1 第一章：數據載入及初步觀察

1.1 載入數據

數據集下載 https://www.kaggle.com/c/titanic/overview

1.1.1 任務一：導入numpy和pandas

#寫入代碼 import numpy as np import pandas as pd import os

【提示】如果加載失敗，學會如何在你的python環境下安裝numpy和pandas這兩個庫

1.1.2 任務二：載入數據

(1) 使用相對路徑載入數據
(2) 使用絕對路徑載入數據

#寫入代碼 test_data = pd.read_csv('test_1.csv') f = open('E://study//master3//數據分析//DataWhale//Titanic//hands-on-data-analysis-master//hands-on-data-analysis-master//第一單元項目集合/train.csv') train_data = pd.read_csv(f) # test_data_t = pd.read_table('./test_1.csv') # os.getcwd() # test_data_t train_data.head(5) PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked01234

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

#寫入代碼 test_data.head(3) Unnamed: 0PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkeda012

0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S	100
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C	100
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S	100

【提示】相對路徑載入報錯時，嘗試使用os.getcwd()查看當前工作目錄。
【思考】知道數據加載的方法后，試試pd.read_csv()和pd.read_table()的不同，如果想讓他們效果一樣，需要怎么做？了解一下’.tsv’和’.csv’的不同，如何加載這兩個數據集？
【總結】加載的數據是所有工作的第一步，我們的工作會接觸到不同的數據格式（eg:.csv;.tsv;.xlsx）,但是加載的方法和思路都是一樣的，在以后工作和做項目的過程中，遇到之前沒有碰到的問題，要多多查資料嗎，使用googel，了解業務邏輯，明白輸入和輸出是什么。

1.1.3 任務三：每1000行為一個數據模塊，逐塊讀取

#寫入代碼 chunker = pd.read_csv('train.csv',chunksize=1000) for piece in chunker:print(type(piece))print(len(piece))print(piece) <class 'pandas.core.frame.DataFrame'> 891PassengerId Survived Pclass \ 0 1 0 3 1 2 1 1 2 3 1 3 3 4 1 1 4 5 0 3 5 6 0 3 6 7 0 1 7 8 0 3 8 9 1 3 9 10 1 2 10 11 1 3 11 12 1 1 12 13 0 3 13 14 0 3 14 15 0 3 15 16 1 2 16 17 0 3 17 18 1 2 18 19 0 3 19 20 1 3 20 21 0 2 21 22 1 2 22 23 1 3 23 24 1 1 24 25 0 3 25 26 1 3 26 27 0 3 27 28 0 1 28 29 1 3 29 30 0 3 .. ... ... ... 861 862 0 2 862 863 1 1 863 864 0 3 864 865 0 2 865 866 1 2 866 867 1 2 867 868 0 1 868 869 0 3 869 870 1 3 870 871 0 3 871 872 1 1 872 873 0 1 873 874 0 3 874 875 1 2 875 876 1 3 876 877 0 3 877 878 0 3 878 879 0 3 879 880 1 1 880 881 1 2 881 882 0 3 882 883 0 3 883 884 0 2 884 885 0 3 885 886 0 3 886 887 0 2 887 888 1 1 888 889 0 3 889 890 1 1 890 891 0 3 Name Sex Age SibSp \ 0 Braund, Mr. Owen Harris male 22.0 1 1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 2 Heikkinen, Miss. Laina female 26.0 0 3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 4 Allen, Mr. William Henry male 35.0 0 5 Moran, Mr. James male NaN 0 6 McCarthy, Mr. Timothy J male 54.0 0 7 Palsson, Master. Gosta Leonard male 2.0 3 8 Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) female 27.0 0 9 Nasser, Mrs. Nicholas (Adele Achem) female 14.0 1 10 Sandstrom, Miss. Marguerite Rut female 4.0 1 11 Bonnell, Miss. Elizabeth female 58.0 0 12 Saundercock, Mr. William Henry male 20.0 0 13 Andersson, Mr. Anders Johan male 39.0 1 14 Vestrom, Miss. Hulda Amanda Adolfina female 14.0 0 15 Hewlett, Mrs. (Mary D Kingcome) female 55.0 0 16 Rice, Master. Eugene male 2.0 4 17 Williams, Mr. Charles Eugene male NaN 0 18 Vander Planke, Mrs. Julius (Emelia Maria Vande... female 31.0 1 19 Masselmani, Mrs. Fatima female NaN 0 20 Fynney, Mr. Joseph J male 35.0 0 21 Beesley, Mr. Lawrence male 34.0 0 22 McGowan, Miss. Anna "Annie" female 15.0 0 23 Sloper, Mr. William Thompson male 28.0 0 24 Palsson, Miss. Torborg Danira female 8.0 3 25 Asplund, Mrs. Carl Oscar (Selma Augusta Emilia... female 38.0 1 26 Emir, Mr. Farred Chehab male NaN 0 27 Fortune, Mr. Charles Alexander male 19.0 3 28 O'Dwyer, Miss. Ellen "Nellie" female NaN 0 29 Todoroff, Mr. Lalio male NaN 0 .. ... ... ... ... 861 Giles, Mr. Frederick Edward male 21.0 1 862 Swift, Mrs. Frederick Joel (Margaret Welles Ba... female 48.0 0 863 Sage, Miss. Dorothy Edith "Dolly" female NaN 8 864 Gill, Mr. John William male 24.0 0 865 Bystrom, Mrs. (Karolina) female 42.0 0 866 Duran y More, Miss. Asuncion female 27.0 1 867 Roebling, Mr. Washington Augustus II male 31.0 0 868 van Melkebeke, Mr. Philemon male NaN 0 869 Johnson, Master. Harold Theodor male 4.0 1 870 Balkic, Mr. Cerin male 26.0 0 871 Beckwith, Mrs. Richard Leonard (Sallie Monypeny) female 47.0 1 872 Carlsson, Mr. Frans Olof male 33.0 0 873 Vander Cruyssen, Mr. Victor male 47.0 0 874 Abelson, Mrs. Samuel (Hannah Wizosky) female 28.0 1 875 Najib, Miss. Adele Kiamie "Jane" female 15.0 0 876 Gustafsson, Mr. Alfred Ossian male 20.0 0 877 Petroff, Mr. Nedelio male 19.0 0 878 Laleff, Mr. Kristo male NaN 0 879 Potter, Mrs. Thomas Jr (Lily Alexenia Wilson) female 56.0 0 880 Shelley, Mrs. William (Imanita Parrish Hall) female 25.0 0 881 Markun, Mr. Johann male 33.0 0 882 Dahlberg, Miss. Gerda Ulrika female 22.0 0 883 Banfield, Mr. Frederick James male 28.0 0 884 Sutehall, Mr. Henry Jr male 25.0 0 885 Rice, Mrs. William (Margaret Norton) female 39.0 0 886 Montvila, Rev. Juozas male 27.0 0 887 Graham, Miss. Margaret Edith female 19.0 0 888 Johnston, Miss. Catherine Helen "Carrie" female NaN 1 889 Behr, Mr. Karl Howell male 26.0 0 890 Dooley, Mr. Patrick male 32.0 0 Parch Ticket Fare Cabin Embarked 0 0 A/5 21171 7.2500 NaN S 1 0 PC 17599 71.2833 C85 C 2 0 STON/O2. 3101282 7.9250 NaN S 3 0 113803 53.1000 C123 S 4 0 373450 8.0500 NaN S 5 0 330877 8.4583 NaN Q 6 0 17463 51.8625 E46 S 7 1 349909 21.0750 NaN S 8 2 347742 11.1333 NaN S 9 0 237736 30.0708 NaN C 10 1 PP 9549 16.7000 G6 S 11 0 113783 26.5500 C103 S 12 0 A/5. 2151 8.0500 NaN S 13 5 347082 31.2750 NaN S 14 0 350406 7.8542 NaN S 15 0 248706 16.0000 NaN S 16 1 382652 29.1250 NaN Q 17 0 244373 13.0000 NaN S 18 0 345763 18.0000 NaN S 19 0 2649 7.2250 NaN C 20 0 239865 26.0000 NaN S 21 0 248698 13.0000 D56 S 22 0 330923 8.0292 NaN Q 23 0 113788 35.5000 A6 S 24 1 349909 21.0750 NaN S 25 5 347077 31.3875 NaN S 26 0 2631 7.2250 NaN C 27 2 19950 263.0000 C23 C25 C27 S 28 0 330959 7.8792 NaN Q 29 0 349216 7.8958 NaN S .. ... ... ... ... ... 861 0 28134 11.5000 NaN S 862 0 17466 25.9292 D17 S 863 2 CA. 2343 69.5500 NaN S 864 0 233866 13.0000 NaN S 865 0 236852 13.0000 NaN S 866 0 SC/PARIS 2149 13.8583 NaN C 867 0 PC 17590 50.4958 A24 S 868 0 345777 9.5000 NaN S 869 1 347742 11.1333 NaN S 870 0 349248 7.8958 NaN S 871 1 11751 52.5542 D35 S 872 0 695 5.0000 B51 B53 B55 S 873 0 345765 9.0000 NaN S 874 0 P/PP 3381 24.0000 NaN C 875 0 2667 7.2250 NaN C 876 0 7534 9.8458 NaN S 877 0 349212 7.8958 NaN S 878 0 349217 7.8958 NaN S 879 1 11767 83.1583 C50 C 880 1 230433 26.0000 NaN S 881 0 349257 7.8958 NaN S 882 0 7552 10.5167 NaN S 883 0 C.A./SOTON 34068 10.5000 NaN S 884 0 SOTON/OQ 392076 7.0500 NaN S 885 5 382652 29.1250 NaN Q 886 0 211536 13.0000 NaN S 887 0 112053 30.0000 B42 S 888 2 W./C. 6607 23.4500 NaN S 889 0 111369 30.0000 C148 C 890 0 370376 7.7500 NaN Q [891 rows x 12 columns]

【思考】什么是逐塊讀取？為什么要逐塊讀取呢？
將文本分成若干塊，每次處理chunksize行的數據，最終返回一個TextParser對象，對該對象進行迭代遍歷，可以完成逐塊統計的合并處理。
因為文本太大，需要一部分數據，或者需要一塊一塊進行處理。
【提示】大家可以chunker(數據塊)是什么類型？用for循環打印出來出處具體的樣子是什么？
DataFrame的數據類型

1.1.4 任務四：將表頭改成中文，索引改為乘客ID [對于某些英文資料，我們可以通過翻譯來更直觀的熟悉我們的數據]

PassengerId => 乘客ID
Survived => 是否幸存
Pclass => 乘客等級(1/2/3等艙位)
Name => 乘客姓名
Sex => 性別
Age => 年齡
SibSp => 堂兄弟/妹個數
Parch => 父母與小孩個數
Ticket => 船票信息
Fare => 票價
Cabin => 客艙
Embarked => 登船港口

#寫入代碼 train_data = pd.read_csv('train.csv',names=['乘客ID','是否幸存','倉位等級','姓名','性別','年齡','兄弟姐妹個數','父母子女個數','船票信息','票價','客艙','登船港口'],index_col='乘客ID',header=0) train_data.head(3) 是否幸存倉位等級姓名性別年齡兄弟姐妹個數父母子女個數船票信息票價客艙登船港口乘客ID123

0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S

【思考】所謂將表頭改為中文其中一個思路是：將英文列名表頭替換成中文。還有其他的方法嗎？

1.2 初步觀察

導入數據后，你可能要對數據的整體結構和樣例進行概覽，比如說，數據大小、有多少列，各列都是什么格式的，是否包含null等

1.2.1 任務一：查看數據的基本信息

#寫入代碼 train_data.info() <class 'pandas.core.frame.DataFrame'> Int64Index: 891 entries, 1 to 891 Data columns (total 11 columns): 是否幸存 891 non-null int64 倉位等級 891 non-null int64 姓名 891 non-null object 性別 891 non-null object 年齡 714 non-null float64 兄弟姐妹個數 891 non-null int64 父母子女個數 891 non-null int64 船票信息 891 non-null object 票價 891 non-null float64 客艙 204 non-null object 登船港口 889 non-null object dtypes: float64(2), int64(4), object(5) memory usage: 83.5+ KB

【提示】有多個函數可以這樣做，你可以做一下總結

train_data.describe() 是否幸存倉位等級年齡兄弟姐妹個數父母子女個數票價countmeanstdmin25%50%75%max

891.000000	891.000000	714.000000	891.000000	891.000000	891.000000
0.383838	2.308642	29.699118	0.523008	0.381594	32.204208
0.486592	0.836071	14.526497	1.102743	0.806057	49.693429
0.000000	1.000000	0.420000	0.000000	0.000000	0.000000
0.000000	2.000000	20.125000	0.000000	0.000000	7.910400
0.000000	3.000000	28.000000	0.000000	0.000000	14.454200
1.000000	3.000000	38.000000	1.000000	0.000000	31.000000
1.000000	3.000000	80.000000	8.000000	6.000000	512.329200

1.2.2 任務二：觀察表格前10行的數據和后15行的數據

#寫入代碼 train_data.head(10) 是否幸存倉位等級姓名性別年齡兄弟姐妹個數父母子女個數船票信息票價客艙登船港口乘客ID12345678910

0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
0	3	Moran, Mr. James	male	NaN	0	0	330877	8.4583	NaN	Q
0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S
0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	NaN	S
1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	347742	11.1333	NaN	S
1	2	Nasser, Mrs. Nicholas (Adele Achem)	female	14.0	1	0	237736	30.0708	NaN	C

#寫入代碼 train_data.tail(15) 是否幸存倉位等級姓名性別年齡兄弟姐妹個數父母子女個數船票信息票價客艙登船港口乘客ID877878879880881882883884885886887888889890891

0	3	Gustafsson, Mr. Alfred Ossian	male	20.0	0	0	7534	9.8458	NaN	S
0	3	Petroff, Mr. Nedelio	male	19.0	0	0	349212	7.8958	NaN	S
0	3	Laleff, Mr. Kristo	male	NaN	0	0	349217	7.8958	NaN	S
1	1	Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)	female	56.0	0	1	11767	83.1583	C50	C
1	2	Shelley, Mrs. William (Imanita Parrish Hall)	female	25.0	0	1	230433	26.0000	NaN	S
0	3	Markun, Mr. Johann	male	33.0	0	0	349257	7.8958	NaN	S
0	3	Dahlberg, Miss. Gerda Ulrika	female	22.0	0	0	7552	10.5167	NaN	S
0	2	Banfield, Mr. Frederick James	male	28.0	0	0	C.A./SOTON 34068	10.5000	NaN	S
0	3	Sutehall, Mr. Henry Jr	male	25.0	0	0	SOTON/OQ 392076	7.0500	NaN	S
0	3	Rice, Mrs. William (Margaret Norton)	female	39.0	0	5	382652	29.1250	NaN	Q
0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S
1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C
0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q

1.2.4 任務三：判斷數據是否為空，為空的地方返回True，其余地方返回False

#寫入代碼 train_data.isnull().head() 是否幸存倉位等級姓名性別年齡兄弟姐妹個數父母子女個數船票信息票價客艙登船港口乘客ID12345

False	False	False	False	False	False	False	False	False	True	False
False	False	False	False	False	False	False	False	False	False	False
False	False	False	False	False	False	False	False	False	True	False
False	False	False	False	False	False	False	False	False	False	False
False	False	False	False	False	False	False	False	False	True	False

【總結】上面的操作都是數據分析中對于數據本身的觀察

【思考】對于一個數據，還可以從哪些方面來觀察？找找答案，這個將對下面的數據分析有很大的幫助

1.3 保存數據

1.3.1 任務一：將你加載并做出改變的數據，在工作目錄下保存為一個新文件train_chinese.csv

#寫入代碼 # 注意：不同的操作系統保存下來可能會有亂碼。大家可以加入`encoding='GBK' 或者 ’encoding = ’uft-8‘‘` train_data.to_csv('train_Chinese.csv',encoding='utf-8')

【總結】數據的加載以及入門，接下來就要接觸數據本身的運算，我們將主要掌握numpy和pandas在工作和項目場景的運用。

1 第一章：數據載入及初步觀察

1.4 知道你的數據叫什么

我們學習pandas的基礎操作，那么上一節通過pandas加載之后的數據，其數據類型是什么呢？

開始前導入numpy和pandas

import numpy as np import pandas as pd

1.4.1 任務一：pandas中有兩個數據類型DateFrame和Series，通過查找簡單了解他們。然后自己寫一個關于這兩個數據類型的小例子🌰[開放題]

https://www.cnblogs.com/lavender1221/p/12664641.html#
Pandas的核心是三大數據結構：Series、DataFrame和Index。絕大多數操作都是圍繞這三種結構進行的。

Series是一個一維的數組對象，它包含一個值序列和一個對應的索引序列。 Numpy的一維數組通過隱式定義的整數索引獲取元素值，而Series用一種顯式定義的索引與元素關聯。顯式索引讓Series對象擁有更強的能力，索引也不再僅僅是整數，還可以是別的類型，比如字符串，索引也不需要連續，也可以重復，自由度非常高。

DataFrame是Pandas的核心數據結構，表示的是二維的矩陣數據表，類似關系型數據庫的結構，每一列可以是不同的值類型，比如數值、字符串、布爾值等等。DataFrame既有行索引，也有列索引，它可以被看做為一個共享相同索引的Series的字典。

創建DataFrame對象的方法有很多，最常用的是利用包含等長度列表或Numpy數組的字典來生成。可以查看DataFrame對象的columns和index屬性。

#寫入代碼 sdata_1 = [7,-2,567,8] example_1 = pd.Series(sdata_1,index = ['a','b','c','d']) example_1 a 7 b -2 c 567 d 8 dtype: int64 sdata_2 = {'a':7,'b':-2,'c':567,'d':8} example_2 = pd.Series(sdata_2) example_2 a 7 b -2 c 567 d 8 dtype: int64 sdata_3 = {'city':['nanjing','wuxi','wuhan','changsha'],'code':['001','002','003','004']} example_3 = pd.DataFrame(sdata_3) example_3 citycode0123

nanjing	001
wuxi	002
wuhan	003
changsha	004

''' #我們舉的例子 sdata = {'Ohio': 35000, 'Texas': 71000, 'Oregon': 16000, 'Utah': 5000} example_1 = pd.Series(sdata) example_1 ''' '''#我們舉的例子data = {'state': ['Ohio', 'Ohio', 'Ohio', 'Nevada', 'Nevada', 'Nevada'], 'year': [2000, 2001, 2002, 2001, 2002, 2003],'pop': [1.5, 1.7, 3.6, 2.4, 2.9, 3.2]}example_2 = pd.DataFrame(data)example_2'''

1.4.2 任務二：根據上節課的方法載入"train.csv"文件

#寫入代碼train_chinese = pd.read_csv('train_Chinese.csv')train_chinese.head()train_data = pd.read_csv('train.csv')

也可以加載上一節課保存的"train_chinese.csv"文件。通過翻譯版train_chinese.csv熟悉了這個數據集，然后我們對trian.csv來進行操作

1.4.3 任務三：查看DataFrame數據的每列的名稱

#寫入代碼train_chinese.columns Index(['乘客ID', '是否幸存', '倉位等級', '姓名', '性別', '年齡', '兄弟姐妹個數', '父母子女個數', '船票信息', '票價', '客艙', '登船港口'], dtype='object') train_data.columns Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp', 'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'], dtype='object') train_data.head() PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked01234

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

1.4.4任務四：查看"Cabin"這列的所有值[有多種方法]

#寫入代碼train_data['Cabin'].head() 0 NaN1 C852 NaN3 C1234 NaNName: Cabin, dtype: object #寫入代碼train_data.Cabin.head() 0 NaN1 C852 NaN3 C1234 NaNName: Cabin, dtype: object

1.4.5 任務五：加載文件"test_1.csv"，然后對比"train.csv"，看看有哪些多出的列，然后將多出的列刪除

經過我們的觀察發現一個測試集test_1.csv有一列是多余的，我們需要將這個多余的列刪去

#寫入代碼test_data = pd.read_csv('test_1.csv')test_data.head() Unnamed: 0PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkeda01234

0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S	100
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C	100
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S	100
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S	100
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S	100

#寫入代碼test_data.pop('a').head()test_data Unnamed: 0PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked01234567891011121314151617181920212223242526272829...861862863864865866867868869870871872873874875876877878879880881882883884885886887888889890

0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S
1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C
2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S
3	4	1	1	Futrelle, Mrs. Jacques Heath (Lily May Peel)	female	35.0	1	0	113803	53.1000	C123	S
4	5	0	3	Allen, Mr. William Henry	male	35.0	0	0	373450	8.0500	NaN	S
5	6	0	3	Moran, Mr. James	male	NaN	0	0	330877	8.4583	NaN	Q
6	7	0	1	McCarthy, Mr. Timothy J	male	54.0	0	0	17463	51.8625	E46	S
7	8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	NaN	S
8	9	1	3	Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)	female	27.0	0	2	347742	11.1333	NaN	S
9	10	1	2	Nasser, Mrs. Nicholas (Adele Achem)	female	14.0	1	0	237736	30.0708	NaN	C
10	11	1	3	Sandstrom, Miss. Marguerite Rut	female	4.0	1	1	PP 9549	16.7000	G6	S
11	12	1	1	Bonnell, Miss. Elizabeth	female	58.0	0	0	113783	26.5500	C103	S
12	13	0	3	Saundercock, Mr. William Henry	male	20.0	0	0	A/5. 2151	8.0500	NaN	S
13	14	0	3	Andersson, Mr. Anders Johan	male	39.0	1	5	347082	31.2750	NaN	S
14	15	0	3	Vestrom, Miss. Hulda Amanda Adolfina	female	14.0	0	0	350406	7.8542	NaN	S
15	16	1	2	Hewlett, Mrs. (Mary D Kingcome)	female	55.0	0	0	248706	16.0000	NaN	S
16	17	0	3	Rice, Master. Eugene	male	2.0	4	1	382652	29.1250	NaN	Q
17	18	1	2	Williams, Mr. Charles Eugene	male	NaN	0	0	244373	13.0000	NaN	S
18	19	0	3	Vander Planke, Mrs. Julius (Emelia Maria Vande...	female	31.0	1	0	345763	18.0000	NaN	S
19	20	1	3	Masselmani, Mrs. Fatima	female	NaN	0	0	2649	7.2250	NaN	C
20	21	0	2	Fynney, Mr. Joseph J	male	35.0	0	0	239865	26.0000	NaN	S
21	22	1	2	Beesley, Mr. Lawrence	male	34.0	0	0	248698	13.0000	D56	S
22	23	1	3	McGowan, Miss. Anna "Annie"	female	15.0	0	0	330923	8.0292	NaN	Q
23	24	1	1	Sloper, Mr. William Thompson	male	28.0	0	0	113788	35.5000	A6	S
24	25	0	3	Palsson, Miss. Torborg Danira	female	8.0	3	1	349909	21.0750	NaN	S
25	26	1	3	Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...	female	38.0	1	5	347077	31.3875	NaN	S
26	27	0	3	Emir, Mr. Farred Chehab	male	NaN	0	0	2631	7.2250	NaN	C
27	28	0	1	Fortune, Mr. Charles Alexander	male	19.0	3	2	19950	263.0000	C23 C25 C27	S
28	29	1	3	O'Dwyer, Miss. Ellen "Nellie"	female	NaN	0	0	330959	7.8792	NaN	Q
29	30	0	3	Todoroff, Mr. Lalio	male	NaN	0	0	349216	7.8958	NaN	S
...	...	...	...	...	...	...	...	...	...	...	...	...
861	862	0	2	Giles, Mr. Frederick Edward	male	21.0	1	0	28134	11.5000	NaN	S
862	863	1	1	Swift, Mrs. Frederick Joel (Margaret Welles Ba...	female	48.0	0	0	17466	25.9292	D17	S
863	864	0	3	Sage, Miss. Dorothy Edith "Dolly"	female	NaN	8	2	CA. 2343	69.5500	NaN	S
864	865	0	2	Gill, Mr. John William	male	24.0	0	0	233866	13.0000	NaN	S
865	866	1	2	Bystrom, Mrs. (Karolina)	female	42.0	0	0	236852	13.0000	NaN	S
866	867	1	2	Duran y More, Miss. Asuncion	female	27.0	1	0	SC/PARIS 2149	13.8583	NaN	C
867	868	0	1	Roebling, Mr. Washington Augustus II	male	31.0	0	0	PC 17590	50.4958	A24	S
868	869	0	3	van Melkebeke, Mr. Philemon	male	NaN	0	0	345777	9.5000	NaN	S
869	870	1	3	Johnson, Master. Harold Theodor	male	4.0	1	1	347742	11.1333	NaN	S
870	871	0	3	Balkic, Mr. Cerin	male	26.0	0	0	349248	7.8958	NaN	S
871	872	1	1	Beckwith, Mrs. Richard Leonard (Sallie Monypeny)	female	47.0	1	1	11751	52.5542	D35	S
872	873	0	1	Carlsson, Mr. Frans Olof	male	33.0	0	0	695	5.0000	B51 B53 B55	S
873	874	0	3	Vander Cruyssen, Mr. Victor	male	47.0	0	0	345765	9.0000	NaN	S
874	875	1	2	Abelson, Mrs. Samuel (Hannah Wizosky)	female	28.0	1	0	P/PP 3381	24.0000	NaN	C
875	876	1	3	Najib, Miss. Adele Kiamie "Jane"	female	15.0	0	0	2667	7.2250	NaN	C
876	877	0	3	Gustafsson, Mr. Alfred Ossian	male	20.0	0	0	7534	9.8458	NaN	S
877	878	0	3	Petroff, Mr. Nedelio	male	19.0	0	0	349212	7.8958	NaN	S
878	879	0	3	Laleff, Mr. Kristo	male	NaN	0	0	349217	7.8958	NaN	S
879	880	1	1	Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)	female	56.0	0	1	11767	83.1583	C50	C
880	881	1	2	Shelley, Mrs. William (Imanita Parrish Hall)	female	25.0	0	1	230433	26.0000	NaN	S
881	882	0	3	Markun, Mr. Johann	male	33.0	0	0	349257	7.8958	NaN	S
882	883	0	3	Dahlberg, Miss. Gerda Ulrika	female	22.0	0	0	7552	10.5167	NaN	S
883	884	0	2	Banfield, Mr. Frederick James	male	28.0	0	0	C.A./SOTON 34068	10.5000	NaN	S
884	885	0	3	Sutehall, Mr. Henry Jr	male	25.0	0	0	SOTON/OQ 392076	7.0500	NaN	S
885	886	0	3	Rice, Mrs. William (Margaret Norton)	female	39.0	0	5	382652	29.1250	NaN	Q
886	887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
887	888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
888	889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S
889	890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C
890	891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q

891 rows × 13 columns

【思考】還有其他的刪除多余的列的方式嗎？

# 思考回答del test_data['a']test_data.head() Unnamed: 0PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked01234

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

1.4.6 任務六： 將[‘PassengerId’,‘Name’,‘Age’,‘Ticket’]這幾個列元素隱藏，只觀察其他幾個列元素

#寫入代碼test_data.drop(['PassengerId','Name','Age','Ticket'],axis=1).head() Unnamed: 0SurvivedPclassSexSibSpParchFareCabinEmbarked01234

0	0	3	male	1	0	7.2500	NaN	S
1	1	1	female	1	0	71.2833	C85	C
2	1	3	female	0	0	7.9250	NaN	S
3	1	1	female	1	0	53.1000	C123	S
4	0	3	male	0	0	8.0500	NaN	S

【思考】對比任務五和任務六，是不是使用了不一樣的方法(函數)，如果使用一樣的函數如何完成上面的不同的要求呢？

【思考回答】

如果想要完全的刪除你的數據結構，使用inplace=True，因為使用inplace就將原數據覆蓋了，所以這里沒有用

1.5 篩選的邏輯

表格數據中，最重要的一個功能就是要具有可篩選的能力，選出我所需要的信息，丟棄無用的信息。

下面我們還是用實戰來學習pandas這個功能。

1.5.1 任務一： 我們以"Age"為篩選條件，顯示年齡在10歲以下的乘客信息。

#寫入代碼test_data[test_data['Age']<10].head() Unnamed: 0PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked710162443

7	8	0	3	Palsson, Master. Gosta Leonard	male	2.0	3	1	349909	21.0750	NaN	S
10	11	1	3	Sandstrom, Miss. Marguerite Rut	female	4.0	1	1	PP 9549	16.7000	G6	S
16	17	0	3	Rice, Master. Eugene	male	2.0	4	1	382652	29.1250	NaN	Q
24	25	0	3	Palsson, Miss. Torborg Danira	female	8.0	3	1	349909	21.0750	NaN	S
43	44	1	2	Laroche, Miss. Simonne Marie Anne Andree	female	3.0	1	2	SC/Paris 2123	41.5792	NaN	C

1.5.2 任務二： 以"Age"為條件，將年齡在10歲以上和50歲以下的乘客信息顯示出來，并將這個數據命名為midage

#寫入代碼midage = test_data[(test_data['Age']>10) & (test_data['Age']<50)]midage.head() Unnamed: 0PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked01234

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

【提示】了解pandas的條件篩選方式以及如何使用交集和并集操作

1.5.3 任務三：將midage的數據中第100行的"Pclass"和"Sex"的數據顯示出來

#寫入代碼midage = midage.reset_index()midage.head() indexUnnamed: 0PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked01234

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

【提示】在抽取數據中，我們希望數據的相對順序保持不變，用什么函數可以達到這個效果呢？
reset_index()函數: 使用索引重置生成一個新的DataFrame或Series，可以把索引用作列。保留原索引，即保持數據的相對順序

midage.loc[[100],['Pclass','Sex']] PclassSex100

2	male

1.5.4 任務四：使用loc方法將midage的數據中第100，105，108行的"Pclass"，"Name"和"Sex"的數據顯示出來

#寫入代碼midage.loc[[100,105,108],['Pclass','Name','Sex']] #因為你主動的延長了行的距離，所以會產生表格形式 PclassNameSex100105108

2	Byles, Rev. Thomas Roussel Davids	male
3	Cribb, Mr. John Hatfield	male
3	Calic, Mr. Jovo	male

1.5.5 任務五：使用iloc方法將midage的數據中第100，105，108行的"Pclass"，"Name"和"Sex"的數據顯示出來

#寫入代碼midage.iloc[[100,105,108],[4,5,6]] #iloc的行和列都按照整數，不能按照列名 PclassNameSex100105108

2	Byles, Rev. Thomas Roussel Davids	male
3	Cribb, Mr. John Hatfield	male
3	Calic, Mr. Jovo	male

【思考】對比iloc和loc的異同
iloc是按照行數取值，而loc按著index名取值

復習：在前面我們已經學習了Pandas基礎，知道利用Pandas讀取csv數據的增刪查改，今天我們要學習的就是探索性數據分析，主要介紹如何利用Pandas進行排序、算術計算以及計算描述函數describe()的使用。

1 第一章：探索性數據分析

開始之前，導入numpy、pandas包和數據

#加載所需的庫 import numpy as np import pandas as pd #載入之前保存的train_chinese.csv數據，關于泰坦尼克號的任務，我們就使用這個數據 train_data = pd.read_csv('train_Chinese.csv')

1.6 了解你的數據嗎？

教材《Python for Data Analysis》第五章

1.6.1 任務一：利用Pandas對示例數據進行排序，要求升序

# 具體請看《利用Python進行數據分析》第五章 排序和排名 部分#自己構建一個都為數字的DataFrame數據''' 我們舉了一個例子 pd.DataFrame() ：創建一個DataFrame對象 np.arange(8).reshape((2, 4)) : 生成一個二維數組（2*4）,第一列：0，1，2，3 第二列：4，5，6，7 index=[2，1] ：DataFrame 對象的索引列 columns=['d', 'a', 'b', 'c'] ：DataFrame 對象的索引行 ''' frame = pd.DataFrame(np.arange(8).reshape(2,4),index=[2,1],columns=['d','a','b','c']) frame dabc21

0	1	2	3
4	5	6	7

【代碼解析】

pd.DataFrame() ：創建一個DataFrame對象

np.arange(8).reshape((2, 4)) : 生成一個二維數組（2*4）,第一列：0，1，2，3 第二列：4，5，6，7

index=['2, 1] ：DataFrame 對象的索引列

columns=[‘d’, ‘a’, ‘b’, ‘c’] ：DataFrame 對象的索引行

【問題】：大多數時候我們都是想根據列的值來排序,所以將你構建的DataFrame中的數據根據某一列，升序排列

#回答代碼 frame.sort_values(by = 'c',ascending = True) dabc21

0	1	2	3
4	5	6	7

【思考】通過書本你能說出Pandas對DataFrame數據的其他排序方式嗎？
sort_index()對索引進行排序，axis=1是對列

frame.sort_index() dabc12

4	5	6	7
0	1	2	3

【總結】下面將不同的排序方式做一個總結

1.讓行索引升序排序

#代碼frame.sort_index() dabc12

4	5	6	7
0	1	2	3

2.讓列索引升序排序

#代碼frame.sort_index(axis=1) abcd21

1	2	3	0
5	6	7	4

3.讓列索引降序排序

#代碼frame.sort_index(axis=1,ascending=False) dcba21

0	3	2	1
4	7	6	5

4.讓任選兩列數據同時降序排序

#代碼frame.sort_values(['a','c'],ascending=False) dabc12

4	5	6	7
0	1	2	3

1.6.2 任務二：對泰坦尼克號數據（trian.csv）按票價和年齡兩列進行綜合排序（降序排列），從這個數據中你可以分析出什么？

'''在開始我們已經導入了train_chinese.csv數據，而且前面我們也學習了導入數據過程，根據上面學習，我們直接對目標列進行排序即可head(20) : 讀取前20條數據'''train_data.head(20) 乘客ID是否幸存倉位等級姓名性別年齡兄弟姐妹個數父母子女個數船票信息票價客艙登船港口012345678910111213141516171819

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

#代碼train_data.sort_values(['票價','年齡'],ascending=False) 乘客ID是否幸存倉位等級姓名性別年齡兄弟姐妹個數父母子女個數船票信息票價客艙登船港口6792587374383418827742311299118380716700557527377779730689856318268609332498708297305195...611477129804825411143654202371818843326872378597263806822179271302277413466481633674732815

680	1	1	Cardeza, Mr. Thomas Drake Martinez	male	36.00	0	1	PC 17755	512.3292	B51 B53 B55	C
259	1	1	Ward, Miss. Anna	female	35.00	0	0	PC 17755	512.3292	NaN	C
738	1	1	Lesurer, Mr. Gustave J	male	35.00	0	0	PC 17755	512.3292	B101	C
439	0	1	Fortune, Mr. Mark	male	64.00	1	4	19950	263.0000	C23 C25 C27	S
342	1	1	Fortune, Miss. Alice Elizabeth	female	24.00	3	2	19950	263.0000	C23 C25 C27	S
89	1	1	Fortune, Miss. Mabel Helen	female	23.00	3	2	19950	263.0000	C23 C25 C27	S
28	0	1	Fortune, Mr. Charles Alexander	male	19.00	3	2	19950	263.0000	C23 C25 C27	S
743	1	1	Ryerson, Miss. Susan Parker "Suzette"	female	21.00	2	2	PC 17608	262.3750	B57 B59 B63 B66	C
312	1	1	Ryerson, Miss. Emily Borie	female	18.00	2	2	PC 17608	262.3750	B57 B59 B63 B66	C
300	1	1	Baxter, Mrs. James (Helene DeLaudeniere Chaput)	female	50.00	0	1	PC 17558	247.5208	B58 B60	C
119	0	1	Baxter, Mr. Quigg Edmond	male	24.00	0	1	PC 17558	247.5208	B58 B60	C
381	1	1	Bidois, Miss. Rosalie	female	42.00	0	0	PC 17757	227.5250	NaN	C
717	1	1	Endres, Miss. Caroline Louise	female	38.00	0	0	PC 17757	227.5250	C45	C
701	1	1	Astor, Mrs. John Jacob (Madeleine Talmadge Force)	female	18.00	1	0	PC 17757	227.5250	C62 C64	C
558	0	1	Robbins, Mr. Victor	male	NaN	0	0	PC 17757	227.5250	NaN	C
528	0	1	Farthing, Mr. John	male	NaN	0	0	PC 17483	221.7792	C95	S
378	0	1	Widener, Mr. Harry Elkins	male	27.00	0	2	113503	211.5000	C82	C
780	1	1	Robert, Mrs. Edward Scott (Elisabeth Walton Mc...	female	43.00	0	1	24160	211.3375	B3	S
731	1	1	Allen, Miss. Elisabeth Walton	female	29.00	0	0	24160	211.3375	B5	S
690	1	1	Madill, Miss. Georgette Alexandra	female	15.00	0	1	24160	211.3375	B5	S
857	1	1	Wick, Mrs. George Dennick (Mary Hitchcock)	female	45.00	1	1	36928	164.8667	NaN	S
319	1	1	Wick, Miss. Mary Natalie	female	31.00	0	2	36928	164.8667	C7	S
269	1	1	Graham, Mrs. William Thompson (Edith Junkins)	female	58.00	0	1	PC 17582	153.4625	C125	S
610	1	1	Shutes, Miss. Elizabeth W	female	40.00	0	0	PC 17582	153.4625	C125	S
333	0	1	Graham, Mr. George Edward	male	38.00	0	1	PC 17582	153.4625	C91	S
499	0	1	Allison, Mrs. Hudson J C (Bessie Waldo Daniels)	female	25.00	1	2	113781	151.5500	C22 C26	S
709	1	1	Cleaver, Miss. Alice	female	22.00	0	0	113781	151.5500	NaN	S
298	0	1	Allison, Miss. Helen Loraine	female	2.00	1	2	113781	151.5500	C22 C26	S
306	1	1	Allison, Master. Hudson Trevor	male	0.92	1	2	113781	151.5500	C22 C26	S
196	1	1	Lurette, Miss. Elise	female	58.00	0	0	PC 17569	146.5208	B80	C
...	...	...	...	...	...	...	...	...	...	...	...
612	0	3	Jardin, Mr. Jose Neto	male	NaN	0	0	SOTON/O.Q. 3101305	7.0500	NaN	S
478	0	3	Braund, Mr. Lewis Richard	male	29.00	1	0	3460	7.0458	NaN	S
130	0	3	Ekstrom, Mr. Johan	male	45.00	0	0	347061	6.9750	NaN	S
805	1	3	Hedman, Mr. Oskar Arvid	male	27.00	0	0	347089	6.9750	NaN	S
826	0	3	Flynn, Mr. John	male	NaN	0	0	368323	6.9500	NaN	Q
412	0	3	Hart, Mr. Henry	male	NaN	0	0	394140	6.8583	NaN	Q
144	0	3	Burke, Mr. Jeremiah	male	19.00	0	0	365222	6.7500	NaN	Q
655	0	3	Hegarty, Miss. Hanora "Nora"	female	18.00	0	0	365226	6.7500	NaN	Q
203	0	3	Johanson, Mr. Jakob Alfred	male	34.00	0	0	3101264	6.4958	NaN	S
372	0	3	Wiklund, Mr. Jakob Alfred	male	18.00	1	0	3101267	6.4958	NaN	S
819	0	3	Holm, Mr. John Fredrik Alexander	male	43.00	0	0	C 7075	6.4500	NaN	S
844	0	3	Lemberopolous, Mr. Peter L	male	34.50	0	0	2683	6.4375	NaN	C
327	0	3	Nysveen, Mr. Johan Hansen	male	61.00	0	0	345364	6.2375	NaN	S
873	0	1	Carlsson, Mr. Frans Olof	male	33.00	0	0	695	5.0000	B51 B53 B55	S
379	0	3	Betros, Mr. Tannous	male	20.00	0	0	2648	4.0125	NaN	C
598	0	3	Johnson, Mr. Alfred	male	49.00	0	0	LINE	0.0000	NaN	S
264	0	1	Harrison, Mr. William	male	40.00	0	0	112059	0.0000	B94	S
807	0	1	Andrews, Mr. Thomas Jr	male	39.00	0	0	112050	0.0000	A36	S
823	0	1	Reuchlin, Jonkheer. John George	male	38.00	0	0	19972	0.0000	NaN	S
180	0	3	Leonard, Mr. Lionel	male	36.00	0	0	LINE	0.0000	NaN	S
272	1	3	Tornquist, Mr. William Henry	male	25.00	0	0	LINE	0.0000	NaN	S
303	0	3	Johnson, Mr. William Cahoone Jr	male	19.00	0	0	LINE	0.0000	NaN	S
278	0	2	Parkes, Mr. Francis "Frank"	male	NaN	0	0	239853	0.0000	NaN	S
414	0	2	Cunningham, Mr. Alfred Fleming	male	NaN	0	0	239853	0.0000	NaN	S
467	0	2	Campbell, Mr. William	male	NaN	0	0	239853	0.0000	NaN	S
482	0	2	Frost, Mr. Anthony Wood "Archie"	male	NaN	0	0	239854	0.0000	NaN	S
634	0	1	Parr, Mr. William Henry Marsh	male	NaN	0	0	112052	0.0000	NaN	S
675	0	2	Watson, Mr. Ennis Hastings	male	NaN	0	0	239856	0.0000	NaN	S
733	0	2	Knight, Mr. Robert J	male	NaN	0	0	239855	0.0000	NaN	S
816	0	1	Fry, Mr. Richard	male	NaN	0	0	112058	0.0000	B102	S

891 rows × 12 columns

【思考】排序后，如果我們僅僅關注年齡和票價兩列。根據常識我知道發現票價越高的應該客艙越好，所以我們會明顯看出，票價前20的乘客中存活的有14人，這是相當高的一個比例，那么我們后面是不是可以進一步分析一下票價和存活之間的關系，年齡和存活之間的關系呢？當你開始發現數據之間的關系了，數據分析就開始了。

當然，這只是我的想法，你還可以有更多想法，歡迎寫在你的學習筆記中。
存活數與男女之間的關系

多做幾個數據的排序

#代碼train_data.sort_values(['兄弟姐妹個數','父母子女個數','性別'],ascending=False).head(20) 乘客ID是否幸存倉位等級姓名性別年齡兄弟姐妹個數父母子女個數船票信息票價客艙登船港口159201324846180792863593864806837118226185068119233541542

160	0	3	Sage, Master. Thomas Henry	male	NaN	8	2	CA. 2343	69.5500	NaN	S
202	0	3	Sage, Mr. Frederick	male	NaN	8	2	CA. 2343	69.5500	NaN	S
325	0	3	Sage, Mr. George John Jr	male	NaN	8	2	CA. 2343	69.5500	NaN	S
847	0	3	Sage, Mr. Douglas Bullen	male	NaN	8	2	CA. 2343	69.5500	NaN	S
181	0	3	Sage, Miss. Constance Gladys	female	NaN	8	2	CA. 2343	69.5500	NaN	S
793	0	3	Sage, Miss. Stella Anna	female	NaN	8	2	CA. 2343	69.5500	NaN	S
864	0	3	Sage, Miss. Dorothy Edith "Dolly"	female	NaN	8	2	CA. 2343	69.5500	NaN	S
60	0	3	Goodwin, Master. William Frederick	male	11.0	5	2	CA 2144	46.9000	NaN	S
387	0	3	Goodwin, Master. Sidney Leonard	male	1.0	5	2	CA 2144	46.9000	NaN	S
481	0	3	Goodwin, Master. Harold Victor	male	9.0	5	2	CA 2144	46.9000	NaN	S
684	0	3	Goodwin, Mr. Charles Edward	male	14.0	5	2	CA 2144	46.9000	NaN	S
72	0	3	Goodwin, Miss. Lillian Amy	female	16.0	5	2	CA 2144	46.9000	NaN	S
183	0	3	Asplund, Master. Clarence Gustaf Hugo	male	9.0	4	2	347077	31.3875	NaN	S
262	1	3	Asplund, Master. Edvin Rojj Felix	male	3.0	4	2	347077	31.3875	NaN	S
851	0	3	Andersson, Master. Sigvard Harald Elias	male	4.0	4	2	347082	31.2750	NaN	S
69	1	3	Andersson, Miss. Erna Alexandra	female	17.0	4	2	3101281	7.9250	NaN	S
120	0	3	Andersson, Miss. Ellis Anna Maria	female	2.0	4	2	347082	31.2750	NaN	S
234	1	3	Asplund, Miss. Lillian Gertrud	female	5.0	4	2	347077	31.3875	NaN	S
542	0	3	Andersson, Miss. Ingeborg Constanzia	female	9.0	4	2	347082	31.2750	NaN	S
543	0	3	Andersson, Miss. Sigrid Elisabeth	female	11.0	4	2	347082	31.2750	NaN	S

#寫下你的思考兄弟姐妹越多的，存活率越低，男性可能比女性存活率低

1.6.3 任務三：利用Pandas進行算術計算，計算兩個DataFrame數據相加結果

# 具體請看《利用Python進行數據分析》第五章 算術運算與數據對齊 部分#自己構建兩個都為數字的DataFrame數據"""我們舉了一個例子：frame1_a = pd.DataFrame(np.arange(9.).reshape(3, 3), columns=['a', 'b', 'c'], index=['one', 'two', 'three'])frame1_b = pd.DataFrame(np.arange(12.).reshape(4, 3), columns=['a', 'e', 'c'], index=['first', 'one', 'two', 'second'])frame1_a""" #代碼frame1_a = pd.DataFrame(np.arange(9.).reshape(3,3),columns=['a','b','c'],index=['one','two','three'])frame1_b = pd.DataFrame(np.arange(12.).reshape(4, 3),columns=['a', 'e', 'c'], index=['first', 'one', 'two', 'second'])

將frame_a和frame_b進行相加

#代碼frame1_a abconetwothree

0.0	1.0	2.0
3.0	4.0	5.0
6.0	7.0	8.0

【提醒】兩個DataFrame相加后，會返回一個新的DataFrame，對應的行和列的值會相加，沒有對應的會變成空值NaN。

當然，DataFrame還有很多算術運算，如減法，除法等，有興趣的同學可以看《利用Python進行數據分析》第五章 算術運算與數據對齊 部分，多在網絡上查找相關學習資料。

frame1_b aecfirstonetwosecond

0.0	1.0	2.0
3.0	4.0	5.0
6.0	7.0	8.0
9.0	10.0	11.0

frame1_a + frame1_b abcefirstonesecondthreetwo

NaN	NaN	NaN	NaN
3.0	NaN	7.0	NaN
NaN	NaN	NaN	NaN
NaN	NaN	NaN	NaN
9.0	NaN	13.0	NaN

1.6.4 任務四：通過泰坦尼克號數據如何計算出在船上最大的家族有多少人？

'''還是用之前導入的chinese_train.csv如果我們想看看在船上，最大的家族有多少人（‘兄弟姐妹個數’+‘父母子女個數’），我們該怎么做呢？'''max(train_data['兄弟姐妹個數']+train_data['父母子女個數']) 10

【提醒】我們只需找出”兄弟姐妹個數“和”父母子女個數“之和最大的數，當然你還可以想出很多方法和思考角度，歡迎你來說出你的看法。

多做幾個數據的相加，看看你能分析出什么？

1.6.5 任務五：學會使用Pandas describe()函數查看數據基本統計信息

#(1) 關鍵知識點示例做一遍（簡單數據）# 具體請看《利用Python進行數據分析》第五章 匯總和計算描述統計 部分#自己構建一個有數字有空值的DataFrame數據"""我們舉了一個例子：frame2 = pd.DataFrame([[1.4, np.nan], [7.1, -4.5], [np.nan, np.nan], [0.75, -1.3] ], index=['a', 'b', 'c', 'd'], columns=['one', 'two'])frame2""" #代碼frame2 = pd.DataFrame([[1.4, np.nan], [7.1, -4.5], [np.nan, np.nan], [0.75, -1.3] ], index=['a', 'b', 'c', 'd'], columns=['one', 'two'])frame2 onetwoabcd

1.40	NaN
7.10	-4.5
NaN	NaN
0.75	-1.3

調用 describe 函數，觀察frame2的數據基本信息

#代碼frame2.describe() onetwocountmeanstdmin25%50%75%max

3.000000	2.000000
3.083333	-2.900000
3.493685	2.262742
0.750000	-4.500000
1.075000	-3.700000
1.400000	-2.900000
4.250000	-2.100000
7.100000	-1.300000

1.6.6 任務六：分別看看泰坦尼克號數據集中 票價、父母子女 這列數據的基本統計數據，你能發現什么？

'''看看泰坦尼克號數據集中 票價 這列數據的基本統計數據''' #代碼train_data['票價'].describe() count 891.000000mean 32.204208std 49.693429min 0.00000025% 7.91040050% 14.45420075% 31.000000max 512.329200Name: 票價, dtype: float64 train_data['父母子女個數'].describe() count 891.000000mean 0.381594std 0.806057min 0.00000025% 0.00000050% 0.00000075% 0.000000max 6.000000Name: 父母子女個數, dtype: float64

【思考】從上面數據我們可以看出，試試在下面寫出你的看法。然后看看我們給出的答案。
【思考】從上面數據我們可以看出，
一共有891個票價數據，
平均值約為：32.20，
標準差約為49.69，說明票價波動特別大，
25%的人的票價是低于7.91的，50%的人的票價低于14.45，75%的人的票價低于31.00，
票價最大值約為512.33，最小值為0。

75%的人沒有子女或父母，說明出玩人員大部分都孤身一身

當然，答案只是我的想法，你還可以有更多想法，歡迎寫在你的學習筆記中。

多做幾個組數據的統計，看看你能分析出什么？

# 寫下你的其他分析

【思考】有更多想法，歡迎寫在你的學習筆記中。

【總結】本節中我們通過Pandas的一些內置函數對數據進行了初步統計查看，這個過程最重要的不是大家得掌握這些函數，而是看懂從這些函數出來的數據，構建自己的數據分析思維，這也是第一章最重要的點，希望大家學完第一章能對數據有個基本認識，了解自己在做什么，為什么這么做，后面的章節我們將開始對數據進行清洗，進一步分析。

總結

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