zhaoyongjie commented on a change in pull request #18782:
URL: https://github.com/apache/superset/pull/18782#discussion_r809088951
##########
File path: superset/utils/pandas_postprocessing/contribution.py
##########
@@ -71,5 +73,7 @@ def contribution(
numeric_df = numeric_df[columns]
axis = 0 if orientation == PostProcessingContributionOrientation.COLUMN
else 1
numeric_df = numeric_df / numeric_df.values.sum(axis=axis, keepdims=True)
+ # replace infinity and nan with 0 in dataframe
+ numeric_df.replace(to_replace=[np.Inf, -np.Inf, np.nan], value=0,
inplace=True)
Review comment:
There is a Dateframe like above:
```
__timestamp a b c
0 2020-07-16 14:49:00 1.0 1.0 NaN
1 2020-07-16 14:50:00 3.0 9.0 NaN
2 2020-07-16 14:51:00 NaN NaN NaN
```
The df will be transformation like following after run `contribution`:
```
contribution(
df, orientation=PostProcessingContributionOrientation.ROW,
)
__timestamp a b c
0 2020-07-16 14:49:00 0.50 0.50 0.0
1 2020-07-16 14:50:00 0.25 0.75 0.0
2 2020-07-16 14:51:00 0.00 0.00 0.0
```
In the other hand, If we didn't replace the `nan` and `inf` values, the row
of `2020-07-16` will be filled out nan in every cell, **but** cell of `row:
2020-07-16 column: c ` have be filled in **0**.
```
__timestamp a b c
0 2020-07-16 14:49:00 0.50 0.50 0.0
1 2020-07-16 14:50:00 0.25 0.75 0.0
2 2020-07-16 14:51:00 NaN NaN NaN
```
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