python - Aggregation in Pandas

Question

1. How can I perform aggregation with Pandas?
2. No DataFrame after aggregation! What happened?
3. How can I aggregate mainly strings columns (to lists, tuples, strings with separator)?
4. How can I aggregate counts?
5. How can I create a new column filled by aggregated values?

I've seen these recurring questions asking about various faces of the pandas aggregate functionality. Most of the information regarding aggregation and its various use cases today is fragmented across dozens of badly worded, unsearchable posts. The aim here is to collate some of the more important points for posterity.

This Q&A is meant to be the next instalment in a series of helpful user-guides:

• How to pivot a dataframe,
• Pandas concat
• How do I operate on a DataFrame with a Series for every column?
• Pandas Merging 101

Please note that this post is not meant to be a replacement for the documentation about aggregation and about groupby, so please read that as well!

Answer 1

Question 1

How can I perform aggregation with Pandas?

Expanded aggregation documentation.

Aggregating functions are the ones that reduce the dimension of the returned objects. It means output Series/DataFrame have less or same rows like original.

Some common aggregating functions are tabulated below:

Function    Description
mean()         Compute mean of groups
sum()         Compute sum of group values
size()         Compute group sizes
count()     Compute count of group
std()         Standard deviation of groups
var()         Compute variance of groups
sem()         Standard error of the mean of groups
describe()     Generates descriptive statistics
first()     Compute first of group values
last()         Compute last of group values
nth()         Take nth value, or a subset if n is a list
min()         Compute min of group values
max()         Compute max of group values

np.random.seed(123)

df = pd.DataFrame({'A' : ['foo', 'foo', 'bar', 'foo', 'bar', 'foo'],
                   'B' : ['one', 'two', 'three','two', 'two', 'one'],
                   'C' : np.random.randint(5, size=6),
                   'D' : np.random.randint(5, size=6),
                   'E' : np.random.randint(5, size=6)})
print (df)
     A      B  C  D  E
0  foo    one  2  3  0
1  foo    two  4  1  0
2  bar  three  2  1  1
3  foo    two  1  0  3
4  bar    two  3  1  4
5  foo    one  2  1  0

Aggregation by filtered columns and Cython implemented functions:

df1 = df.groupby(['A', 'B'], as_index=False)['C'].sum()
print (df1)
     A      B  C
0  bar  three  2
1  bar    two  3
2  foo    one  4
3  foo    two  5

An aggregate function is used for all columns without being specified in the groupby function, here the A, B columns:

df2 = df.groupby(['A', 'B'], as_index=False).sum()
print (df2)
     A      B  C  D  E
0  bar  three  2  1  1
1  bar    two  3  1  4
2  foo    one  4  4  0
3  foo    two  5  1  3

You can also specify only some columns used for aggregation in a list after the groupby function:

df3 = df.groupby(['A', 'B'], as_index=False)['C','D'].sum()
print (df3)
     A      B  C  D
0  bar  three  2  1
1  bar    two  3  1
2  foo    one  4  4
3  foo    two  5  1

Same results by using function DataFrameGroupBy.agg:

df1 = df.groupby(['A', 'B'], as_index=False)['C'].agg('sum')
print (df1)
     A      B  C
0  bar  three  2
1  bar    two  3
2  foo    one  4
3  foo    two  5

df2 = df.groupby(['A', 'B'], as_index=False).agg('sum')
print (df2)
     A      B  C  D  E
0  bar  three  2  1  1
1  bar    two  3  1  4
2  foo    one  4  4  0
3  foo    two  5  1  3

For multiple functions applied for one column use a list of tuples - names of new columns and aggregated functions:

df4 = (df.groupby(['A', 'B'])['C']
         .agg([('average','mean'),('total','sum')])
         .reset_index())
print (df4)
     A      B  average  total
0  bar  three      2.0      2
1  bar    two      3.0      3
2  foo    one      2.0      4
3  foo    two      2.5      5

If want to pass multiple functions is possible pass list of tuples:

df5 = (df.groupby(['A', 'B'])
         .agg([('average','mean'),('total','sum')]))

print (df5)
                C             D             E
          average total average total average total
A   B
bar three     2.0     2     1.0     1     1.0     1
    two       3.0     3     1.0     1     4.0     4
foo one       2.0     4     2.0     4     0.0     0
    two       2.5     5     0.5     1     1.5     3

Then get MultiIndex in columns:

print (df5.columns)
MultiIndex(levels=[['C', 'D', 'E'], ['average', 'total']],
           labels=[[0, 0, 1, 1, 2, 2], [0, 1, 0, 1, 0, 1]])

And for converting to columns, flattening MultiIndex use map with join:

df5.columns = df5.columns.map('_'.join)
df5 = df5.reset_index()
print (df5)
     A      B  C_average  C_total  D_average  D_total  E_average  E_total
0  bar  three        2.0        2        1.0        1        1.0        1
1  bar    two        3.0        3        1.0        1        4.0        4
2  foo    one        2.0        4        2.0        4        0.0        0
3  foo    two        2.5        5        0.5        1        1.5        3

Another solution is pass list of aggregate functions, then flatten MultiIndex and for another columns names use str.replace:

df5 = df.groupby(['A', 'B']).agg(['mean','sum'])

df5.columns = (df5.columns.map('_'.join)
                  .str.replace('sum','total')
                  .str.replace('mean','average'))
df5 = df5.reset_index()
print (df5)
     A      B  C_average  C_total  D_average  D_total  E_average  E_total
0  bar  three        2.0        2        1.0        1        1.0        1
1  bar    two        3.0        3        1.0        1        4.0        4
2  foo    one        2.0        4        2.0        4        0.0        0
3  foo    two        2.5        5        0.5        1        1.5        3

If want specified each column with aggregated function separately pass dictionary:

df6 = (df.groupby(['A', 'B'], as_index=False)
         .agg({'C':'sum','D':'mean'})
         .rename(columns={'C':'C_total', 'D':'D_average'}))
print (df6)
     A      B  C_total  D_average
0  bar  three        2        1.0
1  bar    two        3        1.0
2  foo    one        4        2.0
3  foo    two        5        0.5

You can pass custom function too:

def func(x):
    return x.iat[0] + x.iat[-1]

df7 = (df.groupby(['A', 'B'], as_index=False)
         .agg({'C':'sum','D': func})
         .rename(columns={'C':'C_total', 'D':'D_sum_first_and_last'}))
print (df7)
     A      B  C_total  D_sum_first_and_last
0  bar  three        2                     2
1  bar    two        3                     2
2  foo    one        4                     4
3  foo    two        5                     1

Question 2

No DataFrame after aggregation! What happened?

Aggregation by two or more columns:

df1 = df.groupby(['A', 'B'])['C'].sum()
print (df1)
A    B
bar  three    2
     two      3
foo  one      4
     two      5
Name: C, dtype: int32

First check the Index and type of a Pandas object:

print (df1.index)
MultiIndex(levels=[['bar', 'foo'], ['one', 'three', 'two']],
           labels=[[0, 0, 1, 1], [1, 2, 0, 2]],
           names=['A', 'B'])

print (type(df1))
<class 'pandas.core.series.Series'>

There are two solutions for how to get MultiIndex Series to columns:

• add parameter as_index=False

df1 = df.groupby(['A', 'B'], as_index=False)['C'].sum()
print (df1)
     A      B  C
0  bar  three  2
1  bar    two  3
2  foo    one  4
3  foo    two  5

• use Series.reset_index:

df1 = df.groupby(['A', 'B'])['C'].sum().reset_index()
print (df1)
     A      B  C
0  bar  three  2
1  bar    two  3
2  foo    one  4
3  foo    two  5

---

If group by one column:

df2 = df.groupby('A')['C'].sum()
print (df2)
A
bar    5
foo    9
Name: C, dtype: int32

... get Series with Index:

print (df2.index)
Index(['bar', 'foo'], dtype='object', name='A')

print (type(df2))
<class 'pandas.core.series.Series'>

And the solution is the same like in the MultiIndex Series:

df2 = df.groupby('A', as_index=False)['C'].sum()
print (df2)
     A  C
0  bar  5
1  foo  9

df2 = df.groupby('A')['C'].sum().reset_index()
print (df2)
     A  C
0  bar  5
1  foo  9

Question 3

How can I aggregate mainly strings columns (to lists, tuples, strings with separator)?

df = pd.DataFrame({'A' : ['a', 'c', 'b', 'b', 'a', 'c', 'b'],
                   'B' : ['one', 'two', 'three','two', 'two', 'one', 'three'],
                   'C' : ['three', 'one', 'two', 'two', 'three','two', 'one'],
                   'D' : [1,2,3,2,3,1,2]})
print (df)
   A      B      C  D
0  a    one  three  1
1  c    two    one  2
2  b  three    two  3
3  b    two    two  2
4  a    two  three  3
5  c    one    two  1
6  b  three    one  2

Instead of an aggregation function, it is possible to pass list, tuple, set for converting the column:

df1 = df.groupby('A')['B'].agg(list).reset_index()
print (df1)
   A                    B
0  a           [one, two]
1  b  [three, two, three]
2  c           [two, one]

An alternative is use GroupBy.apply:

df1 = df.groupby('A')['B'].apply(list).reset_index()
print (df1)
   A                    B
0  a           [one, two]
1  b  [three, two, three]
2  c           [two, one]

For converting to strings with a separator, use .join only if it is a string column:

df2 = df.groupby('A')['B'].agg(','.join).reset_index()
print (df2)
   A                B
0  a          one,two
1  b  three,two,three
2  c          two,one

If it is a numeric column, use a lambda function with astype for converting to strings:

df3 = (df.groupby('A')['D']
         .agg(lambda x: ','.join(x.astype(str)))
         .reset_index())
print (df3)
   A      D
0  a    1,3
1  b  3,2,2
2  c    2,1

Another solution is converting to strings before groupby:

df3 = (df.assign(D = df['D'].astype(str))
         .groupby('A')['D']
         .agg(','.join).reset_index())
print (df3)
   A      D
0  a    1,3
1  b  3,2,2
2  c    2,1

For converting all columns, don't pass a list of column(s) after groupby. There isn't any column D, because automatic exclusion of 'nuisance' columns. It means all numeric columns are exclude