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: int64df.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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