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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()

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()

Correct

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

3.

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

n_missing_prices = reviews.price.isnull().sum()

# Check your answer
q3.check()
n_missing_prices

Correct

8996

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

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_per_region = reviews.region_1.fillna("Unknown") \
                            .value_counts().sort_values(ascending=False)

# Check your answer
q4.check()

Correct

#q4.hint()
q4.solution()

Solution:

reviews_per_region = reviews.region_1.fillna('Unknown').value_counts().sort_values(ascending=False)

Keep going

Move on to renaming and combining.

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