How do I create plots in pandas?

.. _10min_tut_04_plotting:

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How do I create plots in pandas?

.. image:: ../../_static/schemas/04_plot_overview.svg :align: center

.. ipython:: python

import pandas as pd
import matplotlib.pyplot as plt

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            Data used for this tutorial:
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.. include:: includes/air_quality_no2.rst

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air_quality = pd.read_csv("data/air_quality_no2.csv", index_col=0, parse_dates=True)
air_quality.head()

.. note:: The index_col=0 and parse_dates=True parameters passed to the read_csv function define the first (0th) column as index of the resulting DataFrame and convert the dates in the column to :class:Timestamp objects, respectively.

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I want a quick visual check of the data.

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@savefig 04_airqual_quick.png
air_quality.plot()
plt.show()

With a DataFrame, pandas creates by default one line plot for each of the columns with numeric data.

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I want to plot only the columns of the data table with the data from Paris.

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# We need to clear the figure here as, within doc generation, the plot
# accumulates data on each plot(). This is not needed when running
# in a notebook, so is suppressed from output.
plt.clf()

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@savefig 04_airqual_paris.png
air_quality["station_paris"].plot()
plt.show()

To plot a specific column, use a selection method from the :ref:subset data tutorial <10min_tut_03_subset> in combination with the :meth:~DataFrame.plot method. Hence, the :meth:~DataFrame.plot method works on both Series and DataFrame.

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I want to visually compare the :math:NO_2 values measured in London versus Paris.

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@savefig 04_airqual_scatter.png
air_quality.plot.scatter(x="station_london", y="station_paris", alpha=0.5)
plt.show()

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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:

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[
    method_name
    for method_name in dir(air_quality.plot)
    if not method_name.startswith("_")
]

.. note:: In many development environments such 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 :meth:DataFrame.plot.box, which refers to a boxplot <https://en.wikipedia.org/wiki/Box_plot>__. The box method is applicable on the air quality example data:

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@savefig 04_airqual_boxplot.png
air_quality.plot.box()
plt.show()

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    <span class="badge badge-info">To user guide</span>

For an introduction to plots other than the default line plot, see the user guide section about :ref:supported plot styles <visualization.other>.

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I want each of the columns in a separate subplot.

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@savefig 04_airqual_area_subplot.png
axs = air_quality.plot.area(figsize=(12, 4), subplots=True)
plt.show()

Separate subplots for each of the data columns are supported by the subplots argument of the plot functions. The builtin options available in each of the pandas plot functions are worth reviewing.

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Some more formatting options are explained in the user guide section on :ref:plot formatting <visualization.formatting>.

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

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fig, axs = plt.subplots(figsize=(12, 4))
air_quality.plot.area(ax=axs)
axs.set_ylabel("NO$_2$ concentration")
@savefig 04_airqual_customized.png
fig.savefig("no2_concentrations.png")
plt.show()

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import os

os.remove("no2_concentrations.png")

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Each of the plot objects created by pandas is a Matplotlib <https://matplotlib.org/>__ 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:

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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. plt.show() # Display the plot

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    <h4>REMEMBER</h4>

• 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.

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    <span class="badge badge-info">To user guide</span>

A full overview of plotting in pandas is provided in the :ref:visualization pages <visualization>.

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