How to show table summary in the last row-CodePudding

I have a data frame where I need to show a totals in the last row in terms of the count of not null values of string value columns and a summation of a one column and the average of another column.

df2 = pd.DataFrame({ 'Name':['John', 'Tom', 'Tom' ,'Ole','Ole','Tom'],
                'To_Count':['Yes', 'Yes','Yes', 'No', np.nan, np.nan],
                 'To_Count1':['Yes', 'Yes','Yes','No', np.nan,np.nan],
                'To_Sum':[100, 200, 300, 500,600, 400],
                'To_Avg':[100, 200, 300, 500, 600, 400],
                })

This is how I code to get this result.

df2.loc["Totals",'To_Count':'To_Count1'] = df2.loc[:,'To_Count':'To_Count1'].count(axis=0)
df2.loc["Totals",'To_Sum'] = df2.loc[:,'To_Sum'].sum(axis=0)
df2.loc["Totals",'To_Avg'] = df2.loc[:,'To_Avg'].mean(axis=0)

However if I run this code again accidently the values get duplicated. Is there a better way to get this result.

Expected result;

CodePudding user response：

Use DataFrame.agg with dictionary:

df2.loc["Totals"] = df2.agg({'To_Sum': 'sum', 
                             'To_Avg': 'mean', 
                             'To_Count': 'count', 
                             'To_Count1':'count'})
print (df2)
        Name To_Count To_Count1  To_Sum  To_Avg
0       John      Yes       Yes   100.0   100.0
1        Tom      Yes       Yes   200.0   200.0
2        Tom      Yes       Yes   300.0   300.0
3        Ole       No        No   500.0   500.0
4        Ole      NaN       NaN   600.0   600.0
5        Tom      NaN       NaN   400.0   400.0
Totals   NaN      4.0       4.0  2100.0   350.0

More dynamic solution if many columns between To_Count and To_Count1:

d = dict.fromkeys(df2.loc[:,'To_Count':'To_Count1'].columns, 'count')
print (d)
df2.loc[:,'To_Count':'To_Count1']

df2.loc["Totals"] = df2.agg({**{'To_Sum': 'sum', 'To_Avg': 'mean'}, **d})
print (df2)
        Name To_Count To_Count1  To_Sum  To_Avg
0       John      Yes       Yes   100.0   100.0
1        Tom      Yes       Yes   200.0   200.0
2        Tom      Yes       Yes   300.0   300.0
3        Ole       No        No   500.0   500.0
4        Ole      NaN       NaN   600.0   600.0
5        Tom      NaN       NaN   400.0   400.0
Totals   NaN      4.0       4.0  2100.0   350.0