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六月：手动学数据分析(task02)

發(fā)布時間：2023/12/8 编程问答 36 豆豆

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復(fù)習(xí)： 在前面我們已經(jīng)學(xué)習(xí)了Pandas基礎(chǔ)，第二章我們開始進入數(shù)據(jù)分析的業(yè)務(wù)部分，在第二章第一節(jié)的內(nèi)容中，我們學(xué)習(xí)了數(shù)據(jù)的清洗，這一部分十分重要，只有數(shù)據(jù)變得相對干凈，我們之后對數(shù)據(jù)的分析才可以更有力。而這一節(jié)，我們要做的是數(shù)據(jù)重構(gòu)，數(shù)據(jù)重構(gòu)依舊屬于數(shù)據(jù)理解（準(zhǔn)備）的范圍。

# Time: 2021-06-16 # 本文有少量備注，并對文章內(nèi)容進行了優(yōu)化 # 目標(biāo)是成為【優(yōu)秀學(xué)習(xí)者】 # 總結(jié)不易，望點贊鼓勵

文章目錄

【task 02】數(shù)據(jù)清洗和特征處理
第二章：數(shù)據(jù)清洗和特征處理
- - 2.1 數(shù)據(jù)的合并
  - - 2.1.1 任務(wù)一：載入四份被分割的數(shù)據(jù)
    - 2.1.2 任務(wù)二：使用concat方法，合并兩CSV文件
    - 2.1.3 任務(wù)三：使用concat方法，兩表縱向合并
    - 2.1.4 任務(wù)四：join方法和append：完成任務(wù)二和任務(wù)三
    - 2.1.5 任務(wù)五：使用pd.merge和append方法：完成任務(wù)二和任務(wù)三的任務(wù)
    - 2.1.6 任務(wù)六：完成的數(shù)據(jù)保存為result.csv
  - 2.2 換一種角度看數(shù)據(jù)
  - - 2.2.1 任務(wù)一：將我們的數(shù)據(jù)變?yōu)镾eries類型的數(shù)據(jù)

【task 02】數(shù)據(jù)清洗和特征處理

<--------感謝評論區(qū)指正，內(nèi)容已更新!--------->

第二章：數(shù)據(jù)清洗和特征處理

import numpy as np import pandas as pd

2.1 數(shù)據(jù)的合并

2.1.1 任務(wù)一：載入四份被分割的數(shù)據(jù)

將data文件夾里面的所有數(shù)據(jù)都載入，我們看到四分?jǐn)?shù)據(jù)，是將上一講完整數(shù)據(jù)行、列進行了切割：

left_up：左上部分
left_down：左下部分
right-up：右上部分
right_down：右下部分

text_left_up = pd.read_csv("data/train-left-up.csv") text_left_down = pd.read_csv("data/train-left-down.csv") text_right_up = pd.read_csv("data/train-right-up.csv") text_right_down = pd.read_csv("data/train-right-down.csv") text_left_up.head() PassengerIdSurvivedPclassName01234

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

【乘客ID】【是否幸存】【艙位等級】【名字】

text_left_down.head() PassengerIdSurvivedPclassName01234

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

【乘客ID】【是否幸存】【艙位等級】【名字】

text_right_down.head() SexAgeSibSpParchTicketFareCabinEmbarked01234

male	31.0	0	0	C.A. 18723	10.500	NaN	S
female	45.0	1	1	F.C.C. 13529	26.250	NaN	S
male	20.0	0	0	345769	9.500	NaN	S
male	25.0	1	0	347076	7.775	NaN	S
female	28.0	0	0	230434	13.000	NaN	S

【性別】【年齡】【兄弟姐妹個數(shù)】【父母孩子個數(shù)】【船票信息】【票價】【船艙】【登船口】

text_right_up.head() SexAgeSibSpParchTicketFareCabinEmbarked01234

male	22.0	1	A/5 21171	7.2500	NaN	S
female	38.0	1	PC 17599	71.2833	C85	C
female	26.0	0	STON/O2. 3101282	7.9250	NaN	S
female	35.0	1	113803	53.1000	C123	S
male	35.0	0	373450	8.0500	NaN	S

【性別】【年齡】【兄弟姐妹個數(shù)】【父母孩子個數(shù)】【船票信息】【票價】【船艙】【登船口】

2.1.2 任務(wù)二：使用concat方法，合并兩CSV文件

pd.concat()

import pandas as pd pd.concat(object,axis=0,join='outer',join_axes=None,ignore_index=False,keys=None,levels=None,names=None,verify_integrity=False)

參數(shù)含義

objs：Series，DataFrame或Panel對象的序列或映射。如果傳遞了dict，則排序的鍵將用作鍵參數(shù)，除非它被傳遞，在這種情況下，將選擇值（見下文）。任何無對象將被靜默刪除，除非它們都是無，在這種情況下將引發(fā)一個ValueError。
axis：{0,1，…}，默認(rèn)為0。0是行，1是列。
join：{‘inner’，‘outer’}，默認(rèn)為“outer”。如何處理其他軸上的索引。outer為聯(lián)合和inner為交集。
ignore_index：boolean，default False。如果為True，請不要使用并置軸上的索引值。結(jié)果軸將被標(biāo)記為0，…，n-1。如果要連接其中并置軸沒有有意義的索引信息的對象，這將非常有用。注意，其他軸上的索引值在連接中仍然受到尊重。
join_axes：Index對象列表。用于其他n-1軸的特定索引，而不是執(zhí)行內(nèi)部/外部設(shè)置邏輯。
keys：序列，默認(rèn)值無。使用傳遞的鍵作為最外層構(gòu)建層次索引。如果為多索引，應(yīng)該使用元組。
levels：序列列表，默認(rèn)值無。用于構(gòu)建MultiIndex的特定級別（唯一值）。否則，它們將從鍵推斷。
names：list，default無。結(jié)果層次索引中的級別的名稱。
verify_integrity：boolean，default False。檢查新連接的軸是否包含重復(fù)項。這相對于實際的數(shù)據(jù)串聯(lián)可能是非常昂貴的。
copy：boolean，default True。如果為False，請勿不必要地復(fù)制數(shù)據(jù)。

【默認(rèn)形式】
默認(rèn)形式是改行，列對齊

frames = [df1, df2, df3] #DF型 result = pd.concat(frames) #默認(rèn)是0 需要合并是行

【用KEY來區(qū)分不同表的來源】

result=pd.concat(frames,keys=['x','y','z'])

【列上的合并，axis=1】

result = pd.concat([df1, df4], axis=1)

默認(rèn)join = ‘outer’,為取并集的關(guān)系，有相同索引的連接【如圖行索引2.3】，確實的NaN

【列上合并，內(nèi)聯(lián)join='inner’取交】

result = pd.concat([df1, df4], axis=1, join='inner')

【join_axes】

如果是join_axes的參數(shù)傳入，可以指定根據(jù)那個軸來對齊數(shù)據(jù)

result=pd.concat([df1,df4],axis=1,join_axes=[df1.index])

列合并，以df1的索引為軸，將df4與其連接，缺失的用NaN

【任務(wù)要求】將數(shù)據(jù)train-left-up.csv和train-right-up.csv橫向合并為一張表，并保存這張表為result_up

list_up = [text_left_up,text_right_up] result_up = pd.concat(list_up,axis=1) result_up PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked01234...434435436437438

1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...
435	0	1	Silvey, Mr. William Baird	male	50.0	1	0	13507	55.9000	E44	S
436	1	1	Carter, Miss. Lucile Polk	female	14.0	1	2	113760	120.0000	B96 B98	S
437	0	3	Ford, Miss. Doolina Margaret "Daisy"	female	21.0	2	2	W./C. 6608	34.3750	NaN	S
438	1	2	Richards, Mrs. Sidney (Emily Hocking)	female	24.0	2	3	29106	18.7500	NaN	S
439	0	1	Fortune, Mr. Mark	male	64.0	1	4	19950	263.0000	C23 C25 C27	S

439 rows × 12 columns

現(xiàn)將表構(gòu)成list，然后在作為concat的輸入

2.1.3 任務(wù)三：使用concat方法，兩表縱向合并

使用concat方法：將train-left-down和train-right-down橫向合并為一張表，并保存這張表為result_down。然后將上邊的result_up和result_down縱向合并為result。

list_down=[text_left_down,text_right_down] result_down = pd.concat(list_down,axis=1) result_down PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked01234...447448449450451

440	0	2	Kvillner, Mr. Johan Henrik Johannesson	male	31.0	0	0	C.A. 18723	10.500	NaN	S
441	1	2	Hart, Mrs. Benjamin (Esther Ada Bloomfield)	female	45.0	1	1	F.C.C. 13529	26.250	NaN	S
442	0	3	Hampe, Mr. Leon	male	20.0	0	0	345769	9.500	NaN	S
443	0	3	Petterson, Mr. Johan Emil	male	25.0	1	0	347076	7.775	NaN	S
444	1	2	Reynaldo, Ms. Encarnacion	female	28.0	0	0	230434	13.000	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...
887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.000	NaN	S
888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.000	B42	S
889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.450	NaN	S
890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.000	C148	C
891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.750	NaN	Q

452 rows × 12 columns

result = pd.concat([result_up,result_down]) result PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked01234...447448449450451

1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...
887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S
890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C
891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q

891 rows × 12 columns

result.loc[1].head() PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked11

2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
441	1	2	Hart, Mrs. Benjamin (Esther Ada Bloomfield)	female	45.0	1	1	F.C.C. 13529	26.2500	NaN	S

我們會發(fā)現(xiàn) 表是拼起來了但是第一列索引是亂的

【解決】用到了drop

drop=True就是把原來的索引index列去掉，重置index。
drop=False就是保留原來的索引，添加重置的index。

result_1 = pd.concat([result_up,result_down]).reset_index(drop = True) result_1 PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked01234...886887888889890

1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...
887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S
890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C
891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q

891 rows × 12 columns

2.1.4 任務(wù)四：join方法和append：完成任務(wù)二和任務(wù)三

resul_up = text_left_up.join(text_right_up) resul_up PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked01234...434435436437438

1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...
435	0	1	Silvey, Mr. William Baird	male	50.0	1	0	13507	55.9000	E44	S
436	1	1	Carter, Miss. Lucile Polk	female	14.0	1	2	113760	120.0000	B96 B98	S
437	0	3	Ford, Miss. Doolina Margaret "Daisy"	female	21.0	2	2	W./C. 6608	34.3750	NaN	S
438	1	2	Richards, Mrs. Sidney (Emily Hocking)	female	24.0	2	3	29106	18.7500	NaN	S
439	0	1	Fortune, Mr. Mark	male	64.0	1	4	19950	263.0000	C23 C25 C27	S

439 rows × 12 columns

result_down = text_left_down.join(text_right_down) result = result_up.append(result_down) result PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked01234...447448449450451

1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...
887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S
890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C
891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q

891 rows × 12 columns

2.1.5 任務(wù)五：使用pd.merge和append方法：完成任務(wù)二和任務(wù)三的任務(wù)

pd.merge()

以index為鏈接鍵,需要同時設(shè)置left_index= True 和 right_index= True，或者left_index設(shè)置的同時，right_on指定某個Key。總的來說就是需要指定left、right鏈接的鍵，可以同時是key、index或者混合使用。

result_up = pd.merge(text_left_up,text_right_up,left_index=True,right_index=True) result_down = pd.merge(text_left_down,text_right_down,left_index=True,right_index=True) result = result_up.append(result_down) result PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked01234...447448449450451

1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...
887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S
890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C
891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q

891 rows × 12 columns

【思考】對比merge、join以及concat的方法的不同以及相同。思考一下在任務(wù)四和任務(wù)五的情況下，為什么都要求使用DataFrame的append方法，如何只要求使用merge或者join可不可以完成任務(wù)四和任務(wù)五呢？

2.1.6 任務(wù)六：完成的數(shù)據(jù)保存為result.csv

result.to_csv('result_task02.csv')

2.2 換一種角度看數(shù)據(jù)

2.2.1 任務(wù)一：將我們的數(shù)據(jù)變?yōu)镾eries類型的數(shù)據(jù)

# 將完整的數(shù)據(jù)加載出來 text = pd.read_csv('result_task02.csv') text.head() # 代碼寫在這里 unit_result=text.stack().head(30) unit_result 0 Unnamed: 0 0PassengerId 1Survived 0Pclass 3Name Braund, Mr. Owen HarrisSex maleAge 22.0SibSp 1Parch 0Ticket A/5 21171Fare 7.25Embarked S 1 Unnamed: 0 1PassengerId 2Survived 1Pclass 1Name Cumings, Mrs. John Bradley (Florence Briggs Th...Sex femaleAge 38.0SibSp 1Parch 0Ticket PC 17599Fare 71.2833Cabin C85Embarked C 2 Unnamed: 0 2PassengerId 3Survived 1Pclass 3Name Heikkinen, Miss. Laina dtype: object #將代碼保存為unit_result,csv unit_result.to_csv('unit_result.csv') test = pd.read_csv('unit_result.csv') test Unnamed: 0Unnamed: 10012345678910111213141516171819

0	Unnamed: 0	0
0	PassengerId	1
0	Survived	0
0	Pclass	3
0	Name	Braund, Mr. Owen Harris
0	Sex	male
0	Age	22.0
0	SibSp	1
0	Parch	0
0	Ticket	A/5 21171
0	Fare	7.25
0	Embarked	S
1	Unnamed: 0	1
1	PassengerId	2
1	Survived	1
1	Pclass	1
1	Name	Cumings, Mrs. John Bradley (Florence Briggs Th...
1	Sex	female
1	Age	38.0
1	SibSp	1

這個stack函數(shù)是干什么的？

stack是棧的意思其實就是講列表傳入到棧中，每條記錄收尾相接

函數(shù)原型為：stack(arrays, axis=0)，arrays可以傳數(shù)組和列表。

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