What does axis in pandas mean?

Question

Here is my code to generate a dataframe:

import pandas as pd import numpy as np  dff = pd.DataFrame(np.random.randn(1,2),columns=list('AB')) 

then I got the dataframe:

+------------+---------+--------+ |            |  A      |  B     | +------------+---------+--------- |      0     | 0.626386| 1.52325| +------------+---------+--------+ 

When I type the commmand :

dff.mean(axis=1) 

I got :

0    1.074821 dtype: float64 

According to the reference of pandas, axis=1 stands for columns and I expect the result of the command to be

A    0.626386 B    1.523255 dtype: float64 

So here is my question: what does axis in pandas mean?

Answer 1

It specifies the axis along which the means are computed. By default axis=0. This is consistent with the numpy.mean usage when axis is specified explicitly (in numpy.mean, axis==None by default, which computes the mean value over the flattened array) , in which axis=0 along the rows (namely, index in pandas), and axis=1 along the columns. For added clarity, one may choose to specify axis='index' (instead of axis=0) or axis='columns' (instead of axis=1).

+------------+---------+--------+ |            |  A      |  B     | +------------+---------+--------- |      0     | 0.626386| 1.52325|----axis=1-----> +------------+---------+--------+              |         |              | axis=0  |              ↓         ↓ 

Answer 2

These answers do help explain this, but it still isn't perfectly intuitive for a non-programmer (i.e. someone like me who is learning Python for the first time in context of data science coursework). I still find using the terms "along" or "for each" wrt to rows and columns to be confusing.

What makes more sense to me is to say it this way:

• Axis 0 will act on all the ROWS in each COLUMN
• Axis 1 will act on all the COLUMNS in each ROW

So a mean on axis 0 will be the mean of all the rows in each column, and a mean on axis 1 will be a mean of all the columns in each row.

Ultimately this is saying the same thing as @zhangxaochen and @Michael, but in a way that is easier for me to internalize.