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You can use the **range** argument to modify the x-axis range in a pandas histogram:

plt.hist(df['var1'], range=[10, 30])

In this particular example, we set the x-axis to range from 10 to 30.

The following example shows how to use the **range** argument in practice.

**Example: Modifying the X-Axis Range in Pandas Histogram**

Suppose we have the following pandas DataFrame:

import pandas as pd import numpy as np #make this example reproducible np.random.seed(1) #create DataFrame df = pd.DataFrame({'team': np.repeat(['A', 'B', 'C'], 100), 'points': np.random.normal(loc=20, scale=2, size=300)}) #view head of DataFrame print(df.head()) team points 0 A 23.248691 1 A 18.776487 2 A 18.943656 3 A 17.854063 4 A 21.730815

If we create a histogram for the **points** variable, pandas will automatically choose the range for the x-axis based on the minimum and maximum values of the **points** variable:

import matplotlib.pyplot as plt #create histogram for points variable plt.hist(df['points'], edgecolor='black')

The x-axis ranges from 14 to 25.

We can use the describe() function to view the minimum and maximum values for the **points** variable:

#summarize distribution of points variable df['points'].describe() count 300.000000 mean 20.148800 std 1.890841 min 14.413830 25% 18.818254 50% 20.176352 75% 21.372843 max 25.056651 Name: points, dtype: float64

We can see that the minimum value is 14.41 and the maximum value is 25.06, which explains why the x-axis in the plot currently ranges from 14 to 25.

However, we can use the **range** argument to force the x-axis to range from 10 to 30 instead:

import matplotlib.pyplot as plt #create histogram for points variable with custom x-axis range plt.hist(df['points'], edgecolor='black', range=[10, 30])

Notice that the x-axis now ranges from 10 to 30.

**Additional Resources**

The following tutorials explain how to perform other common tasks in pandas:

How to Create a Histogram from Pandas DataFrame

How to Create a Histogram from a Pandas Series

How to Plot Histograms by Group in Pandas