lab02.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "lab02.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Lab 2: NumPy and Matlib\n",
        "\n",
        "Kobie Williams \n",
        "\n",
        "Feb 3, 2022 \n",
        "\n",
        "To use numpy and matplotlib for working with data "
      ],
      "metadata": {
        "id": "FgvUBTlp9xOX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np "
      ],
      "metadata": {
        "id": "pR3JZCO4-OVS"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#read the data into a variable called poptable and print it out \n",
        "poptable = np.loadtxt('lab02_populations.csv',dtype=int, delimiter = ',',skiprows=1)\n",
        "poptable"
      ],
      "metadata": {
        "id": "qRx5dN_T-aS4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1714791d-386a-4406-dc45-b2fdebdcb2ec"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 1900, 30000,  4000, 48300],\n",
              "       [ 1901, 47200,  6100, 48200],\n",
              "       [ 1902, 70200,  9800, 41500],\n",
              "       [ 1903, 77400, 35200, 38200],\n",
              "       [ 1904, 36300, 59400, 40600],\n",
              "       [ 1905, 20600, 41700, 39800],\n",
              "       [ 1906, 18100, 19000, 38600],\n",
              "       [ 1907, 21400, 13000, 42300],\n",
              "       [ 1908, 22000,  8300, 44500],\n",
              "       [ 1909, 25400,  9100, 42100],\n",
              "       [ 1910, 27100,  7400, 46000],\n",
              "       [ 1911, 40300,  8000, 46800],\n",
              "       [ 1912, 57000, 12300, 43800],\n",
              "       [ 1913, 76600, 19500, 40900],\n",
              "       [ 1914, 52300, 45700, 39400],\n",
              "       [ 1915, 19500, 51100, 39000],\n",
              "       [ 1916, 11200, 29700, 36700],\n",
              "       [ 1917,  7600, 15800, 41800],\n",
              "       [ 1918, 14600,  9700, 43300],\n",
              "       [ 1919, 16200, 10100, 41300],\n",
              "       [ 1920, 24700,  8600, 47300],\n",
              "       [ 1921,    -1,  9900, 45200]])"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Create the mask and identify rows with a negative column \n",
        "maskData = poptable[:, 1] >= 0 \n",
        "#Setting the mask to positive \n",
        "posRows = maskData\n"
      ],
      "metadata": {
        "id": "pFvpaWtp1VCX"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cleanData = poptable[posRows] \n"
      ],
      "metadata": {
        "id": "vTm59MaI5vca"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(cleanData)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yQbZicVo56Fp",
        "outputId": "0930155f-81d0-4b32-8602-299b30ab2f47"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[ 1900 30000  4000 48300]\n",
            " [ 1901 47200  6100 48200]\n",
            " [ 1902 70200  9800 41500]\n",
            " [ 1903 77400 35200 38200]\n",
            " [ 1904 36300 59400 40600]\n",
            " [ 1905 20600 41700 39800]\n",
            " [ 1906 18100 19000 38600]\n",
            " [ 1907 21400 13000 42300]\n",
            " [ 1908 22000  8300 44500]\n",
            " [ 1909 25400  9100 42100]\n",
            " [ 1910 27100  7400 46000]\n",
            " [ 1911 40300  8000 46800]\n",
            " [ 1912 57000 12300 43800]\n",
            " [ 1913 76600 19500 40900]\n",
            " [ 1914 52300 45700 39400]\n",
            " [ 1915 19500 51100 39000]\n",
            " [ 1916 11200 29700 36700]\n",
            " [ 1917  7600 15800 41800]\n",
            " [ 1918 14600  9700 43300]\n",
            " [ 1919 16200 10100 41300]\n",
            " [ 1920 24700  8600 47300]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#print out the number of rows and columns in poptable \n",
        "print(\"row:\", cleanData.shape[0])\n",
        "print(\"col:\", cleanData.shape[1])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-yUtu2vMHcwh",
        "outputId": "40abb3ef-785f-4ba0-caed-14d906866b92"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "row: 21\n",
            "col: 4\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Minimum number of hares \n",
        "print(cleanData[:,1].min())\n",
        "#Max number of hares \n",
        "print(cleanData[:,1].max())\n",
        "#Average number of hares \n",
        "print(cleanData[:,1].mean())\n",
        "\n",
        "#Minimum number of Lynxes \n",
        "print(cleanData[:,2].min())\n",
        "#Maximum number of Lynxes \n",
        "print(cleanData[:,2].max())\n",
        "#Average number of Lynxes \n",
        "print(cleanData[:,2].mean())\n",
        "\n",
        "#Minimum number of Carrots \n",
        "print(cleanData[:,3].min())\n",
        "#Maximum number of Carrots \n",
        "print(cleanData[:,3].max())\n",
        "#Average number of Carrots \n",
        "print(cleanData[:,3].mean())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "c6DIqJgh7rgn",
        "outputId": "09cc2996-459f-4763-b419-8c34d06b0540"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "7600\n",
            "77400\n",
            "34080.95238095238\n",
            "4000\n",
            "59400\n",
            "20166.666666666668\n",
            "36700\n",
            "48300\n",
            "42400.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#All the years where population is greater than 40000\n",
        "print(cleanData[cleanData[:,1] > 40000][:,0])\n",
        "\n",
        "  "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-3OPC9-T7549",
        "outputId": "bbd1e243-a39b-4519-81c9-2b13140bd5c1"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1901 1902 1903 1911 1912 1913 1914]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "vvLl09kb-zdJ"
      },
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#Plot the number of hares, lynxes, and carrots over time (3 curves on one graph). Include a title, axis labels, and a legend."
      ],
      "metadata": {
        "id": "hjxu2y9B_4bw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#x axis as the years of the study and its range \n",
        "x = np.arange(1900,1921)\n",
        "\n",
        "#hare \n",
        "a = cleanData[:,1] \n",
        "#lynx \n",
        "y = cleanData[:,2] \n",
        "#carrot \n",
        "z = cleanData[:,3] \n",
        "\n",
        "#Plotting the curves \n",
        "plt.plot(x,a,label='hare') \n",
        "plt.plot(x,y,label='lynx')\n",
        "plt.plot(x,z,label='carrot')\n",
        "\n",
        "#title \n",
        "plt.title('Plots of change in hares,lynxes, and carrots over time')\n",
        "\n",
        "#axis labels\n",
        "plt.ylabel('Y') \n",
        "plt.xlabel('X') \n",
        "#legend\n",
        "plt.legend(('hare','lynx','carrot'))\n",
        "plt.show() \n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "DONc5RtAAETu",
        "outputId": "b04afba1-5cae-47ec-9111-59ebeaae2ade"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "CSnhoudzC6ks"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.hist(a,bins=8) \n",
        "plt.xlabel('hare') \n",
        "plt.ylabel('number')\n",
        "plt.grid(True)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 279
        },
        "id": "7fTbgmSfFjAs",
        "outputId": "d89e36a3-5c97-400c-da5e-d0132c3ad3ee"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "yF0xli7iFyfk"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(a,y) \n",
        "plt.xlabel('hare') \n",
        "plt.ylabel('lynx') \n",
        "plt.title(\"scatter plot of hares vs lynx\") \n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "0KbFz-wuF2Mt",
        "outputId": "f3a738a6-8ca9-4c75-9d0d-1be1b28755a0"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": "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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "g. The relationship between the number of lynxes and number of hares looks somewhat inversely proportional with a few outliers. So I assume the lynxes are eating the hares. "
      ],
      "metadata": {
        "id": "BNjcVc5EGDTx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "chCNunW_GQtP"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}