# Pandas for data visualization

`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.

Although not best known for this functionality, Pandas **can be used to create graphs** and visualize data, thanks to its **lightweight syntax** and matplotlib functions.

## ⏱ Quick start

`pandas`

plotting features are a wrapper around the matplotlib library, which is the most popular python library for data visualization.

The `plot`

function is the most basic function to create a chart with pandas. It is a wrapper around the `matplotlib.pyplot.plot`

function.

```
# library
import pandas as pd
import matplotlib.pyplot as plt
# Create data
values=[12, 11, 9, 13, 14, 16, 14, 15, 18, 17, 19, 20]
df=pd.DataFrame({'x': range(1,13), 'y': values })
# plot
df.plot('x', 'y') // This is the plot function of pandas
plt.show()
```

## Three distinct syntaxes

There are 3 ways to build a chart with pandas: the `plot`

method, the function name methods (like `line`

, `bar`

or `hist`

) and the `plot`

+ function name method.

### ➡️ `plot method`

In this case, we have to specify the `kind`

of chart we want to create. The `plot`

method is a wrapper around the `matplotlib.pyplot.plot`

function. The `kind`

argument is used to specify the **type of chart** we want to create.

```
df.plot('x', 'y', kind='line')
plt.show()
```

### ➡️ `function name method`

The function name method is a bit more straightforward. We just have to call **the right function name** to create the chart we want. Matplotlib has various functions to create different types of charts. For example, the`line`

function is used to create line charts.

```
df.line('x', 'y')
plt.show()
```

### ➡️ `plot + function name method`

This method is a combination of the previous two. We use the `plot`

method and need the `function name`

right after it.

```
df.plot.line('x', 'y')
plt.show()
```

The `function name`

method is the most straightforward and the one we recommend. **Most posts** on the gallery use this method.

## Chart examples with Pandas

Pandas offers a wide range of nice charts. Here is a **selection of examples** that you can find on the gallery. Click on the images to see the code!