Converting a Pandas GroupBy output from Series to DataFrame

Question

I\'m starting with input data like this

df1 = pandas.DataFrame( { 
    \"Name\" : [\"Alice\", \"Bob\", \"Mallory\", \"Mallory\", \"Bob\" , \"Mallory\"] , 
           


        

Answer 1

Simply, this should do the task:

import pandas as pd

grouped_df = df1.groupby( [ "Name", "City"] )

pd.DataFrame(grouped_df.size().reset_index(name = "Group_Count"))

Here, grouped_df.size() pulls up the unique groupby count, and reset_index() method resets the name of the column you want it to be. Finally, the pandas Dataframe() function is called upon to create a DataFrame object.

Answer 2

I found this worked for me.

import numpy as np
import pandas as pd

df1 = pd.DataFrame({ 
    "Name" : ["Alice", "Bob", "Mallory", "Mallory", "Bob" , "Mallory"] , 
    "City" : ["Seattle", "Seattle", "Portland", "Seattle", "Seattle", "Portland"]})

df1['City_count'] = 1
df1['Name_count'] = 1

df1.groupby(['Name', 'City'], as_index=False).count()

Answer 3

Below solution may be simpler:

df1.reset_index().groupby( [ "Name", "City"],as_index=False ).count()