I have df with some string values.

so = pd.DataFrame({
"col1": ["row0", "row1", "row2"],
"col2": ["A", "B", "C"],
"col3": ["A", "A", "B"],
"col4":  ["B", "A", "B"],
})

I need to create pivot table where:

1. index is values from column "col1"
2. columns are unique values from columns ['col2':'col4']
3. values at the intersection are count of column name matches for every row

For my example, the answer should be:

Please help... thank you in advance

CodePudding user response：

melt and crosstab:

df2 = so.melt('col1')
pd.crosstab(df2['col1'], df2['value'])

or melt and groupby.count:

so.melt('col1').groupby(['col1', 'value']).size().unstack(fill_value=0)

output:

value  A  B  C
col1          
row0   2  1  0
row1   2  1  0
row2   0  2  1

NB. for the exact output, use .reset_index().rename_axis(columns=None)

CodePudding user response：

here is one way to do it

df.melt('col1').pivot_table(index='col1', columns='value', aggfunc=(lambda x: int(x.size))  ).fillna(0).reset_index()

    col1    variable
value       A         B     C
0   row0    2.0     1.0     0.0
1   row1    2.0     1.0     0.0
2   row2    0.0     2.0     1.0