hands-on-data-analysis 第二单元 2,3节

Posted 2022-06-20 沧夜2021

hands-on-data-analysis 第二单元 2,3节

@[TOC]

第二节 数据重构

万事开头记得导入基本的库：

# 导入基本库
import numpy as np
import pandas as pd

2.1.数据合并——concat横向合并

官方文档：

pandas.concat — pandas 1.4.2 documentation (pydata.org)

对text_left_up,text_right_up两张表，如果横向合并为一张表（就是列与列拼接在一起）

text_left_up：

	PassengerId	Survived	Pclass	Name
0	1	0	3	Braund, Mr. Owen Harris
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...
2	3	1	3	Heikkinen, Miss. Laina
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)
4	5	0	3	Allen, Mr. William Henry

text_right_up:

	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	male	22.0	1.0	0.0	A/5 21171	7.2500	NaN	S
1	female	38.0	1.0	0.0	PC 17599	71.2833	C85	C
2	female	26.0	0.0	0.0	STON/O2. 3101282	7.9250	NaN	S
3	female	35.0	1.0	0.0	113803	53.1000	C123	S
4	male	35.0	0.0	0.0	373450	8.0500	NaN	S

list_up = [text_left_up,text_right_up]
result_up = pd.concat(list_up,axis=1)
result_up.head()

得到：

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1.0	0.0	3.0	Braund, Mr. Owen Harris	male	22.0	1.0	0.0	A/5 21171	7.2500	NaN	S
1	2.0	1.0	1.0	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1.0	0.0	PC 17599	71.2833	C85	C
2	3.0	1.0	3.0	Heikkinen, Miss. Laina	female	26.0	0.0	0.0	STON/O2. 3101282	7.9250	NaN	S
3	4.0	1.0	1.0	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1.0	0.0	113803	53.1000	C123	S
4	5.0	0.0	3.0	Allen, Mr. William Henry	male	35.0	0.0	0.0	373450	8.0500	NaN	S

就好比，把带有小明的学号的表和带有小明成绩的表合在一起。

2.2.数据合并——concat纵向合并

官方文档：

pandas.concat — pandas 1.4.2 documentation (pydata.org)

将train-left-down和train-right-down横向合并为一张表，并保存这张表为result_down。然后将上边的result_up和result_down纵向合并为result。

text_left_down的数据为：

	PassengerId	Survived	Pclass	Name
0	440	0	2	Kvillner, Mr. Johan Henrik Johannesson
1	441	1	2	Hart, Mrs. Benjamin (Esther Ada Bloomfield)
2	442	0	3	Hampe, Mr. Leon
3	443	0	3	Petterson, Mr. Johan Emil
4	444	1	2	Reynaldo, Ms. Encarnacion

text_right_down数据为：

	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	male	31.0	0	0	C.A. 18723	10.500	NaN	S
1	female	45.0	1	1	F.C.C. 13529	26.250	NaN	S
2	male	20.0	0	0	345769	9.500	NaN	S
3	male	25.0	1	0	347076	7.775	NaN	S
4	female	28.0	0	0	230434	13.000	NaN	S

list_down=[text_left_down,text_right_down]
result_down = pd.concat(list_down,axis=1)
result = pd.concat([result_up,result_down])
result.head()

合并后的表为：

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
0	1.0	0.0	3.0	Braund, Mr. Owen Harris	male	22.0	1.0	0.0	A/5 21171	7.2500	NaN	S
1	2.0	1.0	1.0	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1.0	0.0	PC 17599	71.2833	C85	C
2	3.0	1.0	3.0	Heikkinen, Miss. Laina	female	26.0	0.0	0.0	STON/O2. 3101282	7.9250	NaN	S
3	4.0	1.0	1.0	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1.0	0.0	113803	53.1000	C123	S
4	5.0	0.0	3.0	Allen, Mr. William Henry	male	35.0	0.0	0.0	373450	8.0500	NaN	S

2.3.数据合并——join

官方文档：
pandas.DataFrame.join — pandas 1.4.2 documentation (pydata.org)

从官方文档上可以知道，join的方式比较灵活。

可以在 索引 或 列 上将 列 与其他 DataFrame 连接。 也可以通过传递一个列表，一次有效地按索引连接多个 DataFrame 对象。

参数有：

DataFrame.join(other, on=None, how=left, lsuffix=, rsuffix=, sort=False)

2.4. concat 与 join 比较

concat、join等的比较

[Merge, join, concatenate and compare — pandas 1.4.2 documentation (pydata.org)](https://pandas.pydata.org/docs/user_guide/merging.html?highlight=concat join#comparing-objects)

第三节 GroupBy 接口

官方文档：

pandas.DataFrame.groupby — pandas 1.4.2 documentation (pydata.org)