Python | Pandas Series.sum()

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier. Pandas Series.sum() method is used to get the sum of the values for the requested axis.

Syntax: Series.sum(axis=None, skipna=None, level=None, numeric_only=None, min_count=0) Parameters: axis : {index (0)} skipna[boolean, default True] : Exclude NA/null values. If an entire row/column is NA, the result will be NA level[int or level name, default None] : If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a scalar. numeric_only[boolean, default None] : Include only float, int, boolean data. If None, will attempt to use everything, then use only numeric data Returns: Returns the sum of the values for the requested axis

Code #1: By default, the sum of an empty or all-NA Series is 0. Python3 1==

# importing pandas module 
import pandas as pd 

# min_count = 0 is the default
pd.Series([]).sum()

# When passed  min_count = 1,
# sum of an empty series will be NaN
pd.Series([]).sum(min_count = 1)

Output:

0.0
nan

  Code #2: Python3 1==

# importing pandas module 
import pandas as pd 
  
# making data frame csv at url  
data = pd.read_csv("https://media.geeksforgeeks.org/wp-content/uploads/nba.csv") 

# sum of all salary
val = data['Salary'].sum()

val

Output:

2159837111.0

  Code #3: Python3 1==

# importing pandas module 
import pandas as pd 
  
# making a dict of list
data = {'name': ['John', 'Peter', 'Karl'],
        'age' : [23, 42, 19]}

val = pd.DataFrame(data)

# sum of all salary
val['total'] = val['age'].sum()

val

Output: