com_util.py

import pandas as pd
def merge_count(df,columns,value,cname):
    add = pd.DataFrame(df.groupby(columns)[value].count()).reset_index()
    add.columns=columns+[cname]
    df=df.merge(add,on=columns,how="left")
    return df

def merge_nunique(df,columns,value,cname):
    add = pd.DataFrame(df.groupby(columns)[value].nunique()).reset_index()
    add.columns=columns+[cname]
    df=df.merge(add,on=columns,how="left")
    return df

def merge_median(df,columns,value,cname):
    add = pd.DataFrame(df.groupby(columns)[value].median()).reset_index()
    add.columns=columns+[cname]
    df=df.merge(add,on=columns,how="left")
    return df

def merge_mean(df,columns,value,cname):
    add = pd.DataFrame(df.groupby(columns)[value].mean()).reset_index()
    add.columns=columns+[cname]
    df=df.merge(add,on=columns,how="left")
    return df

def merge_sum(df,columns,value,cname):
    add = pd.DataFrame(df.groupby(columns)[value].sum()).reset_index()
    add.columns=columns+[cname]
    df=df.merge(add,on=columns,how="left")
    return df

def merge_max(df,columns,value,cname):
    add = pd.DataFrame(df.groupby(columns)[value].max()).reset_index()
    add.columns=columns+[cname]
    df=df.merge(add,on=columns,how="left")
    return df

def merge_min(df,columns,value,cname):
    add = pd.DataFrame(df.groupby(columns)[value].min()).reset_index()
    add.columns=columns+[cname]
    df=df.merge(add,on=columns,how="left")
    return df

def merge_std(df,columns,value,cname):
    add = pd.DataFrame(df.groupby(columns)[value].std()).reset_index()
    add.columns=columns+[cname]
    df=df.merge(add,on=columns,how="left")
    return df