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

Question

Taking the following example:

>>> df1 = pd.DataFrame({\"x\":[1, 2, 3, 4, 5], 
                        \"y\":[3, 4, 5, 6, 7]}, 
                      ind         


        

Answer 1

First, OP misunderstood the rows and columns in his/her dataframe.

But the acutal output considers rows that are found in both dataframes.(the only common row element 'y')

OP thought the label y is for row. However, y is a column name.

df1 = pd.DataFrame(
         {"x":[1, 2, 3, 4, 5],  # <-- looks like row x but actually col x
          "y":[3, 4, 5, 6, 7]}, # <-- looks like row y but actually col y
          index=['a', 'b', 'c', 'd', 'e'])
print(df1)

            \col   x    y
 index or row\
          a       1     3   |   a
          b       2     4   v   x
          c       3     5   r   i
          d       4     6   o   s
          e       5     7   w   0

               -> column
                 a x i s 1

It is very easy to be misled since in the dictionary, it looks like y and x are two rows.

If you generate df1 from a list of list, it should be more intuitive:

df1 = pd.DataFrame([[1,3], 
                    [2,4],
                    [3,5],
                    [4,6],
                    [5,7]],
                    index=['a', 'b', 'c', 'd', 'e'], columns=["x", "y"])

So back to the problem, concat is a shorthand for concatenate (means to link together in a series or chain on this way [source]) Performing concat along axis 0 means to linking two objects along axis 0.

   1
   1   <-- series 1
   1
^  ^  ^
|  |  |               1
c  a  a               1
o  l  x               1
n  o  i   gives you   2
c  n  s               2
a  g  0               2
t  |  |
|  V  V
v 
   2
   2   <--- series 2
   2

So... think you have the feeling now. What about sum function in pandas? What does sum(axis=0) means?

Suppose data looks like

   1 2
   1 2
   1 2

Maybe...summing along axis 0, you may guess. Yes!!

^  ^  ^
|  |  |               
s  a  a               
u  l  x                
m  o  i   gives you two values 3 6 !
|  n  s               
v  g  0               
   |  |
   V  V

What about dropna? Suppose you have data

   1  2  NaN
  NaN 3   5
   2  4   6

and you only want to keep

2
3
4

On the documentation, it says Return object with labels on given axis omitted where alternately any or all of the data are missing

Should you put dropna(axis=0) or dropna(axis=1)? Think about it and try it out with

df = pd.DataFrame([[1, 2, np.nan],
                   [np.nan, 3, 5],
                   [2, 4, 6]])

# df.dropna(axis=0) or df.dropna(axis=1) ?

Hint: think about the word along.

Answer 2

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.

Answer 3

Data:

In [55]: df1
Out[55]:
   x  y
a  1  3
b  2  4
c  3  5
d  4  6
e  5  7

In [56]: df2
Out[56]:
   y  z
b  1  9
c  3  8
d  5  7
e  7  6
f  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  z
b  2  4  1  9
c  3  5  3  8
d  4  6  5  7
e  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]:
   y
a  3
b  4
c  5
d  6
e  7
b  1
c  3
d  5
e  7
f  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    z
a  1.0  3  NaN
b  2.0  4  NaN
c  3.0  5  NaN
d  4.0  6  NaN
e  5.0  7  NaN
b  NaN  1  9.0
c  NaN  3  8.0
d  NaN  5  7.0
e  NaN  7  6.0
f  NaN  9  5.0

In [63]: pd.concat([df1, df2], axis=1)
Out[63]:
     x    y    y    z
a  1.0  3.0  NaN  NaN
b  2.0  4.0  1.0  9.0
c  3.0  5.0  3.0  8.0
d  4.0  6.0  5.0  7.0
e  5.0  7.0  7.0  6.0
f  NaN  NaN  9.0  5.0

Answer 4

Interpret axis=0 to apply the algorithm down each column, or to the row labels (the index).. A more detailed schema here.

If you apply that general interpretation to your case, the algorithm here is concat. Thus for axis=0, it means:

for each column, take all the rows down (across all the dataframes for concat) , and do contact them when they are in common (because you selected join=inner).

So the meaning would be to take all columns x and concat them down the rows which would stack each chunk of rows one after another. However, here x is not present everywhere, so it is not kept for the final result. The same applies for z. For y the result is kept as y is in all dataframes. This is the result you have.

Answer 5

If someone needs visual description, here is the image: