November 2023
Intermediate to advanced
512 pages
11h 53m
Chinese
Content preview from 利用 Python 进行数据分析：原书第 3 版
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404
|
第
13
章
从图
13-2
不容易看出
Windows
用户在小分组中的相对比例，因此对分组百分比进行标
准化，使其之和为
1
：
再次绘图，如图
13-3
所示：
图 13-3：计数最多时区中的 Windows 用户和非 Windows 用户的百分比
我们还可以用 groupby 的 transform 方法更高效地计算标准化的和：
13.2 MovieLens 1M
数据集
GroupLens
实验室（
https://grouplens.org/datasets/movielens
）收集了大量由
MovieLens
用户提供的从
20
世纪
90
年代末到
21
世纪初的电影评分数据。这些数据包括电影评分、
电影元数据（风格类型和年代），以及关于用户的人口统计学数据（年龄、邮编、性别和
职业）。基于机器学习算法的推荐系统一般都会对此类数据感兴趣。虽然我不会在本书
中详细介绍机器学习技术，但会介绍如何对这种数据进行切片和切块以满足实际需求。
数据分析案例
|
405
MovieLens 1M
数据集包含来自
6000
名用户对
4000
部电影的
100
万条评分数据。它分
为三个表：评分、用户信息和电影信息。可以通过 pandas.read_table 将各个表分别读
取到
pandas
的
DataFrame
对象中。在
Jupyter
的代码框中执行以下代码： ...
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I wanted to learn C and C++, but it didn't click for me until I picked up an O'Reilly book. When I went on the O’Reilly platform, I was astonished to find all the books there, plus live events and sandboxes so you could play around with the technology.Addison B.
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I'm always learning. So when I got on to O'Reilly, I was like a kid in a candy store. There are playlists. There are answers. There's on-demand training. It's worth its weight in gold, in terms of what it allows me to do.