supplement_matplotlib_themes.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "id": "791b699d-6d6e-4fe7-a370-013fb5d0f638",
      "metadata": {
        "id": "791b699d-6d6e-4fe7-a370-013fb5d0f638"
      },
      "source": [
        "### Using Matplotlib Themes\n",
        "\n",
        "Matplotlib default style of plots is quite plain. You can easily improve the look of the charts by applying a pre-configured stylesheet. This notebook shows how to apply the [Seaborn theme](https://seaborn.pydata.org/tutorial/aesthetics.html) along with many of the built-in [Matplotlib themes](https://matplotlib.org/stable/gallery/style_sheets/style_sheets_reference.html) to create aesthetically pleasing charts."
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Setup and Data Download\n",
        "\n",
        "The following blocks of code will install the required packages and download the datasets to your Colab environment."
      ],
      "metadata": {
        "id": "ylmWkzUR3da_"
      },
      "id": "ylmWkzUR3da_"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "d2563b4b-da78-43e7-8119-98d508e298a1",
      "metadata": {
        "id": "d2563b4b-da78-43e7-8119-98d508e298a1"
      },
      "outputs": [],
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import os\n",
        "import pandas as pd\n",
        "import requests"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data_folder = 'data'\n",
        "output_folder = 'output'\n",
        "\n",
        "if not os.path.exists(data_folder):\n",
        "    os.mkdir(data_folder)\n",
        "if not os.path.exists(output_folder):\n",
        "    os.mkdir(output_folder)"
      ],
      "metadata": {
        "id": "RxO6kLcu36tj"
      },
      "id": "RxO6kLcu36tj",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def download(url):\n",
        "    filename = os.path.join(data_folder, os.path.basename(url))\n",
        "    if not os.path.exists(filename):\n",
        "      with requests.get(url, stream=True, allow_redirects=True) as r:\n",
        "          with open(filename, 'wb') as f:\n",
        "              for chunk in r.iter_content(chunk_size=8192):\n",
        "                  f.write(chunk)\n",
        "      print('Downloaded', filename)"
      ],
      "metadata": {
        "id": "jgGbMJrf34QG"
      },
      "id": "jgGbMJrf34QG",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "dca64db9-dd45-412c-af0a-63d30f686348",
      "metadata": {
        "id": "dca64db9-dd45-412c-af0a-63d30f686348"
      },
      "source": [
        "We have 12 different CSV files containing crime data for each month of 2020. We download each of them to the data folder."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "files = [\n",
        "  '2020-01-metropolitan-street.csv',\n",
        "  '2020-02-metropolitan-street.csv',\n",
        "  '2020-03-metropolitan-street.csv',\n",
        "  '2020-04-metropolitan-street.csv',\n",
        "  '2020-05-metropolitan-street.csv',\n",
        "  '2020-06-metropolitan-street.csv',\n",
        "  '2020-07-metropolitan-street.csv',\n",
        "  '2020-08-metropolitan-street.csv',\n",
        "  '2020-09-metropolitan-street.csv',\n",
        "  '2020-10-metropolitan-street.csv',\n",
        "  '2020-11-metropolitan-street.csv',\n",
        "  '2020-12-metropolitan-street.csv'\n",
        "]\n",
        "\n",
        "\n",
        "data_url = 'https://github.com/spatialthoughts/python-dataviz-web/releases/' \\\n",
        "  'download/police.uk/'\n",
        "\n",
        "for f in files:\n",
        "  url = os.path.join(data_url + f)\n",
        "  download(url)\n"
      ],
      "metadata": {
        "id": "dFJeheKI4nQD"
      },
      "id": "dFJeheKI4nQD",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "id": "45d09fdc-508d-4ead-9e06-d00c721b9c62",
      "metadata": {
        "id": "45d09fdc-508d-4ead-9e06-d00c721b9c62"
      },
      "source": [
        "It will be helpful to merge all 12 CSV files into a single dataframe. We can use `pd.concat()` to merge a list of dataframes."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "f301de58-846c-4273-a246-3e78ea548886",
      "metadata": {
        "id": "f301de58-846c-4273-a246-3e78ea548886"
      },
      "outputs": [],
      "source": [
        "dataframe_list = []\n",
        "\n",
        "for f in files:\n",
        "    filepath = os.path.join(data_folder, f)\n",
        "    df = pd.read_csv(filepath)\n",
        "    dataframe_list.append(df)\n",
        "\n",
        "merged_df = pd.concat(dataframe_list)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Create a Bar Chart\n",
        "\n",
        "We can also chart the trend of crime over the year. For this, let's group the data by month."
      ],
      "metadata": {
        "id": "Oh3EVlt2E13e"
      },
      "id": "Oh3EVlt2E13e"
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "id": "adf118df-e809-4c5b-abba-9780eedc2b15",
      "metadata": {
        "id": "adf118df-e809-4c5b-abba-9780eedc2b15"
      },
      "outputs": [],
      "source": [
        "monthly_counts = merged_df.groupby('Month').size()\n",
        "monthly_counts"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use default style\n",
        "plt.style.use('default')\n",
        "\n",
        "fig, ax = plt.subplots(1, 1)\n",
        "fig.set_size_inches(12,6)\n",
        "bars = monthly_counts.plot(kind='bar', ax=ax)\n",
        "\n",
        "ax.set_title('Total Crime by Month (Default Style)', loc='left', pad=20)\n",
        "ax.set_xlabel('')\n",
        "ax.set_ylabel('Total Incidents')\n",
        "ax.set_xticks(np.arange(len(monthly_counts)))\n",
        "\n",
        "# Extra: Add labels on bars\n",
        "for bar in bars.patches:\n",
        "    height = bar.get_height()\n",
        "    ax.annotate(\n",
        "        height,\n",
        "        xy=(bar.get_x() + bar.get_width() / 2, height),\n",
        "        xytext=(0, 10),\n",
        "        fontsize=8,\n",
        "        textcoords=\"offset points\", ha='center', va='bottom',\n",
        "        bbox=dict(facecolor='white', edgecolor='black', boxstyle='round'))\n",
        "\n",
        "# Extra: Custmize X-Axis labels\n",
        "\n",
        "labels = []\n",
        "for date in pd.to_datetime(monthly_counts.index):\n",
        "  labels.append(date.strftime('%b'))\n",
        "ax.set_xticklabels(labels)\n",
        "\n",
        "ax.spines['top'].set_visible(False)\n",
        "ax.spines['right'].set_visible(False)\n",
        "\n",
        "plt.tight_layout()\n",
        "\n",
        "output_path = os.path.join(output_folder, 'default.png')\n",
        "plt.savefig(output_path, dpi=300)\n",
        "\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "yQ2xzL4XKXej",
        "outputId": "bca6e310-daf9-49e2-ce41-94c43dec4fd3",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        }
      },
      "id": "yQ2xzL4XKXej",
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Use seaborn style\n",
        "import seaborn as sns\n",
        "sns.set_theme()\n",
        "\n",
        "fig, ax = plt.subplots(1, 1)\n",
        "fig.set_size_inches(12,6)\n",
        "bars = monthly_counts.plot(kind='bar', ax=ax)\n",
        "\n",
        "ax.set_title('Total Crime by Month (Seaborn Style)', loc='left', pad=20)\n",
        "ax.set_xlabel('')\n",
        "ax.set_ylabel('Total Incidents')\n",
        "ax.set_xticks(np.arange(len(monthly_counts)))\n",
        "\n",
        "# Extra: Add labels on bars\n",
        "for bar in bars.patches:\n",
        "    height = bar.get_height()\n",
        "    ax.annotate(\n",
        "        height,\n",
        "        xy=(bar.get_x() + bar.get_width() / 2, height),\n",
        "        xytext=(0, 10),\n",
        "        fontsize=8,\n",
        "        textcoords=\"offset points\", ha='center', va='bottom',\n",
        "        bbox=dict(facecolor='white', edgecolor='black', boxstyle='round'))\n",
        "\n",
        "# Extra: Custmize X-Axis labels\n",
        "\n",
        "labels = []\n",
        "for date in pd.to_datetime(monthly_counts.index):\n",
        "  labels.append(date.strftime('%b'))\n",
        "ax.set_xticklabels(labels)\n",
        "\n",
        "ax.spines['top'].set_visible(False)\n",
        "ax.spines['right'].set_visible(False)\n",
        "plt.tight_layout()\n",
        "\n",
        "output_path = os.path.join(output_folder, 'seaborn.png')\n",
        "plt.savefig(output_path, dpi=300)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "FzRir9TqNOZu",
        "outputId": "8bedf6db-b1b8-49e9-d634-c2c13a88f286",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 600
        }
      },
      "id": "FzRir9TqNOZu",
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Matplotlib comes with many built-in themes. Let's see what styles are available."
      ],
      "metadata": {
        "id": "HXGmUZ81QfyI"
      },
      "id": "HXGmUZ81QfyI"
    },
    {
      "cell_type": "code",
      "source": [
        "plt.style.available"
      ],
      "metadata": {
        "id": "1y0iZtJaQVci"
      },
      "id": "1y0iZtJaQVci",
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Use ggplot style\n",
        "plt.style.use('ggplot')\n",
        "\n",
        "fig, ax = plt.subplots(1, 1)\n",
        "fig.set_size_inches(12,6)\n",
        "bars = monthly_counts.plot(kind='bar', ax=ax)\n",
        "\n",
        "ax.set_title('Total Crime by Month (ggplot Style)', loc='left', pad=20)\n",
        "ax.set_xlabel('')\n",
        "ax.set_ylabel('Total Incidents')\n",
        "ax.set_xticks(np.arange(len(monthly_counts)))\n",
        "\n",
        "# Extra: Add labels on bars\n",
        "for bar in bars.patches:\n",
        "    height = bar.get_height()\n",
        "    ax.annotate(\n",
        "        height,\n",
        "        xy=(bar.get_x() + bar.get_width() / 2, height),\n",
        "        xytext=(0, 10),\n",
        "        fontsize=8,\n",
        "        textcoords=\"offset points\", ha='center', va='bottom',\n",
        "        bbox=dict(facecolor='white', edgecolor='black', boxstyle='round'))\n",
        "\n",
        "# Extra: Custmize X-Axis labels\n",
        "\n",
        "labels = []\n",
        "for date in pd.to_datetime(monthly_counts.index):\n",
        "  labels.append(date.strftime('%b'))\n",
        "ax.set_xticklabels(labels)\n",
        "\n",
        "ax.spines['top'].set_visible(False)\n",
        "ax.spines['right'].set_visible(False)\n",
        "plt.tight_layout()\n",
        "\n",
        "output_path = os.path.join(output_folder, 'ggplot.png')\n",
        "plt.savefig(output_path, dpi=300)\n",
        "\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "O5iaDKXJNfDm",
        "outputId": "8dbc8bd4-e44f-4d5c-abef-9b2f0c1303e6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        }
      },
      "id": "O5iaDKXJNfDm",
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.10.5"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 5
}