Getting 2 columns after mode(axis=1) DataFrame-CodePudding

I'm trying to find the most frequent value from each row of a DataFrame. I found the way here to do that. But I'm getting two columns instead of one column after doing that.

What do I want to do?

Let's say I have this DataFrame

In [88]: df
Out[88]:
   a  b  c
0  2  3  3
1  1  1  2
2  7  7  8

and I want this

In [89]: df.mode(axis=1)
Out[89]:
   0
0  3
1  1
2  7

I'm trying to apply this in DataFrame but it's not working properly.

My DataFrame looks like.

In [45]: data.head()

    a   b   c   d   e   f
0   1   1   1   1   1   1
1   0   0   0   0   0   0
2   0   0   0   0   1   0
3   0   0   0   0   0   0
4   1   1   1   1   1   1

In [47]: data.shape
Out[48]:(5665, 6)

Getting this output

In [47]: data.mode(axis=1)
Out[48]:
          0   1
    0   1.0 NaN
    1   0.0 NaN
    2   0.0 NaN
    3   0.0 NaN
    4   1.0 NaN

Note: If I apply mode for a few rows data.head().mode(axis=1) it's working fine, but it's not working for full DataFrame.

CodePudding user response：

Is this what you are trying to do?

df['Mode'] = df.mode(axis='columns', numeric_only=True)
df

CodePudding user response：

A set of values can have more than one mode, e.g. the array [0, 0, 0, 1, 1, 1] has mode [0, 1] because both appear equally often. In that case, df.mode will create a second column. If you only want one of the most common values for each row, you can simply drop the second column in the output dataframe.