Pandas — Group By

Grouping large amounts of data and compute operations on these groups — #PySeries#Episode 12

print(~Hello Pandas — Group By lesson!”)

Preparing the DataFrame:

import numpy as np
import pandas as pd
data = { 'Company':['GOOG', 'GOOG' 'MSFT', 'MSFT', 'FB', 'FB',
'Person':['Sam', 'Charlie', 'Amy', 'Vanessa', 'Carl', 'Sarah'],'Sales':[200,120, 340, 124, 243, 350]}
df = pd.DataFrame(data)

df

Aggregating Functions:

Allows you to group together rows based off of a column and perform an aggregate function on them.

df.groupby('Company')
byComp = df.groupby('Company')
byComp.mean()

byComp.std()

byComp.sum()

byComp.sum().loc['FB']

Sales    593 Name: FB, dtype: int64

In one single line!

df.groupby('Company').sum().loc['FB']

Sales    593 Name: FB, dtype: int64

df.groupby('Company').count()

df.groupby('Company').max()

We probably use these aggregate functions with the numeric number (here is inverse of alphabet order:)

df.groupby('Company').min()

That will give you a bunch of information!

df.groupby('Company').describe()

df.groupby(‘Company’).describe().transpose()[‘FB’]

Colab File link:)

Credits & References:

Jose Portilla — Python for Data Science and Machine Learning Bootcamp — Learn how to use NumPy, Pandas, Seaborn , Matplotlib , Plotly , Scikit-Learn , Machine Learning, Tensorflow , and more!

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