src/notebooks/527-introduction-to-histogram-with-pandas.ipynb

{
 "cells": [
  {
   "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`"
   ]
  },
  {
   "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 matplotlib.pyplot as plt"
   ]
  },
  {
   "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 get the Gap Minder dataset using the `read_csv()` function. The data can be accessed using the **url below**. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "url = 'https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/gapminderData.csv'\n",
    "df = pd.read_csv(url)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogram with the hist() function\n",
    "\n",
    "Once we've opened our dataset, we'll now **create the graph**. The following displays the **distribution** of the life expectancy using the `hist()` function. This is probably one of the **shortest ways** to display a [histogram](https://python-graph-gallery.com/histogram/) in Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"lifeExp\"].hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogram with the plot() function\n",
    "\n",
    "We'll now look at how to create a [histogram](https://python-graph-gallery.com/histogram/) using the `plot()` function. This function is **very general** and therefore requires **more arguments** to be specified when it is called.\n",
    "\n",
    "The main argument is `kind`. This specifies the type of chart we want (in our case it's `'hist'`). For example, we could have put `'line'` for a [line chart](https://python-graph-gallery.com/line-chart/) but **not** `'scatter'` since we need 2 variables for a [scatter plot](https://python-graph-gallery.com/scatter-plot/) (this will **trigger an error**)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"lifeExp\"].plot(kind='hist')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histogram with the plot.hist() function\n",
    "\n",
    "And now we'll look at how to create a [histogram](https://python-graph-gallery.com/histogram/) using the `plot.hist()` function. This function is a **combination of the previous 2**, but is *no more* complicated. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[\"lifeExp\"].plot.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Going further\n",
    "\n",
    "This post explains how to create a simple histogram with [pandas](https://python-graph-gallery.com/pandas/) in 3 different ways.\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 a graphical representation of the distribution of a dataset, where <b>data is divided into intervals</b> (bins) and the frequency or count of data points falling into each bin is depicted using bars.<br><a href='https://python-graph-gallery.com/pandas/'>Pandas</a>, a powerful data manipulation library in Python, provides extensive <b>features for data analysis</b>, manipulation, and visualization. In this post, we will explore how to leverage <code>Pandas</code> to create and customize histograms.",
  "family": "distribution",
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "keywords": "pandas, plot, chart, hist, histogram, 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": "Tutorial on how to build histograms using the pandas API in 3 different ways",
  "slug": "527-introduction-to-histogram-with-pandas",
  "title": "Introduction to histograms with Pandas"
 },
 "nbformat": 4,
 "nbformat_minor": 4
}