CODEWITHSUNDEEP
pythonpandasdataframepandas-groupbynumeric
Shadic Mersal
Shadic Mersal

Reputation: 134

DataError: No numeric types to aggregate

I am going to describe males and females groups by gender to visualize how Churn Rate (1-Retention Rate) looks like for each value.

My output

df_data.groupby(by='gender')['Churn'].mean()



error
---------------------------------------------------------------------------
DataError                                 Traceback (most recent call last)
<ipython-input-46-75992efc6958> in <module>()
----> 1 df_data.groupby(by='gender')['Churn'].mean()

1 frames
/usr/local/lib/python3.7/dist-packages/pandas/core/groupby/groupby.py in mean(self, numeric_only)
   1396             "mean",
   1397             alt=lambda x, axis: Series(x).mean(numeric_only=numeric_only),
-> 1398             numeric_only=numeric_only,
   1399         )
   1400 

/usr/local/lib/python3.7/dist-packages/pandas/core/groupby/groupby.py in _cython_agg_general(self, how, alt, numeric_only, min_count)
   1051 
   1052         if len(output) == 0:
-> 1053             raise DataError("No numeric types to aggregate")
   1054 
   1055         return self._wrap_aggregated_output(output, index=self.grouper.result_index)

DataError: No numeric types to aggregate

Upvotes: 2

Views: 5973

Answers (2)

hirayu
hirayu

Reputation: 21

For your precise answer

df_data.Churn = df_data.Churn.astype(int)

Upvotes: 2

DYZ
DYZ

Reputation: 57033

All your columns, even those that look like numbers, are strings. You must convert "numeric" columns into numeric columns with .astype(int) before applying .mean(). For example:

df.tenure = df.tenure.astype(int)

Upvotes: 4

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