Rename Result Columns from Pandas Aggregation ("Futurewarning: Using a Dict With Renaming Is Deprecated")

Rename result columns from Pandas aggregation ("FutureWarning: using a dict with renaming is deprecated")

Use groupby apply and return a Series to rename columns

Use the groupby apply method to perform an aggregation that

• Renames the columns
• Allows for spaces in the names
• Allows you to order the returned columns in any way you choose
• Allows for interactions between columns
• Returns a single level index and NOT a MultiIndex

To do this:

• create a custom function that you pass to apply
• This custom function is passed each group as a DataFrame
• Return a Series
• The index of the Series will be the new columns

Create fake data

df = pd.DataFrame({"User": ["user1", "user2", "user2", "user3", "user2", "user1", "user3"],
                  "Amount": [10.0, 5.0, 8.0, 10.5, 7.5, 8.0, 9],
                  'Score': [9, 1, 8, 7, 7, 6, 9]})

create custom function that returns a Series

The variable x inside of my_agg is a DataFrame

def my_agg(x):
    names = {
        'Amount mean': x['Amount'].mean(),
        'Amount std':  x['Amount'].std(),
        'Amount range': x['Amount'].max() - x['Amount'].min(),
        'Score Max':  x['Score'].max(),
        'Score Sum': x['Score'].sum(),
        'Amount Score Sum': (x['Amount'] * x['Score']).sum()}

    return pd.Series(names, index=['Amount range', 'Amount std', 'Amount mean',
                                   'Score Sum', 'Score Max', 'Amount Score Sum'])

Pass this custom function to the groupby apply method

df.groupby('User').apply(my_agg)

The big downside is that this function will be much slower than agg for the cythonized aggregations

Using a dictionary with groupby agg method

Using a dictionary of dictionaries was removed because of its complexity and somewhat ambiguous nature. There is an ongoing discussion on how to improve this functionality in the future on github Here, you can directly access the aggregating column after the groupby call. Simply pass a list of all the aggregating functions you wish to apply.

df.groupby('User')['Amount'].agg(['sum', 'count'])

Output

       sum  count
User              
user1  18.0      2
user2  20.5      3
user3  10.5      1

It is still possible to use a dictionary to explicitly denote different aggregations for different columns, like here if there was another numeric column named Other.

df = pd.DataFrame({"User": ["user1", "user2", "user2", "user3", "user2", "user1"],
              "Amount": [10.0, 5.0, 8.0, 10.5, 7.5, 8.0],
              'Other': [1,2,3,4,5,6]})

df.groupby('User').agg({'Amount' : ['sum', 'count'], 'Other':['max', 'std']})

Output

      Amount       Other          
         sum count   max       std
User                              
user1   18.0     2     6  3.535534
user2   20.5     3     5  1.527525
user3   10.5     1     4       NaN

Pandas renaming with dict is deprecated

For Reference, this was something deprecated in 2017 (https://github.com/pandas-dev/pandas/issues/18366)

There are multiple approaches (from this SO Answer)

You can change your code

agg = long_df.reset_index().groupby(['RegionVariable', 'EXP'])[features].agg(['count','mean'])

or use something like a custom pd.Series.apply function.

Naming returned columns in Pandas aggregate function?

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

Solution for SpecificationError: nested renamer is not supported while agg() along with groupby()

change

temp['total'] = pd.DataFrame(project_data.groupby(col1)[col2].agg({'total':'count'})).reset_index()['total']

temp['Avg'] = pd.DataFrame(project_data.groupby(col1)[col2].agg({'Avg':'mean'})).reset_index()['Avg']

to

temp['total'] = pd.DataFrame(project_data.groupby(col1)[col2].agg(total='count')).reset_index()['total']
temp['Avg'] = pd.DataFrame(project_data.groupby(col1)[col2].agg(Avg='mean')).reset_index()['Avg']

reason: in new pandas version named aggregation is the recommended replacement for the deprecated “dict-of-dicts” approach to naming the output of column-specific aggregations (Deprecate groupby.agg() with a dictionary when renaming).

source: https://pandas.pydata.org/pandas-docs/stable/whatsnew/v0.25.0.html

Pandas aggregation warning with lambdas (FutureWarning: using a dict with renaming is deprecated)

Use map for flatten columns names:

df.columns = df.columns.map('_'.join)

How to update pandas DataFrame.drop() for Future Warning - all arguments of DataFrame.drop except for the argument 'labels' will be keyword-only

From the documentation, pandas.DataFrame.drop has the following parameters:

Parameters

• labels: single label or list-like Index or column labels to drop.

• axis: {0 or ‘index’, 1 or ‘columns’}, default 0 Whether to drop labels from the index (0 or ‘index’) or columns (1 or ‘columns’).

• index: single label or list-like Alternative to specifying axis (labels, axis=0 is equivalent to index=labels).

• columns: single label or list-like Alternative to specifying axis (labels, axis=1 is equivalent to columns=labels).

• level: int or level name, optional For MultiIndex, level from which the labels will be removed.

• inplace: bool, default False If False, return a copy. Otherwise, do operation inplace and return None.

• errors: {‘ignore’, ‘raise’}, default ‘raise’ If ‘ignore’, suppress error and only existing labels are dropped.

Moving forward, only labels (the first parameter) can be positional.

---

So, for this example, the drop code should be as follows:

df = df.drop('market', axis=1)

or (more legibly) with columns:

df = df.drop(columns='market')

---

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