(cache)Introduction to Pandas — Introducing DataFrames

Introduction to Pandas

3 of 13 · Introducing DataFrames

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On the previous screen, we discovered that our data has 500 rows, 16 columns, and is stored as a pandas.core.frame.DataFrame object — or simply a DataFrame, which is the main pandas data structure.

Remember, one of the key features that makes pandas perfect for working with data is its support for string columns and row labels:

Axis values can have string labels, not just numeric ones.
DataFrames can contain columns with multiple data types, including: integer, float, string, and datetime.

Viewing Rows from the Top

Now, let's see this in action! To get a look at the first few rows of our DataFrame, we can use the DataFrame.head() method. By default, it returns the first five rows of our DataFrame. However, it also accepts an optional integer argument, which specifies the number of rows to display:

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print(f500.head(3))

This displays a neat table showing the top 3 companies in the dataset.

	rank	revenues	revenue_change	profits	assets	profit_change	ceo	industry	sector	previous_rank	country	hq_location	website	years_on_global_500_list	employees	total_stockholder_equity
Walmart	1	485873	0.8	13643.0	198825	-7.2	C. Douglas McMillon	General Merchandisers	Retailing	1	USA	Bentonville, AR	http://www.walmart.com	23	2300000	77798
State Grid	2	315199	-4.4	9571.3	489838	-6.2	Kou Wei	Utilities	Energy	2	China	Beijing, China	http://www.sgcc.com.cn	17	926067	209456
Sinopec Group	3	267518	-9.1	1257.9	310726	-65.0	Wang Yupu	Petroleum Refining	Energy	4	China	Beijing, China	http://www.sinopec.com	19	713288	106523

Viewing Rows from the Bottom

Similarly, we can use the DataFrame.tail() method to reveal the last rows of our DataFrame:

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print(f500.tail(3))

This presents another table, this time showcasing the bottom 3 companies in the dataset.

	rank	revenues	revenue_change	profits	assets	profit_change	ceo	industry	sector	previous_rank	country	hq_location	website	years_on_global_500_list	employees	total_stockholder_equity
Wm. Morrison Supermarkets	498	21741	-11.3	406.4	11630	20.4	David T. Potts	Food and Drug Stores	Food & Drug Stores	437	Britain	Bradford, Britain	http://www.morrisons.com	13	77210	5111
TUI	499	21655	-5.5	1151.7	16247	195.5	Friedrich Joussen	Travel Services	Business Services	467	Germany	Hanover, Germany	http://www.tuigroup.com	23	66779	3006
AutoNation	500	21609	3.6	430.5	10060	-2.7	Michael J. Jackson	Specialty Retailers	Retailing	0	USA	Fort Lauderdale, FL	http://www.autonation.com	12	26000	2310

The f500 variable we created on the previous screen is available to you on this screen.

Now let's practice using these methods to explore the top and bottom Fortune 500 companies!

Instructions

Use the DataFrame.head() method to select the first 6 rows. Assign the result to f500_top_6.
Use the DataFrame.tail() method to select the last 8 rows. Assign the result to f500_bottom_8.
Use the variable inspector to view the two DataFrames created above. The variable inspector uses HTML to create a nicely formatted version of the data.

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