What is the meaning of "axis" attribute in a Pandas DataFrame?

If someone needs visual description, here is the image:

Data:

In [55]: df1Out[55]:   x  ya  1  3b  2  4c  3  5d  4  6e  5  7In [56]: df2Out[56]:   y  zb  1  9c  3  8d  5  7e  7  6f  9  5

Concatenated horizontally (axis=1), using index elements found in both DFs (aligned by indexes for joining):

In [57]: pd.concat([df1, df2], join='inner', axis=1)Out[57]:   x  y  y  zb  2  4  1  9c  3  5  3  8d  4  6  5  7e  5  7  7  6

Concatenated vertically (DEFAULT: axis=0), using columns found in both DFs:

In [58]: pd.concat([df1, df2], join='inner')Out[58]:   ya  3b  4c  5d  6e  7b  1c  3d  5e  7f  9

If you don't use the inner join method - you will have it this way:

In [62]: pd.concat([df1, df2])Out[62]:     x  y    za  1.0  3  NaNb  2.0  4  NaNc  3.0  5  NaNd  4.0  6  NaNe  5.0  7  NaNb  NaN  1  9.0c  NaN  3  8.0d  NaN  5  7.0e  NaN  7  6.0f  NaN  9  5.0In [63]: pd.concat([df1, df2], axis=1)Out[63]:     x    y    y    za  1.0  3.0  NaN  NaNb  2.0  4.0  1.0  9.0c  3.0  5.0  3.0  8.0d  4.0  6.0  5.0  7.0e  5.0  7.0  7.0  6.0f  NaN  NaN  9.0  5.0

This is my trick with axis: just add the operation in your mind to make it sound clear:

• axis 0 = rows
• axis 1 = columns

If you “sum” through axis=0, you are summing all rows, and the output will be a single row with the same number of columns.If you “sum” through axis=1, you are summing all columns, and the output will be a single column with the same number of rows.