This notebook is an exercise in the Pandas course. You can reference the tutorial at this link.

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Introduction

Run the following cell to load your data and some utility functions.

import pandas as pd

reviews = pd.read_csv("../input/wine-reviews/winemag-data-130k-v2.csv", index_col=0)

from learntools.core import binder; binder.bind(globals())
from learntools.pandas.renaming_and_combining import *
print("Setup complete.")

Setup complete.

Exercises

View the first several lines of your data by running the cell below:

reviews.head()

	country	description	designation	points	price	province	region_1	region_2	taster_name	taster_twitter_handle	title	variety	winery
0	Italy	Aromas include tropical fruit, broom, brimston...	Vulkà Bianco	87	NaN	Sicily & Sardinia	Etna	NaN	Kerin O’Keefe	@kerinokeefe	Nicosia 2013 Vulkà Bianco (Etna)	White Blend	Nicosia
1	Portugal	This is ripe and fruity, a wine that is smooth...	Avidagos	87	15.0	Douro	NaN	NaN	Roger Voss	@vossroger	Quinta dos Avidagos 2011 Avidagos Red (Douro)	Portuguese Red	Quinta dos Avidagos
2	US	Tart and snappy, the flavors of lime flesh and...	NaN	87	14.0	Oregon	Willamette Valley	Willamette Valley	Paul Gregutt	@paulgwine	Rainstorm 2013 Pinot Gris (Willamette Valley)	Pinot Gris	Rainstorm
3	US	Pineapple rind, lemon pith and orange blossom ...	Reserve Late Harvest	87	13.0	Michigan	Lake Michigan Shore	NaN	Alexander Peartree	NaN	St. Julian 2013 Reserve Late Harvest Riesling ...	Riesling	St. Julian
4	US	Much like the regular bottling from 2012, this...	Vintner's Reserve Wild Child Block	87	65.0	Oregon	Willamette Valley	Willamette Valley	Paul Gregutt	@paulgwine	Sweet Cheeks 2012 Vintner's Reserve Wild Child...	Pinot Noir	Sweet Cheeks

1.

region_1 and region_2 are pretty uninformative names for locale columns in the dataset. Create a copy of reviews with these columns renamed to region and locale, respectively.

# Your code here
renamed = renamed.rename(columns={'region_1': 'region', 'region_2': 'locale'})

# Check your answer
q1.check()

Correct

#q1.hint()
#q1.solution()

2.

Set the index name in the dataset to wines.

reindexed = reviews.rename_axis('wines')

# Check your answer
q2.check()

Correct

q2.hint()
#q2.solution()

Hint: Use the rename_axis() method.

3.

The Things on Reddit dataset includes product links from a selection of top-ranked forums ("subreddits") on reddit.com. Run the cell below to load a dataframe of products mentioned on the /r/gaming subreddit and another dataframe for products mentioned on the r//movies subreddit.

gaming_products = pd.read_csv("../input/things-on-reddit/top-things/top-things/reddits/g/gaming.csv")
gaming_products['subreddit'] = "r/gaming"
movie_products = pd.read_csv("../input/things-on-reddit/top-things/top-things/reddits/m/movies.csv")
movie_products['subreddit'] = "r/movies"

gaming_products.head()

	name	category	amazon_link	total_mentions	subreddit_mentions	subreddit
0	BOOMco Halo Covenant Needler Blaster	Toys & Games	https://www.amazon.com/BOOMco-Halo-Covenant-Ne...	4.0	4	r/gaming
1	Raspberry PI 3 Model B 1.2GHz 64-bit quad-core...	Electronics	https://www.amazon.com/Raspberry-Model-A1-2GHz...	19.0	3	r/gaming
2	CanaKit 5V 2.5A Raspberry Pi 3 Power Supply / ...	Electronics	https://www.amazon.com/CanaKit-Raspberry-Suppl...	7.0	3	r/gaming
3	Panasonic K-KJ17MCA4BA Advanced Individual Cel...	Electronics	https://www.amazon.com/Panasonic-Advanced-Indi...	29.0	2	r/gaming
4	Mayflash GameCube Controller Adapter for Wii U...	Electronics	https://www.amazon.com/GameCube-Controller-Ada...	24.0	2	r/gaming

movie_products.head()

	name	category	amazon_link	total_mentions	subreddit_mentions	subreddit
0	Marvel Cinematic Universe: Phase One - Avenger...	Movies & TV	https://www.amazon.com/Marvel-Cinematic-Univer...	4.0	3	r/movies
1	On Stranger Tides	Books	https://www.amazon.com/Stranger-Tides-Tim-Powe...	3.0	3	r/movies
2	Superintelligence: Paths, Dangers, Strategies	Books	https://www.amazon.com/Superintelligence-Dange...	7.0	2	r/movies
3	The Secret History of Star Wars	Books	https://www.amazon.com/Secret-History-Star-War...	4.0	2	r/movies
4	2D Glasses 4 Pack - Turns 3D movies back into ...	Electronics	https://www.amazon.com/gp/product/B00K9E7GCC	3.0	2	r/movies

Create a DataFrame of products mentioned on either subreddit.

combined_products = pd.concat([gaming_products, movie_products])

# Check your answer
q3.check()

Correct

combined_products.head()

	name	category	amazon_link	total_mentions	subreddit_mentions	subreddit
0	BOOMco Halo Covenant Needler Blaster	Toys & Games	https://www.amazon.com/BOOMco-Halo-Covenant-Ne...	4.0	4	r/gaming
1	Raspberry PI 3 Model B 1.2GHz 64-bit quad-core...	Electronics	https://www.amazon.com/Raspberry-Model-A1-2GHz...	19.0	3	r/gaming
2	CanaKit 5V 2.5A Raspberry Pi 3 Power Supply / ...	Electronics	https://www.amazon.com/CanaKit-Raspberry-Suppl...	7.0	3	r/gaming
3	Panasonic K-KJ17MCA4BA Advanced Individual Cel...	Electronics	https://www.amazon.com/Panasonic-Advanced-Indi...	29.0	2	r/gaming
4	Mayflash GameCube Controller Adapter for Wii U...	Electronics	https://www.amazon.com/GameCube-Controller-Ada...	24.0	2	r/gaming

combined_products.tail()

	name	category	amazon_link	total_mentions	subreddit_mentions	subreddit
298	Welcome to Night Vale CD: A Novel	Books	https://www.amazon.com/Welcome-Night-Vale-CD-N...	1.0	1	r/movies
299	Ran (StudioCanal Collection) [Blu-ray]	Movies & TV	https://www.amazon.com/StudioCanal-Collection-...	1.0	1	r/movies
300	The Art of John Alvin	Books	https://www.amazon.com/Art-John-Alvin-Andrea/d...	1.0	1	r/movies
301	Apocalypto [Blu-ray]	Movies & TV	https://www.amazon.com/Apocalypto-Blu-ray-Rudy...	1.0	1	r/movies
302	Cinelinx: A Card Game for People Who Love Movi...	Toys & Games	https://www.amazon.com/Cinelinx-Card-Game-Peop...	1.0	1	r/movies

#q3.hint()
#q3.solution()

4.

The Powerlifting Database dataset on Kaggle includes one CSV table for powerlifting meets and a separate one for powerlifting competitors. Run the cell below to load these datasets into dataframes:

powerlifting_meets = pd.read_csv("../input/powerlifting-database/meets.csv")
powerlifting_competitors = pd.read_csv("../input/powerlifting-database/openpowerlifting.csv")

Both tables include references to a MeetID, a unique key for each meet (competition) included in the database. Using this, generate a dataset combining the two tables into one.

powerlifting_meets.head()

	MeetID	MeetPath	Federation	Date	MeetCountry	MeetState	MeetTown	MeetName
0	0	365strong/1601	365Strong	2016-10-29	USA	NC	Charlotte	2016 Junior & Senior National Powerlifting Cha...
1	1	365strong/1602	365Strong	2016-11-19	USA	MO	Ozark	Thanksgiving Powerlifting Classic
2	2	365strong/1603	365Strong	2016-07-09	USA	NC	Charlotte	Charlotte Europa Games
3	3	365strong/1604	365Strong	2016-06-11	USA	SC	Rock Hill	Carolina Cup Push Pull Challenge
4	4	365strong/1605	365Strong	2016-04-10	USA	SC	Rock Hill	Eastern USA Challenge

powerlifting_competitors.head()

	MeetID	Name	Sex	Equipment	Age	Division	BodyweightKg	WeightClassKg	Squat4Kg	BestSquatKg	Bench4Kg	BestBenchKg	Deadlift4Kg	BestDeadliftKg	TotalKg	Place	Wilks
0	0	Angie Belk Terry	F	Wraps	47.0	Mst 45-49	59.60	60	NaN	47.63	NaN	20.41	NaN	70.31	138.35	1	155.05
1	0	Dawn Bogart	F	Single-ply	42.0	Mst 40-44	58.51	60	NaN	142.88	NaN	95.25	NaN	163.29	401.42	1	456.38
2	0	Dawn Bogart	F	Single-ply	42.0	Open Senior	58.51	60	NaN	142.88	NaN	95.25	NaN	163.29	401.42	1	456.38
3	0	Dawn Bogart	F	Raw	42.0	Open Senior	58.51	60	NaN	NaN	NaN	95.25	NaN	NaN	95.25	1	108.29
4	0	Destiny Dula	F	Raw	18.0	Teen 18-19	63.68	67.5	NaN	NaN	NaN	31.75	NaN	90.72	122.47	1	130.47

meets = powerlifting_meets.set_index('MeetID')
competitors = powerlifting_competitors.set_index('MeetID')
powerlifting_combined = meets.join(competitors)

# Check your answer
q4.check()

Correct

powerlifting_combined.columns

Index(['MeetPath', 'Federation', 'Date', 'MeetCountry', 'MeetState',
       'MeetTown', 'MeetName', 'Name', 'Sex', 'Equipment', 'Age', 'Division',
       'BodyweightKg', 'WeightClassKg', 'Squat4Kg', 'BestSquatKg', 'Bench4Kg',
       'BestBenchKg', 'Deadlift4Kg', 'BestDeadliftKg', 'TotalKg', 'Place',
       'Wilks'],
      dtype='object')

#q4.hint()
q4.solution()

Solution:

powerlifting_combined = powerlifting_meets.set_index("MeetID").join(powerlifting_competitors.set_index("MeetID"))

Congratulations!

You've finished the Pandas micro-course. Many data scientists feel efficiency with Pandas is the most useful and practical skill they have, because it allows you to progress quickly in any project you have.

If you'd like to apply your new skills to examining geospatial data, you're encouraged to check out our Geospatial Analysis micro-course.

You can also take advantage of your Pandas skills by entering a Kaggle Competition or by answering a question you find interesting using Kaggle Datasets.

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