August 2022
Intermediate to advanced
441 pages
11h 26m
Chinese
Content preview from Python文本分析
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洞悉文本数据
｜
17
text 0
length 0
dtype: int64
我们需要小心使用
speaker
和
position
两列，因为上述输出表明并非所有记录都
包含这两条信息！为了防止出现问题，我们可以使用一些通用值代替缺失值，例如
“
unknown speaker
”“
unknown position
”或空字符串。
为此，
Pandas
提供了函数
df.fillna()
：
df['speaker'].fillna('unknown', inplace=True)
然而，即便是存在的值也可能会出现问题，因为同一位发言人的姓名有时也会拼写
成不同的形式，甚至会有歧义。下列语句针对
speaker
列包含“
Bush
”的所有文档，
统计每一位发言人的记录数：
df[df['speaker'].str.contains('Bush')]['speaker'].value_counts()
输出结果：
George W. Bush 4
Mr. George W. Bush 2
George Bush 1
Mr. George W Bush 1
Bush 1
Name: speaker, dtype: int64
除非我们消灭这些歧义，否则针对发言人姓名的任何分析都会产生错误的结果。因
此，我们最好检查一下分类属性的各个值。由于这一点，我们将暂时忽略此处的发
言人信息。
1.4.3
绘制值的分布图
可视化五数概括值的一种方法是采用箱形图（ ...
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