CODEWITHSUNDEEP
pythonpandas
Annie
Annie

Reputation: 23

nested renamer is not supported

I am writting this code but appear this error:

Code:

(df.set_index('STNAME')
   .groupby(level=0)['CENSUS2010POP']
   .agg({'avg': np.average, 'sum': np.sum})
)

Error:

D:\Programas core\Paython\ANACONDA\lib\site-packages\pandas\core\base.py in _aggregate(self, arg, *args, **kwargs)
    364                     obj.columns.intersection(keys)
    365                 ) != len(keys):
--> 366                     raise SpecificationError("nested renamer is not supported")
    367 
    368             from pandas.core.reshape.concat import concat

SpecificationError: nested renamer is not supported

Data: enter image description here

Upvotes: 2

Views: 9150

Answers (2)

Rob Raymond
Rob Raymond

Reputation: 31226

  1. please provide data so that it does not need to be run through OCR
  2. three solutions below work, but don't use aggregation defined as dict
head = "ROW SUMLEV REGION DIVISION STATE COUNTY STNAME CTYNAME CENSUS2010POP ESTIMATESBASE2010"
csv = '''1,50,3,6,1,1,Alabama,Autauga County,54571,54571
2,50,3,6,1,3,Alabama,Baldwin County,182265,182265
3,50,3,6,1,5,Alabama,Barbour County,27457,27457
4,50,3,6,1,7,Alabama,Bibb County,22915,22919
5,50,3,6,1,9,Alabama,Blount County,57322,57322'''
head.split(" ")
da = [[i for i in l.split(",")] for l in csv.split("\n")]
df = pd.DataFrame(da, columns=head.split(" "))
df = df.drop(columns=["ROW"])
df.CENSUS2010POP = pd.to_numeric(df.CENSUS2010POP)

df.groupby(["STNAME"])['CENSUS2010POP'].agg(["mean","sum"]).rename({"mean":"avg"}, axis=1)
df.groupby(["STNAME"])['CENSUS2010POP'].agg([np.average, np.sum]).rename({"average":"avg"}, axis=1)
df.set_index("STNAME").groupby(level=0)['CENSUS2010POP'].agg([np.average, np.sum]).rename({"average":"avg"}, axis=1)

Upvotes: 1

Nilesh Ingle
Nilesh Ingle

Reputation: 1883

The pandas named aggregate used here. Two options of codes below.

Option # 1:

Note: Removed {} from .agg().

# Import libraries
import pandas as pd

# DataFrame
df = pd.DataFrame({
    'STNAME': ['Alabama']*5,
    'CENSUS2010POP': [54571,1822865,27457,22915,57322]
})

# Group and aggregate
x = (df.set_index('STNAME')
   .groupby(level=0)['CENSUS2010POP']
   .agg(avg= 'mean', sum= 'sum')
)

print(x)

Output

        avg      sum
STNAME                  
Alabama  397026  1985130

Option # 2:

Note: The output column name sum was changed to total to avoid conflict with np.sum.

# Import libraries
import pandas as pd

# Create DataFrame
df = pd.DataFrame({
    'STNAME': ['Alabama']*5,
    'CENSUS2010POP': [54571,1822865,27457,22915,57322]
})
df = df.set_index('STNAME')

# Create grouped object
g = df.groupby(level=0)

# Named aggregated on grouped object
x = g.aggregate(
        avg=pd.NamedAgg(column='CENSUS2010POP', aggfunc=np.mean),
        total=pd.NamedAgg(column='CENSUS2010POP', aggfunc=np.sum),
    )

# Output
print(x)

Output

            avg    total
STNAME                  
Alabama  397026  1985130

Upvotes: 6

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