groupby

https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.groupby.html

In [1]:

import pandas as pd

In [2]:

sales_df = pd.read_csv('Online Retail.csv', encoding= 'unicode_escape')

In [12]:

sales_df.head()

Out[12]:

	InvoiceNo	StockCode	Description	Quantity	InvoiceDate	UnitPrice	CustomerID	Country
0	536365	85123A	WHITE HANGING HEART T-LIGHT HOLDER	6	2010-12-01 08:26:00	2.55	17850.0	United Kingdom
1	536365	71053	WHITE METAL LANTERN	6	2010-12-01 08:26:00	3.39	17850.0	United Kingdom
2	536365	84406B	CREAM CUPID HEARTS COAT HANGER	8	2010-12-01 08:26:00	2.75	17850.0	United Kingdom
3	536365	84029G	KNITTED UNION FLAG HOT WATER BOTTLE	6	2010-12-01 08:26:00	3.39	17850.0	United Kingdom
4	536365	84029E	RED WOOLLY HOTTIE WHITE HEART.	6	2010-12-01 08:26:00	3.39	17850.0	United Kingdom

In [29]:

sales_df.set_index(keys = ['InvoiceDate'], inplace=True)
sales_df

Out[29]:

	InvoiceNo	StockCode	Description	Quantity	UnitPrice	CustomerID	Country
InvoiceDate
2010-12-01 08:26:00	536365	85123A	WHITE HANGING HEART T-LIGHT HOLDER	6	2.55	17850.0	United Kingdom
2010-12-01 08:26:00	536365	71053	WHITE METAL LANTERN	6	3.39	17850.0	United Kingdom
2010-12-01 08:26:00	536365	84406B	CREAM CUPID HEARTS COAT HANGER	8	2.75	17850.0	United Kingdom
2010-12-01 08:26:00	536365	84029G	KNITTED UNION FLAG HOT WATER BOTTLE	6	3.39	17850.0	United Kingdom
2010-12-01 08:26:00	536365	84029E	RED WOOLLY HOTTIE WHITE HEART.	6	3.39	17850.0	United Kingdom
...	...	...	...	...	...	...	...
2011-12-09 12:50:00	581587	22613	PACK OF 20 SPACEBOY NAPKINS	12	0.85	12680.0	France
2011-12-09 12:50:00	581587	22899	CHILDREN'S APRON DOLLY GIRL	6	2.10	12680.0	France
2011-12-09 12:50:00	581587	23254	CHILDRENS CUTLERY DOLLY GIRL	4	4.15	12680.0	France
2011-12-09 12:50:00	581587	23255	CHILDRENS CUTLERY CIRCUS PARADE	4	4.15	12680.0	France
2011-12-09 12:50:00	581587	22138	BAKING SET 9 PIECE RETROSPOT	3	4.95	12680.0	France

541909 rows × 7 columns

In [30]:

sales_df['Country'].unique()

Out[30]:

array(['United Kingdom', 'France', 'Australia', 'Netherlands', 'Germany',
       'Norway', 'EIRE', 'Switzerland', 'Spain', 'Poland', 'Portugal',
       'Italy', 'Belgium', 'Lithuania', 'Japan', 'Iceland',
       'Channel Islands', 'Denmark', 'Cyprus', 'Sweden', 'Austria',
       'Israel', 'Finland', 'Bahrain', 'Greece', 'Hong Kong', 'Singapore',
       'Lebanon', 'United Arab Emirates', 'Saudi Arabia',
       'Czech Republic', 'Canada', 'Unspecified', 'Brazil', 'USA',
       'European Community', 'Malta', 'RSA'], dtype=object)

In [33]:

# Multi-index DataFrame
sales_df = pd.read_csv('Online Retail.csv', encoding = 'unicode_escape')
sales_df.set_index(keys=['Country','InvoiceDate'], inplace = True)
sales_df

Out[33]:

		InvoiceNo	StockCode	Description	Quantity	UnitPrice	CustomerID
Country	InvoiceDate
United Kingdom	12/1/2010 8:26	536365	85123A	WHITE HANGING HEART T-LIGHT HOLDER	6	2.55	17850.0
	12/1/2010 8:26	536365	71053	WHITE METAL LANTERN	6	3.39	17850.0
	12/1/2010 8:26	536365	84406B	CREAM CUPID HEARTS COAT HANGER	8	2.75	17850.0
	12/1/2010 8:26	536365	84029G	KNITTED UNION FLAG HOT WATER BOTTLE	6	3.39	17850.0
	12/1/2010 8:26	536365	84029E	RED WOOLLY HOTTIE WHITE HEART.	6	3.39	17850.0
...	...	...	...	...	...	...	...
France	12/9/2011 12:50	581587	22613	PACK OF 20 SPACEBOY NAPKINS	12	0.85	12680.0
	12/9/2011 12:50	581587	22899	CHILDREN'S APRON DOLLY GIRL	6	2.10	12680.0
	12/9/2011 12:50	581587	23254	CHILDRENS CUTLERY DOLLY GIRL	4	4.15	12680.0
	12/9/2011 12:50	581587	23255	CHILDRENS CUTLERY CIRCUS PARADE	4	4.15	12680.0
	12/9/2011 12:50	581587	22138	BAKING SET 9 PIECE RETROSPOT	3	4.95	12680.0

541909 rows × 6 columns

In [34]:

# sort countries
sales_df.sort_index(inplace = True)
sales_df

Out[34]:

		InvoiceNo	StockCode	Description	Quantity	UnitPrice	CustomerID
Country	InvoiceDate
Australia	1/10/2011 9:58	540557	22523	CHILDS GARDEN FORK PINK	96	0.85	12415.0
	1/11/2011 9:47	540700	21581	SKULLS DESIGN COTTON TOTE BAG	6	2.25	12393.0
	1/11/2011 9:47	540700	22619	SET OF 6 SOLDIER SKITTLES	8	3.75	12393.0
	1/11/2011 9:47	540700	84997B	RED 3 PIECE RETROSPOT CUTLERY SET	6	3.75	12393.0
	1/11/2011 9:47	540700	20727	LUNCH BAG BLACK SKULL.	20	1.65	12393.0
...	...	...	...	...	...	...	...
Unspecified	9/2/2011 12:17	565303	85227	SET OF 6 3D KIT CARDS FOR KIDS	4	0.85	NaN
	9/2/2011 12:17	565303	22138	BAKING SET 9 PIECE RETROSPOT	2	4.95	NaN
	9/2/2011 12:17	565303	21889	WOODEN BOX OF DOMINOES	5	1.25	NaN
	9/2/2011 12:17	565303	22550	HOLIDAY FUN LUDO	2	3.75	NaN
	9/2/2011 12:17	565303	23127	FELTCRAFT GIRL NICOLE KIT	1	4.95	NaN

541909 rows × 6 columns

In [35]:

# access any element
sales_df.index

Out[35]:

MultiIndex([(  'Australia', '1/10/2011 9:58'),
            (  'Australia', '1/11/2011 9:47'),
            (  'Australia', '1/11/2011 9:47'),
            (  'Australia', '1/11/2011 9:47'),
            (  'Australia', '1/11/2011 9:47'),
            (  'Australia', '1/11/2011 9:47'),
            (  'Australia', '1/11/2011 9:47'),
            (  'Australia', '1/11/2011 9:47'),
            (  'Australia', '1/11/2011 9:47'),
            (  'Australia', '1/11/2011 9:47'),
            ...
            ('Unspecified', '9/2/2011 12:17'),
            ('Unspecified', '9/2/2011 12:17'),
            ('Unspecified', '9/2/2011 12:17'),
            ('Unspecified', '9/2/2011 12:17'),
            ('Unspecified', '9/2/2011 12:17'),
            ('Unspecified', '9/2/2011 12:17'),
            ('Unspecified', '9/2/2011 12:17'),
            ('Unspecified', '9/2/2011 12:17'),
            ('Unspecified', '9/2/2011 12:17'),
            ('Unspecified', '9/2/2011 12:17')],
           names=['Country', 'InvoiceDate'], length=541909)

In [36]:

sales_df.index.names

Out[36]:

FrozenList(['Country', 'InvoiceDate'])

In [37]:

# multiindex objects
type(sales_df.index)

Out[37]:

pandas.core.indexes.multi.MultiIndex

In [38]:

sales_df.index[0]

Out[38]:

('Australia', '1/10/2011 9:58')

[Pandas][DataFrame][MultiIndex]S4_03_Multi_index_dataframe