What is the Difference Between `axis=0` and `axis=1` When Calculating Mean in Pandas?

Axis in Pandas

When dealing with multi-dimensional data structures in Pandas, the concept of axis becomes crucial. Axis in Pandas refers to the orientation of the data along a particular dimension. It specifies the direction in which an operation is to be performed.

In your example:

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

dff is a DataFrame with one row and two columns. The axis=1 argument in dff.mean(axis=1) indicates that the mean should be calculated along the columns of the DataFrame. This means that it will compute the mean value for each column, resulting in a Series with a single element.

The expected result you provided is the mean of individual rows, which can be calculated using axis=0. This would produce a Series with two elements, representing the mean of each row.

To summarize, the axis in Pandas determines the direction of an operation within a multi-dimensional data structure. Axis=0 indicates rows, while axis=1 indicates columns. By understanding the concept of axis, you can effectively manipulate and analyze data in Pandas.

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