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

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Introduction

Now you are ready to get a deeper understanding of your data.

Run the following cell to load your data and some utility functions (including code to check your answers).

import pandas as pd
pd.set_option("display.max_rows", 5)
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.summary_functions_and_maps import *
print("Setup complete.")

reviews.head()

Setup complete.

	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.

What is the median of the points column in the reviews DataFrame?

median_points = reviews.points.median()

# Check your answer
q1.check()

Correct

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

Solution:

median_points = reviews.points.median()

2.

What countries are represented in the dataset? (Your answer should not include any duplicates.)

countries = reviews.country.unique()

# Check your answer
q2.check()

Correct

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

3.

How often does each country appear in the dataset? Create a Series reviews_per_country mapping countries to the count of reviews of wines from that country.

reviews_per_country = reviews.country.value_counts()

# Check your answer
q3.check()

Correct

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

4.

Create variable centered_price containing a version of the price column with the mean price subtracted.

(Note: this 'centering' transformation is a common preprocessing step before applying various machine learning algorithms.)

mean_price = reviews.price.mean()
centered_price = reviews.price.map(lambda r: r - mean_price)

# Check your answer
q4.check()

Correct

q4.hint()
#q4.solution()

Hint: To get the mean of a column in a Pandas DataFrame, use the mean function.

5.

I'm an economical wine buyer. Which wine is the "best bargain"? Create a variable bargain_wine with the title of the wine with the highest points-to-price ratio in the dataset.

bargain_idmax = (reviews.points / reviews.price).idxmax()
bargain_wine = reviews.loc[bargain_idmax, 'title']

# Check your answer
q5.check()

Correct

q5.hint()
q5.solution()

Hint: The idxmax method may be useful here.

Solution:

bargain_idx = (reviews.points / reviews.price).idxmax()
bargain_wine = reviews.loc[bargain_idx, 'title']

6.

There are only so many words you can use when describing a bottle of wine. Is a wine more likely to be "tropical" or "fruity"? Create a Series descriptor_counts counting how many times each of these two words appears in the description column in the dataset.

reviews.description.head()

0    Aromas include tropical fruit, broom, brimston...
1    This is ripe and fruity, a wine that is smooth...
2    Tart and snappy, the flavors of lime flesh and...
3    Pineapple rind, lemon pith and orange blossom ...
4    Much like the regular bottling from 2012, this...
Name: description, dtype: object

# descriptor_counts = reviews.description.map(lambda r: 'tropical' in r or 'fruity' in r).value_counts()
n_tropical = reviews.description.map(lambda d: 'tropical' in d).sum()
n_fruit = reviews.description.map(lambda d: 'fruity' in d).sum()
descriptor_counts = pd.Series([n_tropical, n_fruit], index=['tropical', 'fruity'])

# Check your answer
q6.check()

Correct

q6.hint()
q6.solution()

Hint: Use a map to check each description for the string tropical, then count up the number of times this is True. Repeat this for fruity. Finally, create a Series combining the two values.

Solution:

n_trop = reviews.description.map(lambda desc: "tropical" in desc).sum()
n_fruity = reviews.description.map(lambda desc: "fruity" in desc).sum()
descriptor_counts = pd.Series([n_trop, n_fruity], index=['tropical', 'fruity'])

7.

We'd like to host these wine reviews on our website, but a rating system ranging from 80 to 100 points is too hard to understand - we'd like to translate them into simple star ratings. A score of 95 or higher counts as 3 stars, a score of at least 85 but less than 95 is 2 stars. Any other score is 1 star.

Also, the Canadian Vintners Association bought a lot of ads on the site, so any wines from Canada should automatically get 3 stars, regardless of points.

Create a series star_ratings with the number of stars corresponding to each review in the dataset.

reviews[reviews.country == 'Canada']

	country	description	designation	points	price	province	region_1	region_2	taster_name	taster_twitter_handle	title	variety	winery
454	Canada	An aromatic knockout with notes of peach, papa...	Reserve Icewine	92	30.0	Ontario	Niagara-On-The-Lake	NaN	Sean P. Sullivan	@wawinereport	Pillitteri 2012 Reserve Icewine Vidal (Niagara...	Vidal	Pillitteri
2616	Canada	A slightly earthy, spicy nose leads, followed ...	Fusion	83	12.0	Ontario	Niagara Peninsula	NaN	Susan Kostrzewa	@suskostrzewa	Pillitteri 2004 Fusion Gewürztraminer-Riesling...	Gewürztraminer-Riesling	Pillitteri
...	...	...	...	...	...	...	...	...	...	...	...	...	...
129528	Canada	A delicious though somewhat reserved wine with...	Icewine	89	45.0	Ontario	Niagara Peninsula	NaN	Sean P. Sullivan	@wawinereport	Henry of Pelham 2011 Icewine Vidal (Niagara Pe...	Vidal	Henry of Pelham
129581	Canada	Smooth and engaging, this offers classic varie...	NaN	90	38.0	British Columbia	Okanagan Valley	NaN	Paul Gregutt	@paulgwine	Burrowing Owl 2013 Syrah (Okanagan Valley)	Syrah	Burrowing Owl

257 rows × 13 columns

# este codigo me pasaba como correcto y esta mal 🤷‍
# def calculate_stars_from_points(points):
#     if points > 94:
#         return 3
#     if points > 84:
#         return 2
#     return 1

# star_ratings = reviews.points.map(calculate_stars_from_points)

def calculate_stars(row):
    if row.country == 'Canada':
        return 3
    if row.points > 94:
        return 3
    if row.points > 84:
        return 2
    return 1

star_ratings = reviews.apply(calculate_stars, axis='columns')



# Check your answer
q7.check()

Correct

#q7.hint()
q7.solution()

Solution:

def stars(row):
    if row.country == 'Canada':
        return 3
    elif row.points >= 95:
        return 3
    elif row.points >= 85:
        return 2
    else:
        return 1

star_ratings = reviews.apply(stars, axis='columns')

Keep going

Continue to grouping and sorting.

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