Visualise_&_Manipulate_Data_With_Matplotlib.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Visualise & Manipulate Data With Matplotlib.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyOuuKNLQeptANUKcWV/T8j0",
      "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/github/natnew/Python-Projects-Visualise-And-Manipulate-Data/blob/main/Visualise_%26_Manipulate_Data_With_Matplotlib.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Import libraries"
      ],
      "metadata": {
        "id": "chWT3SYjKavz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# And Generate a plot from a range of numbers"
      ],
      "metadata": {
        "id": "gwdLvVEFKeJh"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "eRc6Mjx5KP9H",
        "outputId": "340eb68e-4529-4705-da99-36ed5f292b09"
      },
      "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"
          }
        }
      ],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Prepare the data\n",
        "x = np.linspace(0, 10, 100)\n",
        "\n",
        "# Plot the data\n",
        "plt.plot(x, x, label='linear')\n",
        "\n",
        "# Add a legend\n",
        "plt.legend()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Change the markers"
      ],
      "metadata": {
        "id": "XK9CVGd7Kklo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "plt.plot([1, 2, 3, 4], [10, 20, 25, 30], color='lightblue', linewidth=3)\n",
        "plt.scatter([0.3, 3.8, 1.2, 2.5], [11, 25, 9, 26], color='darkgreen', marker='^')\n",
        "plt.xlim(0.5, 4.5)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "oR0wCgOAKsvm",
        "outputId": "54f6a066-c9c7-4dd5-ad0d-859a8d6035e4"
      },
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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j7kqRKU2RCYgaAj7GKDIB0UbAxxRFJiD6CPgYylZk4hOZgOgh4GNoaZFpZ2eLeikyAZFDwMfM0iJTb3uTbqXIBEQSAR8jS4tMW5obtIciExBZBHxMZCsy3U2RCYg0Aj4GKDIB8cQzPOKmKDIBsUXAR9hsKq1nKTIBsUXAR1TaXQdfX1xkejNFJiBWCPgIylVk2kKRCYgVAj6CTlBkAiACPnJ+dHFCL1JkAiACPlLOJS/rBYpMAAIEfEQMT83o+xSZACxAwEdAcvraItO9FJmA2CMBqlyuIlMDRSYg9gj4KnalyDRHkQnAtfIGvJk9amaDZnZ8wbKPmNk5MzsSfL2rtGNiKYpMAPIpZA/+05Luz7L8k+6+J/h6srhjYTlZi0xbKDIBWCxvwLv7tyWNlGEWFChrkamdIhOAxdZyDP5DZnY0OITTkWslM3vIzA6Z2aFEIrGGh4NEkQlA4VYb8P8s6UZJeyQNSPrbXCu6+z5373P3vu7u7lU+HCSKTABWZlUB7+4X3D3l7mlJ/yrp7uKOhaWGJykyAViZVQW8mfUsuPqLko7nWhdrNzY9q2fPUWQCsDJ5T5g2s89Juk9Sl5n1S/ozSfeZ2R5JLumspN8q4YyxNjWX0rP9FzVLkQnACuUNeHd/X5bFj5RgFiyRrch07zaKTAAKw9/4FSpXkamjkSITgMIQ8BXI3XWYIhOANSLgK9CJoaReo8gEYI0I+ApzbZFpPUUmAKtCwFeQ7EWmNopMAFaFgK8QQxSZABQZAV8BxqZn9V2KTACKjAQJWeYTmSgyASg+Aj5E80WmKYpMAEqAgA8JRSYApUbAh8DddXhglCITgJIi4ENwYiip15KXr1y/rYsiE4DiI+DLLFuR6ZaNFJkAFB8BX0bnklMUmQCUDQFfJpki0+iV65kiUwdFJgAlQ8CXwdIiU0tdje7dulG16wh3AKVDwJdY7iITmx5AaZEyJZS9yLRRzRSZAJQBAV8iaXd9b2mRaesGdTTWhTsYgNgg4EtgvsiUWFBkumtLu7Y0U2QCUD4EfAlkKzJdT5EJQJkR8EW2tMi0gyITgJAQ8EW0tMjU09KgN1FkAhASAr5IlhaZNjbW6Sd7KDIBCA8BXwTZikz3UGQCEDICfo0oMgGoVKTQGlBkAlDJCPhVosgEoNIR8KtAkQlANSDgV4EiE4BqQMCvEEUmANWCgF+BbEUmPpEJQKUi4AuUq8hEuAOoVAR8ASgyAahGBHweU7MpPdM/QpEJQNUhpZYxm0rr2XMjmppLS5JqKTIBqCIEfA7Zi0wdFJkAVA0CPotcRabNzQ0hTgUAK5M34M3sUTMbNLPjC5ZtNLOnzOyl4L8dpR2zvK4tMrVSZAJQdQrZg/+0pPuXLPuwpG+4+82SvhFcj4SXsxaZmkOcCABWJ2/Au/u3JY0sWfyApMeDy49L+oUizxWKc8kpHaXIBCAiVnsMfrO7DwSXz0vaXKR5QjM1l6LIBCBS1vwiq7u7JM91u5k9ZGaHzOxQIpFY68OVTFNtje7a3C4TRSYA0bDaE7ovmFmPuw+YWY+kwVwruvs+Sfskqa+vL+cvgkqwvX29Gmtr1FxXQ5EJQNVbbYrtl/RgcPlBSU8UZ5zwbWpuoMgEIBIKOU3yc5K+K+kWM+s3sw9K+pikd5jZS5J+NrgOAKggeXdV3f19OW56e5FnAQAUEQeaASCiCPgSGBgd0I0P36jzl86HPQqAGCPgS2Dvgb06O3RWew/sDXsUADFGwBfZwOiAHnv2MaU9rceeeYy9eAChIeCLbO+BvUqnM+8fn0qn2IsHEBoCvojm995nUpm3GZ5JzbAXDyA0BHwRLdx7n8dePICwEPBFtP+F/Vf23ufNpGb0xJHIFH0BVBE6+UXU//H+sEcAgCvYgweAiCLgASCiCHgAiCgCHgAiioAHgIiyzCfulenBzJKSTpftAVevS9JQ2EMUgDmLpxpmlJiz2KplzlvcvXWldyr3aZKn3b2vzI+5YmZ2iDmLpxrmrIYZJeYstmqaczX34xANAEQUAQ8AEVXugN9X5sdbLeYsrmqYsxpmlJiz2CI9Z1lfZAUAlA+HaAAgogh4AIiokgS8md1vZqfN7GUz+3CW2z9gZgkzOxJ8/WYp5sgz46NmNmhmx3Pcbmb2D8G/4aiZ3VXuGYM58s15n5ldWrAt/zSEGa8zs6fN7KSZnTCz38uyTujbs8A5K2F7NprZc2b2QjDnR7Os02BmXwi250Ez663QOUN/rgdz1JjZD8zsQJbbQt+WC2ZZbs6Vb0t3L+qXpBpJP5J0g6R6SS9Ium3JOh+Q9E/FfuwVzvnTku6SdDzH7e+S9FVJJuktkg5W6Jz3SToQ8rbskXRXcLlV0otZvuehb88C56yE7WmSWoLLdZIOSnrLknV+R9K/BJffK+kLFTpn6M/1YI4/kPSf2b63lbAtC5xzxduyFHvwd0t62d3PuPuMpM9LeqAEj7Mm7v5tSSPLrPKApM94xvckbTCznvJMd1UBc4bO3Qfc/fngclLSKUlbl6wW+vYscM7QBdtoPLhaF3wtPRviAUmPB5e/JOntZmZlGlFSwXOGzsy2Sfo5Sf+WY5XQt6VU0JwrVoqA3yrptQXX+5X9SfRLwZ/qXzKz60owx1oV+u+oBPcEfyZ/1cxuD3OQ4M/bO5XZm1uoorbnMnNKFbA9gz/Vj0galPSUu+fcnu4+J+mSpM7yTlnQnFL4z/W/k/RHktI5bq+Iban8c0or3JZhvcj6P5J63f0OSU/p6m9PrNzzkq539zdJ+kdJ/x3WIGbWIunLkn7f3cfCmiOfPHNWxPZ095S775G0TdLdZrYrjDnyKWDOUJ/rZvbzkgbd/XA5H3elCpxzxduyFAF/TtLC3yzbgmVXuPuwu08HV/9N0k+UYI61yvvvqATuPjb/Z7K7Pympzsy6yj2HmdUpE5qfdfevZFmlIrZnvjkrZXsumGdU0tOS7l9y05XtaWa1ktolDZd3uqtyzVkBz/W3Snq3mZ1V5nDx28zsP5asUwnbMu+cq9mWpQj470u62cx2mFm9Mi9a7F+4wpJjr+9W5lhopdkv6deCsz/eIumSuw+EPdRSZrZl/nihmd2tzPe0rD+cweM/IumUu38ix2qhb89C5qyQ7dltZhuCy02S3iHph0tW2y/pweDyeyT9nwevxJVLIXOG/Vx39z9x923u3qtMFv2fu//qktVC35aFzLmabVn0d5N09zkz+5CkrylzRs2j7n7CzP5c0iF33y/pd83s3ZLmlHkB8QPFniMfM/ucMmdMdJlZv6Q/U+ZFIrn7v0h6UpkzP16WNCnp18s9Y4FzvkfSb5vZnKQpSe8t9w+nMnsf75d0LDgeK0kPS9q+YM5K2J6FzFkJ27NH0uNmVqPML5gvuvuBJc+hRyT9u5m9rMxz6L1lnrHQOUN/rmdTgdsyq7VuS96qAAAiiiYrAEQUAQ8AEUXAA0BEEfAAEFEEPABEFAEPABFFwANARP0/qQkdNXCKYT8AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Generate a subplot"
      ],
      "metadata": {
        "id": "yzRt5SswK_Bt"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the necessary packages and modules\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Create a Figure\n",
        "fig = plt.figure()\n",
        "\n",
        "# Set up Axes\n",
        "ax = fig.add_subplot(111)\n",
        "\n",
        "# Scatter the data\n",
        "ax.scatter(np.linspace(0, 1, 5), np.linspace(0, 5, 5))\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "tWKap_bjLA95",
        "outputId": "43492d40-7bea-4038-c1da-95c512f49001"
      },
      "execution_count": 3,
      "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": [
        "#Change the size of the figures"
      ],
      "metadata": {
        "id": "VY6X3XLnLE3n"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import `pyplot` from `matplotlib`\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "# Initialize the plot\n",
        "fig = plt.figure(figsize=(20,10))\n",
        "ax1 = fig.add_subplot(121)\n",
        "ax2 = fig.add_subplot(122)\n",
        "\n",
        "# or replace the three lines of code above by the following line: \n",
        "#fig, (ax1, ax2) = plt.subplots(1,2, figsize=(20,10))\n",
        "\n",
        "# Plot the data\n",
        "ax1.bar([1,2,3],[3,4,5])\n",
        "ax2.barh([0.5,1,2.5],[0,1,2])\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 553
        },
        "id": "tTk-jajELHX7",
        "outputId": "03ed1b26-ac99-4cbd-d79c-b45a4b8fd439"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Customise your pyplot by deleting an axes"
      ],
      "metadata": {
        "id": "7wXiAIyfLgjG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import `pyplot` from `matplotlib`\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Initialize the plot\n",
        "fig = plt.figure()\n",
        "ax1 = fig.add_subplot(131)\n",
        "ax2 = fig.add_subplot(132)\n",
        "ax3 = fig.add_subplot(133)\n",
        "\n",
        "# Plot the data\n",
        "ax1.bar([1,2,3],[3,4,5])\n",
        "ax2.barh([0.5,1,2.5],[0,1,2])\n",
        "ax2.axhline(0.45)\n",
        "ax1.axvline(0.65)\n",
        "ax3.scatter(np.linspace(0, 1, 5), np.linspace(0, 5, 5))\n",
        "\n",
        "# Delete `ax3`\n",
        "fig.delaxes(ax3)\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 267
        },
        "id": "UyyJM264Ll6y",
        "outputId": "d47daac8-9d55-42d0-c5bd-f8931e5a1724"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#Saving the pyplot"
      ],
      "metadata": {
        "id": "7zQi1o4KLsyA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Import the necessary packages and modules\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "\n",
        "# Prepare the data\n",
        "x = np.linspace(0, 10, 100)\n",
        "\n",
        "# Plot the data\n",
        "plt.plot(x, x, label='linear')\n",
        "\n",
        "# Add a legend\n",
        "plt.legend()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 265
        },
        "id": "25nodOGWLuch",
        "outputId": "802aa732-e74d-4f63-bc95-28c497a2b5dc"
      },
      "execution_count": 6,
      "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": [
        "# Save Figure\n",
        "plt.savefig(\"foo.png\")\n",
        "\n",
        "# Save Transparent Figure\n",
        "plt.savefig(\"foo.png\", transparent=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "N_YMpbAjLy7I",
        "outputId": "25aa496e-7ed3-48f6-9671-7b171ffb387b"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 0 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}