Pandas DataFrame.hist()

The hist() function in Pandas DataFrame provides an easy way to visualize the distribution of data in a DataFrame. It generates histograms of the columns of a DataFrame, showing the count of observations that fall within each bin.

Syntax

DataFrame.hist(column=None, by=None, grid=True, xlabelsize=None, xrot=None, ylabelsize=None, yrot=None, ax=None, sharex=False, sharey=False, figsize=None, layout=None, bins=10, **kwargs)

Parameters

• column: The column name(s) of the DataFrame to be plotted. If not specified, all numerical columns will be plotted.

• by: Group the data by a categorical column and display the histograms for each group.

• grid: Display the grid on the plot or not. Default: True.

• xlabelsize: The size of the x-axis label font. Default: None.

• xrot: The rotation degree of the x-axis labels. Default: None.

• ylabelsize: The size of the y-axis label font. Default: None.

• yrot: The rotation degree of the y-axis labels. Default: None.

• ax: The matplotlib.pyplot.axes object to draw the plot onto. Default: None (creates a new figure with a default size).

• sharex: Share the x-axis among subplots or not. Default: False.

• sharey: Share the y-axis among subplots or not. Default: False.

• figsize: The size of the figure as a tuple of (width, height) in inches. Default: (6.4, 4.8).

• layout: The number of rows and columns of the subplot grid. Default: None.

• bins: The number of histogram bins to be used. Default: 10.

• **kwargs: Other parameters passed to the underlying hist() method in matplotlib.

Returns

• np.ndarray or list of np.ndarray: The values of the histogram bins.

• list of matplotlib.artist.Artist: The corresponding list of artists for each histogram (bars, patches, etc.).

Examples

Let's create a DataFrame first:

import pandas as pd
import numpy as np

df = pd.DataFrame(np.random.randn(1000, 4), columns=['A', 'B', 'C', 'D'])

Now, we can visualize the distribution of each column using simple syntax:

df.hist(bins=20, figsize=(10,8))

We can also plot histograms for specific column(s) by providing the column names:

df[['A', 'B']].hist(bins=20, figsize=(10,8))

If we have a categorical column in our DataFrame, we can plot the histograms for each category using the by parameter:

df['E'] = np.random.choice(['X', 'Y'], size=(1000,))
df.hist(column='A', by='E', bins=20, figsize=(10,8))

The hist() function can also be used on grouped data:

g = df.groupby('E')
g.hist(column='A', bins=20, figsize=(10,8))

Conclusion

In this tutorial, we have learned about the hist() function in Pandas DataFrame. We have seen how it can be used to visualize the distribution of data in a DataFrame. We have seen how we can plot histograms for specific columns, for category-wise data, and for grouped data.