Machine Learning - Tests - Basics.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('final_train_data.csv','r') as File:\n",
    "    data = File.read()\n",
    "df = pd.read_csv('final_train_data.csv')\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "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>Unnamed: 0</th>\n",
       "      <th>Country Name</th>\n",
       "      <th>Country Code</th>\n",
       "      <th>Year</th>\n",
       "      <th>Balance</th>\n",
       "      <th>Inflation</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Exports</th>\n",
       "      <th>Trade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>Senegal</td>\n",
       "      <td>SEN</td>\n",
       "      <td>1983</td>\n",
       "      <td>-3.043829e+08</td>\n",
       "      <td>9.274213</td>\n",
       "      <td>-5.326393</td>\n",
       "      <td>6.180000e+08</td>\n",
       "      <td>59.224298</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>Togo</td>\n",
       "      <td>TGO</td>\n",
       "      <td>2009</td>\n",
       "      <td>-1.766692e+08</td>\n",
       "      <td>1.855275</td>\n",
       "      <td>3.510297</td>\n",
       "      <td>9.030260e+08</td>\n",
       "      <td>76.242542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Pakistan</td>\n",
       "      <td>PAK</td>\n",
       "      <td>1992</td>\n",
       "      <td>-1.876092e+09</td>\n",
       "      <td>10.057085</td>\n",
       "      <td>7.705898</td>\n",
       "      <td>7.351000e+09</td>\n",
       "      <td>34.489440</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Nepal</td>\n",
       "      <td>NPL</td>\n",
       "      <td>1988</td>\n",
       "      <td>-2.714506e+08</td>\n",
       "      <td>11.815310</td>\n",
       "      <td>7.696809</td>\n",
       "      <td>1.900000e+08</td>\n",
       "      <td>24.978422</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Malta</td>\n",
       "      <td>MLT</td>\n",
       "      <td>2016</td>\n",
       "      <td>7.269132e+08</td>\n",
       "      <td>1.604548</td>\n",
       "      <td>5.516617</td>\n",
       "      <td>3.017299e+09</td>\n",
       "      <td>83.781471</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0 Country Name Country Code  Year       Balance  Inflation  \\\n",
       "0           0      Senegal          SEN  1983 -3.043829e+08   9.274213   \n",
       "1           1         Togo          TGO  2009 -1.766692e+08   1.855275   \n",
       "2           2     Pakistan          PAK  1992 -1.876092e+09  10.057085   \n",
       "3           3        Nepal          NPL  1988 -2.714506e+08  11.815310   \n",
       "4           5        Malta          MLT  2016  7.269132e+08   1.604548   \n",
       "\n",
       "        GDP       Exports      Trade  \n",
       "0 -5.326393  6.180000e+08  59.224298  \n",
       "1  3.510297  9.030260e+08  76.242542  \n",
       "2  7.705898  7.351000e+09  34.489440  \n",
       "3  7.696809  1.900000e+08  24.978422  \n",
       "4  5.516617  3.017299e+09  83.781471  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "957.7840112"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Trade'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0          5703\n",
       "Country Name    Zimbabwe\n",
       "Country Code         ZWE\n",
       "Year                2016\n",
       "Balance         4.21e+11\n",
       "Inflation        13611.6\n",
       "GDP                 34.5\n",
       "Exports         2.34e+12\n",
       "Trade            957.784\n",
       "dtype: object"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "957.7840112"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Trade'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a17b18f28>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x= 'Year', y='Balance',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "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>Unnamed: 0</th>\n",
       "      <th>Year</th>\n",
       "      <th>Balance</th>\n",
       "      <th>Inflation</th>\n",
       "      <th>GDP</th>\n",
       "      <th>Exports</th>\n",
       "      <th>Trade</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.021524</td>\n",
       "      <td>0.002684</td>\n",
       "      <td>-0.007325</td>\n",
       "      <td>-0.009151</td>\n",
       "      <td>-0.011244</td>\n",
       "      <td>-0.006027</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Year</th>\n",
       "      <td>-0.021524</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.011176</td>\n",
       "      <td>-0.045544</td>\n",
       "      <td>-0.009970</td>\n",
       "      <td>0.213872</td>\n",
       "      <td>0.066026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Balance</th>\n",
       "      <td>0.002684</td>\n",
       "      <td>0.011176</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000919</td>\n",
       "      <td>0.020105</td>\n",
       "      <td>-0.014673</td>\n",
       "      <td>0.076457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Inflation</th>\n",
       "      <td>-0.007325</td>\n",
       "      <td>-0.045544</td>\n",
       "      <td>0.000919</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.091227</td>\n",
       "      <td>-0.020755</td>\n",
       "      <td>-0.024693</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>GDP</th>\n",
       "      <td>-0.009151</td>\n",
       "      <td>-0.009970</td>\n",
       "      <td>0.020105</td>\n",
       "      <td>-0.091227</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.032856</td>\n",
       "      <td>0.105416</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Exports</th>\n",
       "      <td>-0.011244</td>\n",
       "      <td>0.213872</td>\n",
       "      <td>-0.014673</td>\n",
       "      <td>-0.020755</td>\n",
       "      <td>-0.032856</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.030824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Trade</th>\n",
       "      <td>-0.006027</td>\n",
       "      <td>0.066026</td>\n",
       "      <td>0.076457</td>\n",
       "      <td>-0.024693</td>\n",
       "      <td>0.105416</td>\n",
       "      <td>-0.030824</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Unnamed: 0      Year   Balance  Inflation       GDP   Exports  \\\n",
       "Unnamed: 0    1.000000 -0.021524  0.002684  -0.007325 -0.009151 -0.011244   \n",
       "Year         -0.021524  1.000000  0.011176  -0.045544 -0.009970  0.213872   \n",
       "Balance       0.002684  0.011176  1.000000   0.000919  0.020105 -0.014673   \n",
       "Inflation    -0.007325 -0.045544  0.000919   1.000000 -0.091227 -0.020755   \n",
       "GDP          -0.009151 -0.009970  0.020105  -0.091227  1.000000 -0.032856   \n",
       "Exports      -0.011244  0.213872 -0.014673  -0.020755 -0.032856  1.000000   \n",
       "Trade        -0.006027  0.066026  0.076457  -0.024693  0.105416 -0.030824   \n",
       "\n",
       "               Trade  \n",
       "Unnamed: 0 -0.006027  \n",
       "Year        0.066026  \n",
       "Balance     0.076457  \n",
       "Inflation  -0.024693  \n",
       "GDP         0.105416  \n",
       "Exports    -0.030824  \n",
       "Trade       1.000000  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0        int64\n",
       "Country Name     object\n",
       "Country Code     object\n",
       "Year              int64\n",
       "Balance         float64\n",
       "Inflation       float64\n",
       "GDP             float64\n",
       "Exports         float64\n",
       "Trade           float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-78-ad7476465862>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'GDP'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'Trade'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mboxplot\u001b[0;34m(x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth, whis, notch, ax, **kwargs)\u001b[0m\n\u001b[1;32m   2235\u001b[0m     \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mwhis\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwhis\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnotch\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnotch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2236\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2237\u001b[0;31m     \u001b[0mplotter\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2238\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2239\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, ax, boxplot_kws)\u001b[0m\n\u001b[1;32m    547\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mboxplot_kws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    548\u001b[0m         \u001b[0;34m\"\"\"Make the plot.\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 549\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdraw_boxplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mboxplot_kws\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    550\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mannotate_axes\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    551\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0morient\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"h\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mdraw_boxplot\u001b[0;34m(self, ax, kws)\u001b[0m\n\u001b[1;32m    484\u001b[0m                                          \u001b[0mpositions\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    485\u001b[0m                                          \u001b[0mwidths\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwidth\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 486\u001b[0;31m                                          **kws)\n\u001b[0m\u001b[1;32m    487\u001b[0m                 \u001b[0mcolor\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolors\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    488\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrestyle_boxplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0martist_dict\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mprops\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1808\u001b[0m                         \u001b[0;34m\"the Matplotlib list!)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1809\u001b[0m                         RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1810\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1811\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1812\u001b[0m         inner.__doc__ = _add_data_doc(inner.__doc__,\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mboxplot\u001b[0;34m(self, x, notch, sym, vert, whis, positions, widths, patch_artist, bootstrap, usermedians, conf_intervals, meanline, showmeans, showcaps, showbox, showfliers, boxprops, labels, flierprops, medianprops, meanprops, capprops, whiskerprops, manage_xticks, autorange, zorder)\u001b[0m\n\u001b[1;32m   3613\u001b[0m                            \u001b[0mmeanline\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mmeanline\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mshowfliers\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mshowfliers\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3614\u001b[0m                            \u001b[0mcapprops\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcapprops\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwhiskerprops\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mwhiskerprops\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3615\u001b[0;31m                            manage_xticks=manage_xticks, zorder=zorder)\n\u001b[0m\u001b[1;32m   3616\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0martists\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3617\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mbxp\u001b[0;34m(self, bxpstats, positions, widths, vert, patch_artist, shownotches, showmeans, showcaps, showbox, showfliers, boxprops, whiskerprops, flierprops, medianprops, capprops, meanprops, meanline, manage_xticks, zorder)\u001b[0m\n\u001b[1;32m   3967\u001b[0m             ))\n\u001b[1;32m   3968\u001b[0m             whiskers.extend(doplot(\n\u001b[0;32m-> 3969\u001b[0;31m                 \u001b[0mwhisker_x\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwhiskerhi_y\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mfinal_whiskerprops\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3970\u001b[0m             ))\n\u001b[1;32m   3971\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mdoplot\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   3882\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mvert\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3883\u001b[0m             \u001b[0;32mdef\u001b[0m \u001b[0mdoplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3884\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3885\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3886\u001b[0m             \u001b[0;32mdef\u001b[0m \u001b[0mdopatch\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mxs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mys\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/matplotlib/__init__.py\u001b[0m in \u001b[0;36minner\u001b[0;34m(ax, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1808\u001b[0m                         \u001b[0;34m\"the Matplotlib list!)\"\u001b[0m \u001b[0;34m%\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mlabel_namer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1809\u001b[0m                         RuntimeWarning, stacklevel=2)\n\u001b[0;32m-> 1810\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfunc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0max\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1811\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1812\u001b[0m         inner.__doc__ = _add_data_doc(inner.__doc__,\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_axes.py\u001b[0m in \u001b[0;36mplot\u001b[0;34m(self, scalex, scaley, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1610\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1611\u001b[0m         \u001b[0;32mfor\u001b[0m \u001b[0mline\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_get_lines\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1612\u001b[0;31m             \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0madd_line\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1613\u001b[0m             \u001b[0mlines\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1614\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/matplotlib/axes/_base.py\u001b[0m in \u001b[0;36madd_line\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m   1891\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_set_artist_props\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1892\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1893\u001b[0;31m             \u001b[0mline\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mset_clip_path\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpatch\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1894\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1895\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_update_line_limits\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/matplotlib/artist.py\u001b[0m in \u001b[0;36mset_clip_path\u001b[0;34m(self, path, transform)\u001b[0m\n\u001b[1;32m    683\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtransform\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    684\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mRectangle\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 685\u001b[0;31m                 self.clipbox = TransformedBbox(Bbox.unit(),\n\u001b[0m\u001b[1;32m    686\u001b[0m                                                path.get_transform())\n\u001b[1;32m    687\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_clippath\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/anaconda3/lib/python3.7/site-packages/matplotlib/transforms.py\u001b[0m in \u001b[0;36munit\u001b[0;34m()\u001b[0m\n\u001b[1;32m    797\u001b[0m         \u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    798\u001b[0m         \"\"\"\n\u001b[0;32m--> 799\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0mBbox\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m0.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;36m1.0\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1.0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    800\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    801\u001b[0m     \u001b[0;34m@\u001b[0m\u001b[0mstaticmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot(x='GDP',y='Trade',data=df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}