Count the different values row-wise and each result as an extra column pandas dataframe

Question

As can be seen from my title, I have a pandas dataframe with different columns and rows. I want to do a row by row count of the contained values. I want to add the count result for each value in a new column.
My dataframe looks like this:

Col1  Col2  Col3
  2     2    1
  1     1    1
  3     1    2

And this is how the result should look like:

Col1  Col2  Col3  Count1  Count2  Count3
  2     2    1      1        2      0
  1     1    1      3        0      0
  3     1    2      1        1      1

Asked By: newbie

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Source

Answer 1

You can loop through all of the unique values in the dataframe, creating a column for each that gives the count per row of that value.

for v in np.unique(df):
    df[f'Count{v}'] = (df == v).sum(axis=1)

Answered By: mpopken

Answer 2

You can apply value_counts on index axis:

df1 = (df.apply(lambda x: x.value_counts(), axis=1)
         .fillna(0).astype(int).add_prefix('Count'))
out = pd.concat([df, df1], axis=1)
print(out)

# Output
   Col1  Col2  Col3  Count1  Count2  Count3
0     2     2     1       1       2       0
1     1     1     1       3       0       0
2     3     1     2       1       1       1

Answered By: Corralien

Answer 3

Use stack and a crosstab:

# stack values and get rid of column names
s = df.stack().droplevel(1)

# compute a cross-tab, rename columns, join to original
out = df.join(pd.crosstab(s.index, s).add_prefix('Count'))

Output:

   Col1  Col2  Col3  Count1  Count2  Count3
0     2     2     1       1       2       0
1     1     1     1       3       0       0
2     3     1     2       1       1       1

Alternative with groupby.value_counts (likely less efficient):

out = df.join(df.stack()
                .groupby(level=0).value_counts()
                .unstack(level=1, fill_value=0)
                .add_prefix('Count')
              )

Answered By: mozway

Count the different values row-wise and each result as an extra column pandas dataframe

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