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第二章：第二三节数据重构

發布時間：2023/12/8 编程问答 28 豆豆

生活随笔收集整理的這篇文章主要介紹了第二章：第二三节数据重构小編覺得挺不錯的,現在分享給大家,幫大家做個參考.

復習：在前面我們已經學習了Pandas基礎，第二章我們開始進入數據分析的業務部分，在第二章第一節的內容中，我們學習了數據的清洗，這一部分十分重要，只有數據變得相對干凈，我們之后對數據的分析才可以更有力。而這一節，我們要做的是數據重構，數據重構依舊屬于數據理解（準備）的范圍。

開始之前，導入numpy、pandas包和數據

# 導入基本庫 import numpy as np import pandas as pd # 載入data文件中的:train-left-up.csv train_left_up = pd.read_csv('./data/train-left-up.csv')

2 第二章：數據重構

2.4 數據的合并

2.4.1 任務一：將data文件夾里面的所有數據都載入，觀察數據的之間的關系

train_left_down = pd.read_csv('./data/train-left-down.csv') train_right_up = pd.read_csv('./data/train-right-up.csv') train_right_down = pd.read_csv('./data/train-right-down.csv') train_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

train_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

train_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

【提示】結合之前我們加載的train.csv數據，大致預測一下上面的數據是什么

2.4.2：任務二：使用concat方法：將數據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

#pandas.concat(objs, axis=0, join='outer', join_axes=None, ignore_index=False, keys=None, levels=None, names=None, verify_integrity=False, sort=None, copy=True) #pandas.concat()函數以沿著指定的軸將多個dataframe或者series拼接到一起，默認axis=0，join='outer'，以上下的方向拼接，類似數據庫中的全連接（union all）a1 = [train_left_up,train_right_up] result_up = pd.concat(a1,axis=1) result_up.head() PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked01234

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

2.4.3 任務三：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。

a2 = [train_left_down,train_right_down] result_down = pd.concat(a2,axis=1) #result_downresult = pd.concat([result_up,result_down]) result PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked01234567891011121314151617181920212223242526272829...422423424425426427428429430431432433434435436437438439440441442443444445446447448449450451

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
6	0	3	Moran, Mr. James	male	NaN	0	0	330877	8.4583	NaN	Q
7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S
8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	NaN	S
9	1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	347742	11.1333	NaN	S
10	1	2	Nasser, Mrs. Nicholas (Adele Achem)	female	14.0	1	0	237736	30.0708	NaN	C
11	1	3	Sandstrom, Miss. Marguerite Rut	female	4.0	1	1	PP 9549	16.7000	G6	S
12	1	1	Bonnell, Miss. Elizabeth	female	58.0	0	0	113783	26.5500	C103	S
13	0	3	Saundercock, Mr. William Henry	male	20.0	0	0	A/5. 2151	8.0500	NaN	S
14	0	3	Andersson, Mr. Anders Johan	male	39.0	1	5	347082	31.2750	NaN	S
15	0	3	Vestrom, Miss. Hulda Amanda Adolfina	female	14.0	0	0	350406	7.8542	NaN	S
16	1	2	Hewlett, Mrs. (Mary D Kingcome)	female	55.0	0	0	248706	16.0000	NaN	S
17	0	3	Rice, Master. Eugene	male	2.0	4	1	382652	29.1250	NaN	Q
18	1	2	Williams, Mr. Charles Eugene	male	NaN	0	0	244373	13.0000	NaN	S
19	0	3	Vander Planke, Mrs. Julius (Emelia Maria Vande...	female	31.0	1	0	345763	18.0000	NaN	S
20	1	3	Masselmani, Mrs. Fatima	female	NaN	0	0	2649	7.2250	NaN	C
21	0	2	Fynney, Mr. Joseph J	male	35.0	0	0	239865	26.0000	NaN	S
22	1	2	Beesley, Mr. Lawrence	male	34.0	0	0	248698	13.0000	D56	S
23	1	3	McGowan, Miss. Anna "Annie"	female	15.0	0	0	330923	8.0292	NaN	Q
24	1	1	Sloper, Mr. William Thompson	male	28.0	0	0	113788	35.5000	A6	S
25	0	3	Palsson, Miss. Torborg Danira	female	8.0	3	1	349909	21.0750	NaN	S
26	1	3	Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...	female	38.0	1	5	347077	31.3875	NaN	S
27	0	3	Emir, Mr. Farred Chehab	male	NaN	0	0	2631	7.2250	NaN	C
28	0	1	Fortune, Mr. Charles Alexander	male	19.0	3	2	19950	263.0000	C23 C25 C27	S
29	1	3	O'Dwyer, Miss. Ellen "Nellie"	female	NaN	0	0	330959	7.8792	NaN	Q
30	0	3	Todoroff, Mr. Lalio	male	NaN	0	0	349216	7.8958	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...
862	0	2	Giles, Mr. Frederick Edward	male	21.0	1	0	28134	11.5000	NaN	S
863	1	1	Swift, Mrs. Frederick Joel (Margaret Welles Ba...	female	48.0	0	0	17466	25.9292	D17	S
864	0	3	Sage, Miss. Dorothy Edith "Dolly"	female	NaN	8	2	CA. 2343	69.5500	NaN	S
865	0	2	Gill, Mr. John William	male	24.0	0	0	233866	13.0000	NaN	S
866	1	2	Bystrom, Mrs. (Karolina)	female	42.0	0	0	236852	13.0000	NaN	S
867	1	2	Duran y More, Miss. Asuncion	female	27.0	1	0	SC/PARIS 2149	13.8583	NaN	C
868	0	1	Roebling, Mr. Washington Augustus II	male	31.0	0	0	PC 17590	50.4958	A24	S
869	0	3	van Melkebeke, Mr. Philemon	male	NaN	0	0	345777	9.5000	NaN	S
870	1	3	Johnson, Master. Harold Theodor	male	4.0	1	1	347742	11.1333	NaN	S
871	0	3	Balkic, Mr. Cerin	male	26.0	0	0	349248	7.8958	NaN	S
872	1	1	Beckwith, Mrs. Richard Leonard (Sallie Monypeny)	female	47.0	1	1	11751	52.5542	D35	S
873	0	1	Carlsson, Mr. Frans Olof	male	33.0	0	0	695	5.0000	B51 B53 B55	S
874	0	3	Vander Cruyssen, Mr. Victor	male	47.0	0	0	345765	9.0000	NaN	S
875	1	2	Abelson, Mrs. Samuel (Hannah Wizosky)	female	28.0	1	0	P/PP 3381	24.0000	NaN	C
876	1	3	Najib, Miss. Adele Kiamie "Jane"	female	15.0	0	0	2667	7.2250	NaN	C
877	0	3	Gustafsson, Mr. Alfred Ossian	male	20.0	0	0	7534	9.8458	NaN	S
878	0	3	Petroff, Mr. Nedelio	male	19.0	0	0	349212	7.8958	NaN	S
879	0	3	Laleff, Mr. Kristo	male	NaN	0	0	349217	7.8958	NaN	S
880	1	1	Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)	female	56.0	0	1	11767	83.1583	C50	C
881	1	2	Shelley, Mrs. William (Imanita Parrish Hall)	female	25.0	0	1	230433	26.0000	NaN	S
882	0	3	Markun, Mr. Johann	male	33.0	0	0	349257	7.8958	NaN	S
883	0	3	Dahlberg, Miss. Gerda Ulrika	female	22.0	0	0	7552	10.5167	NaN	S
884	0	2	Banfield, Mr. Frederick James	male	28.0	0	0	C.A./SOTON 34068	10.5000	NaN	S
885	0	3	Sutehall, Mr. Henry Jr	male	25.0	0	0	SOTON/OQ 392076	7.0500	NaN	S
886	0	3	Rice, Mrs. William (Margaret Norton)	female	39.0	0	5	382652	29.1250	NaN	Q
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

2.4.4 任務四：使用DataFrame自帶的方法join方法和append：完成任務二和任務三的任務

#DataFrame.join(other, on=None, how='left', lsuffix=' ', rsuffix=' ', sort=False) #主要用于基于行索引上的合并，join方法默認為左外連接how=’left’#DataFrame.append(other, ignore_index=False, verify_integrity=False, sort=None) #append是concat的簡略形式，只不過只能在axis=0上進行合并b1 = train_left_up.join(train_right_up) b2 = train_left_down.join(train_right_down) result1 = b1.append(b2) result1 PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked01234567891011121314151617181920212223242526272829...422423424425426427428429430431432433434435436437438439440441442443444445446447448449450451

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
6	0	3	Moran, Mr. James	male	NaN	0	0	330877	8.4583	NaN	Q
7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S
8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	NaN	S
9	1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	347742	11.1333	NaN	S
10	1	2	Nasser, Mrs. Nicholas (Adele Achem)	female	14.0	1	0	237736	30.0708	NaN	C
11	1	3	Sandstrom, Miss. Marguerite Rut	female	4.0	1	1	PP 9549	16.7000	G6	S
12	1	1	Bonnell, Miss. Elizabeth	female	58.0	0	0	113783	26.5500	C103	S
13	0	3	Saundercock, Mr. William Henry	male	20.0	0	0	A/5. 2151	8.0500	NaN	S
14	0	3	Andersson, Mr. Anders Johan	male	39.0	1	5	347082	31.2750	NaN	S
15	0	3	Vestrom, Miss. Hulda Amanda Adolfina	female	14.0	0	0	350406	7.8542	NaN	S
16	1	2	Hewlett, Mrs. (Mary D Kingcome)	female	55.0	0	0	248706	16.0000	NaN	S
17	0	3	Rice, Master. Eugene	male	2.0	4	1	382652	29.1250	NaN	Q
18	1	2	Williams, Mr. Charles Eugene	male	NaN	0	0	244373	13.0000	NaN	S
19	0	3	Vander Planke, Mrs. Julius (Emelia Maria Vande...	female	31.0	1	0	345763	18.0000	NaN	S
20	1	3	Masselmani, Mrs. Fatima	female	NaN	0	0	2649	7.2250	NaN	C
21	0	2	Fynney, Mr. Joseph J	male	35.0	0	0	239865	26.0000	NaN	S
22	1	2	Beesley, Mr. Lawrence	male	34.0	0	0	248698	13.0000	D56	S
23	1	3	McGowan, Miss. Anna "Annie"	female	15.0	0	0	330923	8.0292	NaN	Q
24	1	1	Sloper, Mr. William Thompson	male	28.0	0	0	113788	35.5000	A6	S
25	0	3	Palsson, Miss. Torborg Danira	female	8.0	3	1	349909	21.0750	NaN	S
26	1	3	Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...	female	38.0	1	5	347077	31.3875	NaN	S
27	0	3	Emir, Mr. Farred Chehab	male	NaN	0	0	2631	7.2250	NaN	C
28	0	1	Fortune, Mr. Charles Alexander	male	19.0	3	2	19950	263.0000	C23 C25 C27	S
29	1	3	O'Dwyer, Miss. Ellen "Nellie"	female	NaN	0	0	330959	7.8792	NaN	Q
30	0	3	Todoroff, Mr. Lalio	male	NaN	0	0	349216	7.8958	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...
862	0	2	Giles, Mr. Frederick Edward	male	21.0	1	0	28134	11.5000	NaN	S
863	1	1	Swift, Mrs. Frederick Joel (Margaret Welles Ba...	female	48.0	0	0	17466	25.9292	D17	S
864	0	3	Sage, Miss. Dorothy Edith "Dolly"	female	NaN	8	2	CA. 2343	69.5500	NaN	S
865	0	2	Gill, Mr. John William	male	24.0	0	0	233866	13.0000	NaN	S
866	1	2	Bystrom, Mrs. (Karolina)	female	42.0	0	0	236852	13.0000	NaN	S
867	1	2	Duran y More, Miss. Asuncion	female	27.0	1	0	SC/PARIS 2149	13.8583	NaN	C
868	0	1	Roebling, Mr. Washington Augustus II	male	31.0	0	0	PC 17590	50.4958	A24	S
869	0	3	van Melkebeke, Mr. Philemon	male	NaN	0	0	345777	9.5000	NaN	S
870	1	3	Johnson, Master. Harold Theodor	male	4.0	1	1	347742	11.1333	NaN	S
871	0	3	Balkic, Mr. Cerin	male	26.0	0	0	349248	7.8958	NaN	S
872	1	1	Beckwith, Mrs. Richard Leonard (Sallie Monypeny)	female	47.0	1	1	11751	52.5542	D35	S
873	0	1	Carlsson, Mr. Frans Olof	male	33.0	0	0	695	5.0000	B51 B53 B55	S
874	0	3	Vander Cruyssen, Mr. Victor	male	47.0	0	0	345765	9.0000	NaN	S
875	1	2	Abelson, Mrs. Samuel (Hannah Wizosky)	female	28.0	1	0	P/PP 3381	24.0000	NaN	C
876	1	3	Najib, Miss. Adele Kiamie "Jane"	female	15.0	0	0	2667	7.2250	NaN	C
877	0	3	Gustafsson, Mr. Alfred Ossian	male	20.0	0	0	7534	9.8458	NaN	S
878	0	3	Petroff, Mr. Nedelio	male	19.0	0	0	349212	7.8958	NaN	S
879	0	3	Laleff, Mr. Kristo	male	NaN	0	0	349217	7.8958	NaN	S
880	1	1	Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)	female	56.0	0	1	11767	83.1583	C50	C
881	1	2	Shelley, Mrs. William (Imanita Parrish Hall)	female	25.0	0	1	230433	26.0000	NaN	S
882	0	3	Markun, Mr. Johann	male	33.0	0	0	349257	7.8958	NaN	S
883	0	3	Dahlberg, Miss. Gerda Ulrika	female	22.0	0	0	7552	10.5167	NaN	S
884	0	2	Banfield, Mr. Frederick James	male	28.0	0	0	C.A./SOTON 34068	10.5000	NaN	S
885	0	3	Sutehall, Mr. Henry Jr	male	25.0	0	0	SOTON/OQ 392076	7.0500	NaN	S
886	0	3	Rice, Mrs. William (Margaret Norton)	female	39.0	0	5	382652	29.1250	NaN	Q
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

2.4.5 任務五：使用Panads的merge方法和DataFrame的append方法：完成任務二和任務三的任務

#how：連接方式，有inner、left、right、outer，默認為inner #left_index/right_index: 如果為True，則使用左側/右側DataFrame中的索引（行標簽）作為其連接鍵。對于具有MultiIndex（分層）的DataFrame，級別數必須與右側DataFrame中的連接鍵數相匹配。 c1 = pd.merge(train_left_up,train_right_up,left_index=True,right_index=True) c2 = pd.merge(train_left_down,train_right_down,left_index=True,right_index=True) result2 = c1.append(c2) result2 PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked01234567891011121314151617181920212223242526272829...422423424425426427428429430431432433434435436437438439440441442443444445446447448449450451

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
6	0	3	Moran, Mr. James	male	NaN	0	0	330877	8.4583	NaN	Q
7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S
8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	NaN	S
9	1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	347742	11.1333	NaN	S
10	1	2	Nasser, Mrs. Nicholas (Adele Achem)	female	14.0	1	0	237736	30.0708	NaN	C
11	1	3	Sandstrom, Miss. Marguerite Rut	female	4.0	1	1	PP 9549	16.7000	G6	S
12	1	1	Bonnell, Miss. Elizabeth	female	58.0	0	0	113783	26.5500	C103	S
13	0	3	Saundercock, Mr. William Henry	male	20.0	0	0	A/5. 2151	8.0500	NaN	S
14	0	3	Andersson, Mr. Anders Johan	male	39.0	1	5	347082	31.2750	NaN	S
15	0	3	Vestrom, Miss. Hulda Amanda Adolfina	female	14.0	0	0	350406	7.8542	NaN	S
16	1	2	Hewlett, Mrs. (Mary D Kingcome)	female	55.0	0	0	248706	16.0000	NaN	S
17	0	3	Rice, Master. Eugene	male	2.0	4	1	382652	29.1250	NaN	Q
18	1	2	Williams, Mr. Charles Eugene	male	NaN	0	0	244373	13.0000	NaN	S
19	0	3	Vander Planke, Mrs. Julius (Emelia Maria Vande...	female	31.0	1	0	345763	18.0000	NaN	S
20	1	3	Masselmani, Mrs. Fatima	female	NaN	0	0	2649	7.2250	NaN	C
21	0	2	Fynney, Mr. Joseph J	male	35.0	0	0	239865	26.0000	NaN	S
22	1	2	Beesley, Mr. Lawrence	male	34.0	0	0	248698	13.0000	D56	S
23	1	3	McGowan, Miss. Anna "Annie"	female	15.0	0	0	330923	8.0292	NaN	Q
24	1	1	Sloper, Mr. William Thompson	male	28.0	0	0	113788	35.5000	A6	S
25	0	3	Palsson, Miss. Torborg Danira	female	8.0	3	1	349909	21.0750	NaN	S
26	1	3	Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...	female	38.0	1	5	347077	31.3875	NaN	S
27	0	3	Emir, Mr. Farred Chehab	male	NaN	0	0	2631	7.2250	NaN	C
28	0	1	Fortune, Mr. Charles Alexander	male	19.0	3	2	19950	263.0000	C23 C25 C27	S
29	1	3	O'Dwyer, Miss. Ellen "Nellie"	female	NaN	0	0	330959	7.8792	NaN	Q
30	0	3	Todoroff, Mr. Lalio	male	NaN	0	0	349216	7.8958	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...
862	0	2	Giles, Mr. Frederick Edward	male	21.0	1	0	28134	11.5000	NaN	S
863	1	1	Swift, Mrs. Frederick Joel (Margaret Welles Ba...	female	48.0	0	0	17466	25.9292	D17	S
864	0	3	Sage, Miss. Dorothy Edith "Dolly"	female	NaN	8	2	CA. 2343	69.5500	NaN	S
865	0	2	Gill, Mr. John William	male	24.0	0	0	233866	13.0000	NaN	S
866	1	2	Bystrom, Mrs. (Karolina)	female	42.0	0	0	236852	13.0000	NaN	S
867	1	2	Duran y More, Miss. Asuncion	female	27.0	1	0	SC/PARIS 2149	13.8583	NaN	C
868	0	1	Roebling, Mr. Washington Augustus II	male	31.0	0	0	PC 17590	50.4958	A24	S
869	0	3	van Melkebeke, Mr. Philemon	male	NaN	0	0	345777	9.5000	NaN	S
870	1	3	Johnson, Master. Harold Theodor	male	4.0	1	1	347742	11.1333	NaN	S
871	0	3	Balkic, Mr. Cerin	male	26.0	0	0	349248	7.8958	NaN	S
872	1	1	Beckwith, Mrs. Richard Leonard (Sallie Monypeny)	female	47.0	1	1	11751	52.5542	D35	S
873	0	1	Carlsson, Mr. Frans Olof	male	33.0	0	0	695	5.0000	B51 B53 B55	S
874	0	3	Vander Cruyssen, Mr. Victor	male	47.0	0	0	345765	9.0000	NaN	S
875	1	2	Abelson, Mrs. Samuel (Hannah Wizosky)	female	28.0	1	0	P/PP 3381	24.0000	NaN	C
876	1	3	Najib, Miss. Adele Kiamie "Jane"	female	15.0	0	0	2667	7.2250	NaN	C
877	0	3	Gustafsson, Mr. Alfred Ossian	male	20.0	0	0	7534	9.8458	NaN	S
878	0	3	Petroff, Mr. Nedelio	male	19.0	0	0	349212	7.8958	NaN	S
879	0	3	Laleff, Mr. Kristo	male	NaN	0	0	349217	7.8958	NaN	S
880	1	1	Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)	female	56.0	0	1	11767	83.1583	C50	C
881	1	2	Shelley, Mrs. William (Imanita Parrish Hall)	female	25.0	0	1	230433	26.0000	NaN	S
882	0	3	Markun, Mr. Johann	male	33.0	0	0	349257	7.8958	NaN	S
883	0	3	Dahlberg, Miss. Gerda Ulrika	female	22.0	0	0	7552	10.5167	NaN	S
884	0	2	Banfield, Mr. Frederick James	male	28.0	0	0	C.A./SOTON 34068	10.5000	NaN	S
885	0	3	Sutehall, Mr. Henry Jr	male	25.0	0	0	SOTON/OQ 392076	7.0500	NaN	S
886	0	3	Rice, Mrs. William (Margaret Norton)	female	39.0	0	5	382652	29.1250	NaN	Q
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。思考一下在任務四和任務五的情況下，為什么都要求使用DataFrame的append方法，如何只要求使用merge或者join可不可以完成任務四和任務五呢？

2.4.6 任務六：完成的數據保存為result.csv

result.to_csv('result.csv')

2.5 換一種角度看數據

2.5.1 任務一：將我們的數據變為Series類型的數據

#DataFrame.stack()，將DataFrame轉Series，且把原來的列索引轉成了最內層的行索引(多層次索引) result_s = result.stack() result_s.head(30)# result_s.to_csv('result_Series.csv') 0 PassengerId 1Survived 0Pclass 3Name Braund, Mr. Owen HarrisSex maleAge 22SibSp 1Parch 0Ticket A/5 21171Fare 7.25Embarked S 1 PassengerId 2Survived 1Pclass 1Name Cumings, Mrs. John Bradley (Florence Briggs Th...Sex femaleAge 38SibSp 1Parch 0Ticket PC 17599Fare 71.2833Cabin C85Embarked C 2 PassengerId 3Survived 1Pclass 3Name Heikkinen, Miss. LainaSex femaleAge 26SibSp 0 dtype: object #寫入代碼 rs = pd.read_csv('result_Series.csv') rs.head(20) 0PassengerId1012345678910111213141516171819

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	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
1	Parch	0
1	Ticket	PC 17599
1	Fare	71.2833

開始之前，導入numpy、pandas包和數據

# 導入基本庫 import numpy as np import pandas as pd # 載入上一個任務人保存的文件中:result.csv，并查看這個文件 result = pd.read_csv('result.csv') result.head() Unnamed: 0PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked01234

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

2 第二章：數據重構

第一部分：數據聚合與運算

2.6 數據運用

2.6.1 任務一：通過教材《Python for Data Analysis》P303、Google or anything來學習了解GroupBy機制

dataframe.groupby()函數主要的作用是進行數據的分組以及分組后地組內運算！

df.groupby([df[屬性],df[屬性])(指分類的屬性，數據的限定定語，可有多個).mean()(指定數據的計算函數)

2.4.2：任務二：計算泰坦尼克號男性與女性的平均票價

sex_fare_mean = result['Fare'].groupby(result['Sex']).mean() sex_fare_mean Sex female 44.479818 male 25.523893 Name: Fare, dtype: float64

2.4.3：任務三：統計泰坦尼克號中男女的存活人數

sex_survived_sum = result['Survived'].groupby(result['Sex']).sum() sex_survived_sum#result['Survived'].groupby(result['Sex']).count() Sex female 233 male 109 Name: Survived, dtype: int64

2.4.4：任務四：計算客艙不同等級的存活人數

result['Survived'].groupby(result['Pclass']).sum() Pclass 1 136 2 87 3 119 Name: Survived, dtype: int64

【提示：】表中的存活那一欄，可以發現如果還活著記為1，死亡記為0

【思考】從數據分析的角度，上面的統計結果可以得出那些結論

#思考心得女性旅客的存活人數最高，客艙等級為1 的存活人數最高

【思考】從任務二到任務三中，這些運算可以通過agg()函數來同時計算。并且可以使用rename函數修改列名。你可以按照提示寫出這個過程嗎？

''' agg()函數通常用于調用groupby()函數之后，對數據做一些聚合操作(sum、count、max、mean等其他聚合函數) agg({'value1':'sum','value2':'mean'}) agg(['mean','max'])colNameDict = {'源數據列名':'新列名'} #將‘源數據列名’改為‘新列名’ df.rename(columns = colNameDict,inplace=True) '''result.groupby(result['Sex']).agg({'Fare':'mean','Survived':'sum'}).rename(columns={'Fare':'Fare_mean','Survived':'Survived_sum'}) Fare_meanSurvived_sumSexfemalemale

44.479818	233
25.523893	109

2.4.5：任務五：統計在不同等級的票中的不同年齡的船票花費的平均值

result['Fare'].groupby([result['Pclass'],result['Age']]).mean() Pclass Age 1 0.92 151.5500002.00 151.5500004.00 81.85830011.00 120.00000014.00 120.00000015.00 211.33750016.00 61.29306717.00 92.26110018.00 169.61250019.00 92.69250021.00 139.20693322.00 91.65666023.00 146.54443324.00 122.99761425.00 99.35696726.00 54.42500027.00 92.95730028.00 47.83020029.00 102.64583330.00 67.01736731.00 87.52750032.00 53.39585033.00 58.65000034.00 26.55000035.00 165.74491136.00 125.62361137.00 45.11806738.00 103.71180039.00 65.91832040.00 69.336660... 3 31.00 11.21607132.00 17.33575833.00 10.84478734.00 9.24895034.50 6.43750035.00 9.73680036.00 12.08193337.00 8.75625038.00 13.74895039.00 21.94583340.00 13.59916040.50 11.12500041.00 20.28332542.00 8.06667543.00 20.46666744.00 10.03125045.00 13.02584045.50 7.22500047.00 10.25000048.00 21.11460049.00 0.00000050.00 8.05000051.00 7.61806755.50 8.05000059.00 7.25000061.00 6.23750063.00 9.58750065.00 7.75000070.50 7.75000074.00 7.775000 Name: Fare, Length: 182, dtype: float64

2.4.6：任務六：將任務二和任務三的數據合并，并保存到sex_fare_survived.csv

pd.concat([sex_fare_mean,sex_survived_sum],axis=1) FareSurvivedSexfemalemale

44.479818	233
25.523893	109

2.4.7：任務七：得出不同年齡的總的存活人數，然后找出存活人數的最高的年齡，最后計算存活人數最高的存活率（存活人數/總人數）

#得出不同年齡的總的存活人數，然后找出存活人數的最高的年齡 Age_Survived_sum = result['Survived'].groupby(result['Age']).sum() Age_Survived_sum[Age_Survived_sum.values==Age_Survived_sum.max()] Age 24.0 15 Name: Survived, dtype: int64 #得出總的存活人數 result['Survived'].sum() 342 #計算存活人數最高的存活率（存活人數/總人數） Age_Survived_sum.max()/result['Survived'].sum() 0.043859649122807015

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