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利用 Python 进行数据分析:原书第 3 版
book

利用 Python 进行数据分析:原书第 3 版

by Wes McKinney
November 2023
Intermediate to advanced
512 pages
11h 53m
Chinese
China Machine Press
Content preview from 利用 Python 进行数据分析:原书第 3 版
数据分析案例
|
425
13-10:使用“ Lesley 式”名字的男女比例随时间的变化趋势
13.4 USDA
食品数据库
美国农业部(
US Department of Agriculture
USDA
)制作了一份有关食物营养信
息的数据库。程序员
Ashley Williams
制作了该数据的
JSON
版本。其中的记录如下
所示:
426
|
13
每种食物都带有若干标识性属性,以及两个有关营养成分和分量的列表。这种形式的数
据不是很适合分析工作,因此我们需要做一些规整化以使其具有更好的形式。
你可以用选择任意
JSON
库将其加载到
Python
中。我用的是
Python
内置的 json
模块:
db 中的每个条目都是一个字典,含有某种食物的全部数据。字段 "nutrients" 是一个字
典列表,其中每个字典对应一种营养成分:
在将字典列表转换为
DataFrame
时,我们可以指定一个需要提取的字段列表。这里,我
们将提取食物的名称、分类、
ID
以及制造商等信息:
数据分析案例
|
427
通过 info.info() ...
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Publisher Resources

ISBN: 9787111726722