Pandas Indexing, Selecting and assigning

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

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Introduction

In this set of exercises we will work with the Wine Reviews dataset.

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

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.indexing_selecting_and_assigning import *
print("Setup complete.")

Setup complete.

Look at an overview of your data by running the following line.

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

reviews.columns

Index(['country', 'description', 'designation', 'points', 'price', 'province',
       'region_1', 'region_2', 'taster_name', 'taster_twitter_handle', 'title',
       'variety', 'winery'],
      dtype='object')

reviews.loc[reviews.country == 'Italy'].count()

country        19540
description    19540
               ...  
variety        19540
winery         19540
Length: 13, dtype: int64

Exercises

1.

Select the description column from reviews and assign the result to the variable desc.

# Your code here
desc = reviews['description']

# Check your answer
q1.check()

<IPython.core.display.Javascript object>

Correct

Follow-up question: what type of object is desc? If you’re not sure, you can check by calling Python’s type function: type(desc).

type(desc)

pandas.core.series.Series

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

2.

Select the first value from the description column of reviews, assigning it to variable first_description.

first_description = desc[0]

# Check your answer
q2.check()
first_description

<IPython.core.display.Javascript object>

Correct:

first_description = reviews.description.iloc[0]

Note that while this is the preferred way to obtain the entry in the DataFrame, many other options will return a valid result, such as reviews.description.loc[0], reviews.description[0], and more!

"Aromas include tropical fruit, broom, brimstone and dried herb. The palate isn't overly expressive, offering unripened apple, citrus and dried sage alongside brisk acidity."

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

3.

Select the first row of data (the first record) from reviews, assigning it to the variable first_row.

first_row = reviews.iloc[0]

# Check your answer
q3.check()
first_row

<IPython.core.display.Javascript object>

Correct

country                                                    Italy
description    Aromas include tropical fruit, broom, brimston...
                                     ...                        
variety                                              White Blend
winery                                                   Nicosia
Name: 0, Length: 13, dtype: object

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

4.

Select the first 10 values from the description column in reviews, assigning the result to variable first_descriptions.

Hint: format your output as a pandas Series.

first_descriptions = reviews['description'].iloc[0:10]

# Check your answer
q4.check()
first_descriptions

<IPython.core.display.Javascript object>

Correct:

first_descriptions = reviews.description.iloc[:10]

Note that many other options will return a valid result, such as desc.head(10) and reviews.loc[:9, "description"].

0    Aromas include tropical fruit, broom, brimston...
1    This is ripe and fruity, a wine that is smooth...
                           ...                        
8    Savory dried thyme notes accent sunnier flavor...
9    This has great depth of flavor with its fresh ...
Name: description, Length: 10, dtype: object

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

5.

Select the records with index labels 1, 2, 3, 5, and 8, assigning the result to the variable sample_reviews.

In other words, generate the following DataFrame:

sample_reviews = reviews.iloc[[1, 2, 3, 5, 8]]

# Check your answer
q5.check()
sample_reviews

<IPython.core.display.Javascript object>

Correct

	country	description	designation	points	price	province	region_1	region_2	taster_name	taster_twitter_handle	title	variety	winery
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
5	Spain	Blackberry and raspberry aromas show a typical...	Ars In Vitro	87	15.0	Northern Spain	Navarra	NaN	Michael Schachner	@wineschach	Tandem 2011 Ars In Vitro Tempranillo-Merlot (N...	Tempranillo-Merlot	Tandem
8	Germany	Savory dried thyme notes accent sunnier flavor...	Shine	87	12.0	Rheinhessen	NaN	NaN	Anna Lee C. Iijima	NaN	Heinz Eifel 2013 Shine Gewürztraminer (Rheinhe...	Gewürztraminer	Heinz Eifel

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

6.

Create a variable df containing the country, province, region_1, and region_2 columns of the records with the index labels 0, 1, 10, and 100. In other words, generate the following DataFrame:

df = reviews.loc[[0, 1, 10, 100], ['country', 'province', 'region_1', 'region_2']]

# Check your answer
q6.check()
df

<IPython.core.display.Javascript object>

Correct

	country	province	region_1	region_2
0	Italy	Sicily & Sardinia	Etna	NaN
1	Portugal	Douro	NaN	NaN
10	US	California	Napa Valley	Napa
100	US	New York	Finger Lakes	Finger Lakes

#q6.hint()
#q6.solution()

7.

Create a variable df containing the country and variety columns of the first 100 records.

Hint: you may use loc or iloc. When working on the answer this question and the several of the ones that follow, keep the following “gotcha” described in the tutorial:

iloc uses the Python stdlib indexing scheme, where the first element of the range is included and the last one excluded. loc, meanwhile, indexes inclusively.

This is particularly confusing when the DataFrame index is a simple numerical list, e.g. 0,...,1000. In this case df.iloc[0:1000] will return 1000 entries, while df.loc[0:1000] return 1001 of them! To get 1000 elements using loc, you will need to go one lower and ask for df.iloc[0:999].

df = reviews.loc[:99, ['country', 'variety']]

# Check your answer
q7.check()
df

<IPython.core.display.Javascript object>

Correct:

cols = ['country', 'variety']
df = reviews.loc[:99, cols]

or

cols_idx = [0, 11]
df = reviews.iloc[:100, cols_idx]

	country	variety
0	Italy	White Blend
1	Portugal	Portuguese Red
...	...	...
98	Italy	Sangiovese
99	US	Bordeaux-style Red Blend

100 rows × 2 columns

#q7.hint()
#q7.solution()

8.

Create a DataFrame italian_wines containing reviews of wines made in Italy. Hint: reviews.country equals what?

italian_wines = reviews[reviews.country == 'Italy']

# Check your answer
q8.check()

<IPython.core.display.Javascript object>

Correct

#q8.hint()
#q8.solution()

9.

Create a DataFrame top_oceania_wines containing all reviews with at least 95 points (out of 100) for wines from Australia or New Zealand.

top_oceania_wines = reviews[(reviews.points >= 95) & ((reviews.country == 'Australia') | (reviews.country == 'New Zealand'))]

# Check your answer
q9.check()
top_oceania_wines

<IPython.core.display.Javascript object>

Correct

	country	description	designation	points	price	province	region_1	region_2	taster_name	taster_twitter_handle	title	variety	winery
345	Australia	This wine contains some material over 100 year...	Rare	100	350.0	Victoria	Rutherglen	NaN	Joe Czerwinski	@JoeCz	Chambers Rosewood Vineyards NV Rare Muscat (Ru...	Muscat	Chambers Rosewood Vineyards
346	Australia	This deep brown wine smells like a damp, mossy...	Rare	98	350.0	Victoria	Rutherglen	NaN	Joe Czerwinski	@JoeCz	Chambers Rosewood Vineyards NV Rare Muscadelle...	Muscadelle	Chambers Rosewood Vineyards
...	...	...	...	...	...	...	...	...	...	...	...	...	...
122507	New Zealand	This blend of Cabernet Sauvignon (62.5%), Merl...	SQM Gimblett Gravels Cabernets/Merlot	95	79.0	Hawke's Bay	NaN	NaN	Joe Czerwinski	@JoeCz	Squawking Magpie 2014 SQM Gimblett Gravels Cab...	Bordeaux-style Red Blend	Squawking Magpie
122939	Australia	Full-bodied and plush yet vibrant and imbued w...	The Factor	98	125.0	South Australia	Barossa Valley	NaN	Joe Czerwinski	@JoeCz	Torbreck 2013 The Factor Shiraz (Barossa Valley)	Shiraz	Torbreck

49 rows × 13 columns

#q9.hint()
#q9.solution()

Keep going

Move on to learn about summary functions and maps.

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