Naming returned columns in Pandas aggregate function?

Question

I'm having trouble with Pandas' groupby functionality. I've read the documentation, but I can't see to figure out how to apply aggregate functions to multiple columns and have custom names for those columns.

This comes very close, but the data structure returned has nested column headings:

data.groupby("Country").agg(
        {"column1": {"foo": sum()}, "column2": {"mean": np.mean, "std": np.std}})

(ie. I want to take the mean and std of column2, but return those columns as "mean" and "std")

What am I missing?

Answer 1

For pandas >= 0.25

The functionality to name returned aggregate columns has been reintroduced in the master branch and is targeted for pandas 0.25. The new syntax is .agg(new_col_name=('col_name', 'agg_func'). Detailed example from the PR linked above:

In [2]: df = pd.DataFrame({'kind': ['cat', 'dog', 'cat', 'dog'],
   ...:                    'height': [9.1, 6.0, 9.5, 34.0],
   ...:                    'weight': [7.9, 7.5, 9.9, 198.0]})
   ...:

In [3]: df
Out[3]:
  kind  height  weight
0  cat     9.1     7.9
1  dog     6.0     7.5
2  cat     9.5     9.9
3  dog    34.0   198.0

In [4]: df.groupby('kind').agg(min_height=('height', 'min'), 
                               max_weight=('weight', 'max'))
Out[4]:
      min_height  max_weight
kind
cat          9.1         9.9
dog          6.0       198.0

It will also be possible to use multiple lambda expressions with this syntax and the two-step rename syntax I suggested earlier (below) as per this PR. Again, copying from the example in the PR:

In [2]: df = pd.DataFrame({"A": ['a', 'a'], 'B': [1, 2], 'C': [3, 4]})

In [3]: df.groupby("A").agg({'B': [lambda x: 0, lambda x: 1]})
Out[3]:
         B
  <lambda> <lambda 1>
A
a        0          1

and then .rename(), or in one go:

In [4]: df.groupby("A").agg(b=('B', lambda x: 0), c=('B', lambda x: 1))
Out[4]:
   b  c
A
a  0  0

---

For pandas < 0.25

The currently accepted answer by unutbu describes are great way of doing this in pandas versions <= 0.20. However, as of pandas 0.20, using this method raises a warning indicating that the syntax will not be available in future versions of pandas.

Series:

FutureWarning: using a dict on a Series for aggregation is deprecated and will be removed in a future version

DataFrames:

FutureWarning: using a dict with renaming is deprecated and will be removed in a future version

According to the pandas 0.20 changelog, the recommended way of renaming columns while aggregating is as follows.

# Create a sample data frame
df = pd.DataFrame({'A': [1, 1, 1, 2, 2],
                   'B': range(5),
                   'C': range(5)})

# ==== SINGLE COLUMN (SERIES) ====
# Syntax soon to be deprecated
df.groupby('A').B.agg({'foo': 'count'})
# Recommended replacement syntax
df.groupby('A').B.agg(['count']).rename(columns={'count': 'foo'})

# ==== MULTI COLUMN ====
# Syntax soon to be deprecated
df.groupby('A').agg({'B': {'foo': 'sum'}, 'C': {'bar': 'min'}})
# Recommended replacement syntax
df.groupby('A').agg({'B': 'sum', 'C': 'min'}).rename(columns={'B': 'foo', 'C': 'bar'})
# As the recommended syntax is more verbose, parentheses can
# be used to introduce line breaks and increase readability
(df.groupby('A')
    .agg({'B': 'sum', 'C': 'min'})
    .rename(columns={'B': 'foo', 'C': 'bar'})
)

Please see the 0.20 changelog for additional details.

Update 2017-01-03 in response to @JunkMechanic's comment.

With the old style dictionary syntax, it was possible to pass multiple lambda functions to .agg, since these would be renamed with the key in the passed dictionary:

>>> df.groupby('A').agg({'B': {'min': lambda x: x.min(), 'max': lambda x: x.max()}})

    B    
  max min
A        
1   2   0
2   4   3

Multiple functions can also be passed to a single column as a list:

>>> df.groupby('A').agg({'B': [np.min, np.max]})

     B     
  amin amax
A          
1    0    2
2    3    4

However, this does not work with lambda functions, since they are anonymous and all return <lambda>, which causes a name collision:

>>> df.groupby('A').agg({'B': [lambda x: x.min(), lambda x: x.max]})
SpecificationError: Function names must be unique, found multiple named <lambda>

To avoid the SpecificationError, named functions can be defined a priori instead of using lambda. Suitable function names also avoid calling .rename on the data frame afterwards. These functions can be passed with the same list syntax as above:

>>> def my_min(x):
>>>     return x.min()

>>> def my_max(x):
>>>     return x.max()

>>> df.groupby('A').agg({'B': [my_min, my_max]})

       B       
  my_min my_max
A              
1      0      2
2      3      4