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

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Introduction

介绍

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

运行以下单元格来加载数据和一些实用函数。

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

练习

1.

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

数据集中points列的数据类型是什么？

# Your code here
#dtype = ____

dtype = reviews['points'].dtype

# Check your answer
q1.check()

dtype

Correct

dtype('int64')

#q1.hint()
q1.solution()

Solution:

dtype = reviews.points.dtype

2.

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

从points列中的条目创建一个Series，但将条目转换为字符串。 提示：字符串在本机 Python 中是str。

#point_strings = ____

# point_strings = reviews['points'].apply(lambda x: str(x))
point_strings = reviews['points'].astype(str)
# Check your answer
q2.check()

point_strings

Correct

0         87
1         87
2         87
3         87
4         87
          ..
129966    90
129967    90
129968    90
129969    90
129970    90
Name: points, Length: 129971, dtype: object

#q2.hint()
q2.solution()

Solution:

point_strings = reviews.points.astype(str)

3.

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

有时价格列为空。 数据集中有多少评论缺少价格？

#n_missing_prices = ____

n_missing_prices = reviews['price'].isnull().sum()

# Check your answer
q3.check()

n_missing_prices

Correct

8996

#q3.hint()
q3.solution()

Solution:

missing_price_reviews = reviews[reviews.price.isnull()]
n_missing_prices = len(missing_price_reviews)
# Cute alternative solution: if we sum a boolean series, True is treated as 1 and False as 0
n_missing_prices = reviews.price.isnull().sum()
# or equivalently:
n_missing_prices = pd.isnull(reviews.price).sum()

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:

最常见的葡萄酒产区有哪些？ 创建一个系列，计算每个值在region_1字段中出现的次数。 该字段经常缺少数据，因此将缺少的值替换为Unknown。 按降序排列。 你的输出应该是这样的：

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

#reviews_per_region = ____

# reviews_per_region = reviews[['region_1']].fillna('Unknown').groupby(['region_1']).size().sort_values(ascending=False)
reviews_per_region = reviews.loc[:,'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

继续

Move on to renaming and combining.

继续重命名和组合。