src/notebooks/529-multi-group-histogram-pandas.ipynb

{
 "cells": [
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Libraries\n",
    "\n",
    "[Pandas](https://python-graph-gallery.com/pandas/) is a popular open-source Python library used for data manipulation and analysis. It provides data structures and functions that make working with structured data, such as tabular data (like `Excel` spreadsheets or `SQL` tables), easy and intuitive.\n",
    "\n",
    "To install [Pandas](https://python-graph-gallery.com/pandas/), you can use the **following command** in your command-line interface (such as `Terminal` or `Command Prompt`):\n",
    "\n",
    "`pip install pandas`"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Matplotlib](https://python-graph-gallery.com/matplotlib/) functionalities have been **integrated into the pandas** library, facilitating their use with `dataframes` and `series`. For this reason, you might also need to **import the [matplotlib](https://python-graph-gallery.com/matplotlib/) library** when building charts with [Pandas](https://python-graph-gallery.com/pandas/).\n",
    "\n",
    "This also means that they use the **same functions**, and if you already know [Matplotlib](https://python-graph-gallery.com/matplotlib/), you'll have no trouble learning plots with [Pandas](https://python-graph-gallery.com/pandas/)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset\n",
    "\n",
    "In order to create graphics with [Pandas](https://python-graph-gallery.com/pandas/), we need to use **pandas objects**: `Dataframes` and `Series`. A dataframe can be seen as an `Excel` table, and a series as a `column` in that table. This means that we must **systematically** convert our data into a format used by pandas.\n",
    "\n",
    "Since [histograms](https://python-graph-gallery.com/histogram/) need quantitative variables, we will create a dataset with 2 columns. The first column is called `\"type\"`, which stores the categories `\"group1\"` and `\"group2\"` repeated a total of 1000 times each.\n",
    "\n",
    "The second column is named `\"value\"`. It holds numbers. The first 1000 numbers are random values from a normal distribution with an **average of 0** and a **standard deviation of 1**. The next 1000 numbers are random values from another normal distribution with an **average of 4** and a **standard deviation of 1**. We concatenate them into one single column thanks to the `concatenate()` function from numpy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Create 2 columns: one categorical and one numerical\n",
    "sample_size = 1000\n",
    "data = {\n",
    "    'type': ['group1'] * sample_size + ['group2'] * sample_size,\n",
    "    'value': np.concatenate([np.random.normal(0, 1, sample_size),\n",
    "                             np.random.normal(4, 1, sample_size)])\n",
    "}\n",
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Basic histogram with 2 groups\n",
    "\n",
    "Once we've opened our dataset, we'll now **create a simple [histogram](https://python-graph-gallery.com/histogram/)**, representing the distributions of the `'value'` variable **with the 2 groups**. We will iterate over all distinct value in the `'type'` variable and use the `hist()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot the histograms of each group\n",
    "for group in df['type'].unique():\n",
    "    \n",
    "    # Filter the dataset on the group\n",
    "    filtered_df = df[df['type']==group]\n",
    "    \n",
    "    # Add the histogram to the graphic\n",
    "    filtered_df['value'].hist(figsize=(8, 4))\n",
    "\n",
    "# Display the plot    \n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Customize histogram with 2 groups\n",
    "\n",
    "The above [histograms](https://python-graph-gallery.com/histogram/) can be easily customized with the following features\n",
    "- change the `bins` argument to the **value we want**\n",
    "- change the `color` argument to the **color we want**\n",
    "- change the `edgecolor` argument to the **color we want**\n",
    "- add a **title** and axis **label**\n",
    "- add a **legend**\n",
    "\n",
    "Our first step will be to get a **list of the labels** in the `type` variable and then define a **list of colors** of the same length as the first list."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Get group names and define colors\n",
    "group_name = df['type'].unique()\n",
    "colors = ['purple', 'orange']\n",
    "\n",
    "# Plot the histograms\n",
    "for i, group in enumerate(group_name):\n",
    "    ax = df[df['type']==group]['value'].hist(figsize=(8, 4),\n",
    "                                        edgecolor='gray',\n",
    "                                        bins=12,\n",
    "                                        color=colors[i]\n",
    "                                       )\n",
    "\n",
    "# Add a legend\n",
    "ax.legend(group_name)\n",
    "\n",
    "# Add a title and axis label\n",
    "ax.set_title('Distribution of 2 different groups')\n",
    "ax.set_xlabel('Value')\n",
    "ax.set_ylabel('Frequency')\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogram with small multiples\n",
    "\n",
    "Now we will see how to create a **chart with small multiple** [histograms](https://python-graph-gallery.com/histogram/) that display the **distribution of several variables** at the same time. First we need a **dataset with more variables** with different distributions.\n",
    "\n",
    "### Create the dataset\n",
    "For this, we will use `numpy` **random functions** and **generate 9 different** numeric variables. **Don't worry** if this seems complicated to you: it's only useful for **generating fake data** and making the graphs readable!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "# Number of data points\n",
    "num_data_points = 1000\n",
    "\n",
    "# Generate data for each distribution\n",
    "normal_data = np.random.normal(loc=0, scale=1, size=num_data_points)\n",
    "uniform_data = np.random.uniform(low=-1, high=1, size=num_data_points)\n",
    "bimodal_data = np.concatenate((np.random.normal(loc=-2, scale=1, size=num_data_points // 2),\n",
    "                               np.random.normal(loc=2, scale=1, size=num_data_points // 2)))\n",
    "poisson_data = np.random.poisson(lam=5, size=num_data_points)\n",
    "exponential_data = np.random.exponential(scale=2, size=num_data_points)\n",
    "gamma_data = np.random.gamma(shape=2, scale=2, size=num_data_points)\n",
    "beta_data = np.random.beta(a=2, b=5, size=num_data_points)\n",
    "lognormal_data = np.random.lognormal(mean=0, sigma=1, size=num_data_points)\n",
    "triangular_data = np.random.triangular(left=-1, mode=0, right=1, size=num_data_points)\n",
    "\n",
    "# Create a DataFrame\n",
    "data = {\n",
    "    'Normal': normal_data,\n",
    "    'Uniform': uniform_data,\n",
    "    'Bimodal': bimodal_data,\n",
    "    'Poisson': poisson_data,\n",
    "    'Exponential': exponential_data,\n",
    "    'Gamma': gamma_data,\n",
    "    'Beta': beta_data,\n",
    "    'LogNormal': lognormal_data,\n",
    "    'Triangular': triangular_data\n",
    "}\n",
    "\n",
    "df = pd.DataFrame(data)"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create the chart\n",
    "\n",
    "Now we can create a small multiple histograms with pandas and matplotlib:\n",
    "- The following code goes through **each column of the dataframe** and creates a [histogram](https://python-graph-gallery.com/histogram/) plot\n",
    "- For each subplot, the code adds a [histogram](https://python-graph-gallery.com/histogram/) of a specific column's data from the dataframe\n",
    "- It adds a **title** and **axis label**\n",
    "- The code **adjusts the layout** (thanks to the `tight_layout()` function) to make sure they fit well together in the figure\n",
    "- Finally, it **displays the entire set of subplots** as a single plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 800x800 with 9 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Initialize a 3x3 charts\n",
    "fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(8, 8))\n",
    "\n",
    "# Flatten the axes array (makes it easier to iterate over)\n",
    "axes = axes.flatten()\n",
    "\n",
    "# Loop through each column and plot a histogram\n",
    "for i, column in enumerate(df.columns):\n",
    "    \n",
    "    # Add the histogram\n",
    "    df[column].hist(ax=axes[i], # Define on which ax we're working on\n",
    "                    edgecolor='white', # Color of the border\n",
    "                    color='#69b3a2' # Color of the bins\n",
    "                   )\n",
    "    \n",
    "    # Add title and axis label\n",
    "    axes[i].set_title(f'{column} distribution') \n",
    "    axes[i].set_xlabel(column) \n",
    "    axes[i].set_ylabel('Frequency') \n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "\n",
    "# Show the plot\n",
    "plt.show()"
   ]
  },
  {
   "attachments": {},
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Going further\n",
    "\n",
    "This post explains how to show the distribution of multiple groups and variables with [pandas](https://python-graph-gallery.com/pandas/).\n",
    "\n",
    "For more examples of **how to create or customize** your plots with Pandas, see the [pandas section](https://python-graph-gallery.com/pandas/). You may also be interested in how to customize your histograms with [Matplotlib and Seaborn](https://python-graph-gallery.com/histogram/)."
   ]
  }
 ],
 "metadata": {
  "chartType": "histogram",
  "description": "A <a href='https://python-graph-gallery.com/histogram/'>histogram</a> is often use for showing the distribution of one variable or one group. However sometimes it's useful to compare the <b>distribution of several groups or variables</b> at the same time.<br> <a href='https://python-graph-gallery.com/pandas/'>Pandas</a>, a powerful <b>data manipulation</b> library in Python, allow us to create easily histograms: check this <a href='https://python-graph-gallery.com/527-introduction-to-histogram-with-pandas/'>introduction to histograms with pandas</a>. Once you understood how to build a basic histogram with pandas, we will explore how to leverage <code>Pandas</code> to show the <b>distribution of mutliple groups and variables</b> at the same time.",
  "family": "distribution",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "pandas, histogram, plot, chart, small, multiple, matplotlib",
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.13"
  },
  "seoDescription": "Show the distribution with histograms of different groups and variables with pandas and matplotlib",
  "slug": "529-multi-group-histogram-pandas",
  "title": "Multi-group histograms with Pandas"
 },
 "nbformat": 4,
 "nbformat_minor": 4
}