================ by Jawad Haider

04 - Groupby¶

Copyright Qalmaqihir For more information, visit us at www.github.com/qalmaqihir/

1 Groupby
2 Great Job! Thats the end of this part.

Groupby¶

The groupby method allows you to group rows of data together and call aggregate functions

import pandas as pd
# Create dataframe
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

	Company	Person	Sales
0	GOOG	Sam	200
1	GOOG	Charlie	120
2	MSFT	Amy	340
3	MSFT	Vanessa	124
4	FB	Carl	243
5	FB	Sarah	350

Now you can use the .groupby() method to group rows together based off of a column name.
For instance let’s group based off of Company. This will create a DataFrameGroupBy object:

df.groupby('Company')

<pandas.core.groupby.DataFrameGroupBy object at 0x113014128>

You can save this object as a new variable:

by_comp = df.groupby("Company")

And then call aggregate methods off the object:

by_comp.mean()

	Sales
Company
FB	296.5
GOOG	160.0
MSFT	232.0

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

	Sales
Company
FB	296.5
GOOG	160.0
MSFT	232.0

More examples of aggregate methods:

by_comp.std()

	Sales
Company
FB	75.660426
GOOG	56.568542
MSFT	152.735065

by_comp.min()

	Person	Sales
Company
FB	Carl	243
GOOG	Charlie	120
MSFT	Amy	124

by_comp.max()

	Person	Sales
Company
FB	Sarah	350
GOOG	Sam	200
MSFT	Vanessa	340

by_comp.count()

	Person	Sales
Company
FB	2	2
GOOG	2	2
MSFT	2	2

by_comp.describe()

		Sales
Company
FB	count	2.000000
	mean	296.500000
	std	75.660426
	min	243.000000
	25%	269.750000
	50%	296.500000
	75%	323.250000
	max	350.000000
GOOG	count	2.000000
	mean	160.000000
	std	56.568542
	min	120.000000
	25%	140.000000
	50%	160.000000
	75%	180.000000
	max	200.000000
MSFT	count	2.000000
	mean	232.000000
	std	152.735065
	min	124.000000
	25%	178.000000
	50%	232.000000
	75%	286.000000
	max	340.000000

by_comp.describe().transpose()

Company	FB								GOOG					MSFT
	count	mean	std	min	25%	50%	75%	max	count	mean	...	75%	max	count	mean	std	min	25%	50%	75%	max
Sales	2.0	296.5	75.660426	243.0	269.75	296.5	323.25	350.0	2.0	160.0	...	180.0	200.0	2.0	232.0	152.735065	124.0	178.0	232.0	286.0	340.0

1 rows × 24 columns

by_comp.describe().transpose()['GOOG']

	count	mean	std	min	25%	50%	75%	max
Sales	2.0	160.0	56.568542	120.0	140.0	160.0	180.0	200.0

Great Job! Thats the end of this part.¶

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