Ambiguity in Pandas Dataframe / Numpy Array “axis” definition

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It’s perhaps simplest to remember it as 0=down and 1=across.

This means:

  • Use axis=0 to apply a method down each column, or to the row labels (the index).
  • Use axis=1 to apply a method across each row, or to the column labels.

Here’s a picture to show the parts of a DataFrame that each axis refers to:

It’s also useful to remember that Pandas follows NumPy’s use of the word axis. The usage is explained in NumPy’s glossary of terms:

Axes are defined for arrays with more than one dimension. A 2-dimensional array has two corresponding axes: the first running vertically downwards across rows (axis 0), and the second running horizontally across columns (axis 1). [my emphasis]

So, concerning the method in the question, df.mean(axis=1), seems to be correctly defined. It takes the mean of entries horizontally across columns, that is, along each individual row. On the other hand, df.mean(axis=0) would be an operation acting vertically downwards across rows.

Similarly, df.drop(name, axis=1) refers to an action on column labels, because they intuitively go across the horizontal axis. Specifying axis=0 would make the method act on rows instead.

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