You can use the logx and logy arguments to create histograms with log scales on the x-axis and y-axis, respectively, in pandas:

#create histogram with log scale on x-axis
df['my_column'].plot(kind='hist', logx=True)

#create histogram with log scale on y-axis
df['my_column'].plot(kind='hist', logy=True)

The following example shows how to use these arguments to create histograms with log scales in pandas.

Related:

Example: Create Histogram with Log Scale in Pandas

Suppose we have the following pandas DataFrame with 5,000 rows:

import pandas as pd
import numpy as np

#make this example reproducible
np.random.seed(1)

#create DataFrame
df = pd.DataFrame({'values': np.random.lognormal(size=5000)})

#view first five rows of DataFrame
print(df.head())

     values
0  5.075096
1  0.542397
2  0.589682
3  0.341992
4  2.375974

We can use the following syntax to create a histogram with a linear scale on both the x-axis and y-axis:

#create histogram
df['values'].plot(kind='hist')

The x-axis and y-axis both currently have a linear scale.

We can use the logx=True argument to convert the x-axis to a log scale:

#create histogram with log scale on x-axis
df['values'].plot(kind='hist', logx=True)

The values on the x-axis now follow a log scale.

And we can use the logy=True argument to convert the y-axis to a log scale:

#create histogram with log scale on y-axis
df['values'].plot(kind='hist', logy=True)

The values on the y-axis now follow a log scale.

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