CODEWITHSUNDEEP
pythonpandasnumpydata-science
gunesevitan
gunesevitan

Reputation: 965

Pandas - Mapping 2 level index dict to a DataFrame column

I'm trying to map the results of a 2 level aggregation to the original categorical feature and use it as a new feature. I created the aggregation like this.

temp_df = pd.concat([X_train[['cat1', 'cont1', 'cat2']], X_test[['cat1', 'cont1', 'cat2']]])
temp_df = temp_df.groupby(['cat1', 'cat2'])['cont1'].agg(['mean']).reset_index().rename(columns={'mean': 'cat1_cont1/cat2_Mean'})

Then I made MultiIndex from the values of first and second categorical feature, and finally casted the new aggregation feature to a dict.

arrays = [list(temp_df['cat1']), list(temp_df['cat2'])]    
temp_df.index = pd.MultiIndex.from_tuples(list(zip(*arrays)), names=['cat1', 'cat2'])
temp_df = temp_df['cat1_cont1/cat2_Mean'].to_dict()

The dict keys are tuples as multi indices. The first values in the tuples are cat1's values and the second values are cat2's values.

{(1000, 'C'): 23.443,
 (1001, 'H'): 50.0,
 (1001, 'W'): 69.5,
 (1002, 'H'): 60.0,
 (1003, 'W'): 42.95,
 (1004, 'H'): 51.0,
 (1004, 'R'): 150.0,
 (1004, 'W'): 226.0,
 (1005, 'H'): 50.0}

When I try to map those values to the original cat1 feature, everything becomes NaN. How can I do this properly?

X_train['cat1'].map(temp_df) # Produces a column of all NaNs

Upvotes: 1

Views: 117

Answers (1)

jezrael
jezrael

Reputation: 862681

You can map by multiple columns, but necessary create tuples from original, here by temp_df[['cat1', 'cat2']].apply(tuple, axis=1):

temp_df = pd.DataFrame({
        'cat1':list('aaaabb'),
         'cat2':[4,5,4,5,5,4],
         'cont1':[7,8,9,4,2,3],

})

new = (temp_df.groupby(['cat1', 'cat2'])['cont1'].agg(['mean'])
             .reset_index()
             .rename(columns={'mean': 'cat1_cont1/cat2_Mean'}))
print (new)
  cat1  cat2  cat1_cont1/cat2_Mean
0    a     4                     8
1    a     5                     6
2    b     4                     3
3    b     5                     2

arrays = [list(new['cat1']), list(new['cat2'])]    
new.index = pd.MultiIndex.from_tuples(list(zip(*arrays)), names=['cat1', 'cat2'])
d = new['cat1_cont1/cat2_Mean'].to_dict()
print (d)
{('a', 4): 8, ('a', 5): 6, ('b', 4): 3, ('b', 5): 2}

temp_df['cat1_cont1/cat2_Mean'] = temp_df[['cat1', 'cat2']].apply(tuple, axis=1).map(d)

For new column filled by aggregate values is simplier use GroupBy.transform function:

temp_df['cat1_cont1/cat2_Mean1'] = temp_df.groupby(['cat1', 'cat2'])['cont1'].transform('mean')

Another solution is use DataFrame.join by Series with MultiIndex: 

s = temp_df.groupby(['cat1', 'cat2'])['cont1'].agg('mean').rename('cat1_cont1/cat2_Mean2')
temp_df = temp_df.join(s, on=['cat1', 'cat2'])

print (temp_df)
  cat1  cat2  cont1  cat1_cont1/cat2_Mean  cat1_cont1/cat2_Mean1  \
0    a     4      7                     8                      8   
1    a     5      8                     6                      6   
2    a     4      9                     8                      8   
3    a     5      4                     6                      6   
4    b     5      2                     2                      2   
5    b     4      3                     3                      3   

   cat1_cont1/cat2_Mean2  
0                      8  
1                      6  
2                      8  
3                      6  
4                      2  
5                      3

Upvotes: 1

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