## Libraries

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.

To install Pandas, you can use the **following command** in your command-line interface (such as `Terminal`

or `Command Prompt`

):

`pip install pandas`

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 library** when building charts with Pandas.

This also means that they use the **same functions**, and if you already know Matplotlib, you'll have no trouble learning plots with Pandas.

```
import pandas as pd
import matplotlib.pyplot as plt
```

## Dataset

In order to create graphics with 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.

Since histograms need quantitative variables, we will get the Gap Minder dataset using the `read_csv()`

function. The data can be accessed using the **url below**.

```
url = 'https://raw.githubusercontent.com/holtzy/The-Python-Graph-Gallery/master/static/data/gapminderData.csv'
df = pd.read_csv(url)
```

## Histogram with the hist() function

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 in Python.

```
df["lifeExp"].hist()
plt.show()
```

## Histogram with the plot() function

We'll now look at how to create a histogram using the `plot()`

function. This function is **very general** and therefore requires **more arguments** to be specified when it is called.

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 but **not** `'scatter'`

since we need 2 variables for a scatter plot (this will **trigger an error**).

```
df["lifeExp"].plot(kind='hist')
plt.show()
```

## Histogram with the plot.hist() function

And now we'll look at how to create a histogram using the `plot.hist()`

function. This function is a **combination of the previous 2**, but is *no more* complicated.

```
df["lifeExp"].plot.hist()
plt.show()
```

## Going further

This post explains how to create a simple histogram with pandas in 3 different ways.

For more examples of **how to create or customize** your plots with Pandas, see the pandas section. You may also be interested in how to customize your histograms with Matplotlib and Seaborn.