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

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.

Answer 2

Let visualize (gonna remember always),

In Pandas:

1. axis=0 means along "indexes". It's a row-wise operation.

Suppose, to perform concat() operation on dataframe1 & dataframe2, we will take dataframe1 & take out 1st row from dataframe1 and place into the new DF, then we take out another row from dataframe1 and put into new DF, we repeat this process until we reach to the bottom of dataframe1. Then, we do the same process for dataframe2.

Basically, stacking dataframe2 on top of dataframe1 or vice a versa.

E.g making a pile of books on a table or floor

2. axis=1 means along "columns". It's a column-wise operation.

Suppose, to perform concat() operation on dataframe1 & dataframe2, we will take out the 1st complete column(a.k.a 1st series) of dataframe1 and place into new DF, then we take out the second column of dataframe1 and keep adjacent to it (sideways), we have to repeat this operation until all columns are finished. Then, we repeat the same process on dataframe2. Basically, stacking dataframe2 sideways.

E.g arranging books on a bookshelf.