How to create plots in pandas?¶

I want a quick visual check of the data.
```
In [7]: air_quality.plot()
Out[7]: <matplotlib.axes._subplots.AxesSubplot at 0x7f2d4d843150>
```
With a DataFrame, pandas creates by default one line plot for each of the columns with numeric data.

I want to plot only the columns of the data table with the data from Paris.
```
In [8]: air_quality["station_paris"].plot()
Out[8]: <matplotlib.axes._subplots.AxesSubplot at 0x7f2d0e8b8090>
```
To plot a specific column, use the selection method of the subset data tutorial in combination with the plot method. Hence, the plot method works on both Series and DataFrame.

I want to visually compare the \(N0_2\) values measured in London versus Paris.

In [9]: air_quality.plot.scatter(x="station_london",
   ...:                          y="station_paris",
   ...:                          alpha=0.5)
   ...: 
Out[9]: <matplotlib.axes._subplots.AxesSubplot at 0x7f2d4d843890>

Apart from the default line plot when using the plot function, a number of alternatives are available to plot data. Let’s use some standard Python to get an overview of the available plot methods:

In [10]: [method_name for method_name in dir(air_quality.plot) if not method_name.startswith("_") ]
Out[10]: 
['area',
 'bar',
 'barh',
 'box',
 'density',
 'hexbin',
 'hist',
 'kde',
 'line',
 'pie',
 'scatter']

Note

In many development environments as well as ipython and jupyter notebook, use the TAB button to get an overview of the available methods, for example air_quality.plot. + TAB.

One of the options is box, which refers to a boxplot. Also the box method is applicable on the air quality example data:

In [11]: air_quality.plot.box()
Out[11]: <matplotlib.axes._subplots.AxesSubplot at 0x7f2d0e801a50>

To user guide

For an introduction to the other plot methods, see Other plots.

I want each of the columns in a separate subplot.
```
In [12]: axs = air_quality.plot.area(figsize=(12, 4), subplots=True)
```
Separate subplots for each of the data columns is supported by the subplots argument of the plot functions. The builtin options available in each of the pandas plot functions that are worthwhile to have a look.

To user guide

Some more formatting options of the pandas plot functionalities are explained in Plot Formatting.

I want to further customize, extend or save the resulting plot.

In [13]: fig, axs = plt.subplots(figsize=(12, 4));

In [14]: air_quality.plot.area(ax=axs);

In [15]: axs.set_ylabel("NO$_2$ concentration");

In [16]: fig.savefig("no2_concentrations.png")

Each of the plot objects created by pandas are a matplotlib object. As Matplotlib provides plenty of options to customize plots, making the link between pandas and matplotlib explicit enables all the power of matplotlib to the plot. This strategy is applied in the previous example:

fig, axs = plt.subplots(figsize=(12, 4))        # Create an empty matplotlib Figure and Axes
air_quality.plot.area(ax=axs)                   # Use pandas to put the area plot on the prepared Figure/Axes
axs.set_ylabel("NO$_2$ concentration")          # Do any matplotlib customization you like
fig.savefig("no2_concentrations.png")           # Save the Figure/Axes using the existing matplotlib method.

REMEMBER

The .plot methods are applicable on both Series and DataFrames
By default, each of the columns is plotted as a different element (line, boxplot,…)
Any plot created by pandas is a matplotlib object.

To user guide

Further details about indexing is provided in Visualization.