Numpy_handson.ipynb

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    "colab": {
      "name": "Numpy-handson.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyMTyt3AkOlLXs11LHNEm++f"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "VxyIQDw0FfAg"
      },
      "source": [
        "#importing\n",
        "import numpy as np"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cuGda0LtFxHL",
        "outputId": "fbb97da5-a7ab-4c5b-a69f-154774bbff52"
      },
      "source": [
        "#the zeros function creates an array containing any number of zeroes\n",
        "np.zeros(5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0., 0., 0., 0., 0.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4yHa1w6ZF2uF",
        "outputId": "fa96bfb1-72c5-49dd-f8ab-c2464a49889c"
      },
      "source": [
        "#create a matrix by providing a tuple\n",
        "np.zeros((3,4))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-SsWXkYWGV7g"
      },
      "source": [
        "- In Numpy, each dimension is calles 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 rank is equal to the shapes's length\n",
        "-The size of an array is the total number of elements, which is the product of all axis lengths (eg. 3*4=12)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UMRbuDKdGQ23",
        "outputId": "944ae61a-1d81-487c-91e4-f0508956047f"
      },
      "source": [
        "a = np.zeros((3,4))\n",
        "a"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0.],\n",
              "       [0., 0., 0., 0.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "id": "A2yOlXqUHCBx",
        "outputId": "d83e808d-5328-4b02-80cb-30d936fe51a6"
      },
      "source": [
        "a.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ucODri-HQbn",
        "outputId": "9ed65c68-1acc-4ef8-87f9-901ad88d1881"
      },
      "source": [
        "a.ndim #equal to len(a.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pzJPfdExHVLR",
        "outputId": "aae4d7dd-cc14-4243-d5d6-c2db95742c3c"
      },
      "source": [
        "a.size"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "12"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pnf4nuWdHWNo",
        "outputId": "eb06ea94-c963-48c1-eb17-293c211cfdea"
      },
      "source": [
        "#N-dimensional arrays\n",
        "np.zeros((2,3,4))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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.]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lsjKH42aHaz4",
        "outputId": "f9efeb52-cf97-412f-9a7c-da755d4fe8dc"
      },
      "source": [
        "#Array type\n",
        "type(np.zeros((3,4)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "numpy.ndarray"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kcI6acqGHrX-",
        "outputId": "635894cf-be96-465c-9b6f-48d328ae2aa8"
      },
      "source": [
        "#np.ones\n",
        "np.ones((3,4))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 1., 1., 1.],\n",
              "       [1., 1., 1., 1.],\n",
              "       [1., 1., 1., 1.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FQHIrQirHxRd",
        "outputId": "4650f983-3cb8-4faa-9063-5ff6bc85a155"
      },
      "source": [
        "#np.full - Creates an array of the given shape initialized with the given value. Here's a 3x4 matrix full of π.\n",
        "np.full((3,4), np.pi)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0TqfcD5fH9EC",
        "outputId": "941f96b3-8631-46e5-b5f3-cae2fee824e2"
      },
      "source": [
        "#np.empty - (its content is not predictable, as it is whatever is in memory at that point):\n",
        "np.empty((2,3))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[4.65134232e-310, 0.00000000e+000, 0.00000000e+000],\n",
              "       [0.00000000e+000, 0.00000000e+000, 0.00000000e+000]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ou3Hw4gIIK6y",
        "outputId": "6960ddd7-61a9-4bb8-f081-8f1a9224dd2f"
      },
      "source": [
        "#np.array\n",
        "np.array([[1,2,3,4], [10, 20, 30, 40]])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1,  2,  3,  4],\n",
              "       [10, 20, 30, 40]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hSkoqPR7I-0T",
        "outputId": "ea31be83-d5c0-4071-b2ac-8efb040f71bb"
      },
      "source": [
        "#np.arange- similar to python's range function\n",
        "np.arange(1,5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3, 4])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kwSou8sQJJpu",
        "outputId": "2eb52194-161d-498f-8868-c69fc8c6310c"
      },
      "source": [
        "np.arange(1.0,5.0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1., 2., 3., 4.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8DE_oKzYJOIF",
        "outputId": "4fa56481-9223-4a1e-ee0d-effe50e69ecc"
      },
      "source": [
        "#using step\n",
        "np.arange(1,5,0.5)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wwZHQCi8JU8f",
        "outputId": "25c28b9a-8a45-430b-8b0b-42370ac75cc7"
      },
      "source": [
        "print(np.arange(0, 5/3, 1/3)) # depending on floating point errors, the max value is 4/3 or 5/3.\n",
        "print(np.arange(0, 5/3, 0.333333333))\n",
        "print(np.arange(0, 5/3, 0.333333334))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nQQj69M-J7Vq"
      },
      "source": [
        "**np.linspace**\n",
        "\n",
        "For this reason, it is generally preferable to use the linspace function instead of arange when working with floats. The linspace function returns an array containing a specific number of points evenly distributed between two values (note that the maximum value is included, contrary to arange):"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bWOQAZK1KRUe",
        "outputId": "7e487ad9-34e8-426d-a4e7-8274ddc20f99"
      },
      "source": [
        "print(np.linspace(0, 5/3, 6))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.         0.33333333 0.66666667 1.         1.33333333 1.66666667]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "S9HdBs3zJ6pM"
      },
      "source": [
        ""
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UZL58-fJKqWO",
        "outputId": "0b32a247-e52d-469d-80f4-e08a309d1647"
      },
      "source": [
        "#array with random values:\n",
        "np.random.rand(3,4)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.85172913, 0.70258329, 0.81552364, 0.39623909],\n",
              "       [0.28263653, 0.43151689, 0.3286936 , 0.85175868],\n",
              "       [0.33976967, 0.81886546, 0.992024  , 0.42947111]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "55vJpsS2Kvy1",
        "outputId": "ae43efb6-855e-4bf5-ad82-bdc2811456a8"
      },
      "source": [
        "np.random.randn(3,4)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.92541772,  1.79321825, -2.3251315 ,  0.10687346],\n",
              "       [-1.67389443, -0.59804435,  0.5046536 , -0.68345491],\n",
              "       [ 0.20736317, -0.2781507 , -0.67293842,  0.2087731 ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G1-oZDvQLGd7"
      },
      "source": [
        "%matplotlib inline\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "a9fzVYdzMiyx",
        "outputId": "8570989a-3450-44de-f57b-2f65af54d28a"
      },
      "source": [
        "plt.hist(np.random.rand(300000), density=True, bins=100, histtype=\"step\", color=\"purple\", label=\"rand\")\n",
        "plt.hist(np.random.randn(300000), density=True, bins=100, histtype=\"step\", color=\"orange\", label=\"rand\")\n",
        "plt.axis([-2.5, 2.5, 0, 1.1])\n",
        "plt.legend(loc= \"upper left\")\n",
        "plt.title(\"Random distribution\")\n",
        "plt.xlabel(\"Value\")\n",
        "plt.ylabel(\"Density\")\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 316
        },
        "id": "PsWgjljkNhnF",
        "outputId": "301e1b57-52cf-4eca-aa79-c00496f4c636"
      },
      "source": [
        "#np.formfunction\n",
        "def my_function(z,y,x):\n",
        "  return x * y + z\n",
        "\n",
        "np.formfunction(my_function, (3,2,10))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "AttributeError",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-31-fac524e72aef>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      3\u001b[0m   \u001b[0;32mreturn\u001b[0m \u001b[0mx\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mz\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 5\u001b[0;31m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformfunction\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmy_function\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.7/dist-packages/numpy/__init__.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(attr)\u001b[0m\n\u001b[1;32m    213\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    214\u001b[0m                 raise AttributeError(\"module {!r} has no attribute \"\n\u001b[0;32m--> 215\u001b[0;31m                                      \"{!r}\".format(__name__, attr))\n\u001b[0m\u001b[1;32m    216\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    217\u001b[0m         \u001b[0;32mdef\u001b[0m \u001b[0m__dir__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mAttributeError\u001b[0m: module 'numpy' has no attribute 'formfunction'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oHGKF4CYOA9F",
        "outputId": "e4bb9f62-84b6-45aa-c2a2-2f0eec962733"
      },
      "source": [
        "#dtype - to check the data type\n",
        "c = np.arange(1,5)\n",
        "print(c.dtype, c)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "int64 [1 2 3 4]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G_EaH26POa2C",
        "outputId": "94c70d99-01bd-4b94-bbff-9ab0c36ad7aa"
      },
      "source": [
        "c = np.arange(1.0, 5.0)\n",
        "print(c.dtype, c)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "float64 [1. 2. 3. 4.]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WaLj8Pc2On7U",
        "outputId": "8a04decf-258a-4f96-f720-adf9346b13c5"
      },
      "source": [
        "#set the data type explicitly\n",
        "d = np.arange(1, 5, dtype=np.complex64)\n",
        "print(d.dtype, d)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "complex64 [1.+0.j 2.+0.j 3.+0.j 4.+0.j]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7bgRBpEtO7Wr",
        "outputId": "9f241e63-8909-472e-e097-548f0a1d4e39"
      },
      "source": [
        "e = np.arange(1, 5, dtype=np.complex64)\n",
        "e.itemsize"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "8"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "C2JlDpI1PNnY",
        "outputId": "30c4b6a6-08ec-4f9f-a295-5782b2b9b930"
      },
      "source": [
        "#data buffer\n",
        "f = np.array([[1,2], [1000, 2000]], dtype=np.int32)\n",
        "f.data\n",
        "\n",
        "# In python 2, f.data is a buffer. In python 3, it is a memoryview."
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<memory at 0x7f16c63cf210>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LMN63MocPwUR",
        "outputId": "b45d6126-fd20-4d44-b257-1dccb9434935"
      },
      "source": [
        "if (hasattr(f.data, \"tobytes\")):\n",
        "  data_bytes = f.data.tobytes() #python 3\n",
        "else:\n",
        "  data_bytes = memoryview(f.data).tobytes() #python 2\n",
        "\n",
        "data_bytes"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "b'\\x01\\x00\\x00\\x00\\x02\\x00\\x00\\x00\\xe8\\x03\\x00\\x00\\xd0\\x07\\x00\\x00'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2dg_nJ0bQxpz",
        "outputId": "25b1f47d-1dee-485e-fe39-0e783eadb3ea"
      },
      "source": [
        "#Reshaping an array\n",
        "#In place\n",
        "g = np.arange(24)\n",
        "print(g)\n",
        "print(\"Rank:\", g.ndim)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9j0ImgggQ4Pf",
        "outputId": "070d6da7-5c0f-48b3-e78d-830719ca4574"
      },
      "source": [
        "g.shape = (6, 4)\n",
        "print(g)\n",
        "print(\"Rank:\", g.ndim)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wgpTSN4pQ4_g",
        "outputId": "a9433d5a-511a-483d-dfec-3527e151bb52"
      },
      "source": [
        "g.shape = (2, 3, 4)\n",
        "print(g)\n",
        "print(\"Rank:\", g.ndim)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2yWSOJm1SuUR",
        "outputId": "77e0f493-ef13-4a32-eaea-d1267ee9d9df"
      },
      "source": [
        "#reshape\n",
        "g2 = g.reshape(4,6)\n",
        "print(g2)\n",
        "print(\"Rank:\", g2.ndim)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pt5BdxvBTQ7O",
        "outputId": "85e47e5a-4fe8-4cdb-e5da-be0f125329f7"
      },
      "source": [
        "g2[1, 2] = 999\n",
        "g2"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fj31wnMOTU9s",
        "outputId": "b1245b0b-b7b3-4984-dc13-3069cfb0127e"
      },
      "source": [
        "g"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "btrg1g6PTWt5",
        "outputId": "e4ca446f-f99c-4ae9-9e6a-df3a11cdb8e3"
      },
      "source": [
        "#ravel - the ravel function returns a new one-dimensional ndarray that also points to the same data:\n",
        "g.ravel()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "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])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "myqSSspVTl9K",
        "outputId": "b0d1317e-4d14-4bfa-b2b7-b8d3f85b0a1e"
      },
      "source": [
        "#Arithmeticoperations\n",
        "\n",
        "a = np.array([14, 23, 32, 41])\n",
        "b = np.array([5, 2, 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)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "a + b  = [19 25 35 43]\n",
            "a - b  = [ 9 21 29 39]\n",
            "a * b  = [70 46 96 82]\n",
            "a / b  = [ 2.8        11.5        10.66666667 20.5       ]\n",
            "a // b  = [ 2 11 10 20]\n",
            "a % b  = [4 1 2 1]\n",
            "a ** b = [537824    529  32768   1681]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0HMEtp4GT_7q",
        "outputId": "1096c4ed-11db-42db-e8c6-8e53741d72ea"
      },
      "source": [
        "#Broadcasting - In general, when NumPy expects arrays of the same shape but finds that this is not the case, it applies the so-called broadcasting rules:\n",
        "\n",
        "#First 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",
        "\n",
        "h = np.arange(5).reshape(1,1,5)\n",
        "h"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[0, 1, 2, 3, 4]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hWnpKvJpUdlJ",
        "outputId": "493af580-65a5-4076-d117-62de191d0714"
      },
      "source": [
        "h + [10, 20, 30, 40, 50]  # same as: h + [[[10, 20, 30, 40, 50]]]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[[10, 21, 32, 43, 54]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0IKSt4u5Wofd",
        "outputId": "483328a2-6244-43cc-ff7b-ded443503cea"
      },
      "source": [
        "#Second rule\n",
        "#Arrays with a 1 along a particular dimension act as if they had the size of the array with the largest shape along that dimension. The value of the array element is repeated along that dimension.\n",
        "\n",
        "k = np.arange(6).reshape(2,3)\n",
        "k"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 1, 2],\n",
              "       [3, 4, 5]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KRj8p8qjWrNe",
        "outputId": "97fa300b-0c03-4f75-b5d4-5389ce0452c2"
      },
      "source": [
        "k + [[100], [200]]  # same as: k + [[100, 100, 100], [200, 200, 200]]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[100, 101, 102],\n",
              "       [203, 204, 205]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "afuzD9C5XCd7",
        "outputId": "fa653448-f7a2-49b3-cec0-aad956265f84"
      },
      "source": [
        "k + [100, 200, 300]  # after rule 1: [[100, 200, 300]], and after rule 2: [[100, 200, 300], [100, 200, 300]]\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[100, 201, 302],\n",
              "       [103, 204, 305]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9UpKuvLqXIDO",
        "outputId": "09426b4d-7c4a-4b5f-e2a2-6f6ec6df07d3"
      },
      "source": [
        "k + 1000  # same as: k + [[1000, 1000, 1000], [1000, 1000, 1000]]\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1000, 1001, 1002],\n",
              "       [1003, 1004, 1005]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7Al8ptRMXM3r",
        "outputId": "8e009bcd-7c8c-4e7c-ff89-d63a598575ea"
      },
      "source": [
        "#Third rule\n",
        "#After rules 1 & 2, the sizes of all arrays must match\n",
        "\n",
        "try:\n",
        "  k + [33, 44]\n",
        "except ValueError as e:\n",
        "  print(e)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "operands could not be broadcast together with shapes (2,3) (2,) \n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kvBFk3JBXeoR",
        "outputId": "7ec24713-d93f-4877-9b31-beb35e11db8c"
      },
      "source": [
        "#Upcasting\n",
        "k1 = np.arange(0, 5, dtype=np.uint8)\n",
        "print(k1.dtype, k1)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "uint8 [0 1 2 3 4]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IYxesmnCw0L1",
        "outputId": "5f7d6a72-e232-4a1c-e277-a759fd261ce4"
      },
      "source": [
        "k2 = k1 + np.array([5, 6, 7, 8, 9], dtype=np.int8)\n",
        "print(k2.dtype, k2)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "int16 [ 5  7  9 11 13]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Di3TwdB8w2ym",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "28be114a-cd91-43e6-d76d-4c1d015c21e3"
      },
      "source": [
        "#Upcasting - 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).\n",
        "\n",
        "k1 = np.arange(0, 5, dtype=np.uint8)\n",
        "print(k1.dtype, k1)"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "uint8 [0 1 2 3 4]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ls4rhagrGZgQ",
        "outputId": "f288ef22-6b34-49c4-9561-0ac0775a2b12"
      },
      "source": [
        "k2 = k1 + np.array([5,6,7,8,9], dtype=np.int8)\n",
        "print(k2.dtype, k2)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "int16 [ 5  7  9 11 13]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JC6PwghwG32O",
        "outputId": "45953a9e-9711-4e97-9f49-84475fd26df1"
      },
      "source": [
        "k3 = k1 + 1.5\n",
        "print(k3.dtype, k3)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "float64 [1.5 2.5 3.5 4.5 5.5]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RugusLquG-O6",
        "outputId": "190cf611-9f67-4e99-8d95-5c70316c279e"
      },
      "source": [
        "#Conditional operators\n",
        "\n",
        "m = np.array([20, -5, 30, 40])\n",
        "m < [15, 16, 35, 36]"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([False,  True,  True, False])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nQtlRKc2HPah",
        "outputId": "2558abbd-ecf5-44ae-804e-5ff1600636c6"
      },
      "source": [
        "m < 25 # equivalent to m < [25, 25, 25, 25]"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ True,  True, False, False])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "abb2hta9HaVx",
        "outputId": "785b80cd-1fae-4e65-a948-f99f0615eaad"
      },
      "source": [
        "m[m < 25]"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([20, -5])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sXHYxMjzHdHE",
        "outputId": "0ea62c58-235d-4cd6-8359-a6ed98d0d602"
      },
      "source": [
        "#Mathematical and statistical functions\n",
        "\n",
        "a = np.array([[-2.5, 3.1, 7], [10, 11, 12]])\n",
        "print(a)\n",
        "print(\"mean =\", a.mean())"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[[-2.5  3.1  7. ]\n",
            " [10.  11.  12. ]]\n",
            "mean = 6.766666666666667\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fzF_CEutHx0t",
        "outputId": "dbedead3-5946-4cb7-ef3f-54764f412fb7"
      },
      "source": [
        "for func in (a.min, a.max, a.sum, a.prod, a.std,a.var):\n",
        "  print(func.__name__, \"=\", func())"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e0i-R7yMIEzp",
        "outputId": "29a1f3c1-3aef-4b75-ba70-34ce99fba21a"
      },
      "source": [
        "c = np.arange(24).reshape(2,3,4)\n",
        "c"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "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]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6dg8F8zkIbjt",
        "outputId": "5df6d58d-5d84-4930-f79e-ea5006ec2a94"
      },
      "source": [
        "c.sum(axis=0) #sum across matrices"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[12, 14, 16, 18],\n",
              "       [20, 22, 24, 26],\n",
              "       [28, 30, 32, 34]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "v4kstVVlImAa",
        "outputId": "3273f701-b616-4bbb-8726-1e96f66ce1a1"
      },
      "source": [
        "c.sum(axis=1)  # sum across rows"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[12, 15, 18, 21],\n",
              "       [48, 51, 54, 57]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EUP0BdN7IqmI",
        "outputId": "2da5f947-51ec-4a0b-ccf0-2b60e551880b"
      },
      "source": [
        "c.sum(axis=(0,2))  # sum across matrices and columns"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 60,  92, 124])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0tKvO7EPItP7",
        "outputId": "9e2f55f0-4abf-444d-a192-713813b6ac52"
      },
      "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"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(60, 92, 124)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0jODGRTwIxMW",
        "outputId": "6634e0b0-cb0d-4760-f32e-761c14db9b44"
      },
      "source": [
        "#Universal functions\n",
        "a = np.array([[-2.5, 3.1, 7], [10, 11, 12]])\n",
        "np.square(a)"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  6.25,   9.61,  49.  ],\n",
              "       [100.  , 121.  , 144.  ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OcTbMWkBI_0z",
        "outputId": "17df499d-0cb0-413e-84ed-53d1ce70f684"
      },
      "source": [
        "print(\"Original ndarray\")\n",
        "print(a)\n",
        "for func in (np.abs, np.sqrt, np.exp, np.log, np.sign, np.ceil, np.modf, np.isnan, np.cos):\n",
        "    print(\"\\n\", func.__name__)\n",
        "    print(func(a))"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Original ndarray\n",
            "[[-2.5  3.1  7. ]\n",
            " [10.  11.  12. ]]\n",
            "\n",
            " absolute\n",
            "[[ 2.5  3.1  7. ]\n",
            " [10.  11.  12. ]]\n",
            "\n",
            " sqrt\n",
            "[[       nan 1.76068169 2.64575131]\n",
            " [3.16227766 3.31662479 3.46410162]]\n",
            "\n",
            " exp\n",
            "[[8.20849986e-02 2.21979513e+01 1.09663316e+03]\n",
            " [2.20264658e+04 5.98741417e+04 1.62754791e+05]]\n",
            "\n",
            " log\n",
            "[[       nan 1.13140211 1.94591015]\n",
            " [2.30258509 2.39789527 2.48490665]]\n",
            "\n",
            " sign\n",
            "[[-1.  1.  1.]\n",
            " [ 1.  1.  1.]]\n",
            "\n",
            " ceil\n",
            "[[-2.  4.  7.]\n",
            " [10. 11. 12.]]\n",
            "\n",
            " modf\n",
            "(array([[-0.5,  0.1,  0. ],\n",
            "       [ 0. ,  0. ,  0. ]]), array([[-2.,  3.,  7.],\n",
            "       [10., 11., 12.]]))\n",
            "\n",
            " isnan\n",
            "[[False False False]\n",
            " [False False False]]\n",
            "\n",
            " cos\n",
            "[[-0.80114362 -0.99913515  0.75390225]\n",
            " [-0.83907153  0.0044257   0.84385396]]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in sqrt\n",
            "  \"\"\"\n",
            "/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:5: RuntimeWarning: invalid value encountered in log\n",
            "  \"\"\"\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jdhsPiKNJERt",
        "outputId": "bab7a453-685b-48c3-9a64-1699068364dd"
      },
      "source": [
        "#Binary ufuncs - There are also many binary ufuncs, that apply elementwise on two ndarrays. Broadcasting rules are applied if the arrays do not have the same shape:\n",
        "a = np.array([1, -2, 3, 4])\n",
        "b = np.array([2, 8, -1, 7])\n",
        "np.add(a, b)  # equivalent to a + b\n"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 3,  6,  2, 11])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D9byKbuAJiz3",
        "outputId": "3396c8b1-96a5-4d22-cf54-fc57bbfbfc4d"
      },
      "source": [
        "np.greater(a, b)  # equivalent to a > b\n"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([False, False,  True, False])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l3Q3HVg6JlIp",
        "outputId": "f80712e5-af75-4a9f-cd87-0664b6639829"
      },
      "source": [
        "np.maximum(a, b)\n"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 8, 3, 7])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f66ywN-_JnFU",
        "outputId": "8189661d-28e5-42c3-da91-7b5e67c99e80"
      },
      "source": [
        "np.copysign(a, b)"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1.,  2., -3.,  4.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "t8h_gW9QJqXo",
        "outputId": "bf8af1f6-1d8b-4cfe-8622-8827a8bf2850"
      },
      "source": [
        "#Array indexing\n",
        "#One-dimensional arrays\n",
        "\n",
        "a = np.array([1, 5, 3, 19, 13, 7, 3])\n",
        "a[3]"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KHJpSgyVJ9Ox",
        "outputId": "d56d88b5-b5f5-42df-f7dc-710888029057"
      },
      "source": [
        "a[2:5]"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 3, 19, 13])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a_NIHBxzKAc9",
        "outputId": "617901a3-0fb7-463f-f2cf-1ae3ea6c2501"
      },
      "source": [
        "a[2:-1]"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 3, 19, 13,  7])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KalmutAAKCVZ",
        "outputId": "abaee0c8-32d7-4c7c-f83b-90174591a03c"
      },
      "source": [
        "a[:2]"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 5])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QBL5fjLfKEWo",
        "outputId": "d37b7c3d-f2cb-4161-88e4-4936d74e8daa"
      },
      "source": [
        "a[2::2]"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 3, 13,  3])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H7kdWbe9KJqR",
        "outputId": "7441155e-fd8d-4b64-9004-2152f6500214"
      },
      "source": [
        "a[::-1]"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 3,  7, 13, 19,  3,  5,  1])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "upahmDfUKL5D",
        "outputId": "26f75d0d-fab6-4246-a0be-1d1280b2b24b"
      },
      "source": [
        "a[3]==999 #modify elements\n",
        "a"
      ],
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1,  5,  3, 19, 13,  7,  3])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QIEe796eKQnQ",
        "outputId": "87028cdd-c5f3-4213-cb97-14ca177e4207"
      },
      "source": [
        "# Differences with regular python arrays\n",
        "a[2:5] = -1\n",
        "a"
      ],
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1,  5, -1, -1, -1,  7,  3])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 30
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d5_VzTegKdzy",
        "outputId": "f5750f0d-8a63-49af-8510-e7a39e2868c3"
      },
      "source": [
        "try:\n",
        "    a[2:5] = [1,2,3,4,5,6]  # too long\n",
        "except ValueError as e:\n",
        "    print(e)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cannot copy sequence with size 6 to array axis with dimension 3\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BuWq7ejMKjU5",
        "outputId": "402546d7-359a-4ca2-81fc-646022f4a0f3"
      },
      "source": [
        "try:\n",
        "    del a[2:5]\n",
        "except ValueError as e:\n",
        "    print(e)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "cannot delete array elements\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KHJXoNVQKoDF",
        "outputId": "86209184-c831-4802-eec9-2017815197b3"
      },
      "source": [
        "a_slice = a[2:6]\n",
        "a_slice[1] = 1000\n",
        "a  # the original array was modified!"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([   1,    5,   -1, 1000,   -1,    7,    3])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2m-6aT6kKvXu",
        "outputId": "b4878320-0dfc-4feb-82fa-ea15b017e9d5"
      },
      "source": [
        "a[3] = 2000\n",
        "a_slice  # similarly, modifying the original array modifies the slice!"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  -1, 2000,   -1,    7])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 34
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AzFq3IYRKyqw",
        "outputId": "984cef59-ea16-42bf-fca5-6dc947b3ece7"
      },
      "source": [
        "another_slice = a[2:6].copy()\n",
        "another_slice[1] = 3000\n",
        "a  # the original array is untouched"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([   1,    5,   -1, 2000,   -1,    7,    3])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 35
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-LkuISifK5kt",
        "outputId": "0821c102-919c-4ef1-e411-610dcb9be761"
      },
      "source": [
        "a[3] = 4000\n",
        "another_slice  # similary, modifying the original array does not affect the slice copy"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([  -1, 3000,   -1,    7])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ejbCNt5kK9y4",
        "outputId": "232c6203-8a10-4513-c053-9fba48bb8c21"
      },
      "source": [
        "b = np.arange(48).reshape(4, 12)\n",
        "b"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "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]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ocS7c5UrLQNx",
        "outputId": "a5a3f5cf-8ea9-4e30-e0ed-11ef7431ccfc"
      },
      "source": [
        "b[1, 2]  # row 1, col 2\n"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "usPRU9aeLSwj",
        "outputId": "15998fdf-1d61-4ef0-f1af-3fc2995a5dc8"
      },
      "source": [
        "b[1, :]  # row 1, all columns\n"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vdsUo-VBLU6j",
        "outputId": "e40961d0-9c3c-405b-c935-038f2e4da340"
      },
      "source": [
        "b[:, 1]  # all rows, column 1\n"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1, 13, 25, 37])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 40
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7A3PnyJbLXX4",
        "outputId": "7b7c8678-2851-4e5b-b0a2-6318d268ae98"
      },
      "source": [
        "b[1, :]\n"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 41
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LNDq8lZ6Lap6",
        "outputId": "d273f45c-be93-4ef4-a0dc-cda16a32c7bf"
      },
      "source": [
        "b[1:2, :]\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)."
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "G1PcsfDXLhzP",
        "outputId": "fd08a02b-2972-431d-852e-616c52dde17f"
      },
      "source": [
        "#Fancy indexing\n",
        "b[(0,2), 2:5]  # rows 0 and 2, columns 2 to 4 (5-1)\n"
      ],
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 2,  3,  4],\n",
              "       [26, 27, 28]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mZRU5SVtLwor",
        "outputId": "2285b985-e60b-430a-809b-d0b4f6a77554"
      },
      "source": [
        "b[:, (-1, 2, -1)]  # all rows, columns -1 (last), 2 and -1 (again, and in this order)"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[11,  2, 11],\n",
              "       [23, 14, 23],\n",
              "       [35, 26, 35],\n",
              "       [47, 38, 47]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sTi6Y9a9L12n",
        "outputId": "4155cb04-9e8c-402e-baa8-fb79ae0bba38"
      },
      "source": [
        "#Higher dimensions\n",
        "c = b.reshape(4,2,6)\n",
        "c"
      ],
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "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]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "du8o3LmKL_Yl",
        "outputId": "75b272a1-a931-493d-83de-03f3ac688e99"
      },
      "source": [
        "c[2, 1, 4]  # matrix 2, row 1, col 4\n"
      ],
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "34"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XLrwRlIeMBho",
        "outputId": "84e479af-0a30-4642-a4c0-b713263b7520"
      },
      "source": [
        "c[2, :, 3]  # matrix 2, all rows, col 3\n"
      ],
      "execution_count": 48,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([27, 33])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dbdLDBXIMMD6",
        "outputId": "58d2dbd5-3f9b-491b-b163-12d20d06eaa4"
      },
      "source": [
        "c[2, 1]  # Return matrix 2, row 1, all columns.  This is equivalent to c[2, 1, :]\n"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([30, 31, 32, 33, 34, 35])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 49
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ipJ59s7LMR2I",
        "outputId": "5a667248-ef87-4a15-f73b-0bd873c9dfc9"
      },
      "source": [
        "#Ellipsis - You may also write an ellipsis (...) to ask that all non-specified axes be entirely included.\n",
        "c[2, ...]"
      ],
      "execution_count": 50,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[24, 25, 26, 27, 28, 29],\n",
              "       [30, 31, 32, 33, 34, 35]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yZG8p1oSMaGe",
        "outputId": "c923520b-4066-460f-f83e-a2eb4c21a154"
      },
      "source": [
        "c[2, 1, ...]  # matrix 2, row 1, all columns.  This is equivalent to c[2, 1, :]"
      ],
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([30, 31, 32, 33, 34, 35])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 51
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i-l0DxXOMe8Z",
        "outputId": "39f55f4a-c27a-4302-fbf0-cb0cda69523d"
      },
      "source": [
        "c[2, ..., 3]  # matrix 2, all rows, column 3.  This is equivalent to c[2, :, 3]\n"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([27, 33])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4teBI_OsMg90",
        "outputId": "3b5d2ed3-bfab-4ff0-d0b6-cd87fd59b8d3"
      },
      "source": [
        "c[..., 3]  # all matrices, all rows, column 3.  This is equivalent to c[:, :, 3]"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 3,  9],\n",
              "       [15, 21],\n",
              "       [27, 33],\n",
              "       [39, 45]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zs9OoaGlMkaF",
        "outputId": "a9003d74-83ed-4497-a722-5c55449e2a3d"
      },
      "source": [
        "#Boolean indexing\n",
        "b = np.arange(48).reshape(4, 12)\n",
        "b"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "execute_result",
          "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]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 54
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "29WCPAq6MrsD",
        "outputId": "20f8c3a7-ef28-4447-a141-d6a9dc3405cc"
      },
      "source": [
        "rows_on = np.array([True, False, True, False])\n",
        "b[rows_on, :]  # Rows 0 and 2, all columns. Equivalent to b[(0, 2), :]"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "execute_result",
          "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]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 55
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lvKJvyp1M0Q1",
        "outputId": "5148a2aa-67bc-4528-9586-11a3399073ad"
      },
      "source": [
        "cols_on = np.array([False, True, False] * 4)\n",
        "b[:, cols_on]  # All rows, columns 1, 4, 7 and 10"
      ],
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1,  4,  7, 10],\n",
              "       [13, 16, 19, 22],\n",
              "       [25, 28, 31, 34],\n",
              "       [37, 40, 43, 46]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_DcrJnqBM6p5",
        "outputId": "394baf2c-7f17-4633-c6a5-6a8caf7cd003"
      },
      "source": [
        "#np.ix_ \n",
        "b[np.ix_(rows_on, cols_on)]"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1,  4,  7, 10],\n",
              "       [25, 28, 31, 34]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rWYMfI4wNGRv",
        "outputId": "563a3f01-a561-4b31-84e1-fe0e9d8bef0e"
      },
      "source": [
        "np.ix_(rows_on, cols_on)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([[0],\n",
              "        [2]]), array([[ 1,  4,  7, 10]]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e89kVXc6NKA2",
        "outputId": "ca57d767-48bc-4be9-fdce-c218bf539f8a"
      },
      "source": [
        "# If you use a boolean array that has the same shape as the ndarray, then you get in return a 1D array containing all the values that have True at their coordinate. This is generally used along with conditional operators:\n",
        "b[b% 3 == 1]"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1,  4,  7, 10, 13, 16, 19, 22, 25, 28, 31, 34, 37, 40, 43, 46])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3ZMrFUfeNUIq",
        "outputId": "26293036-f835-460c-b06d-ed67c7a6e6d8"
      },
      "source": [
        "#Iterating\n",
        "c = np.arange(24).reshape(2, 3, 4)  # A 3D array (composed of two 3x4 matrices)\n",
        "c"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "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]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wHT9WcS2NlyN",
        "outputId": "60dfd9cb-2bf1-4573-bfb3-661a35f0636a"
      },
      "source": [
        "for m in c:\n",
        "    print(\"Item:\")\n",
        "    print(m)"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9-QgtBWiNqY0",
        "outputId": "bca246be-dce8-4c6d-f8cf-6cf3c53016d1"
      },
      "source": [
        "for i in range(len(c)):  # Note that len(c) == c.shape[0]\n",
        "    print(\"Item:\")\n",
        "    print(c[i])"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5IMkGhbFNuXD",
        "outputId": "37bd888c-d054-4350-a125-227a1f2cbf00"
      },
      "source": [
        "for i in c.flat:\n",
        "    print(\"Item:\", i)"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EAe2ty8UN3Aw",
        "outputId": "1a04e400-e98d-4661-afef-cb3b05af6e81"
      },
      "source": [
        "#Stacking arrays\n",
        "# It is often useful to stack together different arrays. NumPy offers several functions to do just that.\n",
        "\n",
        "q1 = np.full((3,4), 1.0)\n",
        "q1"
      ],
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 1., 1., 1.],\n",
              "       [1., 1., 1., 1.],\n",
              "       [1., 1., 1., 1.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SkFv5I2fOENP",
        "outputId": "c019a94c-d485-4046-e3a0-168592f72c14"
      },
      "source": [
        "q2 = np.full((4,4), 2.0)\n",
        "q2"
      ],
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2., 2., 2., 2.],\n",
              "       [2., 2., 2., 2.],\n",
              "       [2., 2., 2., 2.],\n",
              "       [2., 2., 2., 2.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 66
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RQg4txwiOG5L",
        "outputId": "43f9e10d-1c4c-4185-8502-994ce6c1df0e"
      },
      "source": [
        "q3 = np.full((3,4), 3.0)\n",
        "q3"
      ],
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[3., 3., 3., 3.],\n",
              "       [3., 3., 3., 3.],\n",
              "       [3., 3., 3., 3.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vzqyhwp4OJ7r",
        "outputId": "6ffacd69-b14a-4480-b191-8da55b3504a5"
      },
      "source": [
        "#vstack - to stack vertically\n",
        "q4 = np.vstack((q1, q2, q3))\n",
        "q4"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "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.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "o_aFfy2qOQRB",
        "outputId": "8d6ac960-d6b3-45ff-e995-6a1a8f62f7b0"
      },
      "source": [
        "q4.shape"
      ],
      "execution_count": 69,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6SpxXVTsOSyc",
        "outputId": "ee770f0e-508b-44ea-b343-174a17f1511c"
      },
      "source": [
        "#hstack - to stack horizontally\n",
        "\n",
        "q5 = np.hstack((q1, q3))\n",
        "q5"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "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.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4pZLnYlUOh5A",
        "outputId": "d401339a-d076-44b6-9e9b-457e2e46aafd"
      },
      "source": [
        "q5.shape"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 8)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fGsD8X-9OkrV",
        "outputId": "c3dd3675-709c-4a32-bfe2-7c4f7c430637"
      },
      "source": [
        "try:\n",
        "    q5 = np.hstack((q1, q2, q3))\n",
        "except ValueError as e:\n",
        "    print(e)"
      ],
      "execution_count": 72,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "all the input array dimensions for the concatenation axis must match exactly, but along dimension 0, the array at index 0 has size 3 and the array at index 1 has size 4\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UYoVIoqhOo8R",
        "outputId": "1bad810f-05b9-4a0a-c048-c63309825842"
      },
      "source": [
        "#concatenate\n",
        "\n",
        "q7 = np.concatenate((q1, q2, q3), axis=0)  # Equivalent to vstack\n",
        "q7"
      ],
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "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.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2Qj_LePNO8hk",
        "outputId": "80cbc716-f2d9-49da-e0aa-6b9eee3c8a4e"
      },
      "source": [
        "q7.shape"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(10, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 74
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "bWuZgNoGPBTG",
        "outputId": "a84ba838-9569-4174-b15f-20bb1de9f5b0"
      },
      "source": [
        "#stack\n",
        "q8 = np.stack((q1, q3))\n",
        "q8"
      ],
      "execution_count": 77,
      "outputs": [
        {
          "output_type": "execute_result",
          "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.]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 77
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5FMjK0uUPEq-",
        "outputId": "1ef60193-80fd-4a88-f667-58da2c9cb86c"
      },
      "source": [
        "q8.shape"
      ],
      "execution_count": 78,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2, 3, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 78
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s5fQyEkAPR8L",
        "outputId": "630ea083-69d1-477b-e947-a1fc5ae589db"
      },
      "source": [
        "#Splitting arrays\n",
        "# Splitting is the opposite of stacking. For example, let's use the vsplit function to split a matrix vertically.\n",
        "\n",
        "r = np.arange(24).reshape(6,4)\n",
        "r"
      ],
      "execution_count": 79,
      "outputs": [
        {
          "output_type": "execute_result",
          "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]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 79
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q_j3BAJ7Phnt",
        "outputId": "4e80dfb0-84ac-4c2c-82fb-892d36f04399"
      },
      "source": [
        "r1, r2, r3 = np.vsplit(r, 3)\n",
        "r1"
      ],
      "execution_count": 80,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 1, 2, 3],\n",
              "       [4, 5, 6, 7]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 80
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V-OFW-G6eYi5",
        "outputId": "b3f2d8b0-39bf-4ee4-ea09-fec87a67b3f9"
      },
      "source": [
        "r2"
      ],
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 8,  9, 10, 11],\n",
              "       [12, 13, 14, 15]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HVDMN3LlehaX",
        "outputId": "fba0437a-1834-4140-dad2-d7752c590075"
      },
      "source": [
        "r3"
      ],
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[16, 17, 18, 19],\n",
              "       [20, 21, 22, 23]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 82
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RSyjw8BFekAb",
        "outputId": "d6eae47f-b9b8-4974-c04c-df0340194eb6"
      },
      "source": [
        "r4, r5 = np.hsplit(r, 2)\n",
        "r4"
      ],
      "execution_count": 83,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0,  1],\n",
              "       [ 4,  5],\n",
              "       [ 8,  9],\n",
              "       [12, 13],\n",
              "       [16, 17],\n",
              "       [20, 21]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 83
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ijw4izO7eqS6",
        "outputId": "c3753023-3c3d-4e4a-bd9f-25e052e6dfb6"
      },
      "source": [
        "r5"
      ],
      "execution_count": 84,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 2,  3],\n",
              "       [ 6,  7],\n",
              "       [10, 11],\n",
              "       [14, 15],\n",
              "       [18, 19],\n",
              "       [22, 23]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 84
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YrecdwqGetkh",
        "outputId": "a74ad5b1-2605-43cd-db77-0ec82825ff8a"
      },
      "source": [
        "#Transposing arrays - The transpose method creates a new view on an ndarray's data, with axes permuted in the given order.\n",
        "\n",
        "t = np.arange(24).reshape(4,2,3)\n",
        "t"
      ],
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "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]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DypNKM7Uezu2",
        "outputId": "67e85108-7cb2-474e-f63b-127cec8613b9"
      },
      "source": [
        "t1 = t.transpose((1,2,0))\n",
        "t1"
      ],
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "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]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 88
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rwV-RMROhiQ3",
        "outputId": "e4b07f92-0422-4fd1-da10-19bf88373c46"
      },
      "source": [
        "t1.shape"
      ],
      "execution_count": 89,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2, 3, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 89
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c3hi7OamhrlW",
        "outputId": "a0e9505a-9040-459b-c9ab-fd5555a3f19b"
      },
      "source": [
        "t2 = t.transpose()  # equivalent to t.transpose((2, 1, 0))\n",
        "t2"
      ],
      "execution_count": 91,
      "outputs": [
        {
          "output_type": "execute_result",
          "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]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 91
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VEwTLkzmhtkl",
        "outputId": "420f82f9-9231-4eb9-8324-2d4e3cbcbeaf"
      },
      "source": [
        "t2.shape\n"
      ],
      "execution_count": 92,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(3, 2, 4)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 92
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K6-a0uX0h2zf",
        "outputId": "b562217d-11d4-4f2c-fe56-5cca7401f711"
      },
      "source": [
        "t3 = t.swapaxes(0,1)  # equivalent to t.transpose((1, 0, 2))\n",
        "t3"
      ],
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "execute_result",
          "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]]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 93
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WODAZmZSh90b",
        "outputId": "6399deeb-7d1a-4541-af6e-cb5f8dd01c13"
      },
      "source": [
        "t3.shape"
      ],
      "execution_count": 94,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(2, 4, 3)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 94
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oQvQGXCUiAM8",
        "outputId": "be64bd9a-545f-47d1-bf4d-6bd1ea590fdc"
      },
      "source": [
        "# Matrix transpose\n",
        "\n",
        "m1 = np.arange(10).reshape(2,5)\n",
        "m1"
      ],
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 1, 2, 3, 4],\n",
              "       [5, 6, 7, 8, 9]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "S1X8GPvuiL3Y",
        "outputId": "940ec7bb-982e-4f7f-e117-1b58d5c3f272"
      },
      "source": [
        "m1.T\n",
        "#The T attribute is equivalent to calling transpose() when the rank is ≥2:"
      ],
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 5],\n",
              "       [1, 6],\n",
              "       [2, 7],\n",
              "       [3, 8],\n",
              "       [4, 9]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "d8oHp1-liRq2",
        "outputId": "5afd1c1c-b1b9-4b83-dfd4-90d22000fe22"
      },
      "source": [
        "m2 = np.arange(5)\n",
        "m2"
      ],
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xdL-jPFHiaeW",
        "outputId": "6d310695-1781-4b4f-8775-7404bb035649"
      },
      "source": [
        "m2.T"
      ],
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0, 1, 2, 3, 4])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "DeN138ieib9i",
        "outputId": "519d46c0-9967-452e-ea54-3bec64aad24c"
      },
      "source": [
        "m2r = m2.reshape(1,5)\n",
        "m2r"
      ],
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 1, 2, 3, 4]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Eel5hrEMifLT",
        "outputId": "d2ef9059-451b-4d7f-a10b-7953ba58f96f"
      },
      "source": [
        "m2r.T"
      ],
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0],\n",
              "       [1],\n",
              "       [2],\n",
              "       [3],\n",
              "       [4]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 102
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "TpGS0wJMigys",
        "outputId": "0a18d529-7573-4f76-dbdd-6f3ddfd043e4"
      },
      "source": [
        "#Matrix multiplication\n",
        "\n",
        "n1 = np.arange(10).reshape(2, 5)\n",
        "n1"
      ],
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 1, 2, 3, 4],\n",
              "       [5, 6, 7, 8, 9]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1uGY2WHminBG",
        "outputId": "dbf14023-4a4c-4be7-f019-85d05004e771"
      },
      "source": [
        "n2 = np.arange(15).reshape(5,3)\n",
        "n2"
      ],
      "execution_count": 104,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0,  1,  2],\n",
              "       [ 3,  4,  5],\n",
              "       [ 6,  7,  8],\n",
              "       [ 9, 10, 11],\n",
              "       [12, 13, 14]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 104
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tEbMLaQvip9K",
        "outputId": "d147f9d3-1142-4bf4-f4f4-6bb80382e1fe"
      },
      "source": [
        "n1.dot(n2)\n",
        "# Caution: as mentionned previously, n1*n2 is not a matric multiplication, it is an elementwise product (also called a Hadamard product))."
      ],
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 90, 100, 110],\n",
              "       [240, 275, 310]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 106
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zYx3vd_siuib",
        "outputId": "fb1f8526-784d-47d8-9d9e-45fd61836c0f"
      },
      "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",
        "import numpy.linalg as linalg\n",
        "\n",
        "m3 = np.array([[1,2,3],[5,7,11],[21,29,31]])\n",
        "m3"
      ],
      "execution_count": 108,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1,  2,  3],\n",
              "       [ 5,  7, 11],\n",
              "       [21, 29, 31]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 108
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fdmYFG3ci-XK",
        "outputId": "68d3aacb-564f-4c73-c008-d732cdda4de0"
      },
      "source": [
        "linalg.inv(m3)"
      ],
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-2.31818182,  0.56818182,  0.02272727],\n",
              "       [ 1.72727273, -0.72727273,  0.09090909],\n",
              "       [-0.04545455,  0.29545455, -0.06818182]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RXuB0XL5jDtK",
        "outputId": "2c185ed2-a434-4969-de4d-c3e2c0bd7be5"
      },
      "source": [
        "linalg.pinv(m3)"
      ],
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-2.31818182,  0.56818182,  0.02272727],\n",
              "       [ 1.72727273, -0.72727273,  0.09090909],\n",
              "       [-0.04545455,  0.29545455, -0.06818182]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 110
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "959K5SBujHLS",
        "outputId": "937067a2-bb92-4ac1-b57b-4c5fecb0a668"
      },
      "source": [
        "# Identity matrix¶\n",
        "#The product of a matrix by its inverse returns the identiy matrix (with small floating point errors):\n",
        "\n",
        "m3.dot(linalg.inv(m3))"
      ],
      "execution_count": 111,
      "outputs": [
        {
          "output_type": "execute_result",
          "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]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 111
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AYSnRQQHjRxz",
        "outputId": "20ccb618-5f19-4811-c68c-db079ce88ae0"
      },
      "source": [
        "np.eye(3)"
      ],
      "execution_count": 112,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1., 0., 0.],\n",
              "       [0., 1., 0.],\n",
              "       [0., 0., 1.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 112
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cN9uDKvnjUVN",
        "outputId": "a777a83b-f8cb-4193-af05-145904920a00"
      },
      "source": [
        "#QR decomposition\n",
        "\n",
        "q, r = linalg.qr(m3)\n",
        "q"
      ],
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.04627448,  0.98786672,  0.14824986],\n",
              "       [-0.23137241,  0.13377362, -0.96362411],\n",
              "       [-0.97176411, -0.07889213,  0.22237479]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 113
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "taSyfWhfjbDG",
        "outputId": "48b34a03-161f-40e8-b74e-f833de385209"
      },
      "source": [
        "r"
      ],
      "execution_count": 114,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-21.61018278, -29.89331494, -32.80860727],\n",
              "       [  0.        ,   0.62427688,   1.9894538 ],\n",
              "       [  0.        ,   0.        ,  -3.26149699]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 114
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "60gZU8JljcFv",
        "outputId": "8bf7849f-0d2a-439f-a1e5-c641317822b6"
      },
      "source": [
        "q.dot(r)  # q.r equals m3"
      ],
      "execution_count": 115,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1.,  2.,  3.],\n",
              "       [ 5.,  7., 11.],\n",
              "       [21., 29., 31.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3YcpjDfwjejA",
        "outputId": "9ab6e6a9-2810-4a22-a721-6d333cd063e9"
      },
      "source": [
        "#Determinant\n",
        "\n",
        "linalg.det(m3)  # Computes the matrix determinant"
      ],
      "execution_count": 116,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "43.99999999999997"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 116
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jbg7RVTqjmxN",
        "outputId": "90a1c431-d6a9-45e3-ea74-7ae27bc57885"
      },
      "source": [
        "#Eigenvalues and eigenvectors\n",
        "\n",
        "eigenvalues, eigenvectors = linalg.eig(m3)\n",
        "eigenvalues # λ"
      ],
      "execution_count": 118,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([42.26600592, -0.35798416, -2.90802176])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 118
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "XLbt4bgRlGQ1",
        "outputId": "fb8a15d5-ec35-447d-ae34-d1c86e2807dc"
      },
      "source": [
        "eigenvectors # v"
      ],
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.08381182, -0.76283526, -0.18913107],\n",
              "       [-0.3075286 ,  0.64133975, -0.6853186 ],\n",
              "       [-0.94784057, -0.08225377,  0.70325518]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "F5zVjYR3lLy0",
        "outputId": "6e759fab-1464-430b-8f16-a236330402b0"
      },
      "source": [
        "m3.dot(eigenvectors) - eigenvalues * eigenvectors  # m3.v - λ*v = 0"
      ],
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 8.88178420e-15,  2.22044605e-16, -3.10862447e-15],\n",
              "       [ 3.55271368e-15,  2.02615702e-15, -1.11022302e-15],\n",
              "       [ 3.55271368e-14,  3.33413852e-15, -8.43769499e-15]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xw2Jc9Y5lN8f",
        "outputId": "b51c54d8-b17f-4f60-8d70-40878dade187"
      },
      "source": [
        "# Singular Value Decomposition\n",
        "\n",
        "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"
      ],
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "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]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "izxLwSnmlULo",
        "outputId": "8c1f9b8d-a457-4214-be2b-8219d657974e"
      },
      "source": [
        "U, S_diag, V = linalg.svd(m4)\n",
        "U"
      ],
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0.,  1.,  0.,  0.],\n",
              "       [ 1.,  0.,  0.,  0.],\n",
              "       [ 0.,  0.,  0., -1.],\n",
              "       [ 0.,  0.,  1.,  0.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kWsvzIMtlW35",
        "outputId": "b240d90e-7d55-4cfb-b3fa-b09fd68f3235"
      },
      "source": [
        "S_diag"
      ],
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([3.        , 2.23606798, 2.        , 0.        ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 123
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oUG1kaEJlZny",
        "outputId": "f5ac3346-f42a-488b-c5f1-a13058e7981c"
      },
      "source": [
        "#The svd function just returns the values in the diagonal of Σ, but we want the full Σ matrix, so let's create it:\n",
        "\n",
        "S = np.zeros((4, 5))\n",
        "S[np.diag_indices(4)] = S_diag\n",
        "S  # Σ"
      ],
      "execution_count": 124,
      "outputs": [
        {
          "output_type": "execute_result",
          "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.        ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 124
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6LDDYkX4lf1u",
        "outputId": "8a9aecfc-cbc7-4ce3-84e0-92a79b44dbbb"
      },
      "source": [
        "V"
      ],
      "execution_count": 125,
      "outputs": [
        {
          "output_type": "execute_result",
          "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 ]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 125
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QIosWd6tlsAu",
        "outputId": "ae4225b7-be77-435e-c91a-2889b51fecf1"
      },
      "source": [
        "U.dot(S).dot(V) # U.Σ.V == m4"
      ],
      "execution_count": 126,
      "outputs": [
        {
          "output_type": "execute_result",
          "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.]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 126
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jr6KraMqluBs",
        "outputId": "39c23405-2836-4453-b365-d9fd31b2e503"
      },
      "source": [
        "# Diagonal and trace\n",
        "np.diag(m3)  # the values in the diagonal of m3 (top left to bottom right)"
      ],
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 1,  7, 31])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "A-OlEHiSmG3X",
        "outputId": "3acca51d-d3e7-4203-8146-20c5e15f0898"
      },
      "source": [
        "np.trace(m3)  # equivalent to np.diag(m3).sum()"
      ],
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "39"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IrSg4jiZmL-a",
        "outputId": "4c155ba5-4444-4ada-85d7-31b072a8295a"
      },
      "source": [
        "# Solving a system of linear scalar equations\n",
        "\n",
        "coeffs  = np.array([[2, 6], [5, 3]])\n",
        "depvars = np.array([6, -9])\n",
        "solution = linalg.solve(coeffs, depvars)\n",
        "solution"
      ],
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-3.,  2.])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "b-Uim8JUmSRB",
        "outputId": "a08fa0cd-7eda-4958-f97c-bfdc49290925"
      },
      "source": [
        "coeffs.dot(solution), depvars  # yep, it's the same"
      ],
      "execution_count": 131,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([ 6., -9.]), array([ 6, -9]))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 131
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SNDx42gqmU9c",
        "outputId": "7d2eb156-7cc9-4fd5-8398-ac70a42da3c7"
      },
      "source": [
        "np.allclose(coeffs.dot(solution), depvars)"
      ],
      "execution_count": 132,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 132
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YJK6NrAFmWtW"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "2S0O_d9PmYUr"
      },
      "source": [
        "**Vectorization¶**\n",
        "\n",
        "Instead of executing operations on individual array items, one at a time, your code is much more efficient if you try to stick to array operations. This is called vectorization. This way, you can benefit from NumPy's many optimizations.\n",
        "\n",
        "For example, let's say we want to generate a 768x1024 array based on the formula sin(xy/40.5). A bad option would be to do the math in python using nested loops:"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "v3a9lPismiME"
      },
      "source": [
        "import math\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."
      ],
      "execution_count": 133,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jYqSnhBKmmfQ",
        "outputId": "97e539e4-1ab3-4e50-cf3d-4e1c56fe557a"
      },
      "source": [
        "x_coords = np.arange(0, 1024)  # [0, 1, 2, ..., 1023]\n",
        "y_coords = np.arange(0, 768)   # [0, 1, 2, ..., 767]\n",
        "X, Y = np.meshgrid(x_coords, y_coords)\n",
        "X"
      ],
      "execution_count": 134,
      "outputs": [
        {
          "output_type": "execute_result",
          "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]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 134
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jW1LlaN3mpX3",
        "outputId": "2663a6f3-8f47-407a-d8a1-5fc11f35a983"
      },
      "source": [
        "Y"
      ],
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "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]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6EzTvSG1mr4V"
      },
      "source": [
        "#As you can see, 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 the vertical coordinate.\n",
        "\n",
        "#Now we can simply compute the result using array operations:"
      ],
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2xz_bL61mwVg"
      },
      "source": [
        "data = np.sin(X*Y/40.5)"
      ],
      "execution_count": 137,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 345
        },
        "id": "fL7LPVVqmyF2",
        "outputId": "a0ffc47c-a427-4adb-8008-1886e5a8a116"
      },
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.cm as cm\n",
        "fig = plt.figure(1, figsize=(7, 6))\n",
        "plt.imshow(data, cmap=cm.hot, interpolation=\"bicubic\")\n",
        "plt.show()"
      ],
      "execution_count": 139,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WzpkXaQ-m2zl",
        "outputId": "eae402f1-eece-43b3-b2b7-3893030543f6"
      },
      "source": [
        "# Saving and loading\n",
        "\n",
        "a = np.random.rand(2,3)\n",
        "a"
      ],
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.73956686, 0.242247  , 0.65508443],\n",
              "       [0.27049676, 0.66926637, 0.90827614]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gMbS5GpRnHLC"
      },
      "source": [
        "np.save(\"my_array\", a)"
      ],
      "execution_count": 141,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dtM5Wuk2nKDb",
        "outputId": "226b4ea7-b846-4ea7-8dc7-6c23980549e3"
      },
      "source": [
        "with open(\"my_array.npy\", \"rb\") as f:\n",
        "    content = f.read()\n",
        "\n",
        "content"
      ],
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "b'\\x93NUMPY\\x01\\x00v\\x00{\\'descr\\': \\'<f8\\', \\'fortran_order\\': False, \\'shape\\': (2, 3), }                                                          \\n\\x7f\\x9f\\xd5\\x1c\\x88\\xaa\\xe7?<L~\"\\xf3\\x01\\xcf?\\xeawe\\x9es\\xf6\\xe4?\\x1a(\\xaa\\xa5\\xd1O\\xd1?\\x19\\xce\\x9aO\\xa1j\\xe5?\\xb19\\xab\\x1e\\x99\\x10\\xed?'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ifqdt1Q3nMgI",
        "outputId": "cf1b4aa9-3c3e-4df9-d705-76edd6b68859"
      },
      "source": [
        "a_loaded = np.load(\"my_array.npy\")\n",
        "a_loaded"
      ],
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.73956686, 0.242247  , 0.65508443],\n",
              "       [0.27049676, 0.66926637, 0.90827614]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cPlCa18-nQir"
      },
      "source": [
        "# Text format\n",
        "# Let's try saving the array in text format:\n",
        "\n",
        "np.savetxt(\"my_array.csv\", a)"
      ],
      "execution_count": 144,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "E7CrHpILnYRE",
        "outputId": "4a2ef5f6-1dfb-46d6-89c9-c4aaf29597f2"
      },
      "source": [
        "with open(\"my_array.csv\", \"rt\") as f:\n",
        "    print(f.read())"
      ],
      "execution_count": 145,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "7.395668566868919269e-01 2.422470014977039510e-01 6.550844281844068195e-01\n",
            "2.704967611140333128e-01 6.692663721827926127e-01 9.082761382103879955e-01\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bHePe2B1ncIr"
      },
      "source": [
        "np.savetxt(\"my_array.csv\", a, delimiter=\",\")"
      ],
      "execution_count": 146,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OxkoANY0nd77",
        "outputId": "1f651242-5748-41c4-8df5-4fc0e06218d5"
      },
      "source": [
        "a_loaded = np.loadtxt(\"my_array.csv\", delimiter=\",\")\n",
        "a_loaded"
      ],
      "execution_count": 147,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.73956686, 0.242247  , 0.65508443],\n",
              "       [0.27049676, 0.66926637, 0.90827614]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 147
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "STb4CSyjnhzN",
        "outputId": "f839603e-c1b7-4b5f-f1a3-c6d55c0d12d9"
      },
      "source": [
        "# Zipped .npz format\n",
        "# It is also possible to save multiple arrays in one zipped file:\n",
        "\n",
        "b = np.arange(24, dtype=np.uint8).reshape(2, 3, 4)\n",
        "b"
      ],
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "execute_result",
          "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)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 148
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CVK9bxWVnnFZ"
      },
      "source": [
        "np.savez(\"my_arrays\", my_a=a, my_b=b)"
      ],
      "execution_count": 149,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        },
        "id": "eD8mFoZrnpeM",
        "outputId": "7e93c2ac-a732-482b-aae0-ae2d0024c6db"
      },
      "source": [
        "with open(\"my_arrays.npz\", \"rb\") as f:\n",
        "    content = f.read()\n",
        "\n",
        "repr(content)[:180] + \"[...]\""
      ],
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "\"b'PK\\\\x03\\\\x04\\\\x14\\\\x00\\\\x00\\\\x00\\\\x00\\\\x00\\\\x00\\\\x00!\\\\x00h\\\\x19\\\\xbd\\\\xa2\\\\xb0\\\\x00\\\\x00\\\\x00\\\\xb0\\\\x00\\\\x00\\\\x00\\\\x08\\\\x00\\\\x14\\\\x00my_a.npy\\\\x01\\\\x00\\\\x10\\\\x00\\\\xb0\\\\x00\\\\x00\\\\x00\\\\x00\\\\x00\\\\x00\\\\x00\\\\xb0\\\\x00\\\\x00\\\\x[...]\""
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BLmIyOf4nruP",
        "outputId": "32dfe3d1-7c70-4d7a-f5f5-c1ffa6cbd9bd"
      },
      "source": [
        "my_arrays = np.load(\"my_arrays.npz\")\n",
        "my_arrays"
      ],
      "execution_count": 151,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<numpy.lib.npyio.NpzFile at 0x7f1d9b18bf90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 151
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hjisDr_0nvbw",
        "outputId": "3fe64d1a-b915-4821-8686-4a10855aa36d"
      },
      "source": [
        "my_arrays.keys()"
      ],
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "KeysView(<numpy.lib.npyio.NpzFile object at 0x7f1d9b18bf90>)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 152
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YrfcfYzRnw8p",
        "outputId": "fffae7dd-090e-44ea-81a5-0ed63829f83c"
      },
      "source": [
        "my_arrays[\"my_a\"]"
      ],
      "execution_count": 153,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.73956686, 0.242247  , 0.65508443],\n",
              "       [0.27049676, 0.66926637, 0.90827614]])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 153
        }
      ]
    }
  ]
}