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

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Introduction

In these exercises we'll apply groupwise analysis to our dataset.

Run the code cell below to load the data before running the exercises.

import pandas as pd

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

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

Setup complete.

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

Exercises

1.

Who are the most common wine reviewers in the dataset? Create a Series whose index is the taster_twitter_handle category from the dataset, and whose values count how many reviews each person wrote.

# Your code here
reviews_written = reviews.groupby('taster_twitter_handle').taster_twitter_handle.count()
# reviews_written = reviews.groupby('taster_twitter_handle').size()


# Check your answer
q1.check()

Correct:

reviews_written = reviews.groupby('taster_twitter_handle').size()

or

reviews_written = reviews.groupby('taster_twitter_handle').taster_twitter_handle.count()

q1.hint()
#q1.solution()

Hint: Use the groupby operation and size() (or count()).

2.

What is the best wine I can buy for a given amount of money? Create a Series whose index is wine prices and whose values is the maximum number of points a wine costing that much was given in a review. Sort the values by price, ascending (so that 4.0 dollars is at the top and 3300.0 dollars is at the bottom).

best_rating_per_price = reviews.groupby('price').points.max()

# Check your answer
q2.check()

Correct

#q2.hint()
#q2.solution()

3.

What are the minimum and maximum prices for each variety of wine? Create a DataFrame whose index is the variety category from the dataset and whose values are the min and max values thereof.

price_extremes = reviews.groupby('variety').price.agg(['min', 'max'])

# Check your answer
q3.check()

Correct

q3.hint()
q3.solution()

Hint: Use agg().

Solution:

price_extremes = reviews.groupby('variety').price.agg([min, max])

4.

What are the most expensive wine varieties? Create a variable sorted_varieties containing a copy of the dataframe from the previous question where varieties are sorted in descending order based on minimum price, then on maximum price (to break ties).

sorted_varieties = price_extremes.sort_values(['min', 'max'], ascending=False)

# Check your answer
q4.check()

Correct

#q4.hint()
#q4.solution()

5.

Create a Series whose index is reviewers and whose values is the average review score given out by that reviewer. Hint: you will need the taster_name and points columns.

reviewer_mean_ratings = reviews.groupby('taster_name').points.mean()

# Check your answer
q5.check()

85.855421686747

Correct

#q5.hint()
#q5.solution()

Are there significant differences in the average scores assigned by the various reviewers? Run the cell below to use the describe() method to see a summary of the range of values.

reviewer_mean_ratings.describe()

count    19.000000
mean     88.233026
std       1.243610
min      85.855422
25%      87.323501
50%      88.536235
75%      88.975256
max      90.562551
Name: points, dtype: float64

6.

What combination of countries and varieties are most common? Create a Series whose index is a MultiIndexof {country, variety} pairs. For example, a pinot noir produced in the US should map to {"US", "Pinot Noir"}. Sort the values in the Series in descending order based on wine count.

country_variety_counts = reviews.groupby(['country', 'variety']).size().sort_values(ascending=False)

# Check your answer
q6.check()

Correct

q6.hint()
#q6.solution()

Hint: Use groupby(), and provide a list of columns to group by. You may also find size() and sort_values() to be useful!

Keep going

Move on to the data types and missing data.

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