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.