src/notebooks/561-control-colors-in-plottable.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "e758e508-0bd1-41d6-844a-8d59c9a10e71",
   "metadata": {},
   "source": [
    "## Libraries\n",
    "\n",
    "First, you need to install `plottable` with the following command:\n",
    "`pip install plottable`\n",
    "\n",
    "We'll also need the following libraries:\n",
    "- [pandas](https://python-graph-gallery.com/pandas/) for creating a dataframe with our data\n",
    "- [matplotlib](https://python-graph-gallery.com/matplotlib/) for customizing the table and display it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "9de62754-740e-42ca-af5f-6f40e4f27236",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "from plottable import Table"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15a4eed8-9a11-4264-b450-3b99955f93dd",
   "metadata": {},
   "source": [
    "## Dataset\n",
    "\n",
    "Since most of the time, when we want to display a beautiful table, it comes from a **pandas dataframe**, we'll create a (simple) pandas df and use it as the **starting point** we want to make nice. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "269e0ac4-536d-4985-a661-8bca4b26997d",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "data = {'Score': [80, 60, 20, 30, 90],\n",
    "        'Value': [50.42, 17.01, 42.90, 6.83, 92.06],\n",
    "        'Comment': ['Nice', 'Cool', 'Fun', 'Bad', 'Okay']}\n",
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eb96d3d0-596c-444e-bdba-e85d99780ef0",
   "metadata": {},
   "source": [
    "## Change color of one column\n",
    "\n",
    "In order to access one specific column, we use the `columns` attribute from the `Table()` and then use:\n",
    "- `set_facecolor()` function for the **background color**\n",
    "- `set_fontcolor()` function for the **text color**\n",
    "\n",
    "In our case, we change the background color of the *'Value'* column and the text color of the *'Score'* column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7d742b94-e3dc-4568-8b5d-41e249d8fe4a",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Init a figure \n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "\n",
    "# Create the Table() object\n",
    "tab = Table(df)\n",
    "\n",
    "# Change the color\n",
    "tab.columns['Value'].set_facecolor(\"lightblue\")\n",
    "tab.columns['Score'].set_fontcolor(\"green\")\n",
    "\n",
    "# Display the output\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f1373fd8-b869-4313-828c-b4bdd3c46b32",
   "metadata": {},
   "source": [
    "## Change color of all columns\n",
    "\n",
    "If we want to apply the same color for every cell, the easiest way is to iterate over all our columns and use the `set_facecolor()` or `set_fontcolor()` at each iteration. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "72b189b6-c30e-4909-90cf-7a2393c36c0e",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Init a figure \n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "\n",
    "# Create the Table() object\n",
    "tab = Table(df)\n",
    "\n",
    "# Change the color\n",
    "for col in df.columns:\n",
    "    tab.columns[col].set_facecolor(\"lightblue\")\n",
    "    tab.columns[col].set_fontcolor(\"red\")\n",
    "\n",
    "# Display the output\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2f88827-075c-4db9-b53f-ce710db0976f",
   "metadata": {},
   "source": [
    "Keep in mind that the index of our table is **not in our columns**. However, in a similar way as the columns, you can **access** it with `tab.columns['index']`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ea82c1f-67e2-40f0-99e5-0011fec98a5c",
   "metadata": {},
   "source": [
    "## Change color of one row\n",
    "\n",
    "You can access rows with the `rows` attribute from the `Table()` and use the `set_facecolor()` or `set_fontcolor()`, depending on what you want to change. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "9a0bca91-2eb3-4aff-8704-d9623b3017e5",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Init a figure \n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "\n",
    "# Create the Table() object\n",
    "tab = Table(df)\n",
    "\n",
    "# Change the color\n",
    "tab.rows[1].set_facecolor(\"lightblue\")\n",
    "tab.rows[2].set_fontcolor(\"red\")\n",
    "\n",
    "# Display the output\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e712055d-375e-4f7d-8497-4069230afb95",
   "metadata": {},
   "source": [
    "## Change color of all rows\n",
    "\n",
    "`Plottable` has a nice feature that allows us to change color only of **even** or **odd** rows. You can use the `even_row_color` and `odd_row_color` when creating the `Table` object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "6ff5ed4f-5134-4e5a-92f8-cec34814fccc",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Init a figure \n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "\n",
    "# Create the Table() object\n",
    "tab = Table(df,\n",
    "            even_row_color='lightgrey',\n",
    "            odd_row_color='orange')\n",
    "\n",
    "# Display the output\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6336731-e753-4883-b8f2-2da07c068c4e",
   "metadata": {},
   "source": [
    "## Use a gradient of colors\n",
    "\n",
    "We need to **import new elements** from both `plottable` and `matplotlib`:\n",
    "- a colormap from matplotlib: in our case we'll use a **gradient of blues**\n",
    "- `ColumnDefinition` in order to **specify the cmap we want** for which column\n",
    "\n",
    "The main interest of this is that the **color is proportionaly** *stronger* according to values in the cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "07ad4616-e00b-4e9a-8cb5-7797d7fe915c",
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from matplotlib.cm import Blues\n",
    "from plottable import ColumnDefinition\n",
    "\n",
    "# Init a figure \n",
    "fig, ax = plt.subplots(figsize=(5, 5))\n",
    "\n",
    "# Create the Table() object\n",
    "tab = Table(df,\n",
    "            column_definitions=[ColumnDefinition(name=\"Score\", cmap=Blues)])\n",
    "\n",
    "# Display the output\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e281fb0f-5aa2-463f-8dea-06253c008cd0",
   "metadata": {},
   "source": [
    "## Going further \n",
    "\n",
    "This post explains the **how to change colors** with the `plottable` library.\n",
    "\n",
    "You might be interested in:\n",
    "- [Introduction to plottable](https://python-graph-gallery.com/560-introduction-plottable)\n",
    "- [How to add an image in plottable](https://python-graph-gallery.com/562-add-images-in-plottable)"
   ]
  }
 ],
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  "description": " <a href='https://github.com/znstrider/plottable'> Plottable</a> is a python library for plotting <b>nice table outputs</b>. You can custom style, colors, add images and even more with a light python syntax! <br>  In this post, we'll see <b>how change and custom colors</b> of rows and columns with <code>plottable</code>. For an introduction to plottable, see <a href='https://python-graph-gallery.com/560-introduction-plottable'>Introduction to plottable</a> and for an advanced feature check <a href='https://python-graph-gallery.com/562-add-images-in-plottable'>How to add an image in plottable</a>. <br> ",
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