Ambiguity in Pandas Dataframe / Numpy Array “axis” definition

Remember the two points:

• Use axis=0 to apply a method down each column, or to the row labels (the index).
• Use axis=1 to apply a method across each row, or to the column labels.

In simple words 0=down and 1=across

Also remember Pandas also follow the word axis and so do NumPy’s. Refer more in the document.

Axes are used to define arrays for more than one dimension.A 2d array has two axes.Axis 0 is  downwards across rows and axis 1 is horizontally across columns.

Therefore, the code df.mean(axis=1) refers to horizontal across columns and df.mean(axis=0) would mean vertical downwards columns.

Refer to the code below:

// Not realistic but ideal for understanding the axis parameter
df = pd.DataFrame([[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3]],
columns=["idx1", "idx2", "idx3", "idx4"],
index=["idx1", "idx2", "idx3"]
)
 
---------------------------------------1
|        idx1 idx2 idx3 idx4
| idx1    1    1    1    1
| idx2    2    2    2    2
| idx3    3    3    3    3
0

Use df.drop:

A: I wanna remove idx3.
B: **Which one**? // typing while waiting response: df.drop("idx3",
A: The one which is on axis 1
B: OK then it is >> df.drop("idx3", axis=1)
 
// Result
---------------------------------------1
|        idx1 idx2 idx4
| idx1     1    1    1
| idx2     2    2    2
| idx3     3    3    3
0

And df.apply:

A: I wanna apply sum.
B: Which direction? // typing while waiting response: df.apply(lambda x: x.sum(),
A: The one which is on *parallel to axis 0*
B: OK then it is >> df.apply(lambda x: x.sum(), axis=0)
 
// Result
idx1  6
idx2  6
idx3  6
idx4  6

For greater than 2 dimensions the parameter axis has to be changed:

Consider a dataframe with three dimensions a x b x c:

• df.mean(axis=1) returns a dataframe with dimenstion a x 1 x c.
• df.drop(“col4”, axis=1) returns a dataframe with dimension a x (b-1) x c.