Python数据分析入门到进阶：数据清洗（含详细代码）

最新推荐文章于 2024-12-31 14:16:12 发布

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最新推荐文章于 2024-12-31 14:16:12 发布 · 523 阅读

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#python #数据分析 #开发语言

本文详细介绍了Python数据分析中的数据清洗过程，包括查看数据、筛选、替换、重命名列、查找唯一值和缺失值、删除行列、使用groupby分组、按时间段分组等操作，并提供了相关代码示例。

在上一篇文章中，介绍了如何使用python导入数据，导入数据后的第二步往往就是数据清洗，下面我们来看看如何使用pandas进行数据清洗工作

导入相关库

import pandas as pd

dataframe = pd.read_csv(r'C:/Users/DELL/data-science-learning/python数据分析笔记/探索性数据分析/train.csv')
dataframe.head(5)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th…	female	38.0	1	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	373450	8.0500	NaN	S

🥇1.总览数据

查看数据维度

dataframe.shape

(891, 12)

描述性统计分析

dataframe.describe()

.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }

	PassengerId	Survived	Pclass	Age	SibSp	Parch	Fare
count	891.000000	891.000000	891.000000	714.000000	891.000000	891.000000	891.000000
mean	446.000000	0.383838	2.308642	29.699118	0.523008	0.381594	32.204208
std	257.353842	0.486592	0.836071	14.526497	1.102743	0.806057	49.693429
min	1.000000	0.000000	1.000000	0.420000	0.000000	0.000000	0.000000
25%	223.500000	0.000000	2.000000	20.125000	0.000000	0.000000	7.910400
50%	446.000000	0.000000	3.000000	28.000000	0.000000	0.000000	14.454200
75%	668.500000	1.000000	3.000000	38.000000	1.000000	0.000000	31.000000
max	891.000000	1.000000	3.000000	80.000000	8.000000	6.000000	512.329200

🥈2.筛选数据

过滤所有女性和年龄大于60岁的乘客

dataframe[(dataframe['Sex'] == 'female') & (dataframe['Age']>=60)]

.dataframe tbody tr th:only-of-type { vertical-align: middle; } .dataframe tbody tr th { vertical-align: top; } .dataframe thead th { text-align: right; }

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Ticket	Fare	Cabin	Embarked
275	276	1	1	Andrews, Miss. Kornelia Theodosia	female	63.0	1	13502	77.9583	D7	S
366	367	1	1	Warren, Mrs. Frank Manley (Anna Sophia Atkinson)	female	60.0	1	110813	75.2500	D37	C
483	484	1	3	Turkula, Mrs. (Hedwig)	female	63.0	0	4134	9.5875	NaN	S
829	830	1	1	Stone, Mrs. George Nelson (Martha Evelyn)	female	62.0	0	113572	80.0000