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 dataReturns: 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.
# 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:
# importing pandas module import pandas as pd # making data frame csv at url # sum of all salary val = data['Salary'].sum() val |
Output:
2159837111.0
Code #3:
# 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:
