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pythonpandasnumpypandas-groupby
CPBL
CPBL

Reputation: 4030

Weighted average of multiple columns using groupby, dropping NaNs column-wise

I have a situation like Pandas Group Weighted Average of Multiple Columns but where some values of one column are sometimes NaN.

That is, I am doing the following:

import pandas as pd
import numpy as np

df=pd.DataFrame({'category':['a','a','b','b'],
 'var1':np.random.randint(0,100,4),
 'var2':np.random.randint(0,100,4),
 'weights':np.random.randint(0,10,4)})
df.loc[1,'var1']=np.nan
df


      category  var1  var2  weights
0        a      74.0    99        9
1        a       NaN     8        4
2        b      13.0    86        2
3        b      49.0    38        7

def weighted(x, cols, w="weights"):
    # Following fails when NaNs might be present:
    #return pd.Series(np.average(x[cols], weights=x[w], axis=0), cols)
    return pd.Series([np.nan if x.dropna(subset=[c]).empty else np.average(x.dropna(subset=[c])[c], weights =x.dropna(subset=[c])[w] ) for c in cols], cols)

df.groupby('category').apply(weighted, ['var1', 'var2'])


          var1       var2
category                 
a         74.0  57.846154
b         23.0   8.000000

I'd like a nicer way to do this, but np.nanmean does not allow weights. np.average does not allow options to control treatment of NaNs.

Upvotes: 4

Views: 467

Answers (3)

CPBL
CPBL

Reputation: 4030

With no cleaner answer than my proposal, I am suggesting that using the function below is not so bad:

import pandas as pd
import numpy as np

def weighted_means_by_column_ignoring_NaNs(x, cols, w="weights"):
    """ This takes a DataFrame and averages each data column (cols),
        weighting observations by column w, but ignoring individual NaN
        observations within each column.
    """
    return pd.Series([np.nan if x.dropna(subset=[c]).empty else \
                      np.average(x.dropna(subset=[c])[c], 
                      weights =x.dropna(subset=[c])[w] )  \
                      for c in cols], cols)

An example usage is as follows 

df=pd.DataFrame({'category':['a','a','b','b'],
 'var1':np.random.randint(0,100,4),
 'var2':np.random.randint(0,100,4),
 'weights':np.random.randint(0,10,4)})
df.loc[1,'var1']=np.nan
df


      category  var1  var2  weights
0        a      74.0    99        9
1        a       NaN     8        4
2        b      13.0    86        2
3        b      49.0    38        7

df.groupby('category').apply(weighted_means_by_column_ignoring_NaNs), 
        ['var1', 'var2'])


          var1       var2
category                 
a         74.0  57.846154
b         23.0   8.000000

Upvotes: 3

Andy L.
Andy L.

Reputation: 25239

You may pre-process your dataframe with melt, and dropna before call apply and call unstack

wa=lambda x: np.average(x.value, weights=x.weights)
df_avg = (df.melt(['category', 'weights']).dropna().groupby(['category', 'variable'])
                                                   .apply(wa).unstack())

Out[40]:
variable  var1       var2
category
a         74.0  71.000000
b         41.0  48.666667

Note: your desired output doesn't match with the sample. The value of (a, 'var2') is (99 * 9 + 8 * 4) / (9 + 4) = 71

Upvotes: 0

do thuan
do thuan

Reputation: 475

How about set Nan value to zero and create a new column which is var * weight. Then you can use groupby to get the result.

Upvotes: 0

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