afghanistan  1992    16317921      Asia    41.674  649.341395\n",
       "9   Afghanistan  1997    22227415      Asia    41.763  635.341351\n",
       "10  Afghanistan  2002    25268405      Asia    42.129  726.734055"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[7:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "id": "13b01a2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "      <th>life_exp</th>\n",
       "      <th>gdp_cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1987</td>\n",
       "      <td>13867957</td>\n",
       "      <td>Asia</td>\n",
       "      <td>40.822</td>\n",
       "      <td>852.395945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2002</td>\n",
       "      <td>25268405</td>\n",
       "      <td>Asia</td>\n",
       "      <td>42.129</td>\n",
       "      <td>726.734055</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country  year  population continent  life_exp     gdp_cap\n",
       "7   Afghanistan  1987    13867957      Asia    40.822  852.395945\n",
       "10  Afghanistan  2002    25268405      Asia    42.129  726.734055"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[[7,10]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "3e7420a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "      <th>life_exp</th>\n",
       "      <th>gdp_cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1987</td>\n",
       "      <td>13867957</td>\n",
       "      <td>Asia</td>\n",
       "      <td>40.822</td>\n",
       "      <td>852.395945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>2002</td>\n",
       "      <td>25268405</td>\n",
       "      <td>Asia</td>\n",
       "      <td>42.129</td>\n",
       "      <td>726.734055</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        country  year  population continent  life_exp     gdp_cap\n",
       "7   Afghanistan  1987    13867957      Asia    40.822  852.395945\n",
       "10  Afghanistan  2002    25268405      Asia    42.129  726.734055"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[[7,10]] # giving same return ,as indexes are same "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9ed5d332",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "id": "54bea510",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country         Zimbabwe\n",
       "year                2007\n",
       "population      12311143\n",
       "continent         Africa\n",
       "life_exp          43.487\n",
       "gdp_cap       469.709298\n",
       "Name: 1703, dtype: object"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[-1]  # last row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "c87a5556",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(df.iloc[-1]) # row is also a series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "6f2d38f7",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "-1",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\range.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m    384\u001b[0m                 \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 385\u001b[1;33m                     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_range\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    386\u001b[0m                 \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: -1 is not in range",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_3368/1145300078.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    929\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    930\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 931\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    932\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    933\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1162\u001b[0m         \u001b[1;31m# fall thru to straight lookup\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1163\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1164\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1165\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1166\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_slice_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mslice_obj\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mslice\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_label\u001b[1;34m(self, label, axis)\u001b[0m\n\u001b[0;32m   1111\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1112\u001b[0m         \u001b[1;31m# GH#5667 this will fail if the label is not present in the axis.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1113\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1114\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1115\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_handle_lowerdim_multi_index_axis0\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mxs\u001b[1;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[0;32m   3774\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"Expected label or tuple of labels, got {key}\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3775\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3776\u001b[1;33m             \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3777\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3778\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\range.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m    385\u001b[0m                     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_range\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnew_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    386\u001b[0m                 \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 387\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    388\u001b[0m             \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    389\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: -1"
     ]
    }
   ],
   "source": [
    "df.loc[-1]  # will give error as -1 label doesnt exist "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b35ba2f3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "0c090a03",
   "metadata": {},
   "outputs": [],
   "source": [
    "# lets say , if we want to make country column as row labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "id": "f9a4f4d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "temp_df = df.set_index(\"country\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "aec77158",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "      <th>life_exp</th>\n",
       "      <th>gdp_cap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>1952</td>\n",
       "      <td>8425333</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>1957</td>\n",
       "      <td>9240934</td>\n",
       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>1962</td>\n",
       "      <td>10267083</td>\n",
       "      <td>Asia</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>1967</td>\n",
       "      <td>11537966</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Afghanistan</th>\n",
       "      <td>1972</td>\n",
       "      <td>13079460</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>1987</td>\n",
       "      <td>9216418</td>\n",
       "      <td>Africa</td>\n",
       "      <td>62.351</td>\n",
       "      <td>706.157306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>1992</td>\n",
       "      <td>10704340</td>\n",
       "      <td>Africa</td>\n",
       "      <td>60.377</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>1997</td>\n",
       "      <td>11404948</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.809</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>2002</td>\n",
       "      <td>11926563</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>2007</td>\n",
       "      <td>12311143</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             year  population continent  life_exp     gdp_cap\n",
       "country                                                      \n",
       "Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
       "Afghanistan  1957     9240934      Asia    30.332  820.853030\n",
       "Afghanistan  1962    10267083      Asia    31.997  853.100710\n",
       "Afghanistan  1967    11537966      Asia    34.020  836.197138\n",
       "Afghanistan  1972    13079460      Asia    36.088  739.981106\n",
       "...           ...         ...       ...       ...         ...\n",
       "Zimbabwe     1987     9216418    Africa    62.351  706.157306\n",
       "Zimbabwe     1992    10704340    Africa    60.377  693.420786\n",
       "Zimbabwe     1997    11404948    Africa    46.809  792.449960\n",
       "Zimbabwe     2002    11926563    Africa    39.989  672.038623\n",
       "Zimbabwe     2007    12311143    Africa    43.487  469.709298\n",
       "\n",
       "[1704 rows x 5 columns]"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df ## duplicate keys are allowed in pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b057a621",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "id": "c4b600fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "Afghanistan    28.801\n",
       "Afghanistan    30.332\n",
       "Afghanistan    31.997\n",
       "Afghanistan    34.020\n",
       "Afghanistan    36.088\n",
       "                ...  \n",
       "Zimbabwe       62.351\n",
       "Zimbabwe       60.377\n",
       "Zimbabwe       46.809\n",
       "Zimbabwe       39.989\n",
       "Zimbabwe       43.487\n",
       "Name: life_exp, Length: 1704, dtype: float64"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df[\"life_exp\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "a583f9fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>life_exp</th>\n",
       "      <th>gdp_cap</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>48.451</td>\n",
       "      <td>406.884115</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>50.469</td>\n",
       "      <td>518.764268</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>52.358</td>\n",
       "      <td>527.272182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>53.995</td>\n",
       "      <td>569.795071</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>55.635</td>\n",
       "      <td>799.362176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>57.674</td>\n",
       "      <td>685.587682</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>60.363</td>\n",
       "      <td>788.855041</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>62.351</td>\n",
       "      <td>706.157306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>60.377</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>46.809</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          life_exp     gdp_cap\n",
       "country                       \n",
       "Zimbabwe    48.451  406.884115\n",
       "Zimbabwe    50.469  518.764268\n",
       "Zimbabwe    52.358  527.272182\n",
       "Zimbabwe    53.995  569.795071\n",
       "Zimbabwe    55.635  799.362176\n",
       "Zimbabwe    57.674  685.587682\n",
       "Zimbabwe    60.363  788.855041\n",
       "Zimbabwe    62.351  706.157306\n",
       "Zimbabwe    60.377  693.420786\n",
       "Zimbabwe    46.809  792.449960\n",
       "Zimbabwe    39.989  672.038623\n",
       "Zimbabwe    43.487  469.709298"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df[[\"life_exp\",\"gdp_cap\"]].loc[\"Zimbabwe\"]  \n",
    "# only values corresponding to label Zimbabwe\n",
    "\n",
    "# usually iit is recommended to keep row labels unique"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "7c8cddd1",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country\n",
       "Zimbabwe    48.451\n",
       "Zimbabwe    50.469\n",
       "Zimbabwe    52.358\n",
       "Zimbabwe    53.995\n",
       "Zimbabwe    55.635\n",
       "Zimbabwe    57.674\n",
       "Zimbabwe    60.363\n",
       "Zimbabwe    62.351\n",
       "Zimbabwe    60.377\n",
       "Zimbabwe    46.809\n",
       "Zimbabwe    39.989\n",
       "Zimbabwe    43.487\n",
       "Name: life_exp, dtype: float64"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df[\"life_exp\"].loc[\"Zimbabwe\"] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "78cbe444",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>life_exp</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>country</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>48.451</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>50.469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>52.358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>53.995</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>55.635</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>57.674</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>60.363</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>62.351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>60.377</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>46.809</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>39.989</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Zimbabwe</th>\n",
       "      <td>43.487</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          life_exp\n",
       "country           \n",
       "Zimbabwe    48.451\n",
       "Zimbabwe    50.469\n",
       "Zimbabwe    52.358\n",
       "Zimbabwe    53.995\n",
       "Zimbabwe    55.635\n",
       "Zimbabwe    57.674\n",
       "Zimbabwe    60.363\n",
       "Zimbabwe    62.351\n",
       "Zimbabwe    60.377\n",
       "Zimbabwe    46.809\n",
       "Zimbabwe    39.989\n",
       "Zimbabwe    43.487"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df[[\"life_exp\"]].loc[\"Zimbabwe\"]  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "542c89d5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "id": "cc44b009",
   "metadata": {},
   "outputs": [],
   "source": [
    "# add a row to data\n",
    "\n",
    "add_data = {\n",
    "    \"country\":\"India\", \n",
    "    \"year\":2020,\n",
    "    \"life_exp\":69.69,\n",
    "    \"population\":1350000000,\n",
    "    \"continent\":\"Asia\",\n",
    "    \"gdp_cap\" : 1900.71\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "9c2d3086",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.append(add_data, ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "bc454045",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "      <th>life_exp</th>\n",
       "      <th>gdp_cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>9240934</td>\n",
       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083</td>\n",
       "      <td>Asia</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1992</td>\n",
       "      <td>10704340</td>\n",
       "      <td>Africa</td>\n",
       "      <td>60.377</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1997</td>\n",
       "      <td>11404948</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.809</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1704</th>\n",
       "      <td>India</td>\n",
       "      <td>2020</td>\n",
       "      <td>1350000000</td>\n",
       "      <td>Asia</td>\n",
       "      <td>69.690</td>\n",
       "      <td>1900.710000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1705 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year  population continent  life_exp      gdp_cap\n",
       "0     Afghanistan  1952     8425333      Asia    28.801   779.445314\n",
       "1     Afghanistan  1957     9240934      Asia    30.332   820.853030\n",
       "2     Afghanistan  1962    10267083      Asia    31.997   853.100710\n",
       "3     Afghanistan  1967    11537966      Asia    34.020   836.197138\n",
       "4     Afghanistan  1972    13079460      Asia    36.088   739.981106\n",
       "...           ...   ...         ...       ...       ...          ...\n",
       "1700     Zimbabwe  1992    10704340    Africa    60.377   693.420786\n",
       "1701     Zimbabwe  1997    11404948    Africa    46.809   792.449960\n",
       "1702     Zimbabwe  2002    11926563    Africa    39.989   672.038623\n",
       "1703     Zimbabwe  2007    12311143    Africa    43.487   469.709298\n",
       "1704        India  2020  1350000000      Asia    69.690  1900.710000\n",
       "\n",
       "[1705 rows x 6 columns]"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "09df9cd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1704"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.index)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6dd55ebd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "64edca1b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "id": "803997e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "# adding a using a list \n",
    "# we have to make sure abt the order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "id": "082895bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[len(df.index)]  = [\"India\",2020,1350000000,\"Asia\",69.690,1900.71]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "c0e7e08b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "      <th>life_exp</th>\n",
       "      <th>gdp_cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1977</td>\n",
       "      <td>14880372</td>\n",
       "      <td>Asia</td>\n",
       "      <td>38.438</td>\n",
       "      <td>786.113360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1982</td>\n",
       "      <td>12881816</td>\n",
       "      <td>Asia</td>\n",
       "      <td>39.854</td>\n",
       "      <td>978.011439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1987</td>\n",
       "      <td>13867957</td>\n",
       "      <td>Asia</td>\n",
       "      <td>40.822</td>\n",
       "      <td>852.395945</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1992</td>\n",
       "      <td>16317921</td>\n",
       "      <td>Asia</td>\n",
       "      <td>41.674</td>\n",
       "      <td>649.341395</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1997</td>\n",
       "      <td>22227415</td>\n",
       "      <td>Asia</td>\n",
       "      <td>41.763</td>\n",
       "      <td>635.341351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1997</td>\n",
       "      <td>11404948</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.809</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1704</th>\n",
       "      <td>India</td>\n",
       "      <td>2020</td>\n",
       "      <td>1350000000</td>\n",
       "      <td>Asia</td>\n",
       "      <td>69.690</td>\n",
       "      <td>1900.710000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1705</th>\n",
       "      <td>India</td>\n",
       "      <td>2020</td>\n",
       "      <td>1350000000</td>\n",
       "      <td>Asia</td>\n",
       "      <td>69.690</td>\n",
       "      <td>1900.710000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1701 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year  population continent  life_exp      gdp_cap\n",
       "5     Afghanistan  1977    14880372      Asia    38.438   786.113360\n",
       "6     Afghanistan  1982    12881816      Asia    39.854   978.011439\n",
       "7     Afghanistan  1987    13867957      Asia    40.822   852.395945\n",
       "8     Afghanistan  1992    16317921      Asia    41.674   649.341395\n",
       "9     Afghanistan  1997    22227415      Asia    41.763   635.341351\n",
       "...           ...   ...         ...       ...       ...          ...\n",
       "1701     Zimbabwe  1997    11404948    Africa    46.809   792.449960\n",
       "1702     Zimbabwe  2002    11926563    Africa    39.989   672.038623\n",
       "1703     Zimbabwe  2007    12311143    Africa    43.487   469.709298\n",
       "1704        India  2020  1350000000      Asia    69.690  1900.710000\n",
       "1705        India  2020  1350000000      Asia    69.690  1900.710000\n",
       "\n",
       "[1701 rows x 6 columns]"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(-5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7d27751c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#add data using iloc : "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "8f07048e",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "IndexError",
     "evalue": "iloc cannot enlarge its target object",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mIndexError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_3368/1420180100.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m  \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;34m\"India\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2020\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1350000000\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m\"Asia\"\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m69.690\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m1900.71\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__setitem__\u001b[1;34m(self, key, value)\u001b[0m\n\u001b[0;32m    718\u001b[0m             \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    719\u001b[0m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_setitem_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 720\u001b[1;33m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_has_valid_setitem_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    721\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    722\u001b[0m         \u001b[0miloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m\"iloc\"\u001b[0m \u001b[1;32melse\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_has_valid_setitem_indexer\u001b[1;34m(self, indexer)\u001b[0m\n\u001b[0;32m   1459\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1460\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[1;33m>=\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0max\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1461\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"iloc cannot enlarge its target object\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1462\u001b[0m             \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1463\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"iloc cannot enlarge its target object\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mIndexError\u001b[0m: iloc cannot enlarge its target object"
     ]
    }
   ],
   "source": [
    "df.iloc[len(df.index)]  = [\"India\",2020,1350000000,\"Asia\",69.690,1900.71]\n",
    "\n",
    "# not possible, yet tht index doesnt exist . "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4d9d4a78",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6459a299",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e9b68a49",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "015e6d9c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "1130c70c",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "# drop rows, drop duplicates"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "6246b0f3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "1701    False\n",
       "1702    False\n",
       "1703    False\n",
       "1704    False\n",
       "1705     True\n",
       "Length: 1706, dtype: bool"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.duplicated()  \n",
    "# we can use this ask a mast as a fancy index to get duplocate values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "1e7b2bf9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# last row is true tht means it is duplicated data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b49bf7ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "# we can use above output as mask to get those duplicate values \n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "10fe8bfb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "      <th>life_exp</th>\n",
       "      <th>gdp_cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1705</th>\n",
       "      <td>India</td>\n",
       "      <td>2020</td>\n",
       "      <td>1350000000</td>\n",
       "      <td>Asia</td>\n",
       "      <td>69.69</td>\n",
       "      <td>1900.71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     country  year  population continent  life_exp  gdp_cap\n",
       "1705   India  2020  1350000000      Asia     69.69  1900.71"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[df.duplicated()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30a65d95",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "9ea29299",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "      <th>life_exp</th>\n",
       "      <th>gdp_cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>9240934</td>\n",
       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083</td>\n",
       "      <td>Asia</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1992</td>\n",
       "      <td>10704340</td>\n",
       "      <td>Africa</td>\n",
       "      <td>60.377</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1997</td>\n",
       "      <td>11404948</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.809</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1704</th>\n",
       "      <td>India</td>\n",
       "      <td>2020</td>\n",
       "      <td>1350000000</td>\n",
       "      <td>Asia</td>\n",
       "      <td>69.690</td>\n",
       "      <td>1900.710000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1705 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year  population continent  life_exp      gdp_cap\n",
       "0     Afghanistan  1952     8425333      Asia    28.801   779.445314\n",
       "1     Afghanistan  1957     9240934      Asia    30.332   820.853030\n",
       "2     Afghanistan  1962    10267083      Asia    31.997   853.100710\n",
       "3     Afghanistan  1967    11537966      Asia    34.020   836.197138\n",
       "4     Afghanistan  1972    13079460      Asia    36.088   739.981106\n",
       "...           ...   ...         ...       ...       ...          ...\n",
       "1700     Zimbabwe  1992    10704340    Africa    60.377   693.420786\n",
       "1701     Zimbabwe  1997    11404948    Africa    46.809   792.449960\n",
       "1702     Zimbabwe  2002    11926563    Africa    39.989   672.038623\n",
       "1703     Zimbabwe  2007    12311143    Africa    43.487   469.709298\n",
       "1704        India  2020  1350000000      Asia    69.690  1900.710000\n",
       "\n",
       "[1705 rows x 6 columns]"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates()\n",
    "# by default deteles the last duplicaabste \n",
    "\n",
    "# if we want to perticularly delete last duplicate,\n",
    "# df.drop_duplicates(keep = \"first\")\n",
    "\n",
    "# if we want to perticularly delete first duplicate,\n",
    "# df.drop_duplicates(keep = \"last\")\n",
    "\n",
    "# if we want to perticularly delete all duplicated values,\n",
    "# df.drop_duplicates(keep = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "id": "5c848365",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
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       "      <td>Africa</td>\n",
       "      <td>60.377</td>\n",
       "      <td>693.420786</td>\n",
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       "      <th>1701</th>\n",
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       "      <td>792.449960</td>\n",
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       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1705</th>\n",
       "      <td>India</td>\n",
       "      <td>2020</td>\n",
       "      <td>1350000000</td>\n",
       "      <td>Asia</td>\n",
       "      <td>69.690</td>\n",
       "      <td>1900.710000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1705 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year  population continent  life_exp      gdp_cap\n",
       "0     Afghanistan  1952     8425333      Asia    28.801   779.445314\n",
       "1     Afghanistan  1957     9240934      Asia    30.332   820.853030\n",
       "2     Afghanistan  1962    10267083      Asia    31.997   853.100710\n",
       "3     Afghanistan  1967    11537966      Asia    34.020   836.197138\n",
       "4     Afghanistan  1972    13079460      Asia    36.088   739.981106\n",
       "...           ...   ...         ...       ...       ...          ...\n",
       "1700     Zimbabwe  1992    10704340    Africa    60.377   693.420786\n",
       "1701     Zimbabwe  1997    11404948    Africa    46.809   792.449960\n",
       "1702     Zimbabwe  2002    11926563    Africa    39.989   672.038623\n",
       "1703     Zimbabwe  2007    12311143    Africa    43.487   469.709298\n",
       "1705        India  2020  1350000000      Asia    69.690  1900.710000\n",
       "\n",
       "[1705 rows x 6 columns]"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates(keep = \"last\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "33782628",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>population</th>\n",
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       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>9240934</td>\n",
       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083</td>\n",
       "      <td>Asia</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1987</td>\n",
       "      <td>9216418</td>\n",
       "      <td>Africa</td>\n",
       "      <td>62.351</td>\n",
       "      <td>706.157306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1992</td>\n",
       "      <td>10704340</td>\n",
       "      <td>Africa</td>\n",
       "      <td>60.377</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1997</td>\n",
       "      <td>11404948</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.809</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year  population continent  life_exp     gdp_cap\n",
       "0     Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
       "1     Afghanistan  1957     9240934      Asia    30.332  820.853030\n",
       "2     Afghanistan  1962    10267083      Asia    31.997  853.100710\n",
       "3     Afghanistan  1967    11537966      Asia    34.020  836.197138\n",
       "4     Afghanistan  1972    13079460      Asia    36.088  739.981106\n",
       "...           ...   ...         ...       ...       ...         ...\n",
       "1699     Zimbabwe  1987     9216418    Africa    62.351  706.157306\n",
       "1700     Zimbabwe  1992    10704340    Africa    60.377  693.420786\n",
       "1701     Zimbabwe  1997    11404948    Africa    46.809  792.449960\n",
       "1702     Zimbabwe  2002    11926563    Africa    39.989  672.038623\n",
       "1703     Zimbabwe  2007    12311143    Africa    43.487  469.709298\n",
       "\n",
       "[1704 rows x 6 columns]"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop_duplicates(keep = False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "18021fc7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c3beda1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b4248b09",
   "metadata": {},
   "outputs": [],
   "source": [
    "# delete a row\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "9c69c13e",
   "metadata": {},
   "outputs": [],
   "source": [
    "# delete label 1 : \n",
    "\n",
    "df.drop(1,axis = 0, inplace= True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "db2003da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <thead>\n",
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       "      <td>Afghanistan</td>\n",
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       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083</td>\n",
       "      <td>Asia</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1977</td>\n",
       "      <td>14880372</td>\n",
       "      <td>Asia</td>\n",
       "      <td>38.438</td>\n",
       "      <td>786.113360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1997</td>\n",
       "      <td>11404948</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.809</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1704</th>\n",
       "      <td>India</td>\n",
       "      <td>2020</td>\n",
       "      <td>1350000000</td>\n",
       "      <td>Asia</td>\n",
       "      <td>69.690</td>\n",
       "      <td>1900.710000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1705</th>\n",
       "      <td>India</td>\n",
       "      <td>2020</td>\n",
       "      <td>1350000000</td>\n",
       "      <td>Asia</td>\n",
       "      <td>69.690</td>\n",
       "      <td>1900.710000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1705 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year  population continent  life_exp      gdp_cap\n",
       "0     Afghanistan  1952     8425333      Asia    28.801   779.445314\n",
       "2     Afghanistan  1962    10267083      Asia    31.997   853.100710\n",
       "3     Afghanistan  1967    11537966      Asia    34.020   836.197138\n",
       "4     Afghanistan  1972    13079460      Asia    36.088   739.981106\n",
       "5     Afghanistan  1977    14880372      Asia    38.438   786.113360\n",
       "...           ...   ...         ...       ...       ...          ...\n",
       "1701     Zimbabwe  1997    11404948    Africa    46.809   792.449960\n",
       "1702     Zimbabwe  2002    11926563    Africa    39.989   672.038623\n",
       "1703     Zimbabwe  2007    12311143    Africa    43.487   469.709298\n",
       "1704        India  2020  1350000000      Asia    69.690  1900.710000\n",
       "1705        India  2020  1350000000      Asia    69.690  1900.710000\n",
       "\n",
       "[1705 rows x 6 columns]"
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b51d028e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "89dd625d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "country       Afghanistan\n",
       "year                 1962\n",
       "population       10267083\n",
       "continent            Asia\n",
       "life_exp           31.997\n",
       "gdp_cap         853.10071\n",
       "Name: 2, dtype: object"
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "2ff38e51",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "1",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3360\u001b[0m             \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3361\u001b[1;33m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3362\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 1",
      "\nThe above exception was the direct cause of the following exception:\n",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_3368/3260552322.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    929\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    930\u001b[0m             \u001b[0mmaybe_callable\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 931\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    932\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    933\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1162\u001b[0m         \u001b[1;31m# fall thru to straight lookup\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1163\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1164\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1165\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1166\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_slice_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mslice_obj\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mslice\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_label\u001b[1;34m(self, label, axis)\u001b[0m\n\u001b[0;32m   1111\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1112\u001b[0m         \u001b[1;31m# GH#5667 this will fail if the label is not present in the axis.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1113\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mxs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1114\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1115\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_handle_lowerdim_multi_index_axis0\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtup\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mtuple\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mxs\u001b[1;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[0;32m   3774\u001b[0m                 \u001b[1;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"Expected label or tuple of labels, got {key}\"\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3775\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3776\u001b[1;33m             \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3777\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3778\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m   3361\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3362\u001b[0m             \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3363\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3364\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   3365\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0misna\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhasnans\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 1"
     ]
    }
   ],
   "source": [
    "df.loc[1]   # error, it couldnt find label as 1 in df as we dropped it "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "6cc21e5c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "      <th>life_exp</th>\n",
       "      <th>gdp_cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083</td>\n",
       "      <td>Asia</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1977</td>\n",
       "      <td>14880372</td>\n",
       "      <td>Asia</td>\n",
       "      <td>38.438</td>\n",
       "      <td>786.113360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1997</td>\n",
       "      <td>11404948</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.809</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1704</th>\n",
       "      <td>India</td>\n",
       "      <td>2020</td>\n",
       "      <td>1350000000</td>\n",
       "      <td>Asia</td>\n",
       "      <td>69.690</td>\n",
       "      <td>1900.710000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1705</th>\n",
       "      <td>India</td>\n",
       "      <td>2020</td>\n",
       "      <td>1350000000</td>\n",
       "      <td>Asia</td>\n",
       "      <td>69.690</td>\n",
       "      <td>1900.710000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1705 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year  population continent  life_exp      gdp_cap\n",
       "0     Afghanistan  1952     8425333      Asia    28.801   779.445314\n",
       "2     Afghanistan  1962    10267083      Asia    31.997   853.100710\n",
       "3     Afghanistan  1967    11537966      Asia    34.020   836.197138\n",
       "4     Afghanistan  1972    13079460      Asia    36.088   739.981106\n",
       "5     Afghanistan  1977    14880372      Asia    38.438   786.113360\n",
       "...           ...   ...         ...       ...       ...          ...\n",
       "1701     Zimbabwe  1997    11404948    Africa    46.809   792.449960\n",
       "1702     Zimbabwe  2002    11926563    Africa    39.989   672.038623\n",
       "1703     Zimbabwe  2007    12311143    Africa    43.487   469.709298\n",
       "1704        India  2020  1350000000      Asia    69.690  1900.710000\n",
       "1705        India  2020  1350000000      Asia    69.690  1900.710000\n",
       "\n",
       "[1705 rows x 6 columns]"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "f700a0bf",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "f98c54a0",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"gapminder.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "fe67d1ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>gdp_cap</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333</td>\n",
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       "      <td>28.801</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>9240934</td>\n",
       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
       "      <td>820.853030</td>\n",
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       "      <td>1962</td>\n",
       "      <td>10267083</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1987</td>\n",
       "      <td>9216418</td>\n",
       "      <td>Africa</td>\n",
       "      <td>62.351</td>\n",
       "      <td>706.157306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1992</td>\n",
       "      <td>10704340</td>\n",
       "      <td>Africa</td>\n",
       "      <td>60.377</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1997</td>\n",
       "      <td>11404948</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.809</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year  population continent  life_exp     gdp_cap\n",
       "0     Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
       "1     Afghanistan  1957     9240934      Asia    30.332  820.853030\n",
       "2     Afghanistan  1962    10267083      Asia    31.997  853.100710\n",
       "3     Afghanistan  1967    11537966      Asia    34.020  836.197138\n",
       "4     Afghanistan  1972    13079460      Asia    36.088  739.981106\n",
       "...           ...   ...         ...       ...       ...         ...\n",
       "1699     Zimbabwe  1987     9216418    Africa    62.351  706.157306\n",
       "1700     Zimbabwe  1992    10704340    Africa    60.377  693.420786\n",
       "1701     Zimbabwe  1997    11404948    Africa    46.809  792.449960\n",
       "1702     Zimbabwe  2002    11926563    Africa    39.989  672.038623\n",
       "1703     Zimbabwe  2007    12311143    Africa    43.487  469.709298\n",
       "\n",
       "[1704 rows x 6 columns]"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0008926d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# work with rows and columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "345ee6e8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1977</td>\n",
       "      <td>14880372</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1982</td>\n",
       "      <td>12881816</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1987</td>\n",
       "      <td>13867957</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1992</td>\n",
       "      <td>16317921</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year  population continent\n",
       "3  Afghanistan  1967    11537966      Asia\n",
       "4  Afghanistan  1972    13079460      Asia\n",
       "5  Afghanistan  1977    14880372      Asia\n",
       "6  Afghanistan  1982    12881816      Asia\n",
       "7  Afghanistan  1987    13867957      Asia\n",
       "8  Afghanistan  1992    16317921      Asia"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[3:9,0:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "8af7a9f8",
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "cannot do slice indexing on Index with these indexers [2] of type int",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_3368/3001139020.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    923\u001b[0m                 \u001b[1;32mwith\u001b[0m \u001b[0msuppress\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mKeyError\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mIndexError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    924\u001b[0m                     \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtakeable\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_takeable\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 925\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_tuple\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    926\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    927\u001b[0m             \u001b[1;31m# we by definition only have the 0th axis\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple\u001b[1;34m(self, tup)\u001b[0m\n\u001b[0;32m   1107\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_multi_take\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1108\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1109\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_tuple_same_dim\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtup\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1110\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1111\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_get_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_tuple_same_dim\u001b[1;34m(self, tup)\u001b[0m\n\u001b[0;32m    804\u001b[0m                 \u001b[1;32mcontinue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    805\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 806\u001b[1;33m             \u001b[0mretval\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mretval\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mi\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    807\u001b[0m             \u001b[1;31m# We should never have retval.ndim < self.ndim, as that should\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    808\u001b[0m             \u001b[1;31m#  be handled by the _getitem_lowerdim call above.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m   1140\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1141\u001b[0m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1142\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_slice_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1143\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_bool_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1144\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getbool_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_slice_axis\u001b[1;34m(self, slice_obj, axis)\u001b[0m\n\u001b[0;32m   1174\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1175\u001b[0m         \u001b[0mlabels\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1176\u001b[1;33m         \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mlabels\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mslice_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mslice_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mslice_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mslice_obj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1177\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1178\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mslice\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mslice_indexer\u001b[1;34m(self, start, end, step, kind)\u001b[0m\n\u001b[0;32m   5683\u001b[0m         \u001b[0mslice\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5684\u001b[0m         \"\"\"\n\u001b[1;32m-> 5685\u001b[1;33m         \u001b[0mstart_slice\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend_slice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mslice_locs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mend\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mstep\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mstep\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5686\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5687\u001b[0m         \u001b[1;31m# return a slice\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mslice_locs\u001b[1;34m(self, start, end, step, kind)\u001b[0m\n\u001b[0;32m   5885\u001b[0m         \u001b[0mstart_slice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5886\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mstart\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5887\u001b[1;33m             \u001b[0mstart_slice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_slice_bound\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mstart\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m\"left\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5888\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mstart_slice\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5889\u001b[0m             \u001b[0mstart_slice\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;36m0\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_slice_bound\u001b[1;34m(self, label, side, kind)\u001b[0m\n\u001b[0;32m   5795\u001b[0m         \u001b[1;31m# For datetime indices label may be a string that has to be converted\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5796\u001b[0m         \u001b[1;31m# to datetime boundary according to its resolution.\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5797\u001b[1;33m         \u001b[0mlabel\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_slice_bound\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mside\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5798\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5799\u001b[0m         \u001b[1;31m# we need to look up the label\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36m_maybe_cast_slice_bound\u001b[1;34m(self, label, side, kind)\u001b[0m\n\u001b[0;32m   5747\u001b[0m         \u001b[1;31m# reject them, if index does not contain label\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5748\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mis_float\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mlabel\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5749\u001b[1;33m             \u001b[1;32mraise\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_invalid_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"slice\"\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5750\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5751\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mlabel\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: cannot do slice indexing on Index with these indexers [2] of type int"
     ]
    }
   ],
   "source": [
    "df.loc[3:9,2:]  # gives error # doesn't  work.\n",
    "# beacause the loc works with labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "4b07814d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>life_exp</th>\n",
       "      <th>population</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>34.020</td>\n",
       "      <td>11537966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>36.088</td>\n",
       "      <td>13079460</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>38.438</td>\n",
       "      <td>14880372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>39.854</td>\n",
       "      <td>12881816</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>40.822</td>\n",
       "      <td>13867957</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>41.674</td>\n",
       "      <td>16317921</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>41.763</td>\n",
       "      <td>22227415</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  life_exp  population\n",
       "3  Afghanistan    34.020    11537966\n",
       "4  Afghanistan    36.088    13079460\n",
       "5  Afghanistan    38.438    14880372\n",
       "6  Afghanistan    39.854    12881816\n",
       "7  Afghanistan    40.822    13867957\n",
       "8  Afghanistan    41.674    16317921\n",
       "9  Afghanistan    41.763    22227415"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[3:9,[\"country\",\"life_exp\",\"population\"]] \n",
    "\n",
    "# here 9 is included , as it is a label . "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "id": "79e44906",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1977</td>\n",
       "      <td>14880372</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1982</td>\n",
       "      <td>12881816</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1987</td>\n",
       "      <td>13867957</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1992</td>\n",
       "      <td>16317921</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1997</td>\n",
       "      <td>22227415</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       country  year  population continent\n",
       "3  Afghanistan  1967    11537966      Asia\n",
       "4  Afghanistan  1972    13079460      Asia\n",
       "5  Afghanistan  1977    14880372      Asia\n",
       "6  Afghanistan  1982    12881816      Asia\n",
       "7  Afghanistan  1987    13867957      Asia\n",
       "8  Afghanistan  1992    16317921      Asia\n",
       "9  Afghanistan  1997    22227415      Asia"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[3:9,\"country\":\"continent\"] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6dceb772",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "id": "cbc873cc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>gdp_cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>Albania</td>\n",
       "      <td>1967</td>\n",
       "      <td>2760.196931</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2007</td>\n",
       "      <td>6223.367465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>917</th>\n",
       "      <td>Madagascar</td>\n",
       "      <td>1977</td>\n",
       "      <td>1544.228586</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         country  year      gdp_cap\n",
       "0    Afghanistan  1952   779.445314\n",
       "15       Albania  1967  2760.196931\n",
       "35       Algeria  2007  6223.367465\n",
       "917   Madagascar  1977  1544.228586"
      ]
     },
     "execution_count": 218,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df.iloc[[0,15,35,917],[0,1,5]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "db0ad48a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Algeria</td>\n",
       "      <td>2007</td>\n",
       "      <td>33333216</td>\n",
       "      <td>Africa</td>\n",
       "      <td>72.301</td>\n",
       "      <td>6223.367465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Angola</td>\n",
       "      <td>1952</td>\n",
       "      <td>4232095</td>\n",
       "      <td>Africa</td>\n",
       "      <td>30.015</td>\n",
       "      <td>3520.610273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Angola</td>\n",
       "      <td>1957</td>\n",
       "      <td>4561361</td>\n",
       "      <td>Africa</td>\n",
       "      <td>31.999</td>\n",
       "      <td>3827.940465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Angola</td>\n",
       "      <td>1962</td>\n",
       "      <td>4826015</td>\n",
       "      <td>Africa</td>\n",
       "      <td>34.000</td>\n",
       "      <td>4269.276742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Angola</td>\n",
       "      <td>1967</td>\n",
       "      <td>5247469</td>\n",
       "      <td>Africa</td>\n",
       "      <td>35.985</td>\n",
       "      <td>5522.776375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>Angola</td>\n",
       "      <td>1972</td>\n",
       "      <td>5894858</td>\n",
       "      <td>Africa</td>\n",
       "      <td>37.928</td>\n",
       "      <td>5473.288005</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>Angola</td>\n",
       "      <td>1977</td>\n",
       "      <td>6162675</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.483</td>\n",
       "      <td>3008.647355</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    country  year  population continent  life_exp      gdp_cap\n",
       "35  Algeria  2007    33333216    Africa    72.301  6223.367465\n",
       "36   Angola  1952     4232095    Africa    30.015  3520.610273\n",
       "37   Angola  1957     4561361    Africa    31.999  3827.940465\n",
       "38   Angola  1962     4826015    Africa    34.000  4269.276742\n",
       "39   Angola  1967     5247469    Africa    35.985  5522.776375\n",
       "40   Angola  1972     5894858    Africa    37.928  5473.288005\n",
       "41   Angola  1977     6162675    Africa    39.483  3008.647355"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " df.iloc[35:42]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec19e01d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "30520db0",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 223,
   "id": "6e12812c",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1992</td>\n",
       "      <td>10704340</td>\n",
       "      <td>Africa</td>\n",
       "      <td>60.377</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1997</td>\n",
       "      <td>11404948</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.809</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
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      "text/plain": [
       "       country  year  population continent  life_exp     gdp_cap\n",
       "1700  Zimbabwe  1992    10704340    Africa    60.377  693.420786\n",
       "1701  Zimbabwe  1997    11404948    Africa    46.809  792.449960\n",
       "1702  Zimbabwe  2002    11926563    Africa    39.989  672.038623\n",
       "1703  Zimbabwe  2007    12311143    Africa    43.487  469.709298"
      ]
     },
     "execution_count": 223,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[-4:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "14bd68a5",
   "metadata": {},
   "outputs": [
    {
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       "      <td>60.377</td>\n",
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       "      <th>1701</th>\n",
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       "      <td>792.449960</td>\n",
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       "    <tr>\n",
       "      <th>1702</th>\n",
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       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
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      "text/plain": [
       "       country  year  population continent  life_exp     gdp_cap\n",
       "1700  Zimbabwe  1992    10704340    Africa    60.377  693.420786\n",
       "1701  Zimbabwe  1997    11404948    Africa    46.809  792.449960\n",
       "1702  Zimbabwe  2002    11926563    Africa    39.989  672.038623\n",
       "1703  Zimbabwe  2007    12311143    Africa    43.487  469.709298"
      ]
     },
     "execution_count": 224,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1f636985",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "attachments": {
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"
    }
   },
   "cell_type": "markdown",
   "id": "1100d03b",
   "metadata": {},
   "source": [
    "![image.png](attachment:image.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 229,
   "id": "20bbc35a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>continent</th>\n",
       "      <th>life_exp</th>\n",
       "      <th>gdp_cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>Africa</td>\n",
       "      <td>58.014</td>\n",
       "      <td>4910.416756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>Africa</td>\n",
       "      <td>61.368</td>\n",
       "      <td>5745.160213</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>Africa</td>\n",
       "      <td>65.799</td>\n",
       "      <td>5681.358539</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>Africa</td>\n",
       "      <td>67.744</td>\n",
       "      <td>5023.216647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>Africa</td>\n",
       "      <td>69.152</td>\n",
       "      <td>4797.295051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>Africa</td>\n",
       "      <td>70.994</td>\n",
       "      <td>5288.040382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>Africa</td>\n",
       "      <td>72.301</td>\n",
       "      <td>6223.367465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Africa</td>\n",
       "      <td>30.015</td>\n",
       "      <td>3520.610273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>Africa</td>\n",
       "      <td>31.999</td>\n",
       "      <td>3827.940465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>Africa</td>\n",
       "      <td>34.000</td>\n",
       "      <td>4269.276742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>Africa</td>\n",
       "      <td>35.985</td>\n",
       "      <td>5522.776375</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   continent  life_exp      gdp_cap\n",
       "29    Africa    58.014  4910.416756\n",
       "30    Africa    61.368  5745.160213\n",
       "31    Africa    65.799  5681.358539\n",
       "32    Africa    67.744  5023.216647\n",
       "33    Africa    69.152  4797.295051\n",
       "34    Africa    70.994  5288.040382\n",
       "35    Africa    72.301  6223.367465\n",
       "36    Africa    30.015  3520.610273\n",
       "37    Africa    31.999  3827.940465\n",
       "38    Africa    34.000  4269.276742\n",
       "39    Africa    35.985  5522.776375"
      ]
     },
     "execution_count": 229,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[29:40,-3:]   # access last 3 columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f21ff0a8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "id": "7d485bd0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['country', 'year', 'population', 'continent', 'life_exp', 'gdp_cap'], dtype='object')"
      ]
     },
     "execution_count": 243,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "f53d6b8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59.47443936619714"
      ]
     },
     "execution_count": 230,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"life_exp\"].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 231,
   "id": "35e76fba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1704"
      ]
     },
     "execution_count": 231,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"country\"].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "6a04b812",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "142"
      ]
     },
     "execution_count": 238,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df[\"country\"].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "id": "110b826c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12294917.346385501"
      ]
     },
     "execution_count": 239,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"gdp_cap\"].sum() "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd102c99",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac60c736",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "fdd9fe8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "      <th>life_exp</th>\n",
       "      <th>gdp_cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1957</td>\n",
       "      <td>9240934</td>\n",
       "      <td>Asia</td>\n",
       "      <td>30.332</td>\n",
       "      <td>820.853030</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1962</td>\n",
       "      <td>10267083</td>\n",
       "      <td>Asia</td>\n",
       "      <td>31.997</td>\n",
       "      <td>853.100710</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1967</td>\n",
       "      <td>11537966</td>\n",
       "      <td>Asia</td>\n",
       "      <td>34.020</td>\n",
       "      <td>836.197138</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1972</td>\n",
       "      <td>13079460</td>\n",
       "      <td>Asia</td>\n",
       "      <td>36.088</td>\n",
       "      <td>739.981106</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1699</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1987</td>\n",
       "      <td>9216418</td>\n",
       "      <td>Africa</td>\n",
       "      <td>62.351</td>\n",
       "      <td>706.157306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1700</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1992</td>\n",
       "      <td>10704340</td>\n",
       "      <td>Africa</td>\n",
       "      <td>60.377</td>\n",
       "      <td>693.420786</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1701</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>1997</td>\n",
       "      <td>11404948</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.809</td>\n",
       "      <td>792.449960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1702</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2002</td>\n",
       "      <td>11926563</td>\n",
       "      <td>Africa</td>\n",
       "      <td>39.989</td>\n",
       "      <td>672.038623</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          country  year  population continent  life_exp     gdp_cap\n",
       "0     Afghanistan  1952     8425333      Asia    28.801  779.445314\n",
       "1     Afghanistan  1957     9240934      Asia    30.332  820.853030\n",
       "2     Afghanistan  1962    10267083      Asia    31.997  853.100710\n",
       "3     Afghanistan  1967    11537966      Asia    34.020  836.197138\n",
       "4     Afghanistan  1972    13079460      Asia    36.088  739.981106\n",
       "...           ...   ...         ...       ...       ...         ...\n",
       "1699     Zimbabwe  1987     9216418    Africa    62.351  706.157306\n",
       "1700     Zimbabwe  1992    10704340    Africa    60.377  693.420786\n",
       "1701     Zimbabwe  1997    11404948    Africa    46.809  792.449960\n",
       "1702     Zimbabwe  2002    11926563    Africa    39.989  672.038623\n",
       "1703     Zimbabwe  2007    12311143    Africa    43.487  469.709298\n",
       "\n",
       "[1704 rows x 6 columns]"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6142ada3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# sorting\n",
    "\n",
    "# sort per year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "a634d11f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "      <th>life_exp</th>\n",
       "      <th>gdp_cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>Gambia</td>\n",
       "      <td>1952</td>\n",
       "      <td>284320</td>\n",
       "      <td>Africa</td>\n",
       "      <td>30.000</td>\n",
       "      <td>485.230659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Angola</td>\n",
       "      <td>1952</td>\n",
       "      <td>4232095</td>\n",
       "      <td>Africa</td>\n",
       "      <td>30.015</td>\n",
       "      <td>3520.610273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1344</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>1952</td>\n",
       "      <td>2143249</td>\n",
       "      <td>Africa</td>\n",
       "      <td>30.331</td>\n",
       "      <td>879.787736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1032</th>\n",
       "      <td>Mozambique</td>\n",
       "      <td>1952</td>\n",
       "      <td>6446316</td>\n",
       "      <td>Africa</td>\n",
       "      <td>31.286</td>\n",
       "      <td>468.526038</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>Australia</td>\n",
       "      <td>2007</td>\n",
       "      <td>20434176</td>\n",
       "      <td>Oceania</td>\n",
       "      <td>81.235</td>\n",
       "      <td>34435.367440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1487</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>2007</td>\n",
       "      <td>7554661</td>\n",
       "      <td>Europe</td>\n",
       "      <td>81.701</td>\n",
       "      <td>37506.419070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>2007</td>\n",
       "      <td>301931</td>\n",
       "      <td>Europe</td>\n",
       "      <td>81.757</td>\n",
       "      <td>36180.789190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>Hong Kong, China</td>\n",
       "      <td>2007</td>\n",
       "      <td>6980412</td>\n",
       "      <td>Asia</td>\n",
       "      <td>82.208</td>\n",
       "      <td>39724.978670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>Japan</td>\n",
       "      <td>2007</td>\n",
       "      <td>127467972</td>\n",
       "      <td>Asia</td>\n",
       "      <td>82.603</td>\n",
       "      <td>31656.068060</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               country  year  population continent  life_exp       gdp_cap\n",
       "0          Afghanistan  1952     8425333      Asia    28.801    779.445314\n",
       "552             Gambia  1952      284320    Africa    30.000    485.230659\n",
       "36              Angola  1952     4232095    Africa    30.015   3520.610273\n",
       "1344      Sierra Leone  1952     2143249    Africa    30.331    879.787736\n",
       "1032        Mozambique  1952     6446316    Africa    31.286    468.526038\n",
       "...                ...   ...         ...       ...       ...           ...\n",
       "71           Australia  2007    20434176   Oceania    81.235  34435.367440\n",
       "1487       Switzerland  2007     7554661    Europe    81.701  37506.419070\n",
       "695            Iceland  2007      301931    Europe    81.757  36180.789190\n",
       "671   Hong Kong, China  2007     6980412      Asia    82.208  39724.978670\n",
       "803              Japan  2007   127467972      Asia    82.603  31656.068060\n",
       "\n",
       "[1704 rows x 6 columns]"
      ]
     },
     "execution_count": 248,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=[\"year\",\"life_exp\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 259,
   "id": "f7249932",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
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       "      <th>year</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>528</th>\n",
       "      <td>France</td>\n",
       "      <td>1952</td>\n",
       "      <td>42459667</td>\n",
       "      <td>Europe</td>\n",
       "      <td>67.410</td>\n",
       "      <td>7029.809327</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>540</th>\n",
       "      <td>Gabon</td>\n",
       "      <td>1952</td>\n",
       "      <td>420702</td>\n",
       "      <td>Africa</td>\n",
       "      <td>37.003</td>\n",
       "      <td>4293.476475</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1656</th>\n",
       "      <td>West Bank and Gaza</td>\n",
       "      <td>1952</td>\n",
       "      <td>1030585</td>\n",
       "      <td>Asia</td>\n",
       "      <td>43.160</td>\n",
       "      <td>1515.592329</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>Gambia</td>\n",
       "      <td>1952</td>\n",
       "      <td>284320</td>\n",
       "      <td>Africa</td>\n",
       "      <td>30.000</td>\n",
       "      <td>485.230659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1127</th>\n",
       "      <td>Niger</td>\n",
       "      <td>2007</td>\n",
       "      <td>12894865</td>\n",
       "      <td>Africa</td>\n",
       "      <td>56.867</td>\n",
       "      <td>619.676892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1139</th>\n",
       "      <td>Nigeria</td>\n",
       "      <td>2007</td>\n",
       "      <td>135031164</td>\n",
       "      <td>Africa</td>\n",
       "      <td>46.859</td>\n",
       "      <td>2013.977305</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1151</th>\n",
       "      <td>Norway</td>\n",
       "      <td>2007</td>\n",
       "      <td>4627926</td>\n",
       "      <td>Europe</td>\n",
       "      <td>80.196</td>\n",
       "      <td>49357.190170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1175</th>\n",
       "      <td>Pakistan</td>\n",
       "      <td>2007</td>\n",
       "      <td>169270617</td>\n",
       "      <td>Asia</td>\n",
       "      <td>65.483</td>\n",
       "      <td>2605.947580</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1703</th>\n",
       "      <td>Zimbabwe</td>\n",
       "      <td>2007</td>\n",
       "      <td>12311143</td>\n",
       "      <td>Africa</td>\n",
       "      <td>43.487</td>\n",
       "      <td>469.709298</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                 country  year  population continent  life_exp       gdp_cap\n",
       "0            Afghanistan  1952     8425333      Asia    28.801    779.445314\n",
       "528               France  1952    42459667    Europe    67.410   7029.809327\n",
       "540                Gabon  1952      420702    Africa    37.003   4293.476475\n",
       "1656  West Bank and Gaza  1952     1030585      Asia    43.160   1515.592329\n",
       "552               Gambia  1952      284320    Africa    30.000    485.230659\n",
       "...                  ...   ...         ...       ...       ...           ...\n",
       "1127               Niger  2007    12894865    Africa    56.867    619.676892\n",
       "1139             Nigeria  2007   135031164    Africa    46.859   2013.977305\n",
       "1151              Norway  2007     4627926    Europe    80.196  49357.190170\n",
       "1175            Pakistan  2007   169270617      Asia    65.483   2605.947580\n",
       "1703            Zimbabwe  2007    12311143    Africa    43.487    469.709298\n",
       "\n",
       "[1704 rows x 6 columns]"
      ]
     },
     "execution_count": 259,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values([\"year\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "066e08c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# chain sorting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 253,
   "id": "a086e389",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <tbody>\n",
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       "      <th>803</th>\n",
       "      <td>Japan</td>\n",
       "      <td>2007</td>\n",
       "      <td>127467972</td>\n",
       "      <td>Asia</td>\n",
       "      <td>82.603</td>\n",
       "      <td>31656.068060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>Hong Kong, China</td>\n",
       "      <td>2007</td>\n",
       "      <td>6980412</td>\n",
       "      <td>Asia</td>\n",
       "      <td>82.208</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>Japan</td>\n",
       "      <td>2002</td>\n",
       "      <td>127065841</td>\n",
       "      <td>Asia</td>\n",
       "      <td>82.000</td>\n",
       "      <td>28604.591900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>2007</td>\n",
       "      <td>301931</td>\n",
       "      <td>Europe</td>\n",
       "      <td>81.757</td>\n",
       "      <td>36180.789190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1487</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>2007</td>\n",
       "      <td>7554661</td>\n",
       "      <td>Europe</td>\n",
       "      <td>81.701</td>\n",
       "      <td>37506.419070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1344</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>1952</td>\n",
       "      <td>2143249</td>\n",
       "      <td>Africa</td>\n",
       "      <td>30.331</td>\n",
       "      <td>879.787736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Angola</td>\n",
       "      <td>1952</td>\n",
       "      <td>4232095</td>\n",
       "      <td>Africa</td>\n",
       "      <td>30.015</td>\n",
       "      <td>3520.610273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>Gambia</td>\n",
       "      <td>1952</td>\n",
       "      <td>284320</td>\n",
       "      <td>Africa</td>\n",
       "      <td>30.000</td>\n",
       "      <td>485.230659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>1992</td>\n",
       "      <td>7290203</td>\n",
       "      <td>Africa</td>\n",
       "      <td>23.599</td>\n",
       "      <td>737.068595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               country  year  population continent  life_exp       gdp_cap\n",
       "803              Japan  2007   127467972      Asia    82.603  31656.068060\n",
       "671   Hong Kong, China  2007     6980412      Asia    82.208  39724.978670\n",
       "802              Japan  2002   127065841      Asia    82.000  28604.591900\n",
       "695            Iceland  2007      301931    Europe    81.757  36180.789190\n",
       "1487       Switzerland  2007     7554661    Europe    81.701  37506.419070\n",
       "...                ...   ...         ...       ...       ...           ...\n",
       "1344      Sierra Leone  1952     2143249    Africa    30.331    879.787736\n",
       "36              Angola  1952     4232095    Africa    30.015   3520.610273\n",
       "552             Gambia  1952      284320    Africa    30.000    485.230659\n",
       "0          Afghanistan  1952     8425333      Asia    28.801    779.445314\n",
       "1292            Rwanda  1992     7290203    Africa    23.599    737.068595\n",
       "\n",
       "[1704 rows x 6 columns]"
      ]
     },
     "execution_count": 253,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by=[\"life_exp\",\"gdp_cap\"],ascending=[False,True])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "055bb524",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>country</th>\n",
       "      <th>year</th>\n",
       "      <th>population</th>\n",
       "      <th>continent</th>\n",
       "      <th>life_exp</th>\n",
       "      <th>gdp_cap</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>803</th>\n",
       "      <td>Japan</td>\n",
       "      <td>2007</td>\n",
       "      <td>127467972</td>\n",
       "      <td>Asia</td>\n",
       "      <td>82.603</td>\n",
       "      <td>31656.068060</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>671</th>\n",
       "      <td>Hong Kong, China</td>\n",
       "      <td>2007</td>\n",
       "      <td>6980412</td>\n",
       "      <td>Asia</td>\n",
       "      <td>82.208</td>\n",
       "      <td>39724.978670</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>802</th>\n",
       "      <td>Japan</td>\n",
       "      <td>2002</td>\n",
       "      <td>127065841</td>\n",
       "      <td>Asia</td>\n",
       "      <td>82.000</td>\n",
       "      <td>28604.591900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>695</th>\n",
       "      <td>Iceland</td>\n",
       "      <td>2007</td>\n",
       "      <td>301931</td>\n",
       "      <td>Europe</td>\n",
       "      <td>81.757</td>\n",
       "      <td>36180.789190</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1487</th>\n",
       "      <td>Switzerland</td>\n",
       "      <td>2007</td>\n",
       "      <td>7554661</td>\n",
       "      <td>Europe</td>\n",
       "      <td>81.701</td>\n",
       "      <td>37506.419070</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1344</th>\n",
       "      <td>Sierra Leone</td>\n",
       "      <td>1952</td>\n",
       "      <td>2143249</td>\n",
       "      <td>Africa</td>\n",
       "      <td>30.331</td>\n",
       "      <td>879.787736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>Angola</td>\n",
       "      <td>1952</td>\n",
       "      <td>4232095</td>\n",
       "      <td>Africa</td>\n",
       "      <td>30.015</td>\n",
       "      <td>3520.610273</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>552</th>\n",
       "      <td>Gambia</td>\n",
       "      <td>1952</td>\n",
       "      <td>284320</td>\n",
       "      <td>Africa</td>\n",
       "      <td>30.000</td>\n",
       "      <td>485.230659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Afghanistan</td>\n",
       "      <td>1952</td>\n",
       "      <td>8425333</td>\n",
       "      <td>Asia</td>\n",
       "      <td>28.801</td>\n",
       "      <td>779.445314</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1292</th>\n",
       "      <td>Rwanda</td>\n",
       "      <td>1992</td>\n",
       "      <td>7290203</td>\n",
       "      <td>Africa</td>\n",
       "      <td>23.599</td>\n",
       "      <td>737.068595</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1704 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "               country  year  population continent  life_exp       gdp_cap\n",
       "803              Japan  2007   127467972      Asia    82.603  31656.068060\n",
       "671   Hong Kong, China  2007     6980412      Asia    82.208  39724.978670\n",
       "802              Japan  2002   127065841      Asia    82.000  28604.591900\n",
       "695            Iceland  2007      301931    Europe    81.757  36180.789190\n",
       "1487       Switzerland  2007     7554661    Europe    81.701  37506.419070\n",
       "...                ...   ...         ...       ...       ...           ...\n",
       "1344      Sierra Leone  1952     2143249    Africa    30.331    879.787736\n",
       "36              Angola  1952     4232095    Africa    30.015   3520.610273\n",
       "552             Gambia  1952      284320    Africa    30.000    485.230659\n",
       "0          Afghanistan  1952     8425333      Asia    28.801    779.445314\n",
       "1292            Rwanda  1992     7290203    Africa    23.599    737.068595\n",
       "\n",
       "[1704 rows x 6 columns]"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values([\"life_exp\",\"gdp_cap\"],ascending=[False,True])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ef99864f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1053d37d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c6b321e9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4603979",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "456fb724",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2797d9ce",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "8f01203e",
   "metadata": {},
   "source": [
    "## create a data frame from scratch "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0302aeb",
   "metadata": {},
   "outputs": [],
   "source": [
    "# create a series from scratch first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 263,
   "id": "bc7c6cf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "AA    10\n",
       "BB    20\n",
       "CC    30\n",
       "DD    40\n",
       "dtype: int64"
      ]
     },
     "execution_count": 263,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series([10,20,30,40],index = [\"AA\",\"BB\",\"CC\",\"DD\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "872b3c60",
   "metadata": {},
   "outputs": [],
   "source": [
    "# data frame: \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ecb9d457",
   "metadata": {},
   "outputs": [],
   "source": [
    "# approach 1 : row oriented approach "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 267,
   "id": "8a73ed65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AA</th>\n",
       "      <th>BB</th>\n",
       "      <th>CC</th>\n",
       "      <th>DD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "      <td>33</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>32</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AA  BB  CC  DD\n",
       "0  10  20  30  40\n",
       "1  11  22  33  44\n",
       "2  12  22  32  42"
      ]
     },
     "execution_count": 267,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame([[10,20,30,40],[11,22,33,44],[12,22,32,42]],columns=[\"AA\",\"BB\",\"CC\",\"DD\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e3529289",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6f837872",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a32ecc99",
   "metadata": {},
   "outputs": [],
   "source": [
    "# approach 2 : colum oriented approach "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "0b2ad39a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>AA</th>\n",
       "      <th>BB</th>\n",
       "      <th>CC</th>\n",
       "      <th>DD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>20</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "      <td>33</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>22</td>\n",
       "      <td>32</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AA  BB  CC  DD\n",
       "0  10  20  30  40\n",
       "1  11  22  33  44\n",
       "2  12  22  32  42"
      ]
     },
     "execution_count": 268,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame({\n",
    "    \"AA\":[10,11,12],\n",
    "    \"BB\":[20,22,22],\n",
    "    \"CC\":[30,33,32],\n",
    "    \"DD\":[40,44,42]\n",
    "    \n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af46d241",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edf62577",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3942cfd1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 379,
   "id": "1f9b0f2f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'dates':[\"2015-12-06\", \"2011-12-27\", \"2015-09-07\", \"2012-12-21\", \"2020-02-13\"], 'RID':[498, 721, 375, 464, 813], 'Phy':[22, 45, 1, 65, 22], 'Chem':[52, 56, 32, 50, 24], 'Math':[63, 37, 68, 62, 43]}) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 380,
   "id": "a73f16fe",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dates</th>\n",
       "      <th>RID</th>\n",
       "      <th>Phy</th>\n",
       "      <th>Chem</th>\n",
       "      <th>Math</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2015-12-06</td>\n",
       "      <td>498</td>\n",
       "      <td>22</td>\n",
       "      <td>52</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-12-27</td>\n",
       "      <td>721</td>\n",
       "      <td>45</td>\n",
       "      <td>56</td>\n",
       "      <td>37</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2015-09-07</td>\n",
       "      <td>375</td>\n",
       "      <td>1</td>\n",
       "      <td>32</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-12-21</td>\n",
       "      <td>464</td>\n",
       "      <td>65</td>\n",
       "      <td>50</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020-02-13</td>\n",
       "      <td>813</td>\n",
       "      <td>22</td>\n",
       "      <td>24</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        dates  RID  Phy  Chem  Math\n",
       "0  2015-12-06  498   22    52    63\n",
       "1  2011-12-27  721   45    56    37\n",
       "2  2015-09-07  375    1    32    68\n",
       "3  2012-12-21  464   65    50    62\n",
       "4  2020-02-13  813   22    24    43"
      ]
     },
     "execution_count": 380,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 381,
   "id": "589b0d88",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 5 entries, 0 to 4\n",
      "Data columns (total 5 columns):\n",
      " #   Column  Non-Null Count  Dtype \n",
      "---  ------  --------------  ----- \n",
      " 0   dates   5 non-null      object\n",
      " 1   RID     5 non-null      int64 \n",
      " 2   Phy     5 non-null      int64 \n",
      " 3   Chem    5 non-null      int64 \n",
      " 4   Math    5 non-null      int64 \n",
      "dtypes: int64(4), object(1)\n",
      "memory usage: 328.0+ bytes\n"
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   "source": [
    "df.info()"
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  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "8259dd9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df[\"dates\"] = pd.to_datetime(df.dates) "
   ]
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  {
   "cell_type": "code",
   "execution_count": 383,
   "id": "ab3f7bb9",
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   "source": [
    "df = df[df[\"dates\"]>= \"2014-01-05\"]"
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  {
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   "execution_count": 384,
   "id": "f5c4a826",
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     "name": "stderr",
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     "text": [
      "C:\\Users\\sunny\\AppData\\Local\\Temp/ipykernel_3368/745377639.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df[\"average_marks\"] = (df.iloc[:,2:].sum(axis= 1)/3)\n"
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   "execution_count": 386,
   "id": "da15ea07",
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       "       dates  RID  Phy  Chem  Math  average_marks\n",
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   "execution_count": 413,
   "id": "ea374ced",
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   "execution_count": 414,
   "id": "639d2a61",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "[813, 375, 498]"
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     "execution_count": 414,
     "metadata": {},
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  {
   "cell_type": "code",
   "execution_count": 417,
   "id": "7d83d489",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'dates':[\"2015-12-06\", \"2011-12-27\", \"2015-09-07\", \"2012-12-21\", \"2020-02-13\"], 'RID':[498, 721, 375, 464, 813], 'Phy':[22, 45, 1, 65, 22], 'Chem':[52, 56, 32, 50, 24], 'Math':[63, 37, 68, 62, 43]}) "
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   "execution_count": 420,
   "id": "acffa6d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['dates', 'RID', 'Phy', 'Chem', 'Math'], dtype='object')"
      ]
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     "execution_count": 420,
     "metadata": {},
     "output_type": "execute_result"
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   "cell_type": "code",
   "execution_count": 421,
   "id": "5de707fc",
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       "        dates  RID  Phy  Chem  Math\n",
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  {
   "cell_type": "code",
   "execution_count": 425,
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