Furthermore, we can also pass axis=1 to indicate a check for NaN by columns.

Check the following example:

dfx.dropna(how='all', axis=1)

And the output of the preceding code is as follows:

Note that store5 is dropped from the dataframe. By passing in axis=1, we are instructing pandas to drop columns if all the values in the column are NaN. Furthermore, we can also pass another argument, thresh, to specify a minimum number of NaNs that must exist before the column should be dropped:

dfx.dropna(thresh=5, axis=1)

And the output of the preceding code is as follows:

Compared to the preceding, note that even the store4 column is now dropped because it has more than five NaN values.