Pandas tips & tricks

add_column_groupby_transform.py

df = pd.DataFrame({  
'Date': ['2015-05-08', '2015-05-07', '2015-05-06', '2015-05-05', '2015-05-08', '2015-05-07', '2015-05-06', '2015-05-05'],   
'Sym': ['aapl', 'aapl', 'aapl', 'aapl', 'aaww', 'aaww', 'aaww', 'aaww'],  
'Data2': [11, 8, 10, 15, 110, 60, 100, 40],  
'Data3': [5, 8, 6, 1, 50, 100, 60, 120]})  
  
df['Data4'] = df['Data3'].groupby(df['Date']).transform('sum')
df
# Out[74]:
#    Data2  Data3        Date   Sym  Data4
# 0     11      5  2015-05-08  aapl     55
# 1      8      8  2015-05-07  aapl    108
# 2     10      6  2015-05-06  aapl     66
# 3     15      1  2015-05-05  aapl    121
# 4    110     50  2015-05-08  aaww     55
# 5     60    100  2015-05-07  aaww    108
# 6    100     60  2015-05-06  aaww     66
# 7     40    120  2015-05-05  aaww    121

aggregate.py

# How to apply list of functions on pd.Dataframe# How t 
# https://stackoverflow.com/questions/48767067/how-to-pass-list-of-custom-functions-to-pandas-dataframe-aggregate

import pandas as pd
import numpy as np
from scipy.stats import trim_mean

df = pd.DataFrame(np.random.randn(100, 3), columns=['A', 'B', 'C'], index=pd.date_range('1/1/2000', periods=100))

# this works as expected
print(df.agg([np.sum, np.mean]))

# now with a different function, works also
print(df.agg(lambda x: trim_mean(x, 0.2)))

# apply also works
print(df.apply(lambda x: trim_mean(x, 0.2)))

# now with list of lambda: doesn't work
# df.apply([lambda x: trim_mean(x, 0.2)]) # tuple index out of range
# df.agg([lambda x: trim_mean(x, 0.2)]) # tuple index out of range


# solution: wrap list of functions in lambda# solut 
c = ['trim_mean','mean','sum']
print (df.agg(lambda x: pd.Series([trim_mean(x, 0.2), np.mean(x), np.sum(x)], index=c)))