Combine duplicated columns within a DataFrame

Question

If I have a dataframe that has columns that include the same name, is there a way to combine the columns that have the same name with some sort of function (i.e. sum)?

Answer 1

Here is possible simplier solution for common aggregation functions like sum, mean, median, max, min, std - only use parameters axis=1 for working with columns and level:

#coldspeed samples
np.random.seed(0)
df = pd.DataFrame(np.random.choice(50, (5, 5)), columns=list('AABBB'))
print (df)

print (df.sum(axis=1, level=0))
    A    B
0  91    6
1  48   76
2  29   60
3  39  108
4  41   75

df.columns = pd.MultiIndex.from_arrays([['one']*3 + ['two']*2, df.columns])

print (df.sum(axis=1, level=1))
    A    B
0  91    6
1  48   76
2  29   60
3  39  108
4  41   75

print (df.sum(axis=1, level=[0,1]))
  one     two
    A   B   B
0  91   0   6
1  48  19  57
2  29  24  36
3  39  39  69
4  41  37  38

Similar it working for index, then use axis=0 instead axis=1:

np.random.seed(0)
df = pd.DataFrame(np.random.choice(50, (5, 5)), columns=list('ABCDE'), index=list('aabbc'))
print (df)
    A   B   C   D   E
a  44  47   0   3   3
a  39   9  19  21  36
b  23   6  24  24  12
b   1  38  39  23  46
c  24  17  37  25  13

print (df.min(axis=0, level=0))
    A   B   C   D   E
a  39   9   0   3   3
b   1   6  24  23  12
c  24  17  37  25  13

df.index = pd.MultiIndex.from_arrays([['bar']*3 + ['foo']*2, df.index])

print (df.mean(axis=0, level=1))
      A     B     C     D     E
a  41.5  28.0   9.5  12.0  19.5
b  12.0  22.0  31.5  23.5  29.0
c  24.0  17.0  37.0  25.0  13.0

print (df.max(axis=0, level=[0,1]))
        A   B   C   D   E
bar a  44  47  19  21  36
    b  23   6  24  24  12
foo b   1  38  39  23  46
    c  24  17  37  25  13

If need use another functions like first, last, size, count is necessary use coldspeed answer

Answer 2

pandas >= 0.20: df.groupby(level=0, axis=1)

You don't need a lambda here, nor do you explicitly have to query df.columns; groupby accepts a level argument you can specify in conjunction with the axis argument. This is cleaner, IMO.

# Setup
np.random.seed(0)
df = pd.DataFrame(np.random.choice(50, (5, 5)), columns=list('AABBB'))
df

    A   A   B   B   B
0  44  47   0   3   3
1  39   9  19  21  36
2  23   6  24  24  12
3   1  38  39  23  46
4  24  17  37  25  13

<!_ >

df.groupby(level=0, axis=1).sum()

    A    B
0  91    6
1  48   76
2  29   60
3  39  108
4  41   75

---

Handling MultiIndex columns

Another case to consider is when dealing with MultiIndex columns. Consider

df.columns = pd.MultiIndex.from_arrays([['one']*3 + ['two']*2, df.columns])
df
  one         two    
    A   A   B   B   B
0  44  47   0   3   3
1  39   9  19  21  36
2  23   6  24  24  12
3   1  38  39  23  46
4  24  17  37  25  13

To perform aggregation across the upper levels, use

df.groupby(level=1, axis=1).sum()

    A    B
0  91    6
1  48   76
2  29   60
3  39  108
4  41   75

or, if aggregating per upper level only, use

df.groupby(level=[0, 1], axis=1).sum()

  one     two
    A   B   B
0  91   0   6
1  48  19  57
2  29  24  36
3  39  39  69
4  41  37  38

---

Alternate Interpretation: Dropping Duplicate Columns

If you came here looking to find out how to simply drop duplicate columns (without performing any aggregation), use Index.duplicated:

df.loc[:,~df.columns.duplicated()]

    A   B
0  44   0
1  39  19
2  23  24
3   1  39
4  24  37

Or, to keep the last ones, specify keep='last' (default is 'first'),

df.loc[:,~df.columns.duplicated(keep='last')]

    A   B
0  47   3
1   9  36
2   6  12
3  38  46
4  17  13

The groupby alternatives for the two solutions above are df.groupby(level=0, axis=1).first(), and ... .last(), respectively.

Answer 3

I believe this does what you are after:

df.groupby(lambda x:x, axis=1).sum()

Alternatively, between 3% and 15% faster depending on the length of the df:

df.groupby(df.columns, axis=1).sum()

EDIT: To extend this beyond sums, use .agg() (short for .aggregate()):

df.groupby(df.columns, axis=1).agg(numpy.max)