What does the \'axis\' parameter mean in Pandas aggregation functions?

Understanding the Meaning of 'Axis' in Pandas

In many data manipulation operations within Pandas, such as aggregation functions, the concept of 'axis' plays a crucial role. The 'axis' parameter specifies the dimension or direction along which the operation is applied.

By default, 'axis' is set to 0, which corresponds to rows (index) in a DataFrame. However, 'axis' can also be set to 1, indicating columns.

To illustrate, consider the following code:

<code class="python">import pandas as pd
import numpy as np

dff = pd.DataFrame(np.random.randn(1,2),columns=list('AB'))
dff.mean(axis=1)</code>

The expected output is:

A    0.626386
B    1.523255
dtype: float64

However, the actual output is different:

0    1.074821
dtype: float64

This is because the 'axis' parameter is set to 1 by default. In this case, the mean value is calculated along columns, resulting in a single value.

To obtain the desired output, specify 'axis' explicitly as 0:

<code class="python">dff.mean(axis=0)</code>

This will calculate the mean value of each column, producing the expected output.

In summary, 'axis' in Pandas dictates the dimension or direction along which operations are applied. Setting 'axis' to 0 targets rows, while setting it to 1 targets columns. Understanding this concept is essential for manipulating and aggregating data effectively in Pandas.

The above is the detailed content of What does the \'axis\' parameter mean in Pandas aggregation functions?. For more information, please follow other related articles on the PHP Chinese website!