Task1 数据加载及探索性数据分析

1 第一章：数据载入及初步观察

1.1 载入数据

数据集下载 https://www.kaggle.com/c/titanic/overview
下载后数据集存放于相对路径 path= r"…/MyAnswer/titanic"

1.1.1 任务一：导入numpy和pandas

#写入代码
import numpy as np
import pandas as pd

【提示】如果加载失败，学会如何在你的python环境下安装numpy和pandas这两个库

1.1.2 任务二：载入数据

(1) 使用相对路径载入数据
(2) 使用绝对路径载入数据

#写入代码
df_abs = pd.read_csv(r'D:\D SoftWare\坚果云同步\我的坚果云\MyJupyterFolder\hands-on-data-analysis\第一单元项目集合\MyAnswer\titanic\train.csv')
print(df_abs)
# 相对路径出错， os.getcwd()获取当前路径
import os
os.getcwd()
df_realtive = pd.read_csv(r".\MyAnswer\titanic\train.csv")
df_realtive.head()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
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

#写入代码 read_table 读取csv文件
df_table = pd.read_table(r".\MyAnswer\titanic\train.csv")
print(df_table) # 读取文件失败，需要设置正确的分隔符。 每行没遇到Tab键，因此只有一行
df_table = pd.read_table(r".\MyAnswer\titanic\train.csv")
print(df_table) 
df_table.head()

    PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
0    1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/...                             
1    2,1,1,"Cumings, Mrs. John Bradley (Florence Br...                             
2    3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,S...                             
3    4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May ...                             
4    5,0,3,"Allen, Mr. William Henry",male,35,0,0,3...                             
..                                                 ...                             
886  887,0,2,"Montvila, Rev. Juozas",male,27,0,0,21...                             
887  888,1,1,"Graham, Miss. Margaret Edith",female,...                             
888  889,0,3,"Johnston, Miss. Catherine Helen ""Car...                             
889  890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,11...                             
890  891,0,3,"Dooley, Mr. Patrick",male,32,0,0,3703...                             

[891 rows x 1 columns]
    PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
0    1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/...                             
1    2,1,1,"Cumings, Mrs. John Bradley (Florence Br...                             
2    3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,S...                             
3    4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May ...                             
4    5,0,3,"Allen, Mr. William Henry",male,35,0,0,3...                             
..                                                 ...                             
886  887,0,2,"Montvila, Rev. Juozas",male,27,0,0,21...                             
887  888,1,1,"Graham, Miss. Margaret Edith",female,...                             
888  889,0,3,"Johnston, Miss. Catherine Helen ""Car...                             
889  890,1,1,"Behr, Mr. Karl Howell",male,26,0,0,11...                             
890  891,0,3,"Dooley, Mr. Patrick",male,32,0,0,3703...                             

[891 rows x 1 columns]

	PassengerId,Survived,Pclass,Name,Sex,Age,SibSp,Parch,Ticket,Fare,Cabin,Embarked
0	1,0,3,"Braund, Mr. Owen Harris",male,22,1,0,A/...
1	2,1,1,"Cumings, Mrs. John Bradley (Florence Br...
2	3,1,3,"Heikkinen, Miss. Laina",female,26,0,0,S...
3	4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May ...
4	5,0,3,"Allen, Mr. William Henry",male,35,0,0,3...

df_table = pd.read_table(r".\MyAnswer\titanic\train.csv", sep= ',')
df_table.head()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
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】相对路径载入报错时，尝试使用os.getcwd()查看当前工作目录。
【思考】知道数据加载的方法后，试试pd.read_csv()和pd.read_table()的不同，如果想让他们效果一样，需要怎么做？了解一下’.tsv’和’.csv’的不同，如何加载这两个数据集？
1)read_table()的默认分隔符时tab键，因此读取csv文件时，需要指定特定的分隔符 sep=’,’
2)csv tsv txt 本质都是文本文件，csv采用半角逗号分割’，‘，tsv采用tab键分割，txt不指定分隔符，可以为逗号，空格，tab键等。 pd.read_csv(‘aaa.csv’) pd.read_csv(‘bbb.tsv’)
【总结】加载的数据是所有工作的第一步，我们的工作会接触到不同的数据格式（eg:.csv;.tsv;.xlsx）,但是加载的方法和思路都是一样的，在以后工作和做项目的过程中，遇到之前没有碰到的问题，要多多查资料吗，使用googel，了解业务逻辑，明白输入和输出是什么。

1.1.3 任务三：每1000行为一个数据模块，逐块读取

#写入代码
df_chunker = pd.read_csv(r".\MyAnswer\titanic\train.csv", sep= ',', chunksize =300)
df_chunker

<pandas.io.parsers.TextFileReader at 0x162c75d4b08>

【思考】什么是逐块读取？为什么要逐块读取呢？
当文件较大的时候，需要逐块读取文件。

【提示】大家可以chunker(数据块)是什么类型？用for循环打印出来出处具体的样子是什么？
指定chunkersize 后生成一个可迭代对象

for i in df_chunker:  #逐块打印chunker
    print(i)

     PassengerId  Survived  Pclass  \
0              1         0       3   
1              2         1       1   
2              3         1       3   
3              4         1       1   
4              5         0       3   
..           ...       ...     ...   
295          296         0       1   
296          297         0       3   
297          298         0       1   
298          299         1       1   
299          300         1       1   

                                                  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   
..                                                 ...     ...   ...    ...   
295                                  Lewy, Mr. Ervin G    male   NaN      0   
296                                 Hanna, Mr. Mansour    male  23.5      0   
297                       Allison, Miss. Helen Loraine  female   2.0      1   
298                              Saalfeld, Mr. Adolphe    male   NaN      0   
299    Baxter, Mrs. James (Helene DeLaudeniere Chaput)  female  50.0      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  
..     ...               ...       ...      ...      ...  
295      0          PC 17612   27.7208      NaN        C  
296      0              2693    7.2292      NaN        C  
297      2            113781  151.5500  C22 C26        S  
298      0             19988   30.5000     C106        S  
299      1          PC 17558  247.5208  B58 B60        C  

[300 rows x 12 columns]
     PassengerId  Survived  Pclass  \
300          301         1       3   
301          302         1       3   
302          303         0       3   
303          304         1       2   
304          305         0       3   
..           ...       ...     ...   
595          596         0       3   
596          597         1       2   
597          598         0       3   
598          599         0       3   
599          600         1       1   

                                             Name     Sex   Age  SibSp  Parch  \
300      Kelly, Miss. Anna Katherine "Annie Kate"  female   NaN      0      0   
301                            McCoy, Mr. Bernard    male   NaN      2      0   
302               Johnson, Mr. William Cahoone Jr    male  19.0      0      0   
303                           Keane, Miss. Nora A  female   NaN      0      0   
304             Williams, Mr. Howard Hugh "Harry"    male   NaN      0      0   
..                                            ...     ...   ...    ...    ...   
595                   Van Impe, Mr. Jean Baptiste    male  36.0      1      1   
596                    Leitch, Miss. Jessie Wills  female   NaN      0      0   
597                           Johnson, Mr. Alfred    male  49.0      0      0   
598                             Boulos, Mr. Hanna    male   NaN      0      0   
599  Duff Gordon, Sir. Cosmo Edmund ("Mr Morgan")    male  49.0      1      0   

       Ticket     Fare Cabin Embarked  
300      9234   7.7500   NaN        Q  
301    367226  23.2500   NaN        Q  
302      LINE   0.0000   NaN        S  
303    226593  12.3500  E101        Q  
304  A/5 2466   8.0500   NaN        S  
..        ...      ...   ...      ...  
595    345773  24.1500   NaN        S  
596    248727  33.0000   NaN        S  
597      LINE   0.0000   NaN        S  
598      2664   7.2250   NaN        C  
599  PC 17485  56.9292   A20        C  

[300 rows x 12 columns]
     PassengerId  Survived  Pclass  \
600          601         1       2   
601          602         0       3   
602          603         0       1   
603          604         0       3   
604          605         1       1   
..           ...       ...     ...   
886          887         0       2   
887          888         1       1   
888          889         0       3   
889          890         1       1   
890          891         0       3   

                                                  Name     Sex   Age  SibSp  \
600  Jacobsohn, Mrs. Sidney Samuel (Amy Frances Chr...  female  24.0      2   
601                               Slabenoff, Mr. Petco    male   NaN      0   
602                          Harrington, Mr. Charles H    male   NaN      0   
603                          Torber, Mr. Ernst William    male  44.0      0   
604                    Homer, Mr. Harry ("Mr E Haven")    male  35.0      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  
600      1      243847  27.0000   NaN        S  
601      0      349214   7.8958   NaN        S  
602      0      113796  42.4000   NaN        S  
603      0      364511   8.0500   NaN        S  
604      0      111426  26.5500   NaN        C  
..     ...         ...      ...   ...      ...  
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  

[291 rows x 12 columns]

# 一次读取前若干行
#df = pd.read_csv(r".\MyAnswer\titanic\train.csv")
df= pd.read_csv(r".\MyAnswer\titanic\train.csv",  nrows=10)
df.head()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
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

1.1.4 任务四：将表头改成中文，索引改为乘客ID [对于某些英文资料，我们可以通过翻译来更直观的熟悉我们的数据]

PassengerId => 乘客ID
Survived => 是否幸存
Pclass => 乘客等级(1/2/3等舱位)
Name => 乘客姓名
Sex => 性别
Age => 年龄
SibSp => 堂兄弟/妹个数
Parch => 父母与小孩个数
Ticket => 船票信息
Fare => 票价
Cabin => 客舱
Embarked => 登船港口

#方式1：读取文件后修改列名 
df= pd.read_csv(r".\MyAnswer\titanic\train.csv")
df.head(3)
df.columns = ['乘客ID', '是否幸存', '乘客等级(1/2/3等舱位)','乘客姓名', '性别', '年龄','堂兄弟/妹个数',   '父母与小孩个数', '船票信息','票价', '客舱', '登船港口']
df.head(3)

	乘客ID	是否幸存	乘客等级(1/2/3等舱位)	乘客姓名	性别	年龄	堂兄弟/妹个数	父母与小孩个数	船票信息	票价	客舱	登船港口
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

# 方式2：读取文件过程中，直接设置对应的names
df = pd.read_csv('train.csv', names=['乘客ID','是否幸存','仓位等级','姓名','性别','年龄','兄弟姐妹个数','父母子女个数','船票信息','票价','客舱','登船港口'],index_col='乘客ID',header=0)
#设置索引列为'乘客ID'，取代自然生成的索引
df.head(3)

	是否幸存	仓位等级	姓名	性别	年龄	兄弟姐妹个数	父母子女个数	船票信息	票价	客舱	登船港口
乘客ID
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

【思考】所谓将表头改为中文其中一个思路是：将英文列名表头替换成中文。还有其他的方法吗？

1.2 初步观察

导入数据后，你可能要对数据的整体结构和样例进行概览，比如说，数据大小、有多少列，各列都是什么格式的，是否包含null等

1.2.1 任务一：查看数据的基本信息

df.describe()

	是否幸存	仓位等级	年龄	兄弟姐妹个数	父母子女个数	票价
count	891.000000	891.000000	714.000000	891.000000	891.000000	891.000000
mean	0.383838	2.308642	29.699118	0.523008	0.381594	32.204208
std	0.486592	0.836071	14.526497	1.102743	0.806057	49.693429
min	0.000000	1.000000	0.420000	0.000000	0.000000	0.000000
25%	0.000000	2.000000	20.125000	0.000000	0.000000	7.910400
50%	0.000000	3.000000	28.000000	0.000000	0.000000	14.454200
75%	1.000000	3.000000	38.000000	1.000000	0.000000	31.000000
max	1.000000	3.000000	80.000000	8.000000	6.000000	512.329200

df.info()

<class 'pandas.core.frame.DataFrame'>
Int64Index: 891 entries, 1 to 891
Data columns (total 11 columns):
 #   Column  Non-Null Count  Dtype  
---  ------  --------------  -----  
 0   是否幸存    891 non-null    int64  
 1   仓位等级    891 non-null    int64  
 2   姓名      891 non-null    object 
 3   性别      891 non-null    object 
 4   年龄      714 non-null    float64
 5   兄弟姐妹个数  891 non-null    int64  
 6   父母子女个数  891 non-null    int64  
 7   船票信息    891 non-null    object 
 8   票价      891 non-null    float64
 9   客舱      204 non-null    object 
 10  登船港口    889 non-null    object 
dtypes: float64(2), int64(4), object(5)
memory usage: 83.5+ KB

【提示】有多个函数可以这样做，你可以做一下总结

1.2.2 任务二：观察表格前10行的数据和后15行的数据

df.head(10)

	是否幸存	仓位等级	姓名	性别	年龄	兄弟姐妹个数	父母子女个数	船票信息	票价	客舱	登船港口
乘客ID
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

df.tail(15)

	是否幸存	仓位等级	姓名	性别	年龄	兄弟姐妹个数	父母子女个数	船票信息	票价	客舱	登船港口
乘客ID
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

1.2.4 任务三：判断数据是否为空，为空的地方返回True，其余地方返回False

#写入代码
null_data = df.isnull()
print(null_data.head(10))

       是否幸存   仓位等级     姓名     性别     年龄  兄弟姐妹个数  父母子女个数   船票信息     票价     客舱  \
乘客ID                                                                           
1     False  False  False  False  False   False   False  False  False   True   
2     False  False  False  False  False   False   False  False  False  False   
3     False  False  False  False  False   False   False  False  False   True   
4     False  False  False  False  False   False   False  False  False  False   
5     False  False  False  False  False   False   False  False  False   True   
6     False  False  False  False   True   False   False  False  False   True   
7     False  False  False  False  False   False   False  False  False  False   
8     False  False  False  False  False   False   False  False  False   True   
9     False  False  False  False  False   False   False  False  False   True   
10    False  False  False  False  False   False   False  False  False   True   

       登船港口  
乘客ID         
1     False  
2     False  
3     False  
4     False  
5     False  
6     False  
7     False  
8     False  
9     False  
10    False  

【总结】上面的操作都是数据分析中对于数据本身的观察

【思考】对于一个数据，还可以从哪些方面来观察？找找答案，这个将对下面的数据分析有很大的帮助
尝试思考：观察一个数据大小（多少行、多少列）、数据类型（每列数据是数值型or标称型）、每列数据数值分布、数据是否完整

1.3 保存数据

1.3.1 任务一：将你加载并做出改变的数据，在工作目录下保存为一个新文件train_chinese.csv

#写入代码
# 注意：不同的操作系统保存下来可能会有乱码。大家可以加入`encoding='GBK' 或者 ’encoding = ’utf-8‘‘`
df.to_csv(r".\MyAnswer\titanic\train_chinese_GBK.csv", encoding= 'GBK')
df.to_csv(r".\MyAnswer\titanic\train_chinese_utf8.csv", encoding= 'utf-8') # 'utf-8'仍然会乱码？

---------------------------------------------------------------------------

NameError                                 Traceback (most recent call last)

<ipython-input-73-2d27237f9be7> in <module>
      2 # 注意：不同的操作系统保存下来可能会有乱码。大家可以加入`encoding='GBK' 或者 ’encoding = ’utf-8‘‘`
      3 df.to_csv(r".\MyAnswer\titanic\train_chinese_GBK.csv", encoding= 'GBK')
----> 4 df.to_csv(r".\MyAnswer\titanic\train_chinese_utf8.csv", encoding= utf-8)


NameError: name 'utf' is not defined

【总结】数据的加载以及入门，接下来就要接触数据本身的运算，我们将主要掌握numpy和pandas在工作和项目场景的运用。

1.4 知道你的数据叫什么(Series与DataFrame两种基础数据结构)

我们学习pandas的基础操作，那么上一节通过pandas加载之后的数据，其数据类型是什么呢？

开始前导入numpy和pandas

import numpy as np
import pandas as pd

1.4.1 任务一：pandas中有两个数据类型DateFrame和Series，通过查找简单了解他们。然后自己写一个关于这两个数据类型的小例子🌰[开放题]

'''
#我们举的例子
sdata = {'Ohio': 35000, 'Texas': 71000, 'Oregon': 16000, 'Utah': 5000}
example_1 = pd.Series(sdata)
example_1
'''
# DateFrame和Series是pandas的两种基本数据格式
#Series 包含四部分，数据data（list，dict等）, 索引index, 类型dtype, 列名name
my_sr= pd.Series( data=[1,2,3,4,5,6,7,8,9,10,11,12], dtype='float64', name='col_name' )
print(my_sr)

0      1.0
1      2.0
2      3.0
3      4.0
4      5.0
5      6.0
6      7.0
7      8.0
8      9.0
9     10.0
10    11.0
11    12.0
Name: col_name, dtype: float64

'''
#我们举的例子
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
'''
#Series 包含四部分，数据data（list，dict等）, 行索引index, 列索引columns， 类型dtype, 列名name
my_data = np.arange(0, 12).reshape(-1,3)
my_df= pd.DataFrame( data=my_data, dtype='float64', index=['a', 'b','c','d'],columns=['A', 'B', 'C'])
my_df.head()

	A	B	C
a	0.0	1.0	2.0
b	3.0	4.0	5.0
c	6.0	7.0	8.0
d	9.0	10.0	11.0

1.4.2 任务二：根据上节课的方法载入"train.csv"文件

#写入代码
df = pd.read_csv(r".\MyAnswer\titanic\train.csv", encoding= 'GBK') #存储为GBK格式，必须指定对应的读取格式
df.head()

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
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

也可以加载上一节课保存的"train_chinese.csv"文件。通过翻译版train_chinese.csv熟悉了这个数据集，然后我们对trian.csv来进行操作

1.4.3 任务三：查看DataFrame数据的每列的名称

#写入代码
print(df.columns)

Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'SibSp',
       'Parch', 'Ticket', 'Fare', 'Cabin', 'Embarked'],
      dtype='object')

1.4.4任务四：查看"Cabin"这列的所有值[有多种方法]

df['Cabin']

0       NaN
1       C85
2       NaN
3      C123
4       NaN
       ... 
886     NaN
887     B42
888     NaN
889    C148
890     NaN
Name: Cabin, Length: 891, dtype: object

df.Cabin

0       NaN
1       C85
2       NaN
3      C123
4       NaN
       ... 
886     NaN
887     B42
888     NaN
889    C148
890     NaN
Name: Cabin, Length: 891, dtype: object

1.4.5 任务五：加载文件"test_1.csv"，然后对比"train.csv"，看看有哪些多出的列，然后将多出的列删除

经过我们的观察发现一个测试集test_1.csv有一列是多余的，我们需要将这个多余的列删去

df_test= pd.read_csv('test_1.csv')
df_test.head(3)

	Unnamed: 0	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked	a
0	0	1	0	3	Braund, Mr. Owen Harris	male	22.0	1	0	A/5 21171	7.2500	NaN	S	100
1	1	2	1	1	Cumings, Mrs. John Bradley (Florence Briggs Th...	female	38.0	1	0	PC 17599	71.2833	C85	C	100
2	2	3	1	3	Heikkinen, Miss. Laina	female	26.0	0	0	STON/O2. 3101282	7.9250	NaN	S	100

df_train= pd.read_csv(r".\MyAnswer\titanic\train.csv")
df_train.head(3)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
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

# 删除多余的列
del df_test['a']
df_test.head(3)

	Unnamed: 0	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
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

【思考】还有其他的删除多余的列的方式吗？

# df.drop()方法删除列
df_train= pd.read_csv(r".\MyAnswer\titanic\train.csv")

del_df_train = df_train.drop(['Name', 'Sex'], axis=1) # 指定某一列删除，axis必须设置
print('df1:\n',del_df_train.head(3))

df_train= pd.read_csv(r".\MyAnswer\titanic\train.csv")

df_train.drop(['Name', 'Sex'], axis=1, inplace=True) # 指定某一列删除，axis必须设置
print('df2:\n',df_train.head(3))

df_train.drop(df_train.columns[0:3], axis=1, inplace=True)   
print('df3:\n',df_train.head(3))

df1:
    PassengerId  Survived  Pclass   Age  SibSp  Parch            Ticket  \
0            1         0       3  22.0      1      0         A/5 21171   
1            2         1       1  38.0      1      0          PC 17599   
2            3         1       3  26.0      0      0  STON/O2. 3101282   

      Fare Cabin Embarked  
0   7.2500   NaN        S  
1  71.2833   C85        C  
2   7.9250   NaN        S  
df2:
    PassengerId  Survived  Pclass   Age  SibSp  Parch            Ticket  \
0            1         0       3  22.0      1      0         A/5 21171   
1            2         1       1  38.0      1      0          PC 17599   
2            3         1       3  26.0      0      0  STON/O2. 3101282   

      Fare Cabin Embarked  
0   7.2500   NaN        S  
1  71.2833   C85        C  
2   7.9250   NaN        S  
df3:
     Age  SibSp  Parch            Ticket     Fare Cabin Embarked
0  22.0      1      0         A/5 21171   7.2500   NaN        S
1  38.0      1      0          PC 17599  71.2833   C85        C
2  26.0      0      0  STON/O2. 3101282   7.9250   NaN        S

1.4.6 任务六： 将[‘PassengerId’,‘Name’,‘Age’,‘Ticket’]这几个列元素隐藏，只观察其他几个列元素

df_train= pd.read_csv(r".\MyAnswer\titanic\train.csv")
df_train.drop(['PassengerId','Name','Age','Ticket'], axis =1) #inplace =False, 不覆盖原数据，仅返回删除后的数据

【思考】对比任务五和任务六，是不是使用了不一样的方法(函数)，如果使用一样的函数如何完成上面的不同的要求呢？

【思考回答】

如果想要完全的删除你的数据结构，使用inplace=True，因为使用inplace就将原数据覆盖了，所以这里没有用

隐藏某一列数据，与删除某一列数据，都可以使用drop方法

1.5 筛选的逻辑

表格数据中，最重要的一个功能就是要具有可筛选的能力，选出我所需要的信息，丢弃无用的信息。

下面我们还是用实战来学习pandas这个功能。

1.5.1 任务一： 我们以"Age"为筛选条件，显示年龄在10岁以下的乘客信息。

df_train= pd.read_csv(r".\MyAnswer\titanic\train.csv")
df_train['Age']< 10  #返回一个列向量

0      False
1      False
2      False
3      False
4      False
       ...  
886    False
887    False
888    False
889    False
890    False
Name: Age, Length: 891, dtype: bool

df_train[ df_train['Age']< 10 ] 

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
7	8	0	3	Palsson, Master. Gosta Leonard	male	2.00	3	1	349909	21.0750	NaN	S
10	11	1	3	Sandstrom, Miss. Marguerite Rut	female	4.00	1	1	PP 9549	16.7000	G6	S
16	17	0	3	Rice, Master. Eugene	male	2.00	4	1	382652	29.1250	NaN	Q
24	25	0	3	Palsson, Miss. Torborg Danira	female	8.00	3	1	349909	21.0750	NaN	S
43	44	1	2	Laroche, Miss. Simonne Marie Anne Andree	female	3.00	1	2	SC/Paris 2123	41.5792	NaN	C
...	...	...	...	...	...	...	...	...	...	...	...	...
827	828	1	2	Mallet, Master. Andre	male	1.00	0	2	S.C./PARIS 2079	37.0042	NaN	C
831	832	1	2	Richards, Master. George Sibley	male	0.83	1	1	29106	18.7500	NaN	S
850	851	0	3	Andersson, Master. Sigvard Harald Elias	male	4.00	4	2	347082	31.2750	NaN	S
852	853	0	3	Boulos, Miss. Nourelain	female	9.00	1	1	2678	15.2458	NaN	C
869	870	1	3	Johnson, Master. Harold Theodor	male	4.00	1	1	347742	11.1333	NaN	S

62 rows × 12 columns

1.5.2 任务二： 以"Age"为条件，将年龄在10岁以上和50岁以下的乘客信息显示出来，并将这个数据命名为midage

midage= df_train[ (df_train['Age'] <50) & (df_train['Age'] >10) ]
midage.head(10)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
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
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
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

【提示】了解pandas的条件筛选方式以及如何使用交集和并集操作
加括号！ & | 交并集符号
（？）&（？） （？）|（？）

1.5.3 任务三：将midage的数据中第100行的"Pclass"和"Sex"的数据显示出来

#写入代码
midage.reset_index(drop= True)

	PassengerId	Survived	Pclass	Name	Sex	Age	SibSp	Parch	Ticket	Fare	Cabin	Embarked
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
...	...	...	...	...	...	...	...	...	...	...	...	...
571	886	0	3	Rice, Mrs. William (Margaret Norton)	female	39.0	0	5	382652	29.1250	NaN	Q
572	887	0	2	Montvila, Rev. Juozas	male	27.0	0	0	211536	13.0000	NaN	S
573	888	1	1	Graham, Miss. Margaret Edith	female	19.0	0	0	112053	30.0000	B42	S
574	890	1	1	Behr, Mr. Karl Howell	male	26.0	0	0	111369	30.0000	C148	C
575	891	0	3	Dooley, Mr. Patrick	male	32.0	0	0	370376	7.7500	NaN	Q

576 rows × 12 columns

【提示】在抽取数据中，我们希望数据的相对顺序保持不变，用什么函数可以达到这个效果呢？

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

	Pclass	Sex
100	3	female

1.5.4 任务四：使用loc方法将midage的数据中第100，105，108行的"Pclass"，"Name"和"Sex"的数据显示出来

midage.loc[[100,105,108], ['Pclass','Name', 'Sex']]

	Pclass	Name	Sex
100	3	Petranec, Miss. Matilda	female
105	3	Mionoff, Mr. Stoytcho	male
108	3	Rekic, Mr. Tido	male

1.5.5 任务五：使用iloc方法将midage的数据中第100，105，108行的"Pclass"，"Name"和"Sex"的数据显示出来

midage.iloc[[100,105,108],[2,3,4]]

	Pclass	Name	Sex
149	2	Byles, Rev. Thomas Roussel Davids	male
160	3	Cribb, Mr. John Hatfield	male
163	3	Calic, Mr. Jovo	male

【思考】对比iloc和loc的异同
loc 基于元素的索引
iloc 基于位置的索引

1.6 了解你的数据吗？

教材《Python for Data Analysis》第五章

1.6.1 任务一：利用Pandas对示例数据进行排序，要求升序

# 具体请看《利用Python进行数据分析》第五章 排序和排名 部分

#自己构建一个都为数字的DataFrame数据
my_data = np.arange(0, 12).reshape(-1,3)
my_df= pd.DataFrame( data=my_data, dtype='float64', index=['a', 'b','c','d'],columns=['A', 'B', 'C'])
my_df.head()

	A	B	C
a	0.0	1.0	2.0
b	3.0	4.0	5.0
c	6.0	7.0	8.0
d	9.0	10.0	11.0

【问题】：大多数时候我们都是想根据列的值来排序,所以将你构建的DataFrame中的数据根据某一列，升序排列

my_df.sort_values(by='B', ascending=False)

	A	B	C
d	9.0	10.0	11.0
c	6.0	7.0	8.0
b	3.0	4.0	5.0
a	0.0	1.0	2.0

【思考】通过书本你能说出Pandas对DataFrame数据的其他排序方式吗？

【总结】下面将不同的排序方式做一个总结

1.让行索引升序排序

#代码
my_df.sort_index()

	A	B	C
a	0.0	1.0	2.0
b	3.0	4.0	5.0
c	6.0	7.0	8.0
d	9.0	10.0	11.0

2.让列索引升序排序

#代码
my_df.sort_index(axis=1)

	A	B	C
a	0.0	1.0	2.0
b	3.0	4.0	5.0
c	6.0	7.0	8.0
d	9.0	10.0	11.0

3.让列索引降序排序

#代码
my_df.sort_index(axis=1, ascending=False)

	C	B	A
a	2.0	1.0	0.0
b	5.0	4.0	3.0
c	8.0	7.0	6.0
d	11.0	10.0	9.0

4.让任选两列数据同时降序排序

#代码
my_df.sort_values(by=['A', 'B'], ascending=False)

	A	B	C
d	9.0	10.0	11.0
c	6.0	7.0	8.0
b	3.0	4.0	5.0
a	0.0	1.0	2.0

1.6.2 任务二：对泰坦尼克号数据（trian.csv）按票价和年龄两列进行综合排序（降序排列），从这个数据中你可以分析出什么？

'''
在开始我们已经导入了train_chinese.csv数据，而且前面我们也学习了导入数据过程，根据上面学习，我们直接对目标列进行排序即可
head(20) : 读取前20条数据

'''

#代码
df = pd.read_csv(r".\MyAnswer\titanic\train_chinese.csv", encoding= 'GBK') 
df.head()

	乘客ID	是否幸存	仓位等级	姓名	性别	年龄	兄弟姐妹个数	父母子女个数	船票信息	票价	客舱	登船港口
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

df.sort_values(by = ['票价','年龄'], ascending= False).head(30)

	乘客ID	是否幸存	仓位等级	姓名	性别	年龄	兄弟姐妹个数	父母子女个数	船票信息	票价	客舱	登船港口
679	680	1	1	Cardeza, Mr. Thomas Drake Martinez	male	36.00	0	1	PC 17755	512.3292	B51 B53 B55	C
258	259	1	1	Ward, Miss. Anna	female	35.00	0	0	PC 17755	512.3292	NaN	C
737	738	1	1	Lesurer, Mr. Gustave J	male	35.00	0	0	PC 17755	512.3292	B101	C
438	439	0	1	Fortune, Mr. Mark	male	64.00	1	4	19950	263.0000	C23 C25 C27	S
341	342	1	1	Fortune, Miss. Alice Elizabeth	female	24.00	3	2	19950	263.0000	C23 C25 C27	S
88	89	1	1	Fortune, Miss. Mabel Helen	female	23.00	3	2	19950	263.0000	C23 C25 C27	S
27	28	0	1	Fortune, Mr. Charles Alexander	male	19.00	3	2	19950	263.0000	C23 C25 C27	S
742	743	1	1	Ryerson, Miss. Susan Parker "Suzette"	female	21.00	2	2	PC 17608	262.3750	B57 B59 B63 B66	C
311	312	1	1	Ryerson, Miss. Emily Borie	female	18.00	2	2	PC 17608	262.3750	B57 B59 B63 B66	C
299	300	1	1	Baxter, Mrs. James (Helene DeLaudeniere Chaput)	female	50.00	0	1	PC 17558	247.5208	B58 B60	C
118	119	0	1	Baxter, Mr. Quigg Edmond	male	24.00	0	1	PC 17558	247.5208	B58 B60	C
380	381	1	1	Bidois, Miss. Rosalie	female	42.00	0	0	PC 17757	227.5250	NaN	C
716	717	1	1	Endres, Miss. Caroline Louise	female	38.00	0	0	PC 17757	227.5250	C45	C
700	701	1	1	Astor, Mrs. John Jacob (Madeleine Talmadge Force)	female	18.00	1	0	PC 17757	227.5250	C62 C64	C
557	558	0	1	Robbins, Mr. Victor	male	NaN	0	0	PC 17757	227.5250	NaN	C
527	528	0	1	Farthing, Mr. John	male	NaN	0	0	PC 17483	221.7792	C95	S
377	378	0	1	Widener, Mr. Harry Elkins	male	27.00	0	2	113503	211.5000	C82	C
779	780	1	1	Robert, Mrs. Edward Scott (Elisabeth Walton Mc...	female	43.00	0	1	24160	211.3375	B3	S
730	731	1	1	Allen, Miss. Elisabeth Walton	female	29.00	0	0	24160	211.3375	B5	S
689	690	1	1	Madill, Miss. Georgette Alexandra	female	15.00	0	1	24160	211.3375	B5	S
856	857	1	1	Wick, Mrs. George Dennick (Mary Hitchcock)	female	45.00	1	1	36928	164.8667	NaN	S
318	319	1	1	Wick, Miss. Mary Natalie	female	31.00	0	2	36928	164.8667	C7	S
268	269	1	1	Graham, Mrs. William Thompson (Edith Junkins)	female	58.00	0	1	PC 17582	153.4625	C125	S
609	610	1	1	Shutes, Miss. Elizabeth W	female	40.00	0	0	PC 17582	153.4625	C125	S
332	333	0	1	Graham, Mr. George Edward	male	38.00	0	1	PC 17582	153.4625	C91	S
498	499	0	1	Allison, Mrs. Hudson J C (Bessie Waldo Daniels)	female	25.00	1	2	113781	151.5500	C22 C26	S
708	709	1	1	Cleaver, Miss. Alice	female	22.00	0	0	113781	151.5500	NaN	S
297	298	0	1	Allison, Miss. Helen Loraine	female	2.00	1	2	113781	151.5500	C22 C26	S
305	306	1	1	Allison, Master. Hudson Trevor	male	0.92	1	2	113781	151.5500	C22 C26	S
195	196	1	1	Lurette, Miss. Elise	female	58.00	0	0	PC 17569	146.5208	B80	C

【思考】排序后，如果我们仅仅关注年龄和票价两列。根据常识我知道发现票价越高的应该客舱越好，所以我们会明显看出，票价前20的乘客中存活的有14人，这是相当高的一个比例，那么我们后面是不是可以进一步分析一下票价和存活之间的关系，年龄和存活之间的关系呢？当你开始发现数据之间的关系了，数据分析就开始了。

当然，这只是我的想法，你还可以有更多想法，欢迎写在你的学习笔记中。

多做几个数据的排序

#代码
df.sort_values(by = ['年龄','是否幸存'], ascending= False).head(20)

	乘客ID	是否幸存	仓位等级	姓名	性别	年龄	兄弟姐妹个数	父母子女个数	船票信息	票价	客舱	登船港口
630	631	1	1	Barkworth, Mr. Algernon Henry Wilson	male	80.0	0	0	27042	30.0000	A23	S
851	852	0	3	Svensson, Mr. Johan	male	74.0	0	0	347060	7.7750	NaN	S
96	97	0	1	Goldschmidt, Mr. George B	male	71.0	0	0	PC 17754	34.6542	A5	C
493	494	0	1	Artagaveytia, Mr. Ramon	male	71.0	0	0	PC 17609	49.5042	NaN	C
116	117	0	3	Connors, Mr. Patrick	male	70.5	0	0	370369	7.7500	NaN	Q
672	673	0	2	Mitchell, Mr. Henry Michael	male	70.0	0	0	C.A. 24580	10.5000	NaN	S
745	746	0	1	Crosby, Capt. Edward Gifford	male	70.0	1	1	WE/P 5735	71.0000	B22	S
33	34	0	2	Wheadon, Mr. Edward H	male	66.0	0	0	C.A. 24579	10.5000	NaN	S
54	55	0	1	Ostby, Mr. Engelhart Cornelius	male	65.0	0	1	113509	61.9792	B30	C
280	281	0	3	Duane, Mr. Frank	male	65.0	0	0	336439	7.7500	NaN	Q
456	457	0	1	Millet, Mr. Francis Davis	male	65.0	0	0	13509	26.5500	E38	S
438	439	0	1	Fortune, Mr. Mark	male	64.0	1	4	19950	263.0000	C23 C25 C27	S
545	546	0	1	Nicholson, Mr. Arthur Ernest	male	64.0	0	0	693	26.0000	NaN	S
275	276	1	1	Andrews, Miss. Kornelia Theodosia	female	63.0	1	0	13502	77.9583	D7	S
483	484	1	3	Turkula, Mrs. (Hedwig)	female	63.0	0	0	4134	9.5875	NaN	S
570	571	1	2	Harris, Mr. George	male	62.0	0	0	S.W./PP 752	10.5000	NaN	S
829	830	1	1	Stone, Mrs. George Nelson (Martha Evelyn)	female	62.0	0	0	113572	80.0000	B28	NaN
252	253	0	1	Stead, Mr. William Thomas	male	62.0	0	0	113514	26.5500	C87	S
555	556	0	1	Wright, Mr. George	male	62.0	0	0	113807	26.5500	NaN	S
170	171	0	1	Van der hoef, Mr. Wyckoff	male	61.0	0	0	111240	33.5000	B19	S

df.sort_values(by = ['年龄','是否幸存'], ascending= False).tail(20)

	乘客ID	是否幸存	仓位等级	姓名	性别	年龄	兄弟姐妹个数	父母子女个数	船票信息	票价	客舱	登船港口
766	767	0	1	Brewe, Dr. Arthur Jackson	male	NaN	0	0	112379	39.6000	NaN	C
768	769	0	3	Moran, Mr. Daniel J	male	NaN	1	0	371110	24.1500	NaN	Q
773	774	0	3	Elias, Mr. Dibo	male	NaN	0	0	2674	7.2250	NaN	C
776	777	0	3	Tobin, Mr. Roger	male	NaN	0	0	383121	7.7500	F38	Q
778	779	0	3	Kilgannon, Mr. Thomas J	male	NaN	0	0	36865	7.7375	NaN	Q
783	784	0	3	Johnston, Mr. Andrew G	male	NaN	1	2	W./C. 6607	23.4500	NaN	S
790	791	0	3	Keane, Mr. Andrew "Andy"	male	NaN	0	0	12460	7.7500	NaN	Q
792	793	0	3	Sage, Miss. Stella Anna	female	NaN	8	2	CA. 2343	69.5500	NaN	S
793	794	0	1	Hoyt, Mr. William Fisher	male	NaN	0	0	PC 17600	30.6958	NaN	C
815	816	0	1	Fry, Mr. Richard	male	NaN	0	0	112058	0.0000	B102	S
825	826	0	3	Flynn, Mr. John	male	NaN	0	0	368323	6.9500	NaN	Q
826	827	0	3	Lam, Mr. Len	male	NaN	0	0	1601	56.4958	NaN	S
832	833	0	3	Saad, Mr. Amin	male	NaN	0	0	2671	7.2292	NaN	C
837	838	0	3	Sirota, Mr. Maurice	male	NaN	0	0	392092	8.0500	NaN	S
846	847	0	3	Sage, Mr. Douglas Bullen	male	NaN	8	2	CA. 2343	69.5500	NaN	S
859	860	0	3	Razi, Mr. Raihed	male	NaN	0	0	2629	7.2292	NaN	C
863	864	0	3	Sage, Miss. Dorothy Edith "Dolly"	female	NaN	8	2	CA. 2343	69.5500	NaN	S
868	869	0	3	van Melkebeke, Mr. Philemon	male	NaN	0	0	345777	9.5000	NaN	S
878	879	0	3	Laleff, Mr. Kristo	male	NaN	0	0	349217	7.8958	NaN	S
888	889	0	3	Johnston, Miss. Catherine Helen "Carrie"	female	NaN	1	2	W./C. 6607	23.4500	NaN	S

1.6.3 任务三：利用Pandas进行算术计算，计算两个DataFrame数据相加结果

# 具体请看《利用Python进行数据分析》第五章 算术运算与数据对齐 部分

#自己构建两个都为数字的DataFrame数据

"""
我们举了一个例子：
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
"""

#代码
df1 = pd.DataFrame(np.arange(0,9).reshape(3, 3),
                     columns=['a', 'b', 'c'],
                     index=['one', 'two', 'three'])
df1

	a	b	c
one	0	1	2
two	3	4	5
three	6	7	8

df2 = pd.DataFrame(np.arange(1,13).reshape(4, 3),
                     columns=['one', 'two', 'three'],
                     index=['a', 'e', 'c','d' ])
df2

	one	two	three
a	1	2	3
e	4	5	6
c	7	8	9
d	10	11	12

将frame_a和frame_b进行相加

#代码
df1+ df2

	a	b	c	one	three	two
a	NaN	NaN	NaN	NaN	NaN	NaN
c	NaN	NaN	NaN	NaN	NaN	NaN
d	NaN	NaN	NaN	NaN	NaN	NaN
e	NaN	NaN	NaN	NaN	NaN	NaN
one	NaN	NaN	NaN	NaN	NaN	NaN
three	NaN	NaN	NaN	NaN	NaN	NaN
two	NaN	NaN	NaN	NaN	NaN	NaN

【提醒】两个DataFrame相加后，会返回一个新的DataFrame，对应的行和列的值会相加，没有对应的会变成空值NaN。

当然，DataFrame还有很多算术运算，如减法，除法等，有兴趣的同学可以看《利用Python进行数据分析》第五章 算术运算与数据对齐 部分，多在网络上查找相关学习资料。

1.6.4 任务四：通过泰坦尼克号数据如何计算出在船上最大的家族有多少人？

'''
还是用之前导入的chinese_train.csv如果我们想看看在船上，最大的家族有多少人（‘兄弟姐妹个数’+‘父母子女个数’），我们该怎么做呢？
'''

df['兄弟姐妹个数']

0      1
1      1
2      0
3      1
4      0
      ..
886    0
887    0
888    1
889    0
890    0
Name: 兄弟姐妹个数, Length: 891, dtype: int64

df['父母子女个数']

0      0
1      0
2      0
3      0
4      0
      ..
886    0
887    0
888    2
889    0
890    0
Name: 父母子女个数, Length: 891, dtype: int64

max( df['兄弟姐妹个数']+ df['父母子女个数'])

10

【提醒】我们只需找出”兄弟姐妹个数“和”父母子女个数“之和最大的数，当然你还可以想出很多方法和思考角度，欢迎你来说出你的看法。

多做几个数据的相加，看看你能分析出什么？

1.6.5 任务五：学会使用Pandas describe()函数查看数据基本统计信息

调用 describe 函数，观察frame2的数据基本信息

df2.describe()

1.6.6 任务六：分别看看泰坦尼克号数据集中 票价、父母子女 这列数据的基本统计数据，你能发现什么？

'''
看看泰坦尼克号数据集中 票价 这列数据的基本统计数据
'''

#代码
df['票价'].describe()
df['父母子女个数'].describe()

count    891.000000
mean       0.381594
std        0.806057
min        0.000000
25%        0.000000
50%        0.000000
75%        0.000000
max        6.000000
Name: 父母子女个数, dtype: float64

参考资料

[1] hands-on-data-analysis.