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.

介绍

在这组练习中，我们将使用葡萄酒评论数据集。

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

运行以下单元格来加载您的数据和一些实用函数（包括用于检查答案的代码）。

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.

通过运行以下行来查看数据的概况。

reviews.head()

Exercises

练习

1.

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

从reviews中选择description列，并将结果分配给变量desc。

# Your code here
#desc = ____

desc = reviews['description']
desc

# Check your answer
q1.check()

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).

后续问题：desc是什么类型的对象？ 如果你不确定，你可以通过调用Python的type函数来检查：type(desc)。

type(desc)

pandas.core.series.Series

#q1.hint()
q1.solution()

Solution:

desc = reviews.description

or

desc = reviews["description"]

desc is a pandas Series object, with an index matching the reviews DataFrame.
In general, when we select a single column from a DataFrame, we'll get a Series.

2.

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

从reviews的描述列中选择第一个值，将其分配给变量first_description。

#first_description = ____

first_description = reviews['description'][0]

# Check your answer
q2.check()
first_description

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()

Solution:

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!

3.

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

从reviews中选择第一行数据（第一条记录），并将其分配给变量first_row。

#first_row = ____
first_row = reviews.iloc[0]

# Check your answer
q3.check()
first_row

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()

Solution:

first_row = reviews.iloc[0]

4.

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

从reviews的description列中选择前 10 个值，将结果分配给变量first_descriptions。

Hint: format your output as a pandas Series.

提示：将输出格式化为 pandas 系列。

#first_descriptions = ____

first_descriptions = reviews.loc[:9, 'description']

# Check your answer
q4.check()
first_descriptions

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()

Solution:

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"].

5.

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

选择索引标签为1、2、3、5和8的记录，并将结果分配给变量sample_reviews。

In other words, generate the following DataFrame:

换句话说，生成以下 DataFrame：

#sample_reviews = ____

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

# Check your answer
q5.check()
sample_reviews

Correct

#q5.hint()
q5.solution()

Solution:

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

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:

创建一个变量df，其中包含索引标签为0、1、10和100的记录的country、province、region_1和region_2列。 换句话说，生成以下 DataFrame：

#df = ____
df = reviews.loc[[0,1,10,100],['country', 'province', 'region_1', 'region_2']]
# Check your answer
q6.check()
df

Correct

#q6.hint()
q6.solution()

Solution:

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

7.

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

创建一个变量df，其中包含前 100 条记录的country和variety列。

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:

提示：您可以使用loc或iloc。 在回答这个问题以及接下来的几个问题时，请记住教程中描述的以下要点：

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.

iloc 使用 Python stdlib 索引方案，其中包含范围的第一个元素，排除最后一个元素。
同时，loc包含索引。

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].

当 DataFrame 索引是一个简单的数字列表时，例如 0，...，1000。 在这种情况下，df.iloc[0:1000]将返回 1000 个条目，而df.loc[0:1000]则返回其中的 1001 个！ 要使用 loc 获取 1000 个元素，您需要向下一级并请求 df.iloc[0:999]。

#df = ____

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

# Check your answer
q7.check()
df

Correct:

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

or

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

#q7.hint()
q7.solution()

Solution:

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

or

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

8.

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

创建一个DataFrameitalian_wines，其中包含意大利生产的葡萄酒的评论。 提示：reviews.country等于什么？

#italian_wines = ____

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

# Check your answer
q8.check()

Correct

#q8.hint()
q8.solution()

Solution:

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

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.

创建一个DataFrame top_oceania_wines，其中包含来自澳大利亚或新西兰的葡萄酒的至少 95 分（满分 100 分）的所有评论。

#top_oceania_wines = ____

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

# Check your answer
q9.check()
top_oceania_wines

Correct

#q9.hint()
q9.solution()

Solution:

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

Keep going

继续

Move on to learn about summary functions and maps.

继续了解摘要函数和映射。