Pandas axes explained

Question

I'm trying to understand the axis parameter in python pandas . I understand that it's analogous to the numpy axis, but the following example still confuses me:

a = pd.DataFrame([[0, 1, 4], [1, 2, 3]])
print a

   0  1  2
0  0  1  4
1  1  2  3

According to this post , axis=0 runs along the rows (fixed column), while axis=1 runs along the columns (fixed row). Running print a.drop(1, axis=1) yields

   0  2
0  0  4
1  1  3

which results in a dropped column, while print a.drop(1, axis=0) drops a row. Why? That seems backwards to me.

Answer 1

It's slightly confusing, but axis=0 operates on rows , axis=1 operates on columns .

So when you use df.drop(1, axis=1) you are saying drop column number 1 .

The other post has df.mean(axis=1) , which essentially says calculate the mean on columns, per row .

This is similar to indexing numpy arrays, where the first index specifies the row number (0th dimension), the second index the column number (1st dimension), and so on.