51_Numpy.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "4820eb44",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: numpy in c:\\users\\sunny\\anaconda3\\lib\\site-packages (1.20.3)\n"
     ]
    }
   ],
   "source": [
    "!pip install numpy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "006985ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "81b54238",
   "metadata": {},
   "source": [
    "## Content\n",
    "\n",
    "- **Installing and Importing Numpy**\n",
    "\n",
    "- **Introduction to use case**\n",
    "\n",
    "- **Motivation: Why to use Numpy? - How is it different from Python Lists?**\n",
    "\n",
    "\n",
    "- **Creating a Basic Numpy Array** \n",
    "    - From a List - `array()`, `shape`, `ndim`\n",
    "    - From a range and stepsize - `arange()`\n",
    "    - From a range and count of elements - `linspace()`\n",
    "    - `type()` ndarray\n",
    "\n",
    "\n",
    "- **How numpy works under the hood?**\n",
    "\n",
    "    \n",
    "- **2-D arrays (Matrices)**\n",
    "    - `reshape()`\n",
    "    - `resize()`\n",
    "    - Transpose\n",
    "    - Converting Matrix back to Vector - `flatten()`\n",
    "\n",
    "\n",
    "- **Creating some special arrays using Numpy**\n",
    "    - `zeros()`\n",
    "    - `ones()`\n",
    "    - `diag()`\n",
    "    - `identity()`\n",
    "\n",
    "\n",
    "- **Indexing and Slicing** \n",
    "    - Indexing\n",
    "    - Slicing\n",
    "    - Masking (Fancy Indexing)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0992c776",
   "metadata": {},
   "source": [
    "### Use case Introduction: Fitbit\n",
    "\n",
    "\n",
    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)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d8b98740",
   "metadata": {},
   "source": [
    "\n",
    "#### Every row is called a record or data point and every column is a feature\n",
    "\n",
    "#### What kind of questions can we answer using this data?\n",
    "\n",
    "\n",
    "- How many records and features are there in the dataset?\n",
    "- What is the **average step count**?\n",
    "- On which day the **step count was highest/lowest?** \n",
    "- What's the **most frequent mood**?\n",
    "\n",
    "We will try finding \n",
    "- How daily activity affects mood? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e482fb53",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 4, 9, 16, 25]\n"
     ]
    }
   ],
   "source": [
    "a = [1,2,3,4,5]\n",
    "asq = [x**2 for x in a]\n",
    "print(asq)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "1f0293da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 5]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "1b740394",
   "metadata": {},
   "outputs": [],
   "source": [
    "a_np = np.array(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b01f39a7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "5a76dc37",
   "metadata": {},
   "outputs": [],
   "source": [
    "asq = a_np ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1e2458d3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  4,  9, 16, 25], dtype=int32)"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asq"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dc973cac",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6b4e3605",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "49ee3507",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst = range(1000000)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7f3e521d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "82.1 ms ± 2.26 ms per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit [i*i for i in lst]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d9e4b3a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "aeb21c7d",
   "metadata": {},
   "outputs": [],
   "source": [
    "lst_np = np.arange(1000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0b372fbe",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.73 ms ± 12.1 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit lst_np ** 2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e827701",
   "metadata": {},
   "source": [
    "### How numpy works under the hood? \n",
    "\n",
    "\n",
    "#### Numpy is written in C which allows it to **manage memory very efficiently**\n",
    "<img src=\"https://drive.google.com/uc?export=view&id=1dWLBqmxzlstKcpok2aYs0xi-61UF9bmO\" width=\"300\"/>\n",
    "- In **Python List**, we can **store objects of different types together** - int, float, string, etc.\n",
    "- The **actual values** of objects are **stored somewhere else in the memory** \n",
    "- Only **References to those objects** (R1, R2, R3, ...) are stored in the Python List.\n",
    "\n",
    "- So, when we have to access an element in Python List, we **first access the reference** to that element and then that **reference allows us to access the value** of element stored in memory\n",
    "\n",
    "#### C array does all this in one step\n",
    "\n",
    "- **C array stores objects of same data type together**\n",
    "\n",
    "- **Actual values** are **stored in same contiguous memory**\n",
    "\n",
    "- So, when we have to access an element in C array, we **access it directly using indices**.\n",
    "\n",
    "#### This makes NumPy array lose the flexibility to store heterogenous data\n",
    "\n",
    "==> Unlike Python lists, NumPy array can only hold contigous data\n",
    "\n",
    "- So numpy arrays are **NOT** really **Python lists**\n",
    "- They are basically **C arrays**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6cec288",
   "metadata": {},
   "source": [
    "If ypu have to perform element-wise operation, Numpy provides\n",
    "\n",
    "1. Clean Syntax\n",
    "2. Computation Benefit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2adffa2f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "2b331003",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.array([1,2,3,4,5,6])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "4ca8625e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2,  4,  6,  8, 10, 12])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a*2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a5e30cd9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "7814ef65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(6,)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape   # 6 elements as ts 1d "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "19ee23da",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "d13d28da",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 (8,)\n"
     ]
    }
   ],
   "source": [
    "x = np.array([1,2,3,4,5,6,7,8])\n",
    "print(x.ndim,x.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e12ed63d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "eeb5ac84",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4e3077d1",
   "metadata": {},
   "source": [
    "### np.arange(start,end,step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "935c3483",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 55])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0,56,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "f907697e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. , 5.5])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0,6,0.5)    # # valid for arnage, not range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "750e5386",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.  , 0.25, 0.5 , 0.75, 1.  , 1.25, 1.5 , 1.75, 2.  , 2.25, 2.5 ,\n",
       "       2.75, 3.  , 3.25, 3.5 , 3.75, 4.  , 4.25, 4.5 , 4.75])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0,5,0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "0a3cbf1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([5.  , 4.75, 4.5 , 4.25, 4.  , 3.75, 3.5 , 3.25, 3.  , 2.75, 2.5 ,\n",
       "       2.25, 2.  , 1.75, 1.5 , 1.25, 1.  , 0.75, 0.5 , 0.25, 0.  ])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(5,-0.25,-0.25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "3c155c7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([  0.        ,   4.16666667,   8.33333333,  12.5       ,\n",
       "        16.66666667,  20.83333333,  25.        ,  29.16666667,\n",
       "        33.33333333,  37.5       ,  41.66666667,  45.83333333,\n",
       "        50.        ,  54.16666667,  58.33333333,  62.5       ,\n",
       "        66.66666667,  70.83333333,  75.        ,  79.16666667,\n",
       "        83.33333333,  87.5       ,  91.66666667,  95.83333333,\n",
       "       100.        ])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "start, end, flag_count = 0, 100, 25\n",
    "np.linspace(start, end, flag_count) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0d3ee64d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4260f31c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "e968b9c9",
   "metadata": {},
   "source": [
    "### np.linspace(start,stop,step_counts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "9249e722",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.,  25.,  50.,  75., 100.])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# start # end # count \n",
    "np.linspace(0,100,5)  # devides stops into 5 parts , including start & stop"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4bf791e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1.,  34.,  67., 100.])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(1,100,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "b9a0036a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0. , 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1. ])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,1,11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "f85dc940",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0.,  10.,  20.,  30.,  40.,  50.,  60.,  70.,  80.,  90., 100.])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,100,11)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "96f8cd5b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8d77086c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "07343e83",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a5a3f0f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5c7dbd56",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8efa97c4",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.array([1,2,3,4,5,6])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "18ae1c59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "6d4af268",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5 6]\n"
     ]
    }
   ],
   "source": [
    "print(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5b3665e5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "a8a2ee5e",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr2 = np.array([1,2,3,4.0,5,6.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "527755c9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5 6]\n"
     ]
    }
   ],
   "source": [
    "print(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "5def2a7d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 2., 3., 4., 5., 6.])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1,2,3.0,4,5.0,6],dtype= \"float\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e9122e5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "dd87d178",
   "metadata": {},
   "source": [
    "# ---------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "d91af286",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "100000000000000000000"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "100**10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c8a3305c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2a3943ef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "19de7aea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr = np.array([0,10,100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "5f98d3c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,  10, 100])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "ff252d9e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([         0, 1410065408, 1661992960], dtype=int32)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr ** 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "cd0da9b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b9edaf93",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "f0f3068f",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr2 = np.array([0,10,100],dtype = \"int64\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "936f8869",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([                  0,         10000000000, 7766279631452241920],\n",
       "      dtype=int64)"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2**10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "8fce1fe3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "59f10625",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(arr2)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "28c72a88",
   "metadata": {},
   "source": [
    "# ---------------------------------------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4a859e2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b921cefc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['1', '2', '3.5', 'Sunny'], dtype='<U32')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1,2,3.5,\"Sunny\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54f5ed58",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "f754cf5c",
   "metadata": {},
   "source": [
    "#### Another way np array behaves like C arrays and not Python lists\n",
    "\n",
    "- In Python lists, number values can be **arbitrarily large or small**\n",
    "- There's **usually no overflow of number values**\n",
    "\n",
    "\n",
    "#### However, in C, C++ and Java, there's overflow of values\n",
    "\n",
    "- As soon as a **number crosses the max possible** value for a data-type,the number gets **wrapped around to a smaller value**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "852c7894",
   "metadata": {},
   "source": [
    "#  ---------------------------------------------------\n",
    "\n",
    "### 2d arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "cfa97041",
   "metadata": {},
   "outputs": [],
   "source": [
    "M = np.array([[1,2,3,4,5],[6,7,8,9,5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "8b5ede46",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 4, 5],\n",
       "       [6, 7, 8, 9, 5]])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "989de819",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "e8eb6b80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 5)"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M.shape    # 2*5 matrix (n*m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "46a051a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "46d01488",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(M[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "57a8f283",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "(1, 5)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([[1,2,3,4,5]]).shape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f1d7896",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "89185a16",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "9b840291",
   "metadata": {},
   "source": [
    "#### reshape "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "95c33768",
   "metadata": {},
   "outputs": [],
   "source": [
    "M2 = np.arange(1,13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "d1d87624",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "09cfd8ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12,)"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "5f725a1f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M2.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "aede1872",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8],\n",
       "       [ 9, 10, 11, 12]])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M2.reshape(3,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "9066e846",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3],\n",
       "       [ 4,  5,  6],\n",
       "       [ 7,  8,  9],\n",
       "       [10, 11, 12]])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M2.reshape(4,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "3bff11a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1],\n",
       "       [ 2],\n",
       "       [ 3],\n",
       "       [ 4],\n",
       "       [ 5],\n",
       "       [ 6],\n",
       "       [ 7],\n",
       "       [ 8],\n",
       "       [ 9],\n",
       "       [10],\n",
       "       [11],\n",
       "       [12]])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M2.reshape(12,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "fea44daa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M2.reshape(1,12)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4c63a42b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa2d8783",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "a155e701",
   "metadata": {},
   "source": [
    "#### resize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "dd342ec1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "28f5768e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(4)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "000dc571",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0],\n",
       "       [1],\n",
       "       [2],\n",
       "       [3]])"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.reshape(4,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "ef956b09",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1],\n",
       "       [2, 3]])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.reshape(2,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "1bb932fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.reshape(1,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "dc364c33",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "cannot reshape array of size 4 into shape (2,4)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_13044/1324811426.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m: cannot reshape array of size 4 into shape (2,4)"
     ]
    }
   ],
   "source": [
    "a.reshape(2,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a89113fb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "232f45c5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "abb89f19",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.arange(4)\n",
    "x\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e1c5fefd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3],\n",
       "       [0, 1, 2, 3]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.resize(x,(2,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "f8c26ea1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3, 0, 1, 2],\n",
       "       [3, 0, 1, 2, 3, 0, 1]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.resize(x,(2,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "3adfccee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3, 0, 1],\n",
       "       [2, 3, 0, 1, 2, 3],\n",
       "       [0, 1, 2, 3, 0, 1],\n",
       "       [2, 3, 0, 1, 2, 3]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.resize(x,(4,6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "38d8a42e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3],\n",
       "       [0, 0, 0, 0]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.arange(4)\n",
    "y.resize((2,4))\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "3f10c1c0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = np.arange(1,6)\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "id": "767707ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 4, 5, 1, 2],\n",
       "       [3, 4, 5, 1, 2, 3, 4],\n",
       "       [5, 1, 2, 3, 4, 5, 1],\n",
       "       [2, 3, 4, 5, 1, 2, 3],\n",
       "       [4, 5, 1, 2, 3, 4, 5],\n",
       "       [1, 2, 3, 4, 5, 1, 2],\n",
       "       [3, 4, 5, 1, 2, 3, 4]])"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.resize(z,(7,7))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "868cb816",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr = np.arange(1,15)\n",
    "arr.resize(10,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9ba62518",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3],\n",
       "       [ 4,  5,  6],\n",
       "       [ 7,  8,  9],\n",
       "       [10, 11, 12],\n",
       "       [13, 14,  0],\n",
       "       [ 0,  0,  0],\n",
       "       [ 0,  0,  0],\n",
       "       [ 0,  0,  0],\n",
       "       [ 0,  0,  0],\n",
       "       [ 0,  0,  0]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4d788e1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af85a3f1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e432a14",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44484e43",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "e620b019",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr = np.arange(1,13).reshape(3,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "cacd2923",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8],\n",
       "       [ 9, 10, 11, 12]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5f2cdf2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  5,  9],\n",
       "       [ 2,  6, 10],\n",
       "       [ 3,  7, 11],\n",
       "       [ 4,  8, 12]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "c62e65f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  5,  9],\n",
       "       [ 2,  6, 10],\n",
       "       [ 3,  7, 11],\n",
       "       [ 4,  8, 12]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4e66ca7b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "2c6e4b6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "asd = np.array([1,2,3,4])\n",
    "asd.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b0179529",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "id": "51c7271b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12])"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "id": "3a178a22",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12,)"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.flatten().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "4db6548f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 1)"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.reshape(12,1).shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92e07c32",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb3648db",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "981c3059",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2d58df62",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "17f809b1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6d15a6be",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "eba9e3a6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "173c0b46",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "8309bd1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr= np.array([[ 1,  2,  3,  4],\n",
    "       [ 5,  6,  7,  8],\n",
    "       [ 9, 10, 11, 12]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "444e6005",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 4)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "6dd6fae0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2],\n",
       "       [ 3,  4],\n",
       "       [ 5,  6],\n",
       "       [ 7,  8],\n",
       "       [ 9, 10],\n",
       "       [11, 12]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.reshape(6,-1)\n",
    "#IT WILL ARRANGE THE ARRAY IN \"n\" ROWS , \n",
    "# -1 WILL JUST ADJUST THE REST ELEMENTS WITH REQUIRED COLUMS\n",
    "\n",
    "# FIRST ARGUMENT SHOULD BE THE MULTIPLE OF N*M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "a659e130",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3],\n",
       "       [ 4,  5,  6],\n",
       "       [ 7,  8,  9],\n",
       "       [10, 11, 12]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.reshape(4,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "0f935bf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1],\n",
       "       [ 2],\n",
       "       [ 3],\n",
       "       [ 4],\n",
       "       [ 5],\n",
       "       [ 6],\n",
       "       [ 7],\n",
       "       [ 8],\n",
       "       [ 9],\n",
       "       [10],\n",
       "       [11],\n",
       "       [12]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.reshape(12,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "0d66c2a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4,  5,  6],\n",
       "       [ 7,  8,  9, 10, 11, 12]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.reshape(2,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "327baa2c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3],\n",
       "       [ 4,  5,  6],\n",
       "       [ 7,  8,  9],\n",
       "       [10, 11, 12]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.reshape(-1,3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "f706fb05",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8],\n",
       "       [ 9, 10, 11, 12]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.reshape(-1,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "fe4e6882",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4,  5,  6],\n",
       "       [ 7,  8,  9, 10, 11, 12]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.reshape(-1,6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "295da736",
   "metadata": {},
   "outputs": [],
   "source": [
    " "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "57451aad",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2db8b784",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "567bad4f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea21a01b",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94c14a99",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "14ea7b69",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "92fbac97",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0.])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "727ba618",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((5,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54e4b9d3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "id": "c5d70d72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((2,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "67fb18a5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 1., 1., 1., 1.])"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "41dab58d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8f8d6b6f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "id": "8778b758",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9., 9., 9., 9., 9.],\n",
       "       [9., 9., 9., 9., 9.]])"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((2,5))*9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7fa077bb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c528a938",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "afc1d822",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "5110db8c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# diagonal matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "bd3e1683",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, 0, 0],\n",
       "       [0, 2, 0, 0, 0],\n",
       "       [0, 0, 3, 0, 0],\n",
       "       [0, 0, 0, 4, 0],\n",
       "       [0, 0, 0, 0, 5]])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.diag([1,2,3,4,5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9467b5ed",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "id": "49cfe3d2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [4 5 6]\n",
      " [7 8 9]]\n",
      "\n",
      "[3 5 7]\n",
      "\n",
      "[7 5 3]\n"
     ]
    }
   ],
   "source": [
    "a = np.arange(1,10).reshape(3, 3)\n",
    "print(a)\n",
    "# array([[0, 1, 2],\n",
    "#        [3, 4, 5],\n",
    "#        [6, 7, 8]])\n",
    "print()\n",
    "Hflp = np.fliplr(a).diagonal()  # Horizontal flip\n",
    "print(Hflp)\n",
    "#array([2, 4, 6])\n",
    "\n",
    "print()\n",
    "Vflp = np.flipud(a).diagonal()  # Vertical flip\n",
    "print(Vflp)\n",
    "#array([6, 4, 2])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "id": "95135aef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [7, 8, 9]])"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "id": "54351d11",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9, 8, 7],\n",
       "       [6, 5, 4],\n",
       "       [3, 2, 1]])"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.flip(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "id": "d0a5d1eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 6, 9],\n",
       "       [2, 5, 8],\n",
       "       [1, 4, 7]])"
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.rot90(a,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "1ec3a99e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7, 4, 1],\n",
       "       [8, 5, 2],\n",
       "       [9, 6, 3]])"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.rot90(a,-1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f4d5be8f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "571a81b9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0b62384d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "id": "873be031",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.]])"
      ]
     },
     "execution_count": 177,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.identity(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8b68c29e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7e620690",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "96d13324",
   "metadata": {},
   "source": [
    "#### slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "78c9015c",
   "metadata": {},
   "outputs": [],
   "source": [
    "M = np.arange(1,13)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "0ba9cdf5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 12, 6)"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M[1],M[-1],M[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "c499e6a8",
   "metadata": {},
   "outputs": [],
   "source": [
    "M = M.reshape(3,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "id": "8afbc2da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8],\n",
       "       [ 9, 10, 11, 12]])"
      ]
     },
     "execution_count": 185,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "519fff8e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M[1][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "id": "f136b590",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M[1,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "59f87c06",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9aaee4f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f3043aa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4627a87c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "id": "f76dbba6",
   "metadata": {},
   "outputs": [],
   "source": [
    "m = np.array([987,654,321,456,789,123])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 192,
   "id": "2a7e07d7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "123"
      ]
     },
     "execution_count": 192,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "id": "9292e6d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([654, 321, 456])"
      ]
     },
     "execution_count": 193,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m[[1,2,3]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "4157cbd3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([654, 321, 321, 456, 456, 123, 123])"
      ]
     },
     "execution_count": 194,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m[[1,2,2,3,3,-1,-1]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d26a4d38",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0cea65d8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "09ffdcf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8],\n",
       "       [ 9, 10, 11, 12]])"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "id": "b8d15d43",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 9, 8])"
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M[[0,2,1],[0,0,3]]        # IMPORTANT\n",
    "# 1,9,8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc6ab9b8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "557913b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8],\n",
       "       [ 9, 10, 11, 12]])"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "0f03cb30",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8],\n",
       "       [ 9, 10, 11, 12]])"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M[:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 201,
   "id": "5a6512f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 11])"
      ]
     },
     "execution_count": 201,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M[2,1:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 202,
   "id": "9ebb08be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([10, 11, 12])"
      ]
     },
     "execution_count": 202,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M[2,1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "id": "f7b78103",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 7, 8])"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M[1,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9b893f16",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "9852986c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3,  4],\n",
       "       [ 7,  8],\n",
       "       [11, 12]])"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M[:,2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1c855b89",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b55759e6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e6d087f1",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9f67f60e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a4ef8aef",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "94455f98",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ffbcc743",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "54d300b2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a6589aa",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8efd5ba9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "id": "2417f369",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.arange(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "4945a740",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "id": "57f519ca",
   "metadata": {},
   "outputs": [],
   "source": [
    "x[5:] = -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "52e6f186",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4, -1, -1, -1, -1, -1])"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cd6da1e6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "id": "be8209c3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7, -3, -4, -5])"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.arange(1,11)\n",
    "y = np.array([-5,-4,-3])\n",
    "x[7:] = y[::-1]\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c4c05075",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa357e4d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ce111d9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "28ddaaf4",
   "metadata": {},
   "source": [
    "#### fancy indexing : masking\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "8efc1ceb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "M = np.random.randint(1, 25, size=(4, 4))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "b831d941",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[21,  5, 14,  5],\n",
       "       [ 4, 14, 19, 15],\n",
       "       [20, 23, 22, 24],\n",
       "       [14, 23, 15,  1]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "be362dc5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False,  True,  True,  True],\n",
       "       [ True,  True, False, False],\n",
       "       [False, False, False, False],\n",
       "       [ True, False, False,  True]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M < 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f1d16b99",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5, 14,  5,  4, 14, 14,  1])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M[ M < 15 ] # list of elements where element < 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "59c1ff98",
   "metadata": {},
   "outputs": [],
   "source": [
    "## filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b1d7505c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f348f034",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7de3d6ad",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 270,
   "id": "79382fcc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([15, 12,  6, 18, 18])"
      ]
     },
     "execution_count": 270,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M[M%3 == 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "id": "7edfbc1c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 8, 15, 16, 14, 12, 14, 14,  6, 14, 18, 18])"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M[(M%2 == 0)|(M%5==0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "acb8d3b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12,  6, 18, 18])"
      ]
     },
     "execution_count": 274,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M[(M % 2 == 0) & (M % 3 == 0)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "207d2f52",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3715ad9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2450e825",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e27fcc03",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 315,
   "id": "7db98cd5",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.random.randint(20,size = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 316,
   "id": "14d31c10",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  8,  4,  4, 18, 11, 17,  2,  8,  9])"
      ]
     },
     "execution_count": 316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 317,
   "id": "33688723",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1,  8,  4,  4, -1, 11, 17,  2,  8, -1])"
      ]
     },
     "execution_count": 317,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "msk = x%3==0\n",
    "\n",
    "x[msk] = -1\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b2efb721",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "7a323a28",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[30 24 18 12]\n",
      " [25 20 15 10]\n",
      " [20 16 12  8]\n",
      " [ 5  4  3  2]]\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([64])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([5,4,3,2]) \n",
    "y = np.array([[6],\n",
    "              [5],\n",
    "              [4],\n",
    "              [1]])\n",
    "print(x*y)\n",
    "\n",
    "np.dot(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "1ae85cc4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "30+20+12+2\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22c52fd7",
   "metadata": {},
   "source": [
    "# assignments "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 319,
   "id": "8d4b9dbc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([11,  9,  7,  3])"
      ]
     },
     "execution_count": 319,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([5,4,3,2]) \n",
    "y = np.array([6,5,4,1])\n",
    "x+y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0c1b8db3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 321,
   "id": "1b3d3f25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[11, 9, 7, 3]"
      ]
     },
     "execution_count": 321,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = [5,4,3,2] \n",
    "y = [6,5,4,1]\n",
    "\n",
    "\n",
    "asd = []\n",
    "for i in range(len(y)):\n",
    "    asd.append(x[i]+y[i])\n",
    "asd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "818e2c01",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "fbc41e59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = np.array([-5, 9 , 20 , 25, -3, 5, 16, 10])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "4f90edbf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5,  9, 20, 25, -3,  5, 16, 10])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "aba82ba3",
   "metadata": {},
   "outputs": [],
   "source": [
    "x[(x >= -5) & (x <= 15)] *= -1 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1ee4b6d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "      -1    -1          -1  -1       -1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "71e5ba33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  5,  -9,  20,  25,   3,  -5,  16, -10])"
      ]
     },
     "execution_count": 328,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5a2c6993",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b8953dc6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 329,
   "id": "23e47fd2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Astha' 'Raghavendra' 'Ram']\n"
     ]
    }
   ],
   "source": [
    "print(np.sort(np.array(['Ram','Astha','Raghavendra'])))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "81cda8d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False])"
      ]
     },
     "execution_count": 330,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1=np.array(['Ram','Astha','Brahat'])\n",
    "arr2=np.array(['Shyam','Kalyan','Naveen'])\n",
    "arr1>arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7f07eb2e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0379826c",
   "metadata": {},
   "outputs": [],
   "source": [
    "m = 3\n",
    "n = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "id": "2cd408d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "A.shape = (3,)\n",
    "\n",
    "B.shape = (4, 1)\n",
    "\n",
    "C.shape = (4, 3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "6e19b921",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 345,
   "id": "6aa8de46",
   "metadata": {},
   "outputs": [],
   "source": [
    "A = np.array([1,2,3])\n",
    "B = np.array([[1],\n",
    "              [2],\n",
    "              [3],\n",
    "              [4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 346,
   "id": "e5075f3e",
   "metadata": {},
   "outputs": [],
   "source": [
    "C = np.array([[1,2,3],\n",
    "              [4,5,6],\n",
    "              [9,8,7],\n",
    "             [3,2,4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 347,
   "id": "73c5e935",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3,)\n",
      "(4, 1)\n",
      "(4, 3)\n"
     ]
    }
   ],
   "source": [
    "print(A.shape)\n",
    "print(B.shape)\n",
    "print(C.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 353,
   "id": "0f311ee9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 4, 9, 3, 1],\n",
       "       [2, 5, 8, 2, 2],\n",
       "       [3, 6, 7, 4, 3]])"
      ]
     },
     "execution_count": 353,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.concatenate((C.T, A.reshape(3,1)), axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a8b774e9",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "id": "7d3640eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11]])"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.arange(12).reshape(3,4)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "id": "db5926ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True])"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.max(axis=1) == [ 3, 7, 11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "id": "4f0c54b6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.sum() == 11"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "id": "40412e06",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True,  True])"
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.T.max(axis=0) == [ 3, 7, 11]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 361,
   "id": "ce8832b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 361,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i.min() for i in X] == [0, 4, 8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 362,
   "id": "5c2f14f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0, 4, 8]"
      ]
     },
     "execution_count": 362,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[i.min() for i in X]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e0c408e3",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fcc9ffa2",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 363,
   "id": "5fe4e0ef",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.zeros(27).reshape(3, 3, 3)\n",
    "b = np.arange(9).reshape(3, 3) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "id": "6037ac78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]],\n",
       "\n",
       "       [[0., 0., 0.],\n",
       "        [0., 0., 0.],\n",
       "        [0., 0., 0.]]])"
      ]
     },
     "execution_count": 364,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 365,
   "id": "9e4ebb12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5],\n",
       "       [6, 7, 8]])"
      ]
     },
     "execution_count": 365,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 371,
   "id": "c47e23ab",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ True,  True,  True],\n",
       "        [ True,  True,  True],\n",
       "        [ True,  True,  True]],\n",
       "\n",
       "       [[ True,  True,  True],\n",
       "        [ True,  True,  True],\n",
       "        [ True,  True,  True]],\n",
       "\n",
       "       [[ True,  True,  True],\n",
       "        [ True,  True,  True],\n",
       "        [ True,  True,  True]]])"
      ]
     },
     "execution_count": 371,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a+b == a + b[np.newaxis, :, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 367,
   "id": "fbd6e3c1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0., 1., 2.],\n",
       "        [3., 4., 5.],\n",
       "        [6., 7., 8.]],\n",
       "\n",
       "       [[0., 1., 2.],\n",
       "        [3., 4., 5.],\n",
       "        [6., 7., 8.]],\n",
       "\n",
       "       [[0., 1., 2.],\n",
       "        [3., 4., 5.],\n",
       "        [6., 7., 8.]]])"
      ]
     },
     "execution_count": 367,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a + b[np.newaxis, :, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "id": "9f4dae42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[ True,  True,  True],\n",
       "         [ True,  True,  True],\n",
       "         [ True,  True,  True]],\n",
       "\n",
       "        [[ True,  True,  True],\n",
       "         [ True,  True,  True],\n",
       "         [ True,  True,  True]],\n",
       "\n",
       "        [[ True,  True,  True],\n",
       "         [ True,  True,  True],\n",
       "         [ True,  True,  True]]]])"
      ]
     },
     "execution_count": 372,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a+b == b + a[np.newaxis, :, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3373f70d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a5425d38",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cc0d7a83",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "0b6d97ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[ 2,  3,  4,  5],\n",
       "       [ 1,  7,  3,  5],\n",
       "       [ 2,  8,  6,  9],\n",
       "       [11, 23, 12, 19]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr= np.array([[2,3,4,5],[1,7,3,5],[2,8,6,9],[11,23,12,19]])\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "f5c2ee6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# array ([[4,6,8,10],\n",
    "#         [2,14,6,10],\n",
    "#         [4,16,12,18],\n",
    "#         [22,46,24,38]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "defb7dec",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "901e414a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr1= np.array([[2,2,2,2]])\n",
    "def func(x,y):\n",
    "      return x*y\n",
    "vec= np.vectorize(func)\n",
    "e = vec(arr,arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "aff71715",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr1= np.array([[2],[2],[2],[2]])\n",
    "def func(x,y):\n",
    "     return x*y\n",
    "vec= np.vectorize(func)\n",
    "f = vec(arr,arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "126c66ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "arr1= 2   \n",
    "def func(x,y):\n",
    "    return x*y\n",
    "vec= np.vectorize(func)\n",
    "g = vec(arr,arr1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "7fe434b8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "e == f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "87c319e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True],\n",
       "       [ True,  True,  True,  True]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f == g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8ed66e34",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e2b3ef95",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 382,
   "id": "d45f317c",
   "metadata": {},
   "outputs": [],
   "source": [
    "A = np.array([2, 0, 1, 9, 1, 1, 1, 0, 3, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 383,
   "id": "7f3fe790",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  1,  2, 10,  2,  2,  2,  1,  4,  6])"
      ]
     },
     "execution_count": 383,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A+1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 384,
   "id": "7360b5e2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 1, 2, 10, 2, 2, 2, 1, 4, 6]"
      ]
     },
     "execution_count": 384,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "list(map(lambda x:x+1, A))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 385,
   "id": "e3669bb8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 1, 2, 10, 2, 2, 2, 1, 4, 6]"
      ]
     },
     "execution_count": 385,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[x+1 for x in A]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 387,
   "id": "ce16186c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  1,  2, 10,  2,  2,  2,  1,  4,  6])"
      ]
     },
     "execution_count": 387,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A+[1, 1 ,1 ,1, 1, 1 ,1 ,1, 1, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5f8e5dd5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b56823e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 388,
   "id": "58886cc2",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.ones((5,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 389,
   "id": "717b9848",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 389,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 394,
   "id": "9f8bbf47",
   "metadata": {},
   "outputs": [],
   "source": [
    "x[1:-1,1:-1] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 395,
   "id": "22ee84f0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1., 1.],\n",
       "       [1., 0., 0., 0., 1.],\n",
       "       [1., 0., 0., 0., 1.],\n",
       "       [1., 0., 0., 0., 1.],\n",
       "       [1., 1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 395,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 396,
   "id": "aaee0673",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = np.arange(30*32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 399,
   "id": "2ae32f98",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = a.reshape(10,3,32)\n",
    "b = np.arange(30*1).reshape(10,3,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 401,
   "id": "36228d52",
   "metadata": {},
   "outputs": [],
   "source": [
    "c = a+b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 402,
   "id": "0551c9d0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 3, 32)"
      ]
     },
     "execution_count": 402,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 418,
   "id": "933f3575",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 3, 4],\n",
       "       [5, 6, 7]])"
      ]
     },
     "execution_count": 418,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xx = np.array([[2,3,4],\n",
    "               [5,6,7]])\n",
    "xx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 421,
   "id": "e4ee8bf2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   1010,    1011,     100],\n",
       "       [1100101, 1101110,     111]])"
      ]
     },
     "execution_count": 421,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n = len(xx)\n",
    "m = len(xx[0])\n",
    "\n",
    "for i in range(n):\n",
    "    for j in range(m):\n",
    "        xx[i][j] = int(bin(xx[i][j])[2:])\n",
    "        \n",
    "xx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 422,
   "id": "53fb6f1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "def binary(arr):\n",
    "\n",
    "    def func(x):\n",
    "        ls=\"\"\n",
    "        while x>0:\n",
    "            ls = ls+ str(x%2)\n",
    "            x=x//2 \n",
    "        return int(ls[::-1])\n",
    "    vec= np.vectorize(func)\n",
    "    arr= vec(arr)\n",
    "    return arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b64b1d42",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "97cd74b8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "56b02c8f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 448,
   "id": "454de11c",
   "metadata": {},
   "outputs": [],
   "source": [
    "asd = np.array([[5,3,9],\n",
    "                [2,1,4],\n",
    "                [7,6,8]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 449,
   "id": "c2807f75",
   "metadata": {},
   "outputs": [],
   "source": [
    "asd = asd.T\n",
    "\n",
    "xx = np.sort(asd,axis=1).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 450,
   "id": "6b030a5e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 1, 4],\n",
       "       [5, 3, 8],\n",
       "       [7, 6, 9]])"
      ]
     },
     "execution_count": 450,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 458,
   "id": "66e63ee9",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "\n",
    "def sort_marks(M, j):\n",
    "    M = np.array(M)\n",
    "    \n",
    "    M.T\n",
    "        \n",
    "    \n",
    "    \n",
    "    \n",
    "    return M\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 494,
   "id": "e0835eb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "\n",
    "M = [[5,3,9],\n",
    " [2,1,4],\n",
    " [7,6,8]]\n",
    "mm = sort_marks(M, 2)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 495,
   "id": "c88dfa81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 3, 9],\n",
       "       [2, 1, 4],\n",
       "       [7, 6, 8]])"
      ]
     },
     "execution_count": 495,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 496,
   "id": "fd731fe7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 2],\n",
       "       [1, 0, 2],\n",
       "       [1, 0, 2]], dtype=int64)"
      ]
     },
     "execution_count": 496,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mm.argsort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 497,
   "id": "5595fc50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0],\n",
       "       [0, 0],\n",
       "       [0, 2],\n",
       "       [1, 0],\n",
       "       [1, 0],\n",
       "       [1, 0],\n",
       "       [1, 0],\n",
       "       [1, 2],\n",
       "       [2, 1],\n",
       "       [2, 2]])"
      ]
     },
     "execution_count": 497,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "\n",
    "a = np.array([[1, 2],\n",
    "       [0, 0],\n",
    "       [1, 0],\n",
    "       [0, 2],\n",
    "       [2, 1],\n",
    "       [1, 0],\n",
    "       [1, 0],\n",
    "       [0, 0],\n",
    "       [1, 0],\n",
    "      [2, 2]])\n",
    "a[np.lexsort(np.fliplr(a).T)]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 498,
   "id": "18f64550",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 3, 9],\n",
       "       [2, 1, 4],\n",
       "       [7, 6, 8]])"
      ]
     },
     "execution_count": 498,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 499,
   "id": "01abf33d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 1, 4],\n",
       "       [5, 3, 9],\n",
       "       [7, 6, 8]])"
      ]
     },
     "execution_count": 499,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mm[np.lexsort(np.fliplr(mm).T)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 500,
   "id": "cae44f28",
   "metadata": {},
   "outputs": [],
   "source": [
    "a = mm"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 501,
   "id": "33ff8acb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 3, 9],\n",
       "       [2, 1, 4],\n",
       "       [7, 6, 8]])"
      ]
     },
     "execution_count": 501,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "47d3f1e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "A = np.array([[5, 3, 9],\n",
    "       [2, 1, 4],\n",
    "       [7, 6, 8]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "bfb6b03e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 3, 9],\n",
       "       [2, 1, 4],\n",
       "       [7, 6, 8]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "84b850ed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "AxisError",
     "evalue": "axis 2 is out of bounds for array of dimension 2",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAxisError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m~\\AppData\\Local\\Temp/ipykernel_19980/649822024.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mA\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0maxis\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[0m\n\u001b[0m",
      "\u001b[1;32m<__array_function__ internals>\u001b[0m in \u001b[0;36msort\u001b[1;34m(*args, **kwargs)\u001b[0m\n",
      "\u001b[1;32m~\\anaconda3\\lib\\site-packages\\numpy\\core\\fromnumeric.py\u001b[0m in \u001b[0;36msort\u001b[1;34m(a, axis, kind, order)\u001b[0m\n\u001b[0;32m    994\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    995\u001b[0m         \u001b[0ma\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0masanyarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ma\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcopy\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m\"K\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 996\u001b[1;33m     \u001b[0ma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msort\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[0mkind\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mkind\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0morder\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0morder\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    997\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0ma\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    998\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mAxisError\u001b[0m: axis 2 is out of bounds for array of dimension 2"
     ]
    }
   ],
   "source": [
    "np.sort(A,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "011d1067",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "array([[62, 60, 66,  2],\n",
       "       [13, 59, 44, 41],\n",
       "       [25, 86, 74, 68],\n",
       "       [95, 96,  4, 21],\n",
       "       [52, 64, 52, 37]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.random.randint(100, size=(5, 4))\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e229e4ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 4, 0, 3], dtype=int64)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:,0].argsort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "4bf3de8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "b = np.array([[2020, 1, 1, 98],\n",
    "              [2020, 2, 1, 99],\n",
    "              [2021, 3, 6, 43],\n",
    "              [2020, 2, 1, 54],\n",
    "              [2021, 1, 1, 54],\n",
    "              [2020, 1, 2, 74],\n",
    "              [2021, 1, 3, 87],\n",
    "              [2021, 3, 9, 23]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "47f4be91",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2020,    1,    1,   98],\n",
       "       [2020,    2,    1,   99],\n",
       "       [2021,    3,    6,   43],\n",
       "       [2020,    2,    1,   54],\n",
       "       [2021,    1,    1,   54],\n",
       "       [2020,    1,    2,   74],\n",
       "       [2021,    1,    3,   87],\n",
       "       [2021,    3,    9,   23]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fb83d28a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "37c97399",
   "metadata": {},
   "outputs": [],
   "source": [
    "b = b[b[:, 2].argsort()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "4eb54da9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2020,    1,    1,   98],\n",
       "       [2020,    2,    1,   99],\n",
       "       [2020,    2,    1,   54],\n",
       "       [2021,    1,    1,   54],\n",
       "       [2020,    1,    2,   74],\n",
       "       [2021,    1,    3,   87],\n",
       "       [2021,    3,    6,   43],\n",
       "       [2021,    3,    9,   23]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "425e6cea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "A = np.array([[5, 3, 9],\n",
    "               [2, 1, 4],\n",
    "               [7, 6, 8]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "fb1b618b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([3, 1, 6]), array([1, 0, 2], dtype=int64))"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "j = 1\n",
    "A[:, j],A[:, j].argsort()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "24955a17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 1, 4],\n",
       "       [5, 3, 9],\n",
       "       [7, 6, 8]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A[A[:, j].argsort()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "04986f8f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 1, 4],\n",
       "       [5, 3, 9],\n",
       "       [7, 6, 8]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c0e3c482",
   "metadata": {},
   "outputs": [],
   "source": [
    "def sort_marks(arr2d, j):\n",
    "\n",
    "    ans = arr2d[arr2d[:,j].argsort()]\n",
    "    x = []\n",
    "    for i in ans:\n",
    "        x.append(list(i))\n",
    "    \n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "459e8043",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5, 3, 9],\n",
       "       [2, 1, 4],\n",
       "       [7, 6, 8]])"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "829ac0eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[2, 1, 4], [5, 3, 9], [7, 6, 8]]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sort_marks(A,0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7dd32d1e",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da6dbbb4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c22e5ef2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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