“Pandas Sum” Réponses codées

pandas ttable avec des totaux de somme

import numpy as np
import pandas as pd


df = pd.DataFrame({'a': [10,20],'b':[100,200],'c': ['a','b']})

df.loc['Column_Total']= df.sum(numeric_only=True, axis=0)
df.loc[:,'Row_Total'] = df.sum(numeric_only=True, axis=1)

print(df)

                 a      b    c  Row_Total
0             10.0  100.0    a      110.0
1             20.0  200.0    b      220.0
Column_Total  30.0  300.0  NaN      330.0
Yellowed Yak

Pandas Sum

import pandas as pd

data = {'Month': ['Jan ','Feb ','Mar ','Apr ','May ','Jun '],
        'Bill Commission': [1500,2200,3500,1800,3000,2800],
        'Maria Commission': [3200,4100,2500,3000,4700,3400], 
        'Jack Commission': [1700,3100,3300,2700,2400,3100]
        }

df = pd.DataFrame(data,columns=['Month','Bill Commission','Maria Commission','Jack Commission'])
sum_column = df.sum(axis=0)
print (sum_column)
Thankful Tortoise

Pandas Sum

# select numeric columns and calculate the sums
sums = df.select_dtypes(pd.np.number).sum().rename('total')

# append sums to the data frame
df.append(sums)
#         X  MyColumn      Y      Z
#0        A      84.0   13.0   69.0
#1        B      76.0   77.0  127.0
#2        C      28.0   69.0   16.0
#3        D      28.0   28.0   31.0
#4        E      19.0   20.0   85.0
#5        F      84.0  193.0   70.0
#total  NaN     319.0  400.0  398.0
Thankful Tortoise

Pandas Sum

df.at['Total', 'MyColumn'] = df['MyColumn'].sum()
print (df)
         X  MyColumn      Y      Z
0        A      84.0   13.0   69.0
1        B      76.0   77.0  127.0
2        C      28.0   69.0   16.0
3        D      28.0   28.0   31.0
4        E      19.0   20.0   85.0
5        F      84.0  193.0   70.0
Total  NaN     319.0    NaN    NaN
Thankful Tortoise

Pandas Sum

import pandas as pd

data = {'Month': ['Jan ','Feb ','Mar ','Apr ','May ','Jun '],
        'Bill Commission': [1500,2200,3500,1800,3000,2800],
        'Maria Commission': [3200,4100,2500,3000,4700,3400], 
        'Jack Commission': [1700,3100,3300,2700,2400,3100]
        }

df = pd.DataFrame(data,columns=['Month','Bill Commission','Maria Commission','Jack Commission'])
print (df)
Thankful Tortoise

Pandas Sum

>>> sum(ugds)16200904.0>>> ugds.sum()16200904.0
Uninterested Unicorn

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