How to perform pandas groupby() and sum()?

Pandas is a special tool that allows us to perform complex manipulations of data effectively and efficiently. Inside pandas, we mostly deal with a dataset in the form of DataFrame. DataFrames are 2-dimensional data structures in pandas. DataFrames consist of rows, columns, and the data. DataFrame can be created with the help of python dictionaries or lists but in the real world, CSV files are imported and then converted into DataFrames.

pandas.DataFrame.groupby() Method

On the other hand, groupby() is a simple but very useful concept in pandas. By using groupby(), we can create a grouping of certain values and perform some operations on those values.

The groupby() function split the object, apply some operations, and then combines them to create a group, hence large amount of data and computations can be performed on these groups.

One of the simplest methods on groupby objects is the sum() method.

Syntax:

DataFrame.groupby(
    by=None, 
    axis=0, 
    level=None, 
    as_index=True, 
    sort=True, 
    group_keys=True, 
    squeeze=NoDefault.no_default, 
    observed=False, 
    dropna=True
    )

To work with pandas, we need to import pandas package first, below is the syntax:

import pandas as pd

# Importing pandas package import pandas as pd # creating a dictionary of cricketers d = { "Players":['Sachin','Ganguly','Dravid','Yuvraj','Dhoni','Kohli', 'Sachin','Ganguly','Dravid','Yuvraj','Dhoni','Kohli'], "Format":['Test','Test','Test','Test','Test','Test', 'ODI','ODI','ODI','ODI','ODI','ODI'], "Runs":[15921,7212,13228,1900,4876,8043, 18426,11363,10889,8701,10773,12311] } # Now we will create DataFrame df = pd.DataFrame(d) # Viewing the DataFrame print("DataFrame:\n",df,"\n\n") # Performing sum on groupby on Players with runs result = df.groupby(['Players']).sum() # Display Result print("Grouped result:\n",result)

How to perform pandas groupby() and sum()?

pandas.DataFrame.groupby() Method

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