Pandas Data-types and Missing

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

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Introduction

Run the following cell to load your data and some utility functions.

import pandas as pd

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

Setup complete.

Exercises

1.

What is the data type of the points column in the dataset?

# Your code here
dtype = reviews.points.dtype

# Check your answer
q1.check()

<IPython.core.display.Javascript object>

Correct

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

2.

Create a Series from entries in the points column, but convert the entries to strings. Hint: strings are str in native Python.

point_strings = reviews.points.astype('str')

# Check your answer
q2.check()

<IPython.core.display.Javascript object>

Correct

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

3.

Sometimes the price column is null. How many reviews in the dataset are missing a price?

reviews[pd.isnull(reviews.price)]

	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
13	Italy	This is dominated by oak and oak-driven aromas...	Rosso	87	NaN	Sicily & Sardinia	Etna	NaN	Kerin O’Keefe	@kerinokeefe	Masseria Setteporte 2012 Rosso (Etna)	Nerello Mascalese	Masseria Setteporte
30	France	Red cherry fruit comes laced with light tannin...	Nouveau	86	NaN	Beaujolais	Beaujolais-Villages	NaN	Roger Voss	@vossroger	Domaine de la Madone 2012 Nouveau (Beaujolais...	Gamay	Domaine de la Madone
31	Italy	Merlot and Nero d'Avola form the base for this...	Calanìca Nero d'Avola-Merlot	86	NaN	Sicily & Sardinia	Sicilia	NaN	NaN	NaN	Duca di Salaparuta 2010 Calanìca Nero d'Avola-...	Red Blend	Duca di Salaparuta
32	Italy	Part of the extended Calanìca series, this Gri...	Calanìca Grillo-Viognier	86	NaN	Sicily & Sardinia	Sicilia	NaN	NaN	NaN	Duca di Salaparuta 2011 Calanìca Grillo-Viogni...	White Blend	Duca di Salaparuta
...	...	...	...	...	...	...	...	...	...	...	...	...	...
129844	Italy	Doga delle Clavule is a neutral, mineral-drive...	Doga delle Clavule	86	NaN	Tuscany	Morellino di Scansano	NaN	NaN	NaN	Caparzo 2006 Doga delle Clavule (Morellino di...	Sangiovese	Caparzo
129860	Portugal	This rich wine has a firm structure as well as...	Pacheca Superior	90	NaN	Douro	NaN	NaN	Roger Voss	@vossroger	Quinta da Pacheca 2013 Pacheca Superior Red (D...	Portuguese Red	Quinta da Pacheca
129863	Portugal	This mature wine that has 50% Touriga Nacional...	Reserva	90	NaN	Dão	NaN	NaN	Roger Voss	@vossroger	Seacampo 2011 Reserva Red (Dão)	Portuguese Red	Seacampo
129893	Italy	Aromas of passion fruit, hay and a vegetal not...	Corte Menini	91	NaN	Veneto	Soave Classico	NaN	Kerin O’Keefe	@kerinokeefe	Le Mandolare 2015 Corte Menini (Soave Classico)	Garganega	Le Mandolare
129964	France	Initially quite muted, this wine slowly develo...	Domaine Saint-Rémy Herrenweg	90	NaN	Alsace	Alsace	NaN	Roger Voss	@vossroger	Domaine Ehrhart 2013 Domaine Saint-Rémy Herren...	Gewürztraminer	Domaine Ehrhart

8996 rows × 13 columns

type(reviews[pd.isnull(reviews.price)])

pandas.core.frame.DataFrame

n_missing_prices = len(reviews[pd.isnull(reviews.price)])

# Check your answer
q3.check()

<IPython.core.display.Javascript object>

Correct

n_missing_prices

8996

q3.hint()
#q3.solution()

<IPython.core.display.Javascript object>

Hint: Use pd.isnull().

4.

What are the most common wine-producing regions? Create a Series counting the number of times each value occurs in the region_1 field. This field is often missing data, so replace missing values with Unknown. Sort in descending order. Your output should look something like this:

Unknown                    21247
Napa Valley                 4480
                           ...  
Bardolino Superiore            1
Primitivo del Tarantino        1
Name: region_1, Length: 1230, dtype: int64

reviews.region_1.fillna('Unknown').value_counts()

Unknown                    21247
Napa Valley                 4480
Columbia Valley (WA)        4124
Russian River Valley        3091
California                  2629
                           ...  
Lamezia                        1
Trentino Superiore             1
Grave del Friuli               1
Vin Santo di Carmignano        1
Paestum                        1
Name: region_1, Length: 1230, dtype: int64

reviews_per_region = reviews.region_1.fillna('Unknown').value_counts()

# Check your answer
q4.check()

<IPython.core.display.Javascript object>

Correct

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

Keep going

Move on to renaming and combining.

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