tool_numpy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "from __future__ import division, print_function, unicode_literals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Some vocabulary\n",
    "\n",
    "- In NumPy, each dimension is called an **axis**.\n",
    "- The number of axes is called the **rank**.\n",
    "- An array's list of axis lengths is called the **shape** of the array.\n",
    "- The **size** of an array is the total number of elements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.zeros((3,4))\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 4)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.ndim # equal to len(a.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.size"
   ]
  },
  {
   "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.]],\n",
       "\n",
       "       [[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((2,3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(np.zeros((3,4)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.14159265, 3.14159265, 3.14159265, 3.14159265],\n",
       "       [3.14159265, 3.14159265, 3.14159265, 3.14159265],\n",
       "       [3.14159265, 3.14159265, 3.14159265, 3.14159265]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.full((3,4), np.pi)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.14159265, 3.14159265, 3.14159265, 3.14159265],\n",
       "       [3.14159265, 3.14159265, 3.14159265, 3.14159265],\n",
       "       [3.14159265, 3.14159265, 3.14159265, 3.14159265]])"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.empty((3,4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.0038394e-316, 0.0000000e+000, 0.0000000e+000],\n",
       "       [0.0000000e+000, 0.0000000e+000, 0.0000000e+000]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.empty((2,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3,  4],\n",
       "       [10, 20, 30, 40]])"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([[1,2,3,4], [10,20,30,40]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(1,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 2., 3., 4.])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(1., 5.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(1, 5, 0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         0.33333333 0.66666667 1.         1.33333333 1.66666667]\n",
      "[0.         0.33333333 0.66666667 1.         1.33333333 1.66666667]\n",
      "[0.         0.33333333 0.66666667 1.         1.33333334]\n"
     ]
    }
   ],
   "source": [
    "# However, when dealing with floats, the exact number of elements in the array is not always predictible\n",
    "\n",
    "print(np.arange(0, 5/3, 1/3))\n",
    "print(np.arange(0, 5/3, 0.333333333))\n",
    "print(np.arange(0, 5/3, 0.333333334))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.         0.33333333 0.66666667 1.         1.33333333 1.66666667]\n"
     ]
    }
   ],
   "source": [
    "# linspace is preferable when working with floats.\n",
    "# linspace returns an array containing a specific number of points evenly distributed between two values\n",
    "\n",
    "print(np.linspace(0, 5/3, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.98979886, 0.60499195, 0.11955901, 0.47051052],\n",
       "       [0.48492514, 0.86541642, 0.04570781, 0.858861  ],\n",
       "       [0.8875116 , 0.81054092, 0.43703465, 0.49900381]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# uniform distribtion\n",
    "np.random.rand(3,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.35588629, -1.11779741, -0.28268293, -1.11071425],\n",
       "       [ 1.34473334,  0.79804499,  1.90484489,  0.02868008],\n",
       "       [ 0.06562546, -0.51306774, -0.95795734,  0.32198102]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# normal distribution\n",
    "np.random.randn(3,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ephyan/ml/env/lib/python3.6/site-packages/matplotlib/axes/_axes.py:6521: MatplotlibDeprecationWarning: \n",
      "The 'normed' kwarg was deprecated in Matplotlib 2.1 and will be removed in 3.1. Use 'density' instead.\n",
      "  alternative=\"'density'\", removal=\"3.1\")\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(np.random.rand(100000), normed=True, bins=100, histtype='step', color='blue', label='rand')\n",
    "plt.hist(np.random.randn(100000), normed=True, bins=100, histtype='step', color='red', label='randn')\n",
    "plt.axis([-2.5, 2.5, 0, 1.1])\n",
    "plt.xlabel('Value')\n",
    "plt.ylabel('Density')\n",
    "plt.legend(loc='best')\n",
    "plt.title('Random distributions')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
       "        [ 0.,  1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9.]],\n",
       "\n",
       "       [[ 1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.,  1.],\n",
       "        [ 1.,  2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10.]],\n",
       "\n",
       "       [[ 2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.,  2.],\n",
       "        [ 2.,  3.,  4.,  5.,  6.,  7.,  8.,  9., 10., 11.]]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def my_function(z, y, x):\n",
    "    return x * y + z\n",
    "\n",
    "np.fromfunction(my_function, (3, 2, 10))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int64 [1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "c = np.arange(1,5)\n",
    "print(c.dtype, c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64 [1. 2. 3. 4.]\n"
     ]
    }
   ],
   "source": [
    "c = np.arange(1., 5.)\n",
    "print(c.dtype, c)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "complex64 [1.+0.j 2.+0.j 3.+0.j 4.+0.j]\n"
     ]
    }
   ],
   "source": [
    "d = np.arange(1, 5, dtype=np.complex64)\n",
    "print(d.dtype, d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d.itemsize"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## data buffer\n",
    "A array's data is actually stored in memory as a flat (one dimensinaly) byte buffer. It is available via the data **attribute**(you rarely need it though)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<memory at 0x7fb66bc74630>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f = np.array([[1,2], [1000, 2000]], dtype=np.int32)\n",
    "f.data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'\\x01\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\xe8\\x03\\x00\\x00\\xd0\\x07\\x00\\x00'"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "f.data.tobytes()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]\n",
      "Rank:  1\n"
     ]
    }
   ],
   "source": [
    "g = np.arange(24)\n",
    "print(g)\n",
    "print('Rank: ', g.ndim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]\n",
      " [12 13 14 15]\n",
      " [16 17 18 19]\n",
      " [20 21 22 23]]\n",
      "Rank:  2\n"
     ]
    }
   ],
   "source": [
    "g.shape = (6, 4)\n",
    "print(g)\n",
    "print('Rank: ', g.ndim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[[ 0  1  2  3]\n",
      "  [ 4  5  6  7]\n",
      "  [ 8  9 10 11]]\n",
      "\n",
      " [[12 13 14 15]\n",
      "  [16 17 18 19]\n",
      "  [20 21 22 23]]]\n",
      "Rank:  3\n"
     ]
    }
   ],
   "source": [
    "g.shape = (2, 3, 4)\n",
    "print(g)\n",
    "print('Rank: ', g.ndim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0  1  2  3  4  5]\n",
      " [ 6  7  8  9 10 11]\n",
      " [12 13 14 15 16 17]\n",
      " [18 19 20 21 22 23]]\n",
      "Rank:  2\n"
     ]
    }
   ],
   "source": [
    "# reshape returns new ndarry that point to the same data\n",
    "g2 = g.reshape(4, 6)\n",
    "print(g2)\n",
    "print('Rank: ', g2.ndim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   1,   2,   3,   4,   5],\n",
       "       [  6,   7, 999,   9,  10,  11],\n",
       "       [ 12,  13,  14,  15,  16,  17],\n",
       "       [ 18,  19,  20,  21,  22,  23]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g2[1, 2] = 999\n",
    "g2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[  0,   1,   2,   3],\n",
       "        [  4,   5,   6,   7],\n",
       "        [999,   9,  10,  11]],\n",
       "\n",
       "       [[ 12,  13,  14,  15],\n",
       "        [ 16,  17,  18,  19],\n",
       "        [ 20,  21,  22,  23]]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  0,   1,   2,   3,   4,   5,   6,   7, 999,   9,  10,  11,  12,\n",
       "        13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ravel returns new one-dimensional ndarry that also point to the same data\n",
    "g.ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "a + b  = [19 27 35 43]\n",
      "a - b  = [ 9 19 29 39]\n",
      "a * b  = [70 92 96 82]\n",
      "a / b  = [ 2.8         5.75       10.66666667 20.5       ]\n",
      "a // b  = [ 2  5 10 20]\n",
      "a % b  = [4 3 2 1]\n",
      "a ** b = [537824 279841  32768   1681]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([14, 23, 32, 41])\n",
    "b = np.array([5,  4,  3,  2])\n",
    "print(\"a + b  =\", a + b)\n",
    "print(\"a - b  =\", a - b)\n",
    "print(\"a * b  =\", a * b)\n",
    "print(\"a / b  =\", a / b)\n",
    "print(\"a // b  =\", a // b)\n",
    "print(\"a % b  =\", a % b)\n",
    "print(\"a ** b =\", a ** b)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Broadcasting\n",
    "## Broadcasting\n",
    "\n",
    "- **Fisrt rule**: if the arrays do not have the same rank, then a 1 will be prepended to the smaller ranking arrays until their ranks match\n",
    "- **Second rule**: Arrays with a 1 along a particular dimension act as if they had the size of the array with largest shape along that dimension. The value of the array element is repeated along that dimension\n",
    "- **Third rule**: After rules 1 & 2, the sizes of all arrays must match"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0, 1, 2, 3, 4]]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h = np.arange(5).reshape((1,1,5))\n",
    "h"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[10, 21, 32, 43, 54]]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "h + [10, 20, 30, 40, 50]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k = np.arange(6).reshape(2, 3)\n",
    "k"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[100, 101, 102],\n",
       "       [203, 204, 205]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k + [[100], [200]] # same as adding [[100, 100, 100], [200, 200, 200]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[100, 101, 102],\n",
       "       [203, 204, 205]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k + np.array([100, 200]).reshape(2,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[100, 201, 302],\n",
       "       [103, 204, 305]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k + [100, 200, 300] # after rule 1: [[100, 200, 300]], and after rule 2 [[100, 200, 300], [100, 200, 300]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1000, 1001, 1002],\n",
       "       [1003, 1004, 1005]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k + 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "operands could not be broadcast together with shapes (2,3) (2,) \n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    k + [33, 44]\n",
    "except ValueError as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Upcasting\n",
    "when trying to combine arrays with different dtypes, NumPy will upcast to a type capable of handling all **possible** values **(regardless of what the actual values are)**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "uint8 [0 1 2 3 4]\n"
     ]
    }
   ],
   "source": [
    "k1 = np.arange(0, 5, dtype=np.uint8)\n",
    "print(k1.dtype, k1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int16 [ 5  7  9 11 13]\n"
     ]
    }
   ],
   "source": [
    "k2 = k1 + np.array([5, 6, 7, 8, 9], dtype=np.int8)\n",
    "print(k2.dtype, k2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "float64 [1.5 2.5 3.5 4.5 5.5]\n"
     ]
    }
   ],
   "source": [
    "k3 = k1 + 1.5\n",
    "print(k3.dtype, k3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True,  True, False])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m = np.array([20, -5, 30, 40])\n",
    "m < [15, 16, 35, 36]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True, False, False])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m < 25  # equivalent to m < [25, 25, 25, 25]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([20, -5])"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m[m < 25]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mathematical and statistical functions\n",
    "## ndarray methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-2.5  3.1  7. ]\n",
      " [10.  11.  12. ]]\n",
      "mean = 6.766666666666667\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[-2.5, 3.1, 7], [10, 11, 12]])\n",
    "print(a)\n",
    "print(\"mean =\", a.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "min = -2.5\n",
      "max = 12.0\n",
      "sum = 40.6\n",
      "prod = -71610.0\n",
      "std = 5.084835843520964\n",
      "var = 25.855555555555554\n"
     ]
    }
   ],
   "source": [
    "for func in (a.min, a.max, a.sum, a.prod, a.std, a.var):\n",
    "    print(func.__name__, '=', func())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 4,  5,  6,  7],\n",
       "        [ 8,  9, 10, 11]],\n",
       "\n",
       "       [[12, 13, 14, 15],\n",
       "        [16, 17, 18, 19],\n",
       "        [20, 21, 22, 23]]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = np.arange(24).reshape(2,3,4)\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12, 14, 16, 18],\n",
       "       [20, 22, 24, 26],\n",
       "       [28, 30, 32, 34]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12, 15, 18, 21],\n",
       "       [48, 51, 54, 57]])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.sum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 60,  92, 124])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.sum(axis=(0,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(60, 92, 124)"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "0+1+2+3 + 12+13+14+15, 4+5+6+7 + 16+17+18+19, 8+9+10+11 + 20+21+22+23"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## universal functions (ufunc)\n",
    "- elementwise functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.5,  3.1,  7. ],\n",
       "       [10. , 11. , 12. ]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([[-2.5, 3.1, 7], [10, 11, 12]])\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original ndarray\n",
      "[[-2.5  3.1  7. ]\n",
      " [10.  11.  12. ]]\n"
     ]
    }
   ],
   "source": [
    "print('Original ndarray')\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Original ndarray\n",
      "[[-2.5  3.1  7. ]\n",
      " [10.  11.  12. ]]\n",
      "absolute\n",
      "[[ 2.5  3.1  7. ]\n",
      " [10.  11.  12. ]]\n",
      "sqrt\n",
      "[[       nan 1.76068169 2.64575131]\n",
      " [3.16227766 3.31662479 3.46410162]]\n",
      "exp\n",
      "[[8.20849986e-02 2.21979513e+01 1.09663316e+03]\n",
      " [2.20264658e+04 5.98741417e+04 1.62754791e+05]]\n",
      "log\n",
      "[[       nan 1.13140211 1.94591015]\n",
      " [2.30258509 2.39789527 2.48490665]]\n",
      "sign\n",
      "[[-1.  1.  1.]\n",
      " [ 1.  1.  1.]]\n",
      "ceil\n",
      "[[-2.  4.  7.]\n",
      " [10. 11. 12.]]\n",
      "modf\n",
      "(array([[-0.5,  0.1,  0. ],\n",
      "       [ 0. ,  0. ,  0. ]]), array([[-2.,  3.,  7.],\n",
      "       [10., 11., 12.]]))\n",
      "isnan\n",
      "[[False False False]\n",
      " [False False False]]\n",
      "cos\n",
      "[[-0.80114362 -0.99913515  0.75390225]\n",
      " [-0.83907153  0.0044257   0.84385396]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ephyan/ml/env/lib/python3.6/site-packages/ipykernel_launcher.py:6: RuntimeWarning: invalid value encountered in sqrt\n",
      "  \n",
      "/home/ephyan/ml/env/lib/python3.6/site-packages/ipykernel_launcher.py:6: RuntimeWarning: invalid value encountered in log\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "print('Original ndarray')\n",
    "print(a)\n",
    "\n",
    "for func in (np.abs, np.sqrt, np.exp, np.log, np.sign, np.ceil, np.modf, np.isnan, np.cos):\n",
    "    print(func.__name__)\n",
    "    print(func(a))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## binary ufuncs\n",
    "- apply elementwise on two ndarrays\n",
    "- broadcasting rules are applied if the arrays do not have the same shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  6,  2, 11])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1, -2, 3, 4])\n",
    "b = np.array([2, 8, -1, 7])\n",
    "np.add(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False,  True, False])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.greater(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([2, 8, 3, 7])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.maximum(a, b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.,  2., -3.,  4.])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.copysign(a, b) # copy sign from b to a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Array indexing\n",
    "## One-dimensional arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "19"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.array([1, 5, 3, 19, 13, 7, 3])\n",
    "a[3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3, 19, 13])"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3, 19, 13,  7])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3, 13,  3])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2::2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3,  7, 13, 19,  3,  5,  1])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[::-1] # reverse ordder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1,   5,   3, 999,  13,   7,   3])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[3] = 999\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1,   5, 997, 998, 999,   7,   3])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2:5] = [997, 998, 999]\n",
    "a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Differences with regular python arrays\n",
    "- Contray to regular python arrays, if you assign a single value to an ndarray slice, it is copied across the whole slice, thanks to broadcasting rules\n",
    "- cannot grow or shrink ndarrays naively, cannot delete elements either\n",
    "- ndarray **slices are views** on the same data buffer. This means if you create a slice and modify it, you are actually going to **modify the original ndarray as well**!\n",
    "- if you want a copy of the data, you need to use the copy method"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  5, -1, -1, -1,  7,  3])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2:5] = -1\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cannot copy sequence with size 6 to array axis with dimension 3\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    a[2:5] = [1,2,3,4,5,6]  # too long\n",
    "except ValueError as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "cannot delete array elements\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    del a[2:5]\n",
    "except ValueError as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1,    5,   -1, 1000,   -1,    7,    3])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_slice = a[2:6]\n",
    "a_slice[1] = 1000\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  -1, 2000,   -1,    7])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[3] = 2000\n",
    "a_slice  # similarly, modifying the original array modifies the slice!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1,    5,   -1, 2000,   -1,    7,    3])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "another_slice = a[2:6].copy()\n",
    "another_slice[1] = 3000\n",
    "a  # the original array is untouched"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  -1, 3000,   -1,    7])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[3] = 4000\n",
    "another_slice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11],\n",
       "       [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23],\n",
       "       [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35],\n",
       "       [36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47]])"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.arange(48).reshape(4, 12)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "14"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[1, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[1, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1, 13, 25, 37])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[:, 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "**Caution**: note the subtle difference between these two expressions:\n",
    "The first expression returns row 1 as a 1D array of shape (12,), while the second returns that same row as a 2D array of shape (1, 12)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[1, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[1:2, :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Fancy indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2,  3,  4],\n",
       "       [26, 27, 28]])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[(0,2), 2:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[11,  2, 11],\n",
       "       [23, 14, 23],\n",
       "       [35, 26, 35],\n",
       "       [47, 38, 47]])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[:, (-1, 2, -1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([41, 33, 37, 33])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[(-1, 2, -1, 2), (5, 9, 1, 9)]   # returns a 1D array with b[-1, 5], b[2, 9], b[-1, 1] and b[2, 9]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3,  4,  5],\n",
       "        [ 6,  7,  8,  9, 10, 11]],\n",
       "\n",
       "       [[12, 13, 14, 15, 16, 17],\n",
       "        [18, 19, 20, 21, 22, 23]],\n",
       "\n",
       "       [[24, 25, 26, 27, 28, 29],\n",
       "        [30, 31, 32, 33, 34, 35]],\n",
       "\n",
       "       [[36, 37, 38, 39, 40, 41],\n",
       "        [42, 43, 44, 45, 46, 47]]])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = b.reshape(4, 2, 6)\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "34"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[2, 1, 4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   1,   2,   3,   4,   5,   6,   7,   8,   9,  10,  11],\n",
       "       [ 12,  13,  14,  15,  16,  17,  18,  19,  20,  21,  22,  23],\n",
       "       [ 24,  25,  26,  27,  28,  29,  30,  31,  32,  33, 100,  35],\n",
       "       [ 36,  37,  38,  39,  40,  41,  42,  43,  44,  45,  46,  47]])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[2, 1, 4] = 100\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([27, 33])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[2, :, 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 30,  31,  32,  33, 100,  35])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[2, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 24,  25,  26,  27,  28,  29],\n",
       "       [ 30,  31,  32,  33, 100,  35]])"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[2, ...]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 30,  31,  32,  33, 100,  35])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[2, 1, ...]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([27, 33])"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[2, ..., 3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3,  9],\n",
       "       [15, 21],\n",
       "       [27, 33],\n",
       "       [39, 45]])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[..., 3] # equivalent to c[:, :, 3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Boolean indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11],\n",
       "       [12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23],\n",
       "       [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35],\n",
       "       [36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47]])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.arange(48).reshape(4, 12)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11],\n",
       "       [24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35]])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "row_on = np.array([True, False, True, False])\n",
    "b[row_on, :]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  4,  7, 10],\n",
       "       [13, 16, 19, 22],\n",
       "       [25, 28, 31, 34],\n",
       "       [37, 40, 43, 46]])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cols_on = np.array([False, True, False] * 4)\n",
    "b[:, cols_on]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### np.ix_\n",
    "You cannot use boolean indexing this way on multiple axes, but you can work around this by using ix_ function:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  4,  7, 10],\n",
       "       [25, 28, 31, 34]])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[np.ix_(row_on, cols_on)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([[0],\n",
       "        [2]]), array([[ 1,  4,  7, 10]]))"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ix_(row_on, cols_on)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  4,  7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43, 46])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if you use a boolean array that has the same shape as ndarray, then you get in return a 1D array\n",
    "# containing all the values that have True at their coordinate\n",
    "b[b % 3 == 1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Iterating\n",
    "**Note**: iterating over multidimensional arrays is done with respect to the first axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 4,  5,  6,  7],\n",
       "        [ 8,  9, 10, 11]],\n",
       "\n",
       "       [[12, 13, 14, 15],\n",
       "        [16, 17, 18, 19],\n",
       "        [20, 21, 22, 23]]])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = np.arange(24).reshape(2, 3, 4)\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "item: \n",
      "[[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]]\n",
      "item: \n",
      "[[12 13 14 15]\n",
      " [16 17 18 19]\n",
      " [20 21 22 23]]\n"
     ]
    }
   ],
   "source": [
    "for m in c:\n",
    "    print('item: ')\n",
    "    print(m)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "item: \n",
      "[[ 0  1  2  3]\n",
      " [ 4  5  6  7]\n",
      " [ 8  9 10 11]]\n",
      "item: \n",
      "[[12 13 14 15]\n",
      " [16 17 18 19]\n",
      " [20 21 22 23]]\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(c)):  # len(c) == c.shape[0]\n",
    "    print('item: ')\n",
    "    print(c[i])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Item: 0\n",
      "Item: 1\n",
      "Item: 2\n",
      "Item: 3\n",
      "Item: 4\n",
      "Item: 5\n",
      "Item: 6\n",
      "Item: 7\n",
      "Item: 8\n",
      "Item: 9\n",
      "Item: 10\n",
      "Item: 11\n",
      "Item: 12\n",
      "Item: 13\n",
      "Item: 14\n",
      "Item: 15\n",
      "Item: 16\n",
      "Item: 17\n",
      "Item: 18\n",
      "Item: 19\n",
      "Item: 20\n",
      "Item: 21\n",
      "Item: 22\n",
      "Item: 23\n"
     ]
    }
   ],
   "source": [
    "# if you want to iterate all the elements, simply iterate over flat attribute\n",
    "for i in c.flat:\n",
    "    print('Item:', i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Stacking arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q1 = np.full((3, 4), 1.0)\n",
    "q1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2., 2., 2., 2.],\n",
       "       [2., 2., 2., 2.],\n",
       "       [2., 2., 2., 2.],\n",
       "       [2., 2., 2., 2.]])"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q2 = np.full((4, 4), 2.0)\n",
    "q2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3., 3., 3., 3.],\n",
       "       [3., 3., 3., 3.],\n",
       "       [3., 3., 3., 3.]])"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q3 = np.full((3, 4), 3.0)\n",
    "q3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## vstack\n",
    "- stack vertically"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.],\n",
       "       [2., 2., 2., 2.],\n",
       "       [2., 2., 2., 2.],\n",
       "       [2., 2., 2., 2.],\n",
       "       [2., 2., 2., 2.],\n",
       "       [3., 3., 3., 3.],\n",
       "       [3., 3., 3., 3.],\n",
       "       [3., 3., 3., 3.]])"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q4 = np.vstack((q1, q2, q3))\n",
    "q4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 4)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q4.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## hstack\n",
    "- stack horizontally"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1., 3., 3., 3., 3.],\n",
       "       [1., 1., 1., 1., 3., 3., 3., 3.],\n",
       "       [1., 1., 1., 1., 3., 3., 3., 3.]])"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q5 = np.hstack((q1, q3))\n",
    "q5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 8)"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q5.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all the input array dimensions except for the concatenation axis must match exactly\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    q5 = np.hstack((q1, q2, q3))\n",
    "except ValueError as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## concatenate\n",
    "- stack arrays along any given **existing** axis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.],\n",
       "       [2., 2., 2., 2.],\n",
       "       [2., 2., 2., 2.],\n",
       "       [2., 2., 2., 2.],\n",
       "       [2., 2., 2., 2.],\n",
       "       [3., 3., 3., 3.],\n",
       "       [3., 3., 3., 3.],\n",
       "       [3., 3., 3., 3.]])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q7 = np.concatenate((q1, q2, q3), axis=0)  # Equivalent to vstack\n",
    "q7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 4)"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q7.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## stack\n",
    "- stack arrays along a new axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.],\n",
       "        [1., 1., 1., 1.]],\n",
       "\n",
       "       [[3., 3., 3., 3.],\n",
       "        [3., 3., 3., 3.],\n",
       "        [3., 3., 3., 3.]]])"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q8 = np.stack((q1, q3))\n",
    "q8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3, 4)"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q8.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "all input arrays must have the same shape\n"
     ]
    }
   ],
   "source": [
    "try:\n",
    "    np.stack((q1, q2, q3))\n",
    "except ValueError as e:\n",
    "    print(e)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Splitting arrays\n",
    "- vsplit\n",
    "- hsplit"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11],\n",
       "       [12, 13, 14, 15],\n",
       "       [16, 17, 18, 19],\n",
       "       [20, 21, 22, 23]])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r = np.arange(24).reshape(6,4)\n",
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3],\n",
       "       [4, 5, 6, 7]])"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r1, r2, r3 = np.vsplit(r, 3)\n",
    "r1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 8,  9, 10, 11],\n",
       "       [12, 13, 14, 15]])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[16, 17, 18, 19],\n",
       "       [20, 21, 22, 23]])"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1],\n",
       "       [ 4,  5],\n",
       "       [ 8,  9],\n",
       "       [12, 13],\n",
       "       [16, 17],\n",
       "       [20, 21]])"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r4, r5 = np.hsplit(r, 2)\n",
    "r4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2,  3],\n",
       "       [ 6,  7],\n",
       "       [10, 11],\n",
       "       [14, 15],\n",
       "       [18, 19],\n",
       "       [22, 23]])"
      ]
     },
     "execution_count": 127,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1],\n",
       "       [ 4,  5],\n",
       "       [ 8,  9],\n",
       "       [12, 13],\n",
       "       [16, 17],\n",
       "       [20, 21]])"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r6, r7 = np.split(r, 2, axis=1)\n",
    "r6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2,  3],\n",
       "       [ 6,  7],\n",
       "       [10, 11],\n",
       "       [14, 15],\n",
       "       [18, 19],\n",
       "       [22, 23]])"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r7"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Transposing arrays\n",
    "The transpose method creates a new **view** on a ndarray's data, with axes permuted in the give order"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2],\n",
       "        [ 3,  4,  5]],\n",
       "\n",
       "       [[ 6,  7,  8],\n",
       "        [ 9, 10, 11]],\n",
       "\n",
       "       [[12, 13, 14],\n",
       "        [15, 16, 17]],\n",
       "\n",
       "       [[18, 19, 20],\n",
       "        [21, 22, 23]]])"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t = np.arange(24).reshape(4,2,3)\n",
    "t"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  6, 12, 18],\n",
       "        [ 1,  7, 13, 19],\n",
       "        [ 2,  8, 14, 20]],\n",
       "\n",
       "       [[ 3,  9, 15, 21],\n",
       "        [ 4, 10, 16, 22],\n",
       "        [ 5, 11, 17, 23]]])"
      ]
     },
     "execution_count": 131,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t1 = t.transpose((1, 2, 0))\n",
    "t1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  6, 12, 18],\n",
       "        [ 3,  9, 15, 21]],\n",
       "\n",
       "       [[ 1,  7, 13, 19],\n",
       "        [ 4, 10, 16, 22]],\n",
       "\n",
       "       [[ 2,  8, 14, 20],\n",
       "        [ 5, 11, 17, 23]]])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t2 = t.transpose()  # equivalent to t.transpose((2, 1, 0))\n",
    "t2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 2, 4)"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## swapaxes \n",
    "- swap two axes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2],\n",
       "        [ 6,  7,  8],\n",
       "        [12, 13, 14],\n",
       "        [18, 19, 20]],\n",
       "\n",
       "       [[ 3,  4,  5],\n",
       "        [ 9, 10, 11],\n",
       "        [15, 16, 17],\n",
       "        [21, 22, 23]]])"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t3 = t.swapaxes(0, 1)\n",
    "t3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 4, 3)"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "t3.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Linear algebra\n",
    "## Matrix transpose\n",
    "- The T **attribute** is equivalent to calling transpose() when rank is ≥2:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3, 4],\n",
       "       [5, 6, 7, 8, 9]])"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m1 = np.arange(10).reshape(2,5)\n",
    "m1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 5],\n",
       "       [1, 6],\n",
       "       [2, 7],\n",
       "       [3, 8],\n",
       "       [4, 9]])"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m1.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The T attribute has no effect on rank 0 (empty) or rank 1 arrays\n",
    "m2 = np.arange(5)\n",
    "m2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4])"
      ]
     },
     "execution_count": 139,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3, 4]])"
      ]
     },
     "execution_count": 140,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2r = m2.reshape(1, 5)\n",
    "m2r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0],\n",
       "       [1],\n",
       "       [2],\n",
       "       [3],\n",
       "       [4]])"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m2r.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matrix dot product"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2, 3, 4],\n",
       "       [5, 6, 7, 8, 9]])"
      ]
     },
     "execution_count": 142,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n1 = np.arange(10).reshape(2, 5)\n",
    "n1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2],\n",
       "       [ 3,  4,  5],\n",
       "       [ 6,  7,  8],\n",
       "       [ 9, 10, 11],\n",
       "       [12, 13, 14]])"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n2 = np.arange(15).reshape(5,3)\n",
    "n2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 90, 100, 110],\n",
       "       [240, 275, 310]])"
      ]
     },
     "execution_count": 144,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "n1.dot(n2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matrix inverse and pseudo-inverse\n",
    "Many of the linear algebra functions are available in the numpy.linalg module, in particular the inv function to compute a square matrix's inverse\n",
    "\n",
    "a pseudoinverse $A+$ of a matrix A is a generalization of the inverse matrix."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1,  2,  3],\n",
       "       [ 5,  7, 11],\n",
       "       [21, 29, 31]])"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy.linalg as linalg\n",
    "\n",
    "m3 = np.array([[1,2,3],[5,7,11],[21,29,31]])\n",
    "m3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.31818182,  0.56818182,  0.02272727],\n",
       "       [ 1.72727273, -0.72727273,  0.09090909],\n",
       "       [-0.04545455,  0.29545455, -0.06818182]])"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linalg.inv(m3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.31818182,  0.56818182,  0.02272727],\n",
       "       [ 1.72727273, -0.72727273,  0.09090909],\n",
       "       [-0.04545455,  0.29545455, -0.06818182]])"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linalg.pinv(m3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Identity matrix\n",
    "The product of a matrix by its inverse returns the identiy matrix (with small floating point errors):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.00000000e+00, -1.66533454e-16,  0.00000000e+00],\n",
       "       [ 6.31439345e-16,  1.00000000e+00, -1.38777878e-16],\n",
       "       [ 5.21110932e-15, -2.38697950e-15,  1.00000000e+00]])"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3.dot(linalg.inv(m3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 149,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0.],\n",
       "       [0., 1., 0.],\n",
       "       [0., 0., 1.]])"
      ]
     },
     "execution_count": 149,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.eye(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## QR decomposition\n",
    "In linear algebra, a QR decomposition (also called a QR factorization) of a matrix is a decomposition of a matrix A into a product A = QR of an orthogonal matrix Q and an upper triangular matrix R"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.04627448,  0.98786672,  0.14824986],\n",
       "       [-0.23137241,  0.13377362, -0.96362411],\n",
       "       [-0.97176411, -0.07889213,  0.22237479]])"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q, r = linalg.qr(m3)\n",
    "q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-21.61018278, -29.89331494, -32.80860727],\n",
       "       [  0.        ,   0.62427688,   1.9894538 ],\n",
       "       [  0.        ,   0.        ,  -3.26149699]])"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "r"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  2.,  3.],\n",
       "       [ 5.,  7., 11.],\n",
       "       [21., 29., 31.]])"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q.dot(r)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Determinant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43.99999999999997"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "linalg.det(m3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Eigenvalues and eigenvectors\n",
    "In linear algebra, an eigenvector or characteristic vector of a linear transformation is a non-zero vector that changes by only a scalar factor when that linear transformation is applied to it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([42.26600592, -0.35798416, -2.90802176])"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eigenvalues, eigenvectors = linalg.eig(m3)\n",
    "eigenvalues"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.08381182, -0.76283526, -0.18913107],\n",
       "       [-0.3075286 ,  0.64133975, -0.6853186 ],\n",
       "       [-0.94784057, -0.08225377,  0.70325518]])"
      ]
     },
     "execution_count": 155,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eigenvectors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6.66133815e-15,  1.66533454e-15, -3.10862447e-15],\n",
       "       [ 7.10542736e-15,  5.16253706e-15, -5.32907052e-15],\n",
       "       [ 3.55271368e-14,  4.94743135e-15, -9.76996262e-15]])"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m3.dot(eigenvectors) - eigenvalues * eigenvectors  # m3.v - λ*v = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Singular Value Decomposition\n",
    "In linear algebra, the singular-value decomposition (SVD) is a factorization of a real or complex matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0, 0, 2],\n",
       "       [0, 0, 3, 0, 0],\n",
       "       [0, 0, 0, 0, 0],\n",
       "       [0, 2, 0, 0, 0]])"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "m4 = np.array([[1,0,0,0,2], [0,0,3,0,0], [0,0,0,0,0], [0,2,0,0,0]])\n",
    "m4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  1.,  0.,  0.],\n",
       "       [ 1.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0., -1.],\n",
       "       [ 0.,  0.,  1.,  0.]])"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "U, S_diag, V = linalg.svd(m4)\n",
    "U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3.        , 2.23606798, 2.        , 0.        ])"
      ]
     },
     "execution_count": 159,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S_diag"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3.        , 0.        , 0.        , 0.        , 0.        ],\n",
       "       [0.        , 2.23606798, 0.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 2.        , 0.        , 0.        ],\n",
       "       [0.        , 0.        , 0.        , 0.        , 0.        ]])"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The svd function just returns the values in the diagonal of Σ, but we want the full Σ matrix\n",
    "S = np.zeros((4, 5))\n",
    "S[np.diag_indices(4)] = S_diag\n",
    "S"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.        ,  0.        ,  1.        , -0.        ,  0.        ],\n",
       "       [ 0.4472136 ,  0.        ,  0.        ,  0.        ,  0.89442719],\n",
       "       [-0.        ,  1.        ,  0.        , -0.        ,  0.        ],\n",
       "       [ 0.        ,  0.        ,  0.        ,  1.        ,  0.        ],\n",
       "       [-0.89442719,  0.        ,  0.        ,  0.        ,  0.4472136 ]])"
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "V"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 2.],\n",
       "       [0., 0., 3., 0., 0.],\n",
       "       [0., 0., 0., 0., 0.],\n",
       "       [0., 2., 0., 0., 0.]])"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "U.dot(S).dot(V)  # U.Σ.V == m4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Solving a system of linear scalar equations\n",
    "The solve function solves a system of linear scalar equations, such as:\n",
    "\n",
    "- $2x + 6y = 6$\n",
    "- $5x + 3y = -9$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 163,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-3.,  2.])"
      ]
     },
     "execution_count": 163,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeffs = np.array([[2, 6], [5, 3]])\n",
    "depvars = np.array([6, -9])\n",
    "solution = linalg.solve(coeffs, depvars)\n",
    "solution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 6., -9.]), array([ 6, -9]))"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "coeffs.dot(solution), depvars"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 165,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 165,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Another way to check the solution:\n",
    "np.allclose(coeffs.dot(solution), depvars)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vectorization\n",
    "- The code is more efficient if stick to array operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 166,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "\n",
    "# generate a 768x1024 array\n",
    "data = np.empty((768, 1024))\n",
    "for y in range(768):\n",
    "    for x in range(1024):\n",
    "        data[y, x] = math.sin(x*y/40.5)  # BAD! Very inefficient."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## meshgrid\n",
    "- generate coordinate **matrics from coordinates vectors**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[   0,    1,    2, ..., 1021, 1022, 1023],\n",
       "       [   0,    1,    2, ..., 1021, 1022, 1023],\n",
       "       [   0,    1,    2, ..., 1021, 1022, 1023],\n",
       "       ...,\n",
       "       [   0,    1,    2, ..., 1021, 1022, 1023],\n",
       "       [   0,    1,    2, ..., 1021, 1022, 1023],\n",
       "       [   0,    1,    2, ..., 1021, 1022, 1023]])"
      ]
     },
     "execution_count": 167,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_coords = np.arange(0, 1024)\n",
    "y_coords = np.arange(0, 768)\n",
    "X, Y = np.meshgrid(x_coords, y_coords)\n",
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 1024)"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   0,   0, ...,   0,   0,   0],\n",
       "       [  1,   1,   1, ...,   1,   1,   1],\n",
       "       [  2,   2,   2, ...,   2,   2,   2],\n",
       "       ...,\n",
       "       [765, 765, 765, ..., 765, 765, 765],\n",
       "       [766, 766, 766, ..., 766, 766, 766],\n",
       "       [767, 767, 767, ..., 767, 767, 767]])"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(768, 1024)"
      ]
     },
     "execution_count": 170,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Y.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "both X and Y are 768x1024 arrays, and all values in X correspond to the horizontal coordinate, while all values in Y correspond to the vertical coordinate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.sin(X*Y/40.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x432 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.cm as cm\n",
    "\n",
    "fig = plt.figure(1, figsize=(7, 6))\n",
    "plt.imshow(data, cmap=cm.hot, interpolation='bicubic')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Saving and loading\n",
    "## Binary .npy format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 173,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.96541808, 0.17542739, 0.80582247],\n",
       "       [0.11175705, 0.52535614, 0.21497442]])"
      ]
     },
     "execution_count": 173,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.random.rand(2,3)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save('my_array', a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 175,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b\"\\x93NUMPY\\x01\\x00v\\x00{'descr': '<f8', 'fortran_order': False, 'shape': (2, 3), }                                                          \\np\\x84ow\\xb4\\xe4\\xee?\\xc0\\xd7\\xb8\\x9bgt\\xc6?\\x05\\xe0\\x845L\\xc9\\xe9?0\\xc4\\x1d \\x1c\\x9c\\xbc?\\x03}\\x1e\\xad\\xb7\\xcf\\xe0?\\x10!V.H\\x84\\xcb?\""
      ]
     },
     "execution_count": 175,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open(\"my_array.npy\", \"rb\") as f:\n",
    "    content = f.read()\n",
    "\n",
    "content"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.96541808, 0.17542739, 0.80582247],\n",
       "       [0.11175705, 0.52535614, 0.21497442]])"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_loaded = np.load('my_array.npy')\n",
    "a_loaded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Text format"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt('my_array.csv', a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "9.654180844046447163e-01 1.754273901330503094e-01 8.058224721235097698e-01\n",
      "1.117570475014566522e-01 5.253561383468937640e-01 2.149744249584348843e-01\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open('my_array.csv', 'rt') as f:\n",
    "    content = f.read()\n",
    "    \n",
    "print(content)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savetxt('my_array.csv', a, delimiter=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.96541808, 0.17542739, 0.80582247],\n",
       "       [0.11175705, 0.52535614, 0.21497442]])"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_loaded = np.loadtxt('my_array.csv', delimiter=',')\n",
    "a_loaded"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Zipped .npz format\n",
    "It is possible to save multiple arrays in one zipped file:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 4,  5,  6,  7],\n",
       "        [ 8,  9, 10, 11]],\n",
       "\n",
       "       [[12, 13, 14, 15],\n",
       "        [16, 17, 18, 19],\n",
       "        [20, 21, 22, 23]]], dtype=uint8)"
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.arange(24, dtype=np.uint8).reshape(2, 3, 4)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savez('my_arrays', my_a=a, my_b=b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 183,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'b\"PK\\\\x03\\\\x04\\\\x14\\\\x00\\\\x00\\\\x00\\\\x00\\\\x00\\\\x00\\\\x00!\\\\x00\\\\xd0\\\\x81\\\\xc8\\\\xae\\\\xb0\\\\x00\\\\x00\\\\x00\\\\xb0\\\\x00\\\\x00\\\\x00\\\\x08\\\\x00\\\\x00\\\\x00my_a.npy\\\\x93NUMPY\\\\x01\\\\x00v\\\\x00{\\'descr\\': \\'<f8\\', \\'fortran_order\\': Fal[...]'"
      ]
     },
     "execution_count": 183,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "with open('my_arrays.npz', 'rb') as f:\n",
    "    content = f.read()\n",
    "    \n",
    "repr(content)[:180] + '[...]'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<numpy.lib.npyio.NpzFile at 0x7fb6696b8a58>"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_arrays = np.load(\"my_arrays.npz\")\n",
    "my_arrays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "my_a\n",
      "my_b\n"
     ]
    }
   ],
   "source": [
    "for k in my_arrays.keys():\n",
    "    print(k)\n",
    "# my_arrays.keys()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.96541808, 0.17542739, 0.80582247],\n",
       "       [0.11175705, 0.52535614, 0.21497442]])"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_arrays[\"my_a\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 187,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3],\n",
       "        [ 4,  5,  6,  7],\n",
       "        [ 8,  9, 10, 11]],\n",
       "\n",
       "       [[12, 13, 14, 15],\n",
       "        [16, 17, 18, 19],\n",
       "        [20, 21, 22, 23]]], dtype=uint8)"
      ]
     },
     "execution_count": 187,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_arrays[\"my_b\"]"
   ]
  }
 ],
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