# Histogram

A Histogram represents the **distribution** of a numeric variable for one or several groups. The values are split in **bins**, each bin is represented as a **bar**.

This page showcases many histograms built with **python**, using the most popular libraries like `seaborn`

and `matplotlib`

.

Examples start with **very simple**, beginner-friendly histograms and progressively increase in complexity. At the end of the page, some **polished & publication-ready** histograms are provided, ready to be used in your next project 🔥!

## ⏱ Quick start (Seaborn)

`Seaborn`

is definitely the best library to quickly build a histogram thanks to its `displot()`

.

Note the importance of the `bins`

parameter: try several values to see which represents your data the best. 🔥

```
# library & dataset
import seaborn as sns
df = sns.load_dataset('iris')
# Plot the histogram thanks to the displot function
sns.displot( data=df["sepal_length"], kde=True )
```

## Histogram charts with `Seaborn`

`Seaborn`

is a python library allowing to make better charts easily. It is well adapted to build histogram thanks to its `displot`

function. The following charts will guide you through its usage, going from a very basic histogram to something much more customized.

## Quick start (Matplotlib)

Matplotlib can also build decent histograms easily. It provides a `hist()`

function that accept a vector of numeric values as input.

It also provides all the options you can think of to customize the binning and the genreral appearance.

```
# library & dataset
import matplotlib.pyplot as plt
hours = [17, 20, 22, 25, 26, 27, 30, 31, 32, 38, 40, 40, 45, 55]
# Initialize layout
fig, ax = plt.subplots(figsize = (9, 9))
#plot
ax.hist(hours, bins=5, edgecolor="black");
```

## Histograms with `Matplotlib`

As usual matplotlib is perfectly skilled to build nice histogram, but require some more work camparing to seaborn to get a good looking figure.

The examples below should help you to get started with matplotlib histograms. They go from a very basic version and then show how to customize it, like adding annotation.

## Quick start (Pandas)

Pandas can build decent histograms easily. It provides different functions like `hist()`

and `plot()`

that need a pandas dataframe (or series) as input.

Since it's based on matplotlib, it provides all the options you can think of to customize the binning and the genreral appearance.

```
# library & dataset
import pandas as pd
import matplotlib.pyplot as plt
time = [17, 25, 42, 35, 26, 27, 20, 11, 22, 32, 35, 30, 45, 55]
# Convert to a pandas format
time = pd.Series(time)
#plot
time.hist()
plt.show
```

## Histograms with `Pandas`

Pandas is not the most common Python library to build histograms, but it can be used to build decent ones. It provides different functions like `hist()`

and `plot()`

from matplotlib.

The examples below should help you to get started with basic pandas histograms.

## Histograms with `Plotnine`

Plotnine allows us to use the **grammar-of-graphics** to build histograms. It provides a `geom_histogram()`

function that can be used to build histograms.

The examples below should help you to get started with** basic plotnine histograms**.

## Best python histogram examples

The web is full of astonishing charts made by awesome bloggers, (often using R). The Python graph gallery tries to display (or translate from R) some of the best creations and explain how their source code works. If you want to display your work here, please drop me a word or even better, submit a Pull Request!