Python Pandas DataFrame sum() For suming rows and columns by using axis , skipna,numeric_only,min

We can sum number of in rows or columns by using sum().

import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex','Ron','King','Jack'],
         'ID':[1,2,3,4,5,6],
         'MATH':[80,40,70,70,70,30],
         'ENGLISH':[80,70,40,50,60,30]}
my_data = pd.DataFrame(data=my_dict)
print(my_data.sum())

Output

NAME       RaviRajuAlexRonKingJack
ID                              21
MATH                           360
ENGLISH                        330

Using axis

We will use option axis=0 ( default ) by adding to above code.

( The last line is only changed )

print(my_data.sum(axis=1))

Output is here

level option

Sum of two column values.

import pandas as pd 
my_dict={
  'name':['Alex','King','Ravi','Raju','John'],
  'mark':[7,8,5,6,3],
  'math':[70,80,50,60,30]
  	}
df = pd.DataFrame(data=my_dict) # create DataFrame
df.set_index('name',inplace=True) # added index to Name column 
print(df[['math','mark']].sum())

Sum of column values ( of all rows ) Output

math    290
mark     29
dtype: int64

Using Axis ( In above code default value for axis = 0 ), We can use axis=1 to sum values of columns in each row.

print(df[['math','mark']].sum(axis=1))

Output

name
Alex    77
King    88
Ravi    55
Raju    66
John    33
dtype: int64

For MultiIndex (hierarchical) axis we can specify the level.

import pandas as pd 
my_dict=pd.MultiIndex.from_arrays(
         [[1,2,3,4,5,6],
         [80,40,70,70,70,30],
         [80,70,40,50,60,30]],
names=['id','math','eng'])
my_data = pd.Series([4, 2, 0, 8,3,4], name='marks', index=my_dict)
print(my_data.sum(level='math'))

Output

Handling NA data

We will use skipna=True to ignore the null or NA data. Let us check what happens if it is set to True ( skipna=True )

import numpy as np
import pandas as pd 
my_dict={'NAME':['Ravi','Raju','Alex','Ron','King','Jack'],
         'ID':[1,2,3,4,5,6],
         'MATH':[80,40,70,70,70,30],
         'ENGLISH':[80,70,np.nan,50,60,30]}
my_data = pd.DataFrame(data=my_dict)
print(my_data.sum(skipna=False))

Output

NAME       RaviRajuAlexRonKingJack
ID                              21
MATH                           360
ENGLISH                        NaN

Check the sum of ENGLISH it is returned as NAN, the value will change to 290 if we set skipna=True

numeric_only

Default value is None, we can set it to True ( numeric_only=True ) to include only float, int, boolean columns. We can included all by setting it to False ( numeric_only=False ) . Let us see the outputs .

print(my_data.sum(numeric_only=False))

Output is here

NAME       RaviRajuAlexRonKingJack
ID                              21
MATH                           360
ENGLISH                      330.5

Now let us change the option numeric_only to True ( numeric_only=True ).

print(my_data.sum(numeric_only=True))

Output

ID          21.0
MATH       360.0
ENGLISH    330.5

min_count

Default value is 0, it accepts int. It is required number of valid values to perform.

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