I have a dataset that looks like this ( some other cols):

Value         Theme       Country
-1.975767     Weather     China
-0.540979     Fruits      China
-2.359127     Fruits      China
-2.815604     Corona      Brazil
-0.929755     Weather     UK
-0.929755     Weather     UK

I want to find standard deviations for the values after grouping by Themes and Countries (as explained here calculate standard deviation by grouping two columns

df = pd.read_csv('./Brazil.csv')
df['std'] = df.groupby(['themes', 'country'])['value'].std()

However, currently, I get this error:

File /usr/local/Cellar/ipython/8.0.1/libexec/lib/python3.10/site-packages/pandas/core/frame.py:3656, in DataFrame.__setitem__(self, key, value)
   3653     self._setitem_array([key], value)
   3654 else:
   3655     # set column
-> 3656     self._set_item(key, value)

File /usr/local/Cellar/ipython/8.0.1/libexec/lib/python3.10/site-packages/pandas/core/frame.py:3833, in DataFrame._set_item(self, key, value)
   3823 def _set_item(self, key, value) -> None:
   3824     """
   3825     Add series to DataFrame in specified column.
   3826 
   (...)
   3831     ensure homogeneity.
   3832     """
-> 3833     value = self._sanitize_column(value)
   3835     if (
   3836         key in self.columns
   3837         and value.ndim == 1
   3838         and not is_extension_array_dtype(value)
   3839     ):
   3840         # broadcast across multiple columns if necessary
   3841         if not self.columns.is_unique or isinstance(self.columns, MultiIndex):

File /usr/local/Cellar/ipython/8.0.1/libexec/lib/python3.10/site-packages/pandas/core/frame.py:4534, in DataFrame._sanitize_column(self, value)
   4532 # We should never get here with DataFrame value
   4533 if isinstance(value, Series):
-> 4534     return _reindex_for_setitem(value, self.index)
   4536 if is_list_like(value):
   4537     com.require_length_match(value, self.index)

File /usr/local/Cellar/ipython/8.0.1/libexec/lib/python3.10/site-packages/pandas/core/frame.py:10985, in _reindex_for_setitem(value, index)
  10981     if not value.index.is_unique:
  10982         # duplicate axis
  10983         raise err
> 10985     raise TypeError(
  10986         "incompatible index of inserted column with frame index"
  10987     ) from err
  10988 return reindexed_value

TypeError: incompatible index of inserted column with frame index

CodePudding user response：

Use DataFrame.expanding with remove first level for new column by DataFrame.droplevel should be simplier solution:

df['std']  = (df.groupby(['Theme', 'Country'])['Value']
                .expanding()
                .std()
                .droplevel([0,1]))
print (df)
      Value    Theme Country       std
0 -1.975767  Weather   China       NaN
1 -0.540979   Fruits   China       NaN
2 -2.359127   Fruits   China  1.285625
3 -2.815604   Corona  Brazil       NaN
4 -0.929755  Weather      UK       NaN
5 -0.929755  Weather      UK  0.000000

CodePudding user response：

You can use a rolling method to calculate cumulative standard deviations for each group.

Code

import pandas as pd

# Create a sample dataframe
import io
text_csv = '''Value,Theme,Country
-1.975767,Weather,China
-0.540979,Fruits,China
-2.359127,Fruits,China
-2.815604,Corona,Brazil
-0.929755,Weather,UK
-0.929755,Weather,UK'''
df = pd.read_csv(io.StringIO(text_csv))

# Calculate cumulative standard deviations
df_std = df.groupby(['Theme', 'Country'], as_index=False)['Value'].rolling(len(df), min_periods=1).std()

# Merge the original df with the cumulative std values
df_std = df.join(df_std.drop(['Theme', 'Country'], axis=1).rename(columns={'Value': 'CorrectedStd'}))

Output