src/notebooks/550-intro-table-with-pandas.ipynb

{
  "cells": [
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {
        "tags": []
      },
      "source": [
        "## Libraries\n",
        "\n",
        "First, you need to install the following librairies:\n",
        "- [matplotlib](https://python-graph-gallery.com/matplotlib/) is used for plot creating the charts\n",
        "- [pandas](https://python-graph-gallery.com/pandas/) is used to put the data into a dataframe and custom the table\n",
        "- `numpy` is used to generate some data\n",
        "\n",
        "For some reasons, we need to use an older pandas version (1.5.3) than the lastest available. It means we have to run `pip install pandas==1.5.3` in the terminal before."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 71,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import matplotlib as mpl\n",
        "\n",
        "# data generation\n",
        "import numpy as np"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Dataset\n",
        "\n",
        "When creating **nice output tables**, we first need to have the dataframe with the values we want. \n",
        "\n",
        "In this post, we'll use *fake weather data* from different cities. We'll take a look at different simple features of [pandas](https://python-graph-gallery.com/pandas/)  to make this table more **aesthetically appealing**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 72,
      "metadata": {
        "tags": []
      },
      "outputs": [],
      "source": [
        "sample_size = 6\n",
        "\n",
        "new_york = np.random.uniform(20,60,sample_size)\n",
        "paris = np.random.uniform(20,40,sample_size)\n",
        "london = np.random.uniform(5,30,sample_size)\n",
        "\n",
        "df = pd.DataFrame({'new_york': new_york,\n",
        "                   'paris': paris,\n",
        "                   'london': london},\n",
        "                 \n",
        "                 # generate date values in the index of the dataframe\n",
        "                 index=pd.date_range(start=\"2020-01-01\", periods=sample_size).strftime(\"%d-%m-%Y\"))"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Default output\n",
        "\n",
        "The default result **isn't very pretty**, but it's from this base that we'll build something more pleasing to the eye."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 73,
      "metadata": {
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>new_york</th>\n",
              "      <th>paris</th>\n",
              "      <th>london</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>01-01-2020</th>\n",
              "      <td>23.620452</td>\n",
              "      <td>24.961700</td>\n",
              "      <td>15.059958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>02-01-2020</th>\n",
              "      <td>53.725231</td>\n",
              "      <td>30.954143</td>\n",
              "      <td>21.996464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>03-01-2020</th>\n",
              "      <td>52.312884</td>\n",
              "      <td>28.618204</td>\n",
              "      <td>23.748652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>04-01-2020</th>\n",
              "      <td>29.841392</td>\n",
              "      <td>21.862396</td>\n",
              "      <td>18.777793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>05-01-2020</th>\n",
              "      <td>52.857303</td>\n",
              "      <td>24.430124</td>\n",
              "      <td>10.540992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>06-01-2020</th>\n",
              "      <td>52.577723</td>\n",
              "      <td>31.604297</td>\n",
              "      <td>10.159980</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "             new_york      paris     london\n",
              "01-01-2020  23.620452  24.961700  15.059958\n",
              "02-01-2020  53.725231  30.954143  21.996464\n",
              "03-01-2020  52.312884  28.618204  23.748652\n",
              "04-01-2020  29.841392  21.862396  18.777793\n",
              "05-01-2020  52.857303  24.430124  10.540992\n",
              "06-01-2020  52.577723  31.604297  10.159980"
            ]
          },
          "execution_count": 73,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "df"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Change colors\n",
        "\n",
        "A clean way to apply modifications to [Pandas](https://python-graph-gallery.com) tables is to create a function that performs the **modifications**, then apply this function to our data frame.\n",
        "\n",
        "- We use a colormap named *\"Reds\"*, that will put the **background color** of each cell depending on the value in the cell.\n",
        "\n",
        "Once the function is defined, we use the `style()` and `pipe()` from [Pandas](https://python-graph-gallery.com) to **apply this function** to the dataframe. "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 74,
      "metadata": {
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_8094a_row0_col0, #T_8094a_row2_col2 {\n",
              "  background-color: #fca78b;\n",
              "  color: #000000;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_8094a_row0_col1 {\n",
              "  background-color: #fc9e80;\n",
              "  color: #000000;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_8094a_row0_col2 {\n",
              "  background-color: #fee3d6;\n",
              "  color: #000000;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_8094a_row1_col0 {\n",
              "  background-color: #67000d;\n",
              "  color: #f1f1f1;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_8094a_row1_col1 {\n",
              "  background-color: #fb7151;\n",
              "  color: #f1f1f1;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_8094a_row1_col2 {\n",
              "  background-color: #fcb499;\n",
              "  color: #000000;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_8094a_row2_col0 {\n",
              "  background-color: #77040f;\n",
              "  color: #f1f1f1;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_8094a_row2_col1 {\n",
              "  background-color: #fc8262;\n",
              "  color: #f1f1f1;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_8094a_row3_col0 {\n",
              "  background-color: #fb7a5a;\n",
              "  color: #f1f1f1;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_8094a_row3_col1 {\n",
              "  background-color: #fcb69b;\n",
              "  color: #000000;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_8094a_row3_col2 {\n",
              "  background-color: #fdcbb6;\n",
              "  color: #000000;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_8094a_row4_col0 {\n",
              "  background-color: #71020e;\n",
              "  color: #f1f1f1;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_8094a_row4_col1 {\n",
              "  background-color: #fca285;\n",
              "  color: #000000;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_8094a_row4_col2 {\n",
              "  background-color: #fff4ee;\n",
              "  color: #000000;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_8094a_row5_col0 {\n",
              "  background-color: #73030f;\n",
              "  color: #f1f1f1;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_8094a_row5_col1 {\n",
              "  background-color: #fb6c4c;\n",
              "  color: #f1f1f1;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_8094a_row5_col2 {\n",
              "  background-color: #fff5f0;\n",
              "  color: #000000;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_8094a\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_8094a_level0_col0\" class=\"col_heading level0 col0\" >new_york</th>\n",
              "      <th id=\"T_8094a_level0_col1\" class=\"col_heading level0 col1\" >paris</th>\n",
              "      <th id=\"T_8094a_level0_col2\" class=\"col_heading level0 col2\" >london</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_8094a_level0_row0\" class=\"row_heading level0 row0\" >01-01-2020</th>\n",
              "      <td id=\"T_8094a_row0_col0\" class=\"data row0 col0\" >23.620452</td>\n",
              "      <td id=\"T_8094a_row0_col1\" class=\"data row0 col1\" >24.961700</td>\n",
              "      <td id=\"T_8094a_row0_col2\" class=\"data row0 col2\" >15.059958</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8094a_level0_row1\" class=\"row_heading level0 row1\" >02-01-2020</th>\n",
              "      <td id=\"T_8094a_row1_col0\" class=\"data row1 col0\" >53.725231</td>\n",
              "      <td id=\"T_8094a_row1_col1\" class=\"data row1 col1\" >30.954143</td>\n",
              "      <td id=\"T_8094a_row1_col2\" class=\"data row1 col2\" >21.996464</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8094a_level0_row2\" class=\"row_heading level0 row2\" >03-01-2020</th>\n",
              "      <td id=\"T_8094a_row2_col0\" class=\"data row2 col0\" >52.312884</td>\n",
              "      <td id=\"T_8094a_row2_col1\" class=\"data row2 col1\" >28.618204</td>\n",
              "      <td id=\"T_8094a_row2_col2\" class=\"data row2 col2\" >23.748652</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8094a_level0_row3\" class=\"row_heading level0 row3\" >04-01-2020</th>\n",
              "      <td id=\"T_8094a_row3_col0\" class=\"data row3 col0\" >29.841392</td>\n",
              "      <td id=\"T_8094a_row3_col1\" class=\"data row3 col1\" >21.862396</td>\n",
              "      <td id=\"T_8094a_row3_col2\" class=\"data row3 col2\" >18.777793</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8094a_level0_row4\" class=\"row_heading level0 row4\" >05-01-2020</th>\n",
              "      <td id=\"T_8094a_row4_col0\" class=\"data row4 col0\" >52.857303</td>\n",
              "      <td id=\"T_8094a_row4_col1\" class=\"data row4 col1\" >24.430124</td>\n",
              "      <td id=\"T_8094a_row4_col2\" class=\"data row4 col2\" >10.540992</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_8094a_level0_row5\" class=\"row_heading level0 row5\" >06-01-2020</th>\n",
              "      <td id=\"T_8094a_row5_col0\" class=\"data row5 col0\" >52.577723</td>\n",
              "      <td id=\"T_8094a_row5_col1\" class=\"data row5 col1\" >31.604297</td>\n",
              "      <td id=\"T_8094a_row5_col2\" class=\"data row5 col2\" >10.159980</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7ff4f1095700>"
            ]
          },
          "execution_count": 74,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def custom_table(styler):\n",
        "    styler.background_gradient(cmap=\"Reds\", axis=None)\n",
        "    \n",
        "    # center text and increase font size\n",
        "    styler.applymap(lambda x: 'text-align: center; font-size: 14px;') \n",
        "    return styler\n",
        "\n",
        "df.style.pipe(custom_table)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "Here, for example, it's much easier to see that temperatures in New York are **higher** compared to London."
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Add aggregate metrics\n",
        "\n",
        "To make our table more meaningful, we can then add **aggregation measures**. To do this, we use pandas' `agg()` and `concat()` functions.\n",
        "\n",
        "We also add a line to our function to **round off the values** in our table, using the `format()` function."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 75,
      "metadata": {
        "tags": []
      },
      "outputs": [
        {
          "data": {
            "text/html": [
              "<style type=\"text/css\">\n",
              "#T_7457d_row0_col0, #T_7457d_row2_col2, #T_7457d_row7_col2 {\n",
              "  background-color: #b3d3e8;\n",
              "  color: #000000;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_7457d_row0_col1 {\n",
              "  background-color: #aacfe5;\n",
              "  color: #000000;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_7457d_row0_col2 {\n",
              "  background-color: #e1edf8;\n",
              "  color: #000000;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_7457d_row1_col0, #T_7457d_row7_col0 {\n",
              "  background-color: #08306b;\n",
              "  color: #f1f1f1;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_7457d_row1_col1 {\n",
              "  background-color: #74b3d8;\n",
              "  color: #000000;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_7457d_row1_col2 {\n",
              "  background-color: #bfd8ed;\n",
              "  color: #000000;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_7457d_row2_col0 {\n",
              "  background-color: #083877;\n",
              "  color: #f1f1f1;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_7457d_row2_col1 {\n",
              "  background-color: #8abfdd;\n",
              "  color: #000000;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_7457d_row3_col0 {\n",
              "  background-color: #7fb9da;\n",
              "  color: #000000;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_7457d_row3_col1 {\n",
              "  background-color: #c1d9ed;\n",
              "  color: #000000;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_7457d_row3_col2 {\n",
              "  background-color: #d0e2f2;\n",
              "  color: #000000;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_7457d_row4_col0 {\n",
              "  background-color: #083573;\n",
              "  color: #f1f1f1;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_7457d_row4_col1 {\n",
              "  background-color: #aed1e7;\n",
              "  color: #000000;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_7457d_row4_col2 {\n",
              "  background-color: #f5fafe;\n",
              "  color: #000000;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_7457d_row5_col0 {\n",
              "  background-color: #083674;\n",
              "  color: #f1f1f1;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_7457d_row5_col1, #T_7457d_row7_col1 {\n",
              "  background-color: #6dafd7;\n",
              "  color: #f1f1f1;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_7457d_row5_col2 {\n",
              "  background-color: #f7fbff;\n",
              "  color: #000000;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_7457d_row6_col0 {\n",
              "  background-color: #1b69af;\n",
              "  color: #f1f1f1;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_7457d_row6_col1 {\n",
              "  background-color: #99c7e0;\n",
              "  color: #000000;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "#T_7457d_row6_col2 {\n",
              "  background-color: #d9e8f5;\n",
              "  color: #000000;\n",
              "  text-align: center;\n",
              "  font-size: 14px;\n",
              "}\n",
              "</style>\n",
              "<table id=\"T_7457d\">\n",
              "  <thead>\n",
              "    <tr>\n",
              "      <th class=\"blank level0\" >&nbsp;</th>\n",
              "      <th id=\"T_7457d_level0_col0\" class=\"col_heading level0 col0\" >new_york</th>\n",
              "      <th id=\"T_7457d_level0_col1\" class=\"col_heading level0 col1\" >paris</th>\n",
              "      <th id=\"T_7457d_level0_col2\" class=\"col_heading level0 col2\" >london</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th id=\"T_7457d_level0_row0\" class=\"row_heading level0 row0\" >01-01-2020</th>\n",
              "      <td id=\"T_7457d_row0_col0\" class=\"data row0 col0\" >23.62</td>\n",
              "      <td id=\"T_7457d_row0_col1\" class=\"data row0 col1\" >24.96</td>\n",
              "      <td id=\"T_7457d_row0_col2\" class=\"data row0 col2\" >15.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7457d_level0_row1\" class=\"row_heading level0 row1\" >02-01-2020</th>\n",
              "      <td id=\"T_7457d_row1_col0\" class=\"data row1 col0\" >53.73</td>\n",
              "      <td id=\"T_7457d_row1_col1\" class=\"data row1 col1\" >30.95</td>\n",
              "      <td id=\"T_7457d_row1_col2\" class=\"data row1 col2\" >22.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7457d_level0_row2\" class=\"row_heading level0 row2\" >03-01-2020</th>\n",
              "      <td id=\"T_7457d_row2_col0\" class=\"data row2 col0\" >52.31</td>\n",
              "      <td id=\"T_7457d_row2_col1\" class=\"data row2 col1\" >28.62</td>\n",
              "      <td id=\"T_7457d_row2_col2\" class=\"data row2 col2\" >23.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7457d_level0_row3\" class=\"row_heading level0 row3\" >04-01-2020</th>\n",
              "      <td id=\"T_7457d_row3_col0\" class=\"data row3 col0\" >29.84</td>\n",
              "      <td id=\"T_7457d_row3_col1\" class=\"data row3 col1\" >21.86</td>\n",
              "      <td id=\"T_7457d_row3_col2\" class=\"data row3 col2\" >18.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7457d_level0_row4\" class=\"row_heading level0 row4\" >05-01-2020</th>\n",
              "      <td id=\"T_7457d_row4_col0\" class=\"data row4 col0\" >52.86</td>\n",
              "      <td id=\"T_7457d_row4_col1\" class=\"data row4 col1\" >24.43</td>\n",
              "      <td id=\"T_7457d_row4_col2\" class=\"data row4 col2\" >10.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7457d_level0_row5\" class=\"row_heading level0 row5\" >06-01-2020</th>\n",
              "      <td id=\"T_7457d_row5_col0\" class=\"data row5 col0\" >52.58</td>\n",
              "      <td id=\"T_7457d_row5_col1\" class=\"data row5 col1\" >31.60</td>\n",
              "      <td id=\"T_7457d_row5_col2\" class=\"data row5 col2\" >10.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7457d_level0_row6\" class=\"row_heading level0 row6\" >mean</th>\n",
              "      <td id=\"T_7457d_row6_col0\" class=\"data row6 col0\" >44.16</td>\n",
              "      <td id=\"T_7457d_row6_col1\" class=\"data row6 col1\" >27.07</td>\n",
              "      <td id=\"T_7457d_row6_col2\" class=\"data row6 col2\" >16.71</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th id=\"T_7457d_level0_row7\" class=\"row_heading level0 row7\" >max</th>\n",
              "      <td id=\"T_7457d_row7_col0\" class=\"data row7 col0\" >53.73</td>\n",
              "      <td id=\"T_7457d_row7_col1\" class=\"data row7 col1\" >31.60</td>\n",
              "      <td id=\"T_7457d_row7_col2\" class=\"data row7 col2\" >23.75</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n"
            ],
            "text/plain": [
              "<pandas.io.formats.style.Styler at 0x7ff4e1007760>"
            ]
          },
          "execution_count": 75,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "def custom_table(styler):\n",
        "    styler.background_gradient(cmap=\"Blues\", axis=None)\n",
        "    styler.format(precision=2)\n",
        "\n",
        "    # center text and increase font size\n",
        "    styler.applymap(lambda x: 'text-align: center; font-size: 14px;') \n",
        "    return styler\n",
        "\n",
        "agg_metrics = df.agg([\"mean\", \"max\"])\n",
        "pd.concat([df, agg_metrics]).style.pipe(custom_table)"
      ]
    },
    {
      "attachments": {},
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Going further\n",
        "\n",
        "This post explains how to create a simple custom table with [pandas](https://python-graph-gallery.com/pandas/).\n",
        "\n",
        "For more examples of **how to create or customize** your tables, see the [table section](https://python-graph-gallery.com/table/). You may also be interested in how to [add HTML and CCS to your table](https://python-graph-gallery.com/5xx-pandas-table-with-html-and-css/)."
      ]
    }
  ],
  "metadata": {
    "chartType": "colors",
    "description": "<b>Creating elegant tables</b> with the <a href='https://python-graph-gallery.com/pandas/'>Pandas</a> library in Python is a useful way to organize and display <b>structured data</b>. <a href='https://python-graph-gallery.com/pandas/'>Pandas</a> tables allow you to present information in a neat and organized format, making it easy to analyze and understand various datasets.<br>  You can use Pandas to create tables that display data such as <b>numerical values, text, and categorical</b> information. These tables can be <b>customized and styled</b> to enhance their visual appeal, making them a versatile tool for <i>data presentation and analysis</i>.",
    "family": "general",
    "kernelspec": {
      "display_name": "Python 3 (ipykernel)",
      "language": "python",
      "name": "python3"
    },
    "keywords": "table, pandas, customization, styler",
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.4"
    },
    "seoDescription": "How to custom a table with pandas",
    "slug": "550-intro-table-with-pandas",
    "title": "Introduction to table with Pandas"
  },
  "nbformat": 4,
  "nbformat_minor": 4
}