Renaming and combining workbook

Introduction

This is the worbook part of the "Renaming and combining" section of the Advanced Pandas tutorial. For the reference section, click here.

Renaming is covered in its own section in the "Essential Basic Functionality" section of the extensive official documentation. Combining is covered by the "Merge, join, concatenate" section there.

In [1]:
import pandas as pd

from learntools.advanced_pandas.renaming_and_combining import *

pd.set_option('max_rows', 5)
reviews = pd.read_csv("../input/wine-reviews/winemag-data-130k-v2.csv", index_col=0)

Checking Answers

Check your answers in each exercise using the check_qN function (replacing N with the number of the exercise). For example here's how you would check an incorrect answer to exercise 1:

In [2]:
check_q1(pd.DataFrame())
Out[2]:
False

If you get stuck, use the answer_qN function to see the code with the correct answer.

For the first set of questions, running the check_qN on the correct answer returns True.

For the second set of questions, using this function to check a correct answer will present an informative graph!

Exercises

Look at your data by running the cell below:

In [3]:
reviews.head()
Out[3]:
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

Exercise 1: region_1 and region_2 are pretty uninformative names for locale columns in the dataset. Rename these columns to region and locale.

In [4]:
# Your code here

Exercise 2: Set the index name in the dataset to wines.

In [5]:
# Your code here

Exercise 3: The Things on Reddit dataset includes product links from a selection of top-ranked forums ("subreddits") on Reddit.com. Create a DataFrame of products mentioned on either subreddit. Use the following data:

In [6]:
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"
In [7]:
# Your code here

Exercise 4: The Powerlifting Database dataset on Kaggle includes one CSV table for powerlifting meets and a separate one for powerlifting competitors. 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. Use the following data:

In [8]:
powerlifting_meets = pd.read_csv("../input/powerlifting-database/meets.csv")
powerlifting_competitors = pd.read_csv("../input/powerlifting-database/openpowerlifting.csv")
In [9]:
# Your code here

Keep going

Continue to the method chaining workbook.