How Does \'Axis\' Work in Pandas: Rows vs. Columns?

Understanding "Axis" in Pandas

When working with Pandas, the concept of "axis" plays a crucial role in various operations, including statistical calculations like mean. In this context, the axis parameter specifies the direction along which the operation is performed.

By default, the axis value is 0, which indicates operations along the rows (index) of the DataFrame. However, one can explicitly set the axis value to 1 to perform operations along the columns instead.

Consider the following example:

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

# Generate a DataFrame with random values
dff = pd.DataFrame(np.random.randn(1, 2), columns=list('AB'))

# Calculate the mean along each column
mean_columns = dff.mean(axis=1)</code>

In this case, specifying axis=1 means that the mean function will calculate the mean value for each column in the DataFrame. The expected output would be:

0    1.074821
dtype: float64

This is different from the result you might expect if you had used axis=0, which would have calculated the mean value for each row, resulting in the following output:

A    0.626386
B    1.523255
dtype: float64

To clarify further, the axis parameter in Pandas aligns with the usage of axis in NumPy's mean function. When axis is not explicitly specified in NumPy's mean, it defaults to None, which flattens the array before calculating the mean. Therefore, specifying axis=0 in Pandas corresponds to calculating the mean along the rows (since the index in Pandas represents the rows), while specifying axis=1 corresponds to calculating the mean along the columns.

For greater clarity, you can also use axis='index' instead of axis=0 and axis='columns' instead of axis=1, making it explicitly clear which axis the operation is performed on.

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