Website Notebook Classwork 4.ipynb

website-notebook-classwork-4.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Website Notebook Classwork 4.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPyR/kn906u+RAflOtixvq6",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/gist/flyfir248/740356d7fea9de964074f4ebc8d81442/website-notebook-classwork-4.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dLPbS_bNbtxB",
        "outputId": "c1d9b38c-2629-4fcd-81bd-17bbb3266e1d"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 0  1  2  3  4]\n",
            " [ 5  6  7  8  9]\n",
            " [10 11 12 13 14]\n",
            " [15 16 17 18 19]\n",
            " [20 21 22 23 24]]\n"
          ]
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "Array2D = np.arange(0, 25).reshape([5,5])\n",
        "\n",
        "print(Array2D)"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(Array2D, axis = 1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Mek_16p-b594",
        "outputId": "cae49e5a-032a-43f2-cd57-11a1259d0486"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 2.,  7., 12., 17., 22.])"
            ]
          },
          "metadata": {},
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "np.mean(Array2D, axis = 0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nfA-bE_Ob7II",
        "outputId": "f8d827a0-bc56-4184-ef6f-9fde0abf7ef9"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([10., 11., 12., 13., 14.])"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    }
  ]
}