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" (http://archive.is/e20r8#60.7%) section of the extensive official documentation. Combining is covered by the "Merge, join, concatenate" (http://archive.is/f5FsP) section there.
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)
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:
check_q1(pd.DataFrame())
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!
Look at your data by running the cell below:
reviews.head()
Exercise 1: region_1 and region_2 are pretty uninformative names for locale columns in the dataset. Rename these columns to region and locale.
reviews.rename(columns={'region_1': 'region', 'region_2': 'locale'})
print(check_q1(reviews.rename(columns={'region_1': 'region', 'region_2': 'locale'})))
print('-------')
print(answer_q1())
Exercise 2: Set the index name in the dataset to wines.
reviews.rename_axis("wines", axis='rows')
print(check_q2(reviews.rename_axis("wines", axis='rows')))
print('-------')
print(answer_q2())
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:
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"
pd.concat([gaming_products, movie_products])
print(check_q3(pd.concat([gaming_products, movie_products])))
print('-------')
print(answer_q3())
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:
powerlifting_meets = pd.read_csv("../input/powerlifting-database/meets.csv")
powerlifting_competitors = pd.read_csv("../input/powerlifting-database/openpowerlifting.csv")
powerlifting_meets.set_index("MeetID").join(powerlifting_competitors.set_index("MeetID"))
print(check_q4(powerlifting_meets.set_index("MeetID").join(powerlifting_competitors.set_index("MeetID"))))
print('-------')
print(answer_q4())