1.Array-Intro.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Array : \n",
    "```numpy.array``` ,   ```numpy.array.shape```,   ```numpy.reshape```, ```numpy.concatenate```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 5, 7, 'apple', 2.4]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L = [1,3,5,7,\"apple\",2.4]\n",
    "L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'apple'"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 3, 5, 7, 1, 3, 5, 7]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "L + L"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A = np.array([1,2,3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = np.array([[2,3,5],[3,5,7],[6,8,9]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros([3,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1.],\n",
       "       [1., 1., 1.],\n",
       "       [1., 1., 1.]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones([3,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.24328245, 0.97738463, 0.18213799, 0.83052358, 0.3653109 ],\n",
       "       [0.63924704, 0.32634262, 0.93738712, 0.04190707, 0.16856443],\n",
       "       [0.02439421, 0.64339841, 0.25104598, 0.07552312, 0.33859142],\n",
       "       [0.24346396, 0.09796353, 0.3732192 , 0.2614549 , 0.13293965],\n",
       "       [0.13669362, 0.3105436 , 0.51570164, 0.04806873, 0.88910613]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.random.rand(5,5)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 3, 4],\n",
       "       [2, 4, 7],\n",
       "       [3, 7, 9]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.array([[1,3,4],[2,4,7],[3,7,9]])\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "LL = [[1,3,4],[2,4,7],[3,7,9]]\n",
    "LL[1][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[1,2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Array Indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.53015554, 0.34082372, 0.17875984, 0.64703264, 0.2235947 ],\n",
       "       [0.34290729, 0.66984187, 0.40666649, 0.82657358, 0.66325498],\n",
       "       [0.22065645, 0.93000416, 0.57442424, 0.70437704, 0.55194144],\n",
       "       [0.06551909, 0.43306581, 0.40549847, 0.31129663, 0.99645275],\n",
       "       [0.26043963, 0.73481174, 0.05637365, 0.2926374 , 0.82444519]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.random.rand(5,5)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.34290729, 0.66984187, 0.40666649, 0.82657358, 0.66325498])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7043770359239762"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[2,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.22065645, 0.93000416, 0.57442424, 0.70437704, 0.55194144],\n",
       "       [0.06551909, 0.43306581, 0.40549847, 0.31129663, 0.99645275],\n",
       "       [0.26043963, 0.73481174, 0.05637365, 0.2926374 , 0.82444519]])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.34082372, 0.66984187, 0.93000416, 0.43306581, 0.73481174])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.64703264, 0.82657358, 0.70437704, 0.31129663, 0.2926374 ])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[:,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.55330373, 0.06741463],\n",
       "       [0.97854604, 0.18927008]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[1:3,2:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.530155537186793\n",
      "0.6698418707020536\n",
      "0.5744242448498665\n",
      "0.31129663385625317\n",
      "0.8244451854101277\n"
     ]
    }
   ],
   "source": [
    "for i in range(5):\n",
    "    for j in range(5):\n",
    "        if i== j:\n",
    "            print(X[i,j])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.53015554, 0.66984187, 0.57442424, 0.31129663, 0.82444519])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.diag(X)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Visualization of an array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "sns.set()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = np.random.rand(10,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1a174a4240>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [15,10])\n",
    "sns.heatmap(X, annot=True,cmap=\"YlGnBu\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Subarray : \n",
    "Splitting to 4 subarrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 8 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = [15,10])\n",
    "\n",
    "plt.subplot(2,2,1)\n",
    "sns.heatmap(X[0:5,0:5], annot=True)\n",
    "\n",
    "plt.subplot(2,2,2)\n",
    "sns.heatmap(X[5:10,0:5], annot=True)\n",
    "\n",
    "plt.subplot(2,2,3)\n",
    "sns.heatmap(X[0:5,5:10], annot=True)\n",
    "\n",
    "plt.subplot(2,2,4)\n",
    "sns.heatmap(X[5:10,5:10], annot=True)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Shape:\n",
    "Change shape of array:  from [10 by 10] to [20 by 5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Find Array shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 10)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Modify shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.93852928, 0.5629153 , 0.40031156, 0.05806378, 0.97983286],\n",
       "       [0.94297538, 0.10800161, 0.11069516, 0.90720548, 0.19857628],\n",
       "       [0.58431666, 0.82412861, 0.92679995, 0.77921264, 0.5673404 ],\n",
       "       [0.03117379, 0.36952646, 0.44022574, 0.33607312, 0.4144617 ],\n",
       "       [0.43455739, 0.74942589, 0.51639448, 0.61336095, 0.48473164],\n",
       "       [0.40440422, 0.60614083, 0.57705755, 0.79288636, 0.21180724],\n",
       "       [0.72241987, 0.19907833, 0.80578831, 0.36106624, 0.0778791 ],\n",
       "       [0.11341762, 0.05541729, 0.67124287, 0.59567082, 0.27976907],\n",
       "       [0.24750836, 0.52605463, 0.97923593, 0.1903222 , 0.26829744],\n",
       "       [0.46525502, 0.34985357, 0.44309043, 0.69688965, 0.33400436],\n",
       "       [0.23468938, 0.94940977, 0.89626526, 0.86187936, 0.99052044],\n",
       "       [0.96050669, 0.0065963 , 0.49770304, 0.85285478, 0.65581363],\n",
       "       [0.09482151, 0.57702584, 0.86883705, 0.14260322, 0.10167723],\n",
       "       [0.2856549 , 0.11293518, 0.75423858, 0.12482127, 0.68321163],\n",
       "       [0.34892935, 0.23105718, 0.39435598, 0.2528592 , 0.74207706],\n",
       "       [0.8628569 , 0.08761234, 0.70950303, 0.60906093, 0.15525657],\n",
       "       [0.09659796, 0.40521082, 0.3779406 , 0.03447772, 0.06621547],\n",
       "       [0.76406415, 0.18869095, 0.73287798, 0.98307838, 0.41521145],\n",
       "       [0.30999207, 0.0850545 , 0.79054693, 0.72950549, 0.94548169],\n",
       "       [0.88379333, 0.51067006, 0.08175909, 0.63589428, 0.01402678]])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape = (20,5)\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Reshaping the Array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33,\n",
       "       34])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.arange(35)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "XX = np.reshape(X, (7, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14],\n",
       "       [15, 16, 17, 18, 19],\n",
       "       [20, 21, 22, 23, 24],\n",
       "       [25, 26, 27, 28, 29],\n",
       "       [30, 31, 32, 33, 34]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "XX"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14],\n",
       "       [15, 16, 17, 18, 19],\n",
       "       [20, 21, 22, 23, 24],\n",
       "       [25, 26, 27, 28, 29],\n",
       "       [30, 31, 32, 33, 34]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.arange(35).reshape(7,5)\n",
    "X"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Flatten the Array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 5)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X = np.random.rand(4,5)\n",
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.46425663, 0.26273357, 0.76715497, 0.02803551, 0.06077554],\n",
       "       [0.45085724, 0.20021709, 0.83866571, 0.93010498, 0.82287586],\n",
       "       [0.64015274, 0.40214994, 0.46140888, 0.10875569, 0.90314464],\n",
       "       [0.1996534 , 0.00575243, 0.19888495, 0.80968552, 0.66819322]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(20,)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y = X.flatten()\n",
    "Y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.46425663, 0.26273357, 0.76715497, 0.02803551, 0.06077554,\n",
       "       0.45085724, 0.20021709, 0.83866571, 0.93010498, 0.82287586,\n",
       "       0.64015274, 0.40214994, 0.46140888, 0.10875569, 0.90314464,\n",
       "       0.1996534 , 0.00575243, 0.19888495, 0.80968552, 0.66819322])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Concatenate the Array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "A = np.random.rand(3,4)\n",
    "B = np.random.rand(3,4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Verticle addition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "C = np.concatenate((A,B),axis =0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.72279121, 0.69991245, 0.02998703, 0.12220733],\n",
       "       [0.0016784 , 0.57873713, 0.43938855, 0.20021548],\n",
       "       [0.18972308, 0.44890851, 0.76709578, 0.73554246],\n",
       "       [0.89593371, 0.48033999, 0.45776717, 0.65204457],\n",
       "       [0.53473347, 0.76373758, 0.67908483, 0.74560973],\n",
       "       [0.32444887, 0.838872  , 0.72398484, 0.81445926]])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "C"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Horizontal Addition"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "D = np.concatenate((A,B),axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.72279121, 0.69991245, 0.02998703, 0.12220733, 0.89593371,\n",
       "        0.48033999, 0.45776717, 0.65204457],\n",
       "       [0.0016784 , 0.57873713, 0.43938855, 0.20021548, 0.53473347,\n",
       "        0.76373758, 0.67908483, 0.74560973],\n",
       "       [0.18972308, 0.44890851, 0.76709578, 0.73554246, 0.32444887,\n",
       "        0.838872  , 0.72398484, 0.81445926]])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "D"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Row sum and column sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X = np.random.rand(8,6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### sum of all elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24.566949569026413"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Row sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4.20288936, 2.3996539 , 2.46388761, 4.30291271, 2.60671914,\n",
       "       2.23461285, 2.68832592, 3.6679481 ])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(X,axis =1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Column sum"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.69453055, 3.84555591, 5.87500707, 2.90605569, 4.41441644,\n",
       "       3.83138392])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(X,axis =0)"
   ]
  }
 ],
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