1.导入库

#导入库
import numpy as np
import pandas as pd

2.查看数据

#读取文件
data = pd.read_csv('titanic_data.csv')

data
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
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 × 12 columns

#查看数据维度
data.shape

(891, 12)

#查看数据基本信息
data.info()

<class 'pandas.core.frame.DataFrame'>
RangeIndex: 891 entries, 0 to 890
Data columns (total 12 columns):
PassengerId    891 non-null int64
Survived       891 non-null int64
Pclass         891 non-null int64
Name           891 non-null object
Sex            891 non-null object
Age            714 non-null float64
SibSp          891 non-null int64
Parch          891 non-null int64
Ticket         891 non-null object
Fare           891 non-null float64
Cabin          204 non-null object
Embarked       889 non-null object
dtypes: float64(2), int64(5), object(5)
memory usage: 83.6+ KB

#查看空值.把函数分步运行理解
data.isnull().sum().sort_values(ascending=False)

Cabin          687
Age            177
Embarked         2
Fare             0
Ticket           0
Parch            0
SibSp            0
Sex              0
Name             0
Pclass           0
Survived         0
PassengerId      0
dtype: int64

#查看每一列的数据格式
data.dtypes

PassengerId      int64
Survived         int64
Pclass           int64
Name            object
Sex             object
Age            float64
SibSp            int64
Parch            int64
Ticket          object
Fare           float64
Cabin           object
Embarked        object
dtype: object

#查看具体每一列的的格式
data['Sex'].dtype

dtype('O')

#查看整个表的空值
data.isnull()
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
0 False False False False False False False False False False True False
1 False False False False False False False False False False False False
2 False False False False False False False False False False True False
3 False False False False False False False False False False False False
4 False False False False False False False False False False True False
5 False False False False False True False False False False True False
6 False False False False False False False False False False False False
7 False False False False False False False False False False True False
8 False False False False False False False False False False True False
9 False False False False False False False False False False True False
10 False False False False False False False False False False False False
11 False False False False False False False False False False False False
12 False False False False False False False False False False True False
13 False False False False False False False False False False True False
14 False False False False False False False False False False True False
15 False False False False False False False False False False True False
16 False False False False False False False False False False True False
17 False False False False False True False False False False True False
18 False False False False False False False False False False True False
19 False False False False False True False False False False True False
20 False False False False False False False False False False True False
21 False False False False False False False False False False False False
22 False False False False False False False False False False True False
23 False False False False False False False False False False False False
24 False False False False False False False False False False True False
25 False False False False False False False False False False True False
26 False False False False False True False False False False True False
27 False False False False False False False False False False False False
28 False False False False False True False False False False True False
29 False False False False False True False False False False True False
... ... ... ... ... ... ... ... ... ... ... ... ...
861 False False False False False False False False False False True False
862 False False False False False False False False False False False False
863 False False False False False True False False False False True False
864 False False False False False False False False False False True False
865 False False False False False False False False False False True False
866 False False False False False False False False False False True False
867 False False False False False False False False False False False False
868 False False False False False True False False False False True False
869 False False False False False False False False False False True False
870 False False False False False False False False False False True False
871 False False False False False False False False False False False False
872 False False False False False False False False False False False False
873 False False False False False False False False False False True False
874 False False False False False False False False False False True False
875 False False False False False False False False False False True False
876 False False False False False False False False False False True False
877 False False False False False False False False False False True False
878 False False False False False True False False False False True False
879 False False False False False False False False False False False False
880 False False False False False False False False False False True False
881 False False False False False False False False False False True False
882 False False False False False False False False False False True False
883 False False False False False False False False False False True False
884 False False False False False False False False False False True False
885 False False False False False False False False False False True False
886 False False False False False False False False False False True False
887 False False False False False False False False False False False False
888 False False False False False True False False False False True False
889 False False False False False False False False False False False False
890 False False False False False False False False False False True False

891 rows × 12 columns

#查看具体某一列的空值
data['Age'].isnull()

0      False
1      False
2      False
3      False
4      False
5       True
6      False
7      False
8      False
9      False
10     False
11     False
12     False
13     False
14     False
15     False
16     False
17      True
18     False
19      True
20     False
21     False
22     False
23     False
24     False
25     False
26      True
27     False
28      True
29      True...
861    False
862    False
863     True
864    False
865    False
866    False
867    False
868     True
869    False
870    False
871    False
872    False
873    False
874    False
875    False
876    False
877    False
878     True
879    False
880    False
881    False
882    False
883    False
884    False
885    False
886    False
887    False
888     True
889    False
890    False
Name: Age, Length: 891, dtype: bool

#查看某列的取值种类
data['Parch'].unique()

array([0, 1, 2, 5, 3, 4, 6], dtype=int64)

#查看某一列(值不同)的值分布
data['Parch'].value_counts(dropna=False) #不把空值丢弃

0    678
1    118
2     80
5      5
3      5
4      4
6      1
Name: Parch, dtype: int64

#查看数据表的值,就是不包括表头和索引
data.values

array([[1, 0, 3, ..., 7.25, nan, 'S'],[2, 1, 1, ..., 71.2833, 'C85', 'C'],[3, 1, 3, ..., 7.925, nan, 'S'],...,[889, 0, 3, ..., 23.45, nan, 'S'],[890, 1, 1, ..., 30.0, 'C148', 'C'],[891, 0, 3, ..., 7.75, nan, 'Q']], dtype=object)

#查看前几行
data.head(4)
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 
#查看后几行
data.tail(4)
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked
887 888 1 1 Graham, Miss. Margaret Edith female 19.0 0 0 112053 30.00 B42 S
888 889 0 3 Johnston, Miss. Catherine Helen "Carrie" female NaN 1 2 W./C. 6607 23.45 NaN S
889 890 1 1 Behr, Mr. Karl Howell male 26.0 0 0 111369 30.00 C148 C
890 891 0 3 Dooley, Mr. Patrick male 32.0 0 0 370376 7.75 NaN Q

3.清洗数据

#-----------------------------------------以上是查看数据
#-----------------------------------------以下是清洗数据

#用0填充空值
data['Age'].fillna(value=0)

0      22.0
1      38.0
2      26.0
3      35.0
4      35.0
5       0.0
6      54.0
7       2.0
8      27.0
9      14.0
10      4.0
11     58.0
12     20.0
13     39.0
14     14.0
15     55.0
16      2.0
17      0.0
18     31.0
19      0.0
20     35.0
21     34.0
22     15.0
23     28.0
24      8.0
25     38.0
26      0.0
27     19.0
28      0.0
29      0.0...
861    21.0
862    48.0
863     0.0
864    24.0
865    42.0
866    27.0
867    31.0
868     0.0
869     4.0
870    26.0
871    47.0
872    33.0
873    47.0
874    28.0
875    15.0
876    20.0
877    19.0
878     0.0
879    56.0
880    25.0
881    33.0
882    22.0
883    28.0
884    25.0
885    39.0
886    27.0
887    19.0
888     0.0
889    26.0
890    32.0
Name: Age, Length: 891, dtype: float64

#使用空值前面一个数填充数据(真正的表数据没变)
data['Age'].fillna(method='ffill')

0      22.0
1      38.0
2      26.0
3      35.0
4      35.0
5      35.0
6      54.0
7       2.0
8      27.0
9      14.0
10      4.0
11     58.0
12     20.0
13     39.0
14     14.0
15     55.0
16      2.0
17      2.0
18     31.0
19     31.0
20     35.0
21     34.0
22     15.0
23     28.0
24      8.0
25     38.0
26     38.0
27     19.0
28     19.0
29     19.0...
861    21.0
862    48.0
863    48.0
864    24.0
865    42.0
866    27.0
867    31.0
868    31.0
869     4.0
870    26.0
871    47.0
872    33.0
873    47.0
874    28.0
875    15.0
876    20.0
877    19.0
878    19.0
879    56.0
880    25.0
881    33.0
882    22.0
883    28.0
884    25.0
885    39.0
886    27.0
887    19.0
888    19.0
889    26.0
890    32.0
Name: Age, Length: 891, dtype: float64

#使用空值后面一个数填充数据(真正的表数据没变)
data['Age'].fillna(method='bfill')

0      22.0
1      38.0
2      26.0
3      35.0
4      35.0
5      54.0
6      54.0
7       2.0
8      27.0
9      14.0
10      4.0
11     58.0
12     20.0
13     39.0
14     14.0
15     55.0
16      2.0
17     31.0
18     31.0
19     35.0
20     35.0
21     34.0
22     15.0
23     28.0
24      8.0
25     38.0
26     19.0
27     19.0
28     40.0
29     40.0...
861    21.0
862    48.0
863    24.0
864    24.0
865    42.0
866    27.0
867    31.0
868     4.0
869     4.0
870    26.0
871    47.0
872    33.0
873    47.0
874    28.0
875    15.0
876    20.0
877    19.0
878    56.0
879    56.0
880    25.0
881    33.0
882    22.0
883    28.0
884    25.0
885    39.0
886    27.0
887    19.0
888    26.0
889    26.0
890    32.0
Name: Age, Length: 891, dtype: float64

#查看元数据
data['Age']

0      22.0
1      38.0
2      26.0
3      35.0
4      35.0
5       NaN
6      54.0
7       2.0
8      27.0
9      14.0
10      4.0
11     58.0
12     20.0
13     39.0
14     14.0
15     55.0
16      2.0
17      NaN
18     31.0
19      NaN
20     35.0
21     34.0
22     15.0
23     28.0
24      8.0
25     38.0
26      NaN
27     19.0
28      NaN
29      NaN...
861    21.0
862    48.0
863     NaN
864    24.0
865    42.0
866    27.0
867    31.0
868     NaN
869     4.0
870    26.0
871    47.0
872    33.0
873    47.0
874    28.0
875    15.0
876    20.0
877    19.0
878     NaN
879    56.0
880    25.0
881    33.0
882    22.0
883    28.0
884    25.0
885    39.0
886    27.0
887    19.0
888     NaN
889    26.0
890    32.0
Name: Age, Length: 891, dtype: float64

#空值依然是空值，不影响原数据
#因为填充时有个inplace参数，该参数默认为False不改变原数据，若要改变原数据需要设置为True

#使用均值改变原数据空值
data['Age'].fillna(data['Age'].mean(),inplace=True)

data['Age']

0      22.000000
1      38.000000
2      26.000000
3      35.000000
4      35.000000
5      29.699118
6      54.000000
7       2.000000
8      27.000000
9      14.000000
10      4.000000
11     58.000000
12     20.000000
13     39.000000
14     14.000000
15     55.000000
16      2.000000
17     29.699118
18     31.000000
19     29.699118
20     35.000000
21     34.000000
22     15.000000
23     28.000000
24      8.000000
25     38.000000
26     29.699118
27     19.000000
28     29.699118
29     29.699118...
861    21.000000
862    48.000000
863    29.699118
864    24.000000
865    42.000000
866    27.000000
867    31.000000
868    29.699118
869     4.000000
870    26.000000
871    47.000000
872    33.000000
873    47.000000
874    28.000000
875    15.000000
876    20.000000
877    19.000000
878    29.699118
879    56.000000
880    25.000000
881    33.000000
882    22.000000
883    28.000000
884    25.000000
885    39.000000
886    27.000000
887    19.000000
888    29.699118
889    26.000000
890    32.000000
Name: Age, Length: 891, dtype: float64

#清除字符空格
'  a bb'.strip()
#data['a']=data['a'].map(str.strip())   对于a列的每一个字符都进行操作

'a bb'

data['Name']=data['Name'].map(str.strip)

data['Name']

0                                Braund, Mr. Owen Harris
1      Cumings, Mrs. John Bradley (Florence Briggs Th...
2                                 Heikkinen, Miss. Laina
3           Futrelle, Mrs. Jacques Heath (Lily May Peel)
4                               Allen, Mr. William Henry
5                                       Moran, Mr. James
6                                McCarthy, Mr. Timothy J
7                         Palsson, Master. Gosta Leonard
8      Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)
9                    Nasser, Mrs. Nicholas (Adele Achem)
10                       Sandstrom, Miss. Marguerite Rut
11                              Bonnell, Miss. Elizabeth
12                        Saundercock, Mr. William Henry
13                           Andersson, Mr. Anders Johan
14                  Vestrom, Miss. Hulda Amanda Adolfina
15                       Hewlett, Mrs. (Mary D Kingcome)
16                                  Rice, Master. Eugene
17                          Williams, Mr. Charles Eugene
18     Vander Planke, Mrs. Julius (Emelia Maria Vande...
19                               Masselmani, Mrs. Fatima
20                                  Fynney, Mr. Joseph J
21                                 Beesley, Mr. Lawrence
22                           McGowan, Miss. Anna "Annie"
23                          Sloper, Mr. William Thompson
24                         Palsson, Miss. Torborg Danira
25     Asplund, Mrs. Carl Oscar (Selma Augusta Emilia...
26                               Emir, Mr. Farred Chehab
27                        Fortune, Mr. Charles Alexander
28                         O'Dwyer, Miss. Ellen "Nellie"
29                                   Todoroff, Mr. Lalio...
861                          Giles, Mr. Frederick Edward
862    Swift, Mrs. Frederick Joel (Margaret Welles Ba...
863                    Sage, Miss. Dorothy Edith "Dolly"
864                               Gill, Mr. John William
865                             Bystrom, Mrs. (Karolina)
866                         Duran y More, Miss. Asuncion
867                 Roebling, Mr. Washington Augustus II
868                          van Melkebeke, Mr. Philemon
869                      Johnson, Master. Harold Theodor
870                                    Balkic, Mr. Cerin
871     Beckwith, Mrs. Richard Leonard (Sallie Monypeny)
872                             Carlsson, Mr. Frans Olof
873                          Vander Cruyssen, Mr. Victor
874                Abelson, Mrs. Samuel (Hannah Wizosky)
875                     Najib, Miss. Adele Kiamie "Jane"
876                        Gustafsson, Mr. Alfred Ossian
877                                 Petroff, Mr. Nedelio
878                                   Laleff, Mr. Kristo
879        Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)
880         Shelley, Mrs. William (Imanita Parrish Hall)
881                                   Markun, Mr. Johann
882                         Dahlberg, Miss. Gerda Ulrika
883                        Banfield, Mr. Frederick James
884                               Sutehall, Mr. Henry Jr
885                 Rice, Mrs. William (Margaret Norton)
886                                Montvila, Rev. Juozas
887                         Graham, Miss. Margaret Edith
888             Johnston, Miss. Catherine Helen "Carrie"
889                                Behr, Mr. Karl Howell
890                                  Dooley, Mr. Patrick
Name: Name, Length: 891, dtype: object

#大小写转换
data['Name']=data['Name'].str.lower()

data['Name']

0                                braund, mr. owen harris
1      cumings, mrs. john bradley (florence briggs th...
2                                 heikkinen, miss. laina
3           futrelle, mrs. jacques heath (lily may peel)
4                               allen, mr. william henry
5                                       moran, mr. james
6                                mccarthy, mr. timothy j
7                         palsson, master. gosta leonard
8      johnson, mrs. oscar w (elisabeth vilhelmina berg)
9                    nasser, mrs. nicholas (adele achem)
10                       sandstrom, miss. marguerite rut
11                              bonnell, miss. elizabeth
12                        saundercock, mr. william henry
13                           andersson, mr. anders johan
14                  vestrom, miss. hulda amanda adolfina
15                       hewlett, mrs. (mary d kingcome)
16                                  rice, master. eugene
17                          williams, mr. charles eugene
18     vander planke, mrs. julius (emelia maria vande...
19                               masselmani, mrs. fatima
20                                  fynney, mr. joseph j
21                                 beesley, mr. lawrence
22                           mcgowan, miss. anna "annie"
23                          sloper, mr. william thompson
24                         palsson, miss. torborg danira
25     asplund, mrs. carl oscar (selma augusta emilia...
26                               emir, mr. farred chehab
27                        fortune, mr. charles alexander
28                         o'dwyer, miss. ellen "nellie"
29                                   todoroff, mr. lalio...
861                          giles, mr. frederick edward
862    swift, mrs. frederick joel (margaret welles ba...
863                    sage, miss. dorothy edith "dolly"
864                               gill, mr. john william
865                             bystrom, mrs. (karolina)
866                         duran y more, miss. asuncion
867                 roebling, mr. washington augustus ii
868                          van melkebeke, mr. philemon
869                      johnson, master. harold theodor
870                                    balkic, mr. cerin
871     beckwith, mrs. richard leonard (sallie monypeny)
872                             carlsson, mr. frans olof
873                          vander cruyssen, mr. victor
874                abelson, mrs. samuel (hannah wizosky)
875                     najib, miss. adele kiamie "jane"
876                        gustafsson, mr. alfred ossian
877                                 petroff, mr. nedelio
878                                   laleff, mr. kristo
879        potter, mrs. thomas jr (lily alexenia wilson)
880         shelley, mrs. william (imanita parrish hall)
881                                   markun, mr. johann
882                         dahlberg, miss. gerda ulrika
883                        banfield, mr. frederick james
884                               sutehall, mr. henry jr
885                 rice, mrs. william (margaret norton)
886                                montvila, rev. juozas
887                         graham, miss. margaret edith
888             johnston, miss. catherine helen "carrie"
889                                behr, mr. karl howell
890                                  dooley, mr. patrick
Name: Name, Length: 891, dtype: object

#更改数据格式
data['Survived'].dtype

dtype('int64')

data['Survived'].astype('str')

0      0
1      1
2      1
3      1
4      0
5      0
6      0
7      0
8      1
9      1
10     1
11     1
12     0
13     0
14     0
15     1
16     0
17     1
18     0
19     1
20     0
21     1
22     1
23     1
24     0
25     1
26     0
27     0
28     1
29     0..
861    0
862    1
863    0
864    0
865    1
866    1
867    0
868    0
869    1
870    0
871    1
872    0
873    0
874    1
875    1
876    0
877    0
878    0
879    1
880    1
881    0
882    0
883    0
884    0
885    0
886    0
887    1
888    0
889    1
890    0
Name: Survived, Length: 891, dtype: object

#改变列名
data.rename(columns={'Sex':'性别 ','Name':'姓名'}).head()
PassengerId Survived Pclass 姓名 性别 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 
#打印列名（依然不会在原表上改）
data.columns

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

#删除某列重复值（删除后出现的）
data['Sex'].drop_duplicates()

0      male
1    female
Name: Sex, dtype: object

#删除某列重复值（删除先出现的）保留后面的
data['Sex'].drop_duplicates(keep='last')

888    female
890      male
Name: Sex, dtype: object

#数据替换
data['Survived'].replace(0,'died')

0      died
1         1
2         1
3         1
4      died
5      died
6      died
7      died
8         1
9         1
10        1
11        1
12     died
13     died
14     died
15        1
16     died
17        1
18     died
19        1
20     died
21        1
22        1
23        1
24     died
25        1
26     died
27     died
28        1
29     died...
861    died
862       1
863    died
864    died
865       1
866       1
867    died
868    died
869       1
870    died
871       1
872    died
873    died
874       1
875       1
876    died
877    died
878    died
879       1
880       1
881    died
882    died
883    died
884    died
885    died
886    died
887       1
888    died
889       1
890    died
Name: Survived, Length: 891, dtype: object

4.数据合并

#--------------------------------------数据合并

data1= pd.DataFrame({'PassengerId':[9,19,29,39999],'Sur':[0,1,0,1],'Name':['xiaoming','xiaohong','xiaozhang','xiaozhao'],'time':[10.0,22.0,30.0,40.0]})
data1
PassengerId Sur Name time
0 9 0 xiaoming 10.0
1 19 1 xiaohong 22.0
2 29 0 xiaozhang 30.0
3 39999 1 xiaozhao 40.0 
#merge合并两张表
data_inner = pd.merge(data,data1,on='PassengerId',how='inner')#根据'PassengerId'字段合并，取交集，也就是内连接
data_inner
PassengerId Survived Pclass Name_x Sex Age SibSp Parch Ticket Fare Cabin Embarked Sur Name_y time
0 9 1 3 johnson, mrs. oscar w (elisabeth vilhelmina berg) female 27.000000 0 2 347742 11.1333 NaN S 0 xiaoming 10.0
1 19 0 3 vander planke, mrs. julius (emelia maria vande... female 31.000000 1 0 345763 18.0000 NaN S 1 xiaohong 22.0
2 29 1 3 o'dwyer, miss. ellen "nellie" female 29.699118 0 0 330959 7.8792 NaN Q 0 xiaozhang 30.0 
#左连接
data_left = pd.merge(data,data1,on='PassengerId',how='left')
data_left.head(30)
PassengerId Survived Pclass Name_x Sex Age SibSp Parch Ticket Fare Cabin Embarked Sur Name_y time
0 1 0 3 braund, mr. owen harris male 22.000000 1 0 A/5 21171 7.2500 NaN S NaN NaN NaN
1 2 1 1 cumings, mrs. john bradley (florence briggs th... female 38.000000 1 0 PC 17599 71.2833 C85 C NaN NaN NaN
2 3 1 3 heikkinen, miss. laina female 26.000000 0 0 STON/O2. 3101282 7.9250 NaN S NaN NaN NaN
3 4 1 1 futrelle, mrs. jacques heath (lily may peel) female 35.000000 1 0 113803 53.1000 C123 S NaN NaN NaN
4 5 0 3 allen, mr. william henry male 35.000000 0 0 373450 8.0500 NaN S NaN NaN NaN
5 6 0 3 moran, mr. james male 29.699118 0 0 330877 8.4583 NaN Q NaN NaN NaN
6 7 0 1 mccarthy, mr. timothy j male 54.000000 0 0 17463 51.8625 E46 S NaN NaN NaN
7 8 0 3 palsson, master. gosta leonard male 2.000000 3 1 349909 21.0750 NaN S NaN NaN NaN
8 9 1 3 johnson, mrs. oscar w (elisabeth vilhelmina berg) female 27.000000 0 2 347742 11.1333 NaN S 0.0 xiaoming 10.0
9 10 1 2 nasser, mrs. nicholas (adele achem) female 14.000000 1 0 237736 30.0708 NaN C NaN NaN NaN
10 11 1 3 sandstrom, miss. marguerite rut female 4.000000 1 1 PP 9549 16.7000 G6 S NaN NaN NaN
11 12 1 1 bonnell, miss. elizabeth female 58.000000 0 0 113783 26.5500 C103 S NaN NaN NaN
12 13 0 3 saundercock, mr. william henry male 20.000000 0 0 A/5. 2151 8.0500 NaN S NaN NaN NaN
13 14 0 3 andersson, mr. anders johan male 39.000000 1 5 347082 31.2750 NaN S NaN NaN NaN
14 15 0 3 vestrom, miss. hulda amanda adolfina female 14.000000 0 0 350406 7.8542 NaN S NaN NaN NaN
15 16 1 2 hewlett, mrs. (mary d kingcome) female 55.000000 0 0 248706 16.0000 NaN S NaN NaN NaN
16 17 0 3 rice, master. eugene male 2.000000 4 1 382652 29.1250 NaN Q NaN NaN NaN
17 18 1 2 williams, mr. charles eugene male 29.699118 0 0 244373 13.0000 NaN S NaN NaN NaN
18 19 0 3 vander planke, mrs. julius (emelia maria vande... female 31.000000 1 0 345763 18.0000 NaN S 1.0 xiaohong 22.0
19 20 1 3 masselmani, mrs. fatima female 29.699118 0 0 2649 7.2250 NaN C NaN NaN NaN
20 21 0 2 fynney, mr. joseph j male 35.000000 0 0 239865 26.0000 NaN S NaN NaN NaN
21 22 1 2 beesley, mr. lawrence male 34.000000 0 0 248698 13.0000 D56 S NaN NaN NaN
22 23 1 3 mcgowan, miss. anna "annie" female 15.000000 0 0 330923 8.0292 NaN Q NaN NaN NaN
23 24 1 1 sloper, mr. william thompson male 28.000000 0 0 113788 35.5000 A6 S NaN NaN NaN
24 25 0 3 palsson, miss. torborg danira female 8.000000 3 1 349909 21.0750 NaN S NaN NaN NaN
25 26 1 3 asplund, mrs. carl oscar (selma augusta emilia... female 38.000000 1 5 347077 31.3875 NaN S NaN NaN NaN
26 27 0 3 emir, mr. farred chehab male 29.699118 0 0 2631 7.2250 NaN C NaN NaN NaN
27 28 0 1 fortune, mr. charles alexander male 19.000000 3 2 19950 263.0000 C23 C25 C27 S NaN NaN NaN
28 29 1 3 o'dwyer, miss. ellen "nellie" female 29.699118 0 0 330959 7.8792 NaN Q 0.0 xiaozhang 30.0
29 30 0 3 todoroff, mr. lalio male 29.699118 0 0 349216 7.8958 NaN S NaN NaN NaN 
#右连接
data_right = pd.merge(data,data1,on='PassengerId',how='right')
data_right
PassengerId Survived Pclass Name_x Sex Age SibSp Parch Ticket Fare Cabin Embarked Sur Name_y time
0 9 1.0 3.0 johnson, mrs. oscar w (elisabeth vilhelmina berg) female 27.000000 0.0 2.0 347742 11.1333 NaN S 0 xiaoming 10.0
1 19 0.0 3.0 vander planke, mrs. julius (emelia maria vande... female 31.000000 1.0 0.0 345763 18.0000 NaN S 1 xiaohong 22.0
2 29 1.0 3.0 o'dwyer, miss. ellen "nellie" female 29.699118 0.0 0.0 330959 7.8792 NaN Q 0 xiaozhang 30.0
3 39999 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1 xiaozhao 40.0 
#全连接
data_outer = pd.merge(data,data1,on='PassengerId',how='outer')
data_outer.head(20)#打印前20条看
PassengerId Survived Pclass Name_x Sex Age SibSp Parch Ticket Fare Cabin Embarked Sur Name_y time
0 1 0.0 3.0 braund, mr. owen harris male 22.000000 1.0 0.0 A/5 21171 7.2500 NaN S NaN NaN NaN
1 2 1.0 1.0 cumings, mrs. john bradley (florence briggs th... female 38.000000 1.0 0.0 PC 17599 71.2833 C85 C NaN NaN NaN
2 3 1.0 3.0 heikkinen, miss. laina female 26.000000 0.0 0.0 STON/O2. 3101282 7.9250 NaN S NaN NaN NaN
3 4 1.0 1.0 futrelle, mrs. jacques heath (lily may peel) female 35.000000 1.0 0.0 113803 53.1000 C123 S NaN NaN NaN
4 5 0.0 3.0 allen, mr. william henry male 35.000000 0.0 0.0 373450 8.0500 NaN S NaN NaN NaN
5 6 0.0 3.0 moran, mr. james male 29.699118 0.0 0.0 330877 8.4583 NaN Q NaN NaN NaN
6 7 0.0 1.0 mccarthy, mr. timothy j male 54.000000 0.0 0.0 17463 51.8625 E46 S NaN NaN NaN
7 8 0.0 3.0 palsson, master. gosta leonard male 2.000000 3.0 1.0 349909 21.0750 NaN S NaN NaN NaN
8 9 1.0 3.0 johnson, mrs. oscar w (elisabeth vilhelmina berg) female 27.000000 0.0 2.0 347742 11.1333 NaN S 0.0 xiaoming 10.0
9 10 1.0 2.0 nasser, mrs. nicholas (adele achem) female 14.000000 1.0 0.0 237736 30.0708 NaN C NaN NaN NaN
10 11 1.0 3.0 sandstrom, miss. marguerite rut female 4.000000 1.0 1.0 PP 9549 16.7000 G6 S NaN NaN NaN
11 12 1.0 1.0 bonnell, miss. elizabeth female 58.000000 0.0 0.0 113783 26.5500 C103 S NaN NaN NaN
12 13 0.0 3.0 saundercock, mr. william henry male 20.000000 0.0 0.0 A/5. 2151 8.0500 NaN S NaN NaN NaN
13 14 0.0 3.0 andersson, mr. anders johan male 39.000000 1.0 5.0 347082 31.2750 NaN S NaN NaN NaN
14 15 0.0 3.0 vestrom, miss. hulda amanda adolfina female 14.000000 0.0 0.0 350406 7.8542 NaN S NaN NaN NaN
15 16 1.0 2.0 hewlett, mrs. (mary d kingcome) female 55.000000 0.0 0.0 248706 16.0000 NaN S NaN NaN NaN
16 17 0.0 3.0 rice, master. eugene male 2.000000 4.0 1.0 382652 29.1250 NaN Q NaN NaN NaN
17 18 1.0 2.0 williams, mr. charles eugene male 29.699118 0.0 0.0 244373 13.0000 NaN S NaN NaN NaN
18 19 0.0 3.0 vander planke, mrs. julius (emelia maria vande... female 31.000000 1.0 0.0 345763 18.0000 NaN S 1.0 xiaohong 22.0
19 20 1.0 3.0 masselmani, mrs. fatima female 29.699118 0.0 0.0 2649 7.2250 NaN C NaN NaN NaN 
data_outer.tail(20)#打印后20条看
PassengerId Survived Pclass Name_x Sex Age SibSp Parch Ticket Fare Cabin Embarked Sur Name_y time
872 873 0.0 1.0 carlsson, mr. frans olof male 33.000000 0.0 0.0 695 5.0000 B51 B53 B55 S NaN NaN NaN
873 874 0.0 3.0 vander cruyssen, mr. victor male 47.000000 0.0 0.0 345765 9.0000 NaN S NaN NaN NaN
874 875 1.0 2.0 abelson, mrs. samuel (hannah wizosky) female 28.000000 1.0 0.0 P/PP 3381 24.0000 NaN C NaN NaN NaN
875 876 1.0 3.0 najib, miss. adele kiamie "jane" female 15.000000 0.0 0.0 2667 7.2250 NaN C NaN NaN NaN
876 877 0.0 3.0 gustafsson, mr. alfred ossian male 20.000000 0.0 0.0 7534 9.8458 NaN S NaN NaN NaN
877 878 0.0 3.0 petroff, mr. nedelio male 19.000000 0.0 0.0 349212 7.8958 NaN S NaN NaN NaN
878 879 0.0 3.0 laleff, mr. kristo male 29.699118 0.0 0.0 349217 7.8958 NaN S NaN NaN NaN
879 880 1.0 1.0 potter, mrs. thomas jr (lily alexenia wilson) female 56.000000 0.0 1.0 11767 83.1583 C50 C NaN NaN NaN
880 881 1.0 2.0 shelley, mrs. william (imanita parrish hall) female 25.000000 0.0 1.0 230433 26.0000 NaN S NaN NaN NaN
881 882 0.0 3.0 markun, mr. johann male 33.000000 0.0 0.0 349257 7.8958 NaN S NaN NaN NaN
882 883 0.0 3.0 dahlberg, miss. gerda ulrika female 22.000000 0.0 0.0 7552 10.5167 NaN S NaN NaN NaN
883 884 0.0 2.0 banfield, mr. frederick james male 28.000000 0.0 0.0 C.A./SOTON 34068 10.5000 NaN S NaN NaN NaN
884 885 0.0 3.0 sutehall, mr. henry jr male 25.000000 0.0 0.0 SOTON/OQ 392076 7.0500 NaN S NaN NaN NaN
885 886 0.0 3.0 rice, mrs. william (margaret norton) female 39.000000 0.0 5.0 382652 29.1250 NaN Q NaN NaN NaN
886 887 0.0 2.0 montvila, rev. juozas male 27.000000 0.0 0.0 211536 13.0000 NaN S NaN NaN NaN
887 888 1.0 1.0 graham, miss. margaret edith female 19.000000 0.0 0.0 112053 30.0000 B42 S NaN NaN NaN
888 889 0.0 3.0 johnston, miss. catherine helen "carrie" female 29.699118 1.0 2.0 W./C. 6607 23.4500 NaN S NaN NaN NaN
889 890 1.0 1.0 behr, mr. karl howell male 26.000000 0.0 0.0 111369 30.0000 C148 C NaN NaN NaN
890 891 0.0 3.0 dooley, mr. patrick male 32.000000 0.0 0.0 370376 7.7500 NaN Q NaN NaN NaN
891 39999 NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN NaN 1.0 xiaozhao 40.0 
#追加数据
data2 = pd.DataFrame({'id':[1,2,3],'Sex':['famle','male','male']})
result = data1.append(data2)
result
Name PassengerId Sex Sur id time
0 xiaoming 9.0 NaN 0.0 NaN 10.0
1 xiaohong 19.0 NaN 1.0 NaN 22.0
2 xiaozhang 29.0 NaN 0.0 NaN 30.0
3 xiaozhao 39999.0 NaN 1.0 NaN 40.0
0 NaN NaN famle NaN 1.0 NaN
1 NaN NaN male NaN 2.0 NaN
2 NaN NaN male NaN 3.0 NaN 
#设置索引列
data_inner.set_index('time')
PassengerId Survived Pclass Name_x Sex Age SibSp Parch Ticket Fare Cabin Embarked Sur Name_y
time
10.0 9 1 3 johnson, mrs. oscar w (elisabeth vilhelmina berg) female 27.000000 0 2 347742 11.1333 NaN S 0 xiaoming
22.0 19 0 3 vander planke, mrs. julius (emelia maria vande... female 31.000000 1 0 345763 18.0000 NaN S 1 xiaohong
30.0 29 1 3 o'dwyer, miss. ellen "nellie" female 29.699118 0 0 330959 7.8792 NaN Q 0 xiaozhang 
#还原索引
data_inner.reset_index(drop=True)
PassengerId Survived Pclass Name_x Sex Age SibSp Parch Ticket Fare Cabin Embarked Sur Name_y time
0 9 1 3 johnson, mrs. oscar w (elisabeth vilhelmina berg) female 27.000000 0 2 347742 11.1333 NaN S 0 xiaoming 10.0
1 19 0 3 vander planke, mrs. julius (emelia maria vande... female 31.000000 1 0 345763 18.0000 NaN S 1 xiaohong 22.0
2 29 1 3 o'dwyer, miss. ellen "nellie" female 29.699118 0 0 330959 7.8792 NaN Q 0 xiaozhang 30.0 
#按照特定列值排序
data_inner.sort_values(by=['Fare'])
PassengerId Survived Pclass Name_x Sex Age SibSp Parch Ticket Fare Cabin Embarked Sur Name_y time
2 29 1 3 o'dwyer, miss. ellen "nellie" female 29.699118 0 0 330959 7.8792 NaN Q 0 xiaozhang 30.0
0 9 1 3 johnson, mrs. oscar w (elisabeth vilhelmina berg) female 27.000000 0 2 347742 11.1333 NaN S 0 xiaoming 10.0
1 19 0 3 vander planke, mrs. julius (emelia maria vande... female 31.000000 1 0 345763 18.0000 NaN S 1 xiaohong 22.0 
#还原回按照索引排序
data_inner.sort_index()
PassengerId Survived Pclass Name_x Sex Age SibSp Parch Ticket Fare Cabin Embarked Sur Name_y time
0 9 1 3 johnson, mrs. oscar w (elisabeth vilhelmina berg) female 27.000000 0 2 347742 11.1333 NaN S 0 xiaoming 10.0
1 19 0 3 vander planke, mrs. julius (emelia maria vande... female 31.000000 1 0 345763 18.0000 NaN S 1 xiaohong 22.0
2 29 1 3 o'dwyer, miss. ellen "nellie" female 29.699118 0 0 330959 7.8792 NaN Q 0 xiaozhang 30.0 
#生成一个新列标记某项数据的大小关系
data_inner['level']=np.where(data_inner['Fare']>10.0,'good','oh')
data_inner
PassengerId Survived Pclass Name_x Sex Age SibSp Parch Ticket Fare Cabin Embarked Sur Name_y time level
0 9 1 3 johnson, mrs. oscar w (elisabeth vilhelmina berg) female 27.000000 0 2 347742 11.1333 NaN S 0 xiaoming 10.0 good
1 19 0 3 vander planke, mrs. julius (emelia maria vande... female 31.000000 1 0 345763 18.0000 NaN S 1 xiaohong 22.0 good
2 29 1 3 o'dwyer, miss. ellen "nellie" female 29.699118 0 0 330959 7.8792 NaN Q 0 xiaozhang 30.0 oh 
#生成一个新列标记符合多个条件的数据
data.loc[(data['Sex']=='male') & (data['Age']<=10.0),'tip']=1
data.head(50)
PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked tip
0 1 0 3 braund, mr. owen harris male 22.000000 1 0 A/5 21171 7.2500 NaN S NaN
1 2 1 1 cumings, mrs. john bradley (florence briggs th... female 38.000000 1 0 PC 17599 71.2833 C85 C NaN
2 3 1 3 heikkinen, miss. laina female 26.000000 0 0 STON/O2. 3101282 7.9250 NaN S NaN
3 4 1 1 futrelle, mrs. jacques heath (lily may peel) female 35.000000 1 0 113803 53.1000 C123 S NaN
4 5 0 3 allen, mr. william henry male 35.000000 0 0 373450 8.0500 NaN S NaN
5 6 0 3 moran, mr. james male 29.699118 0 0 330877 8.4583 NaN Q NaN
6 7 0 1 mccarthy, mr. timothy j male 54.000000 0 0 17463 51.8625 E46 S NaN
7 8 0 3 palsson, master. gosta leonard male 2.000000 3 1 349909 21.0750 NaN S 1.0
8 9 1 3 johnson, mrs. oscar w (elisabeth vilhelmina berg) female 27.000000 0 2 347742 11.1333 NaN S NaN
9 10 1 2 nasser, mrs. nicholas (adele achem) female 14.000000 1 0 237736 30.0708 NaN C NaN
10 11 1 3 sandstrom, miss. marguerite rut female 4.000000 1 1 PP 9549 16.7000 G6 S NaN
11 12 1 1 bonnell, miss. elizabeth female 58.000000 0 0 113783 26.5500 C103 S NaN
12 13 0 3 saundercock, mr. william henry male 20.000000 0 0 A/5. 2151 8.0500 NaN S NaN
13 14 0 3 andersson, mr. anders johan male 39.000000 1 5 347082 31.2750 NaN S NaN
14 15 0 3 vestrom, miss. hulda amanda adolfina female 14.000000 0 0 350406 7.8542 NaN S NaN
15 16 1 2 hewlett, mrs. (mary d kingcome) female 55.000000 0 0 248706 16.0000 NaN S NaN
16 17 0 3 rice, master. eugene male 2.000000 4 1 382652 29.1250 NaN Q 1.0
17 18 1 2 williams, mr. charles eugene male 29.699118 0 0 244373 13.0000 NaN S NaN
18 19 0 3 vander planke, mrs. julius (emelia maria vande... female 31.000000 1 0 345763 18.0000 NaN S NaN
19 20 1 3 masselmani, mrs. fatima female 29.699118 0 0 2649 7.2250 NaN C NaN
20 21 0 2 fynney, mr. joseph j male 35.000000 0 0 239865 26.0000 NaN S NaN
21 22 1 2 beesley, mr. lawrence male 34.000000 0 0 248698 13.0000 D56 S NaN
22 23 1 3 mcgowan, miss. anna "annie" female 15.000000 0 0 330923 8.0292 NaN Q NaN
23 24 1 1 sloper, mr. william thompson male 28.000000 0 0 113788 35.5000 A6 S NaN
24 25 0 3 palsson, miss. torborg danira female 8.000000 3 1 349909 21.0750 NaN S NaN
25 26 1 3 asplund, mrs. carl oscar (selma augusta emilia... female 38.000000 1 5 347077 31.3875 NaN S NaN
26 27 0 3 emir, mr. farred chehab male 29.699118 0 0 2631 7.2250 NaN C NaN
27 28 0 1 fortune, mr. charles alexander male 19.000000 3 2 19950 263.0000 C23 C25 C27 S NaN
28 29 1 3 o'dwyer, miss. ellen "nellie" female 29.699118 0 0 330959 7.8792 NaN Q NaN
29 30 0 3 todoroff, mr. lalio male 29.699118 0 0 349216 7.8958 NaN S NaN
30 31 0 1 uruchurtu, don. manuel e male 40.000000 0 0 PC 17601 27.7208 NaN C NaN
31 32 1 1 spencer, mrs. william augustus (marie eugenie) female 29.699118 1 0 PC 17569 146.5208 B78 C NaN
32 33 1 3 glynn, miss. mary agatha female 29.699118 0 0 335677 7.7500 NaN Q NaN
33 34 0 2 wheadon, mr. edward h male 66.000000 0 0 C.A. 24579 10.5000 NaN S NaN
34 35 0 1 meyer, mr. edgar joseph male 28.000000 1 0 PC 17604 82.1708 NaN C NaN
35 36 0 1 holverson, mr. alexander oskar male 42.000000 1 0 113789 52.0000 NaN S NaN
36 37 1 3 mamee, mr. hanna male 29.699118 0 0 2677 7.2292 NaN C NaN
37 38 0 3 cann, mr. ernest charles male 21.000000 0 0 A./5. 2152 8.0500 NaN S NaN
38 39 0 3 vander planke, miss. augusta maria female 18.000000 2 0 345764 18.0000 NaN S NaN
39 40 1 3 nicola-yarred, miss. jamila female 14.000000 1 0 2651 11.2417 NaN C NaN
40 41 0 3 ahlin, mrs. johan (johanna persdotter larsson) female 40.000000 1 0 7546 9.4750 NaN S NaN
41 42 0 2 turpin, mrs. william john robert (dorothy ann ... female 27.000000 1 0 11668 21.0000 NaN S NaN
42 43 0 3 kraeff, mr. theodor male 29.699118 0 0 349253 7.8958 NaN C NaN
43 44 1 2 laroche, miss. simonne marie anne andree female 3.000000 1 2 SC/Paris 2123 41.5792 NaN C NaN
44 45 1 3 devaney, miss. margaret delia female 19.000000 0 0 330958 7.8792 NaN Q NaN
45 46 0 3 rogers, mr. william john male 29.699118 0 0 S.C./A.4. 23567 8.0500 NaN S NaN
46 47 0 3 lennon, mr. denis male 29.699118 1 0 370371 15.5000 NaN Q NaN
47 48 1 3 o'driscoll, miss. bridget female 29.699118 0 0 14311 7.7500 NaN Q NaN
48 49 0 3 samaan, mr. youssef male 29.699118 2 0 2662 21.6792 NaN C NaN
49 50 0 3 arnold-franchi, mrs. josef (josefine franchi) female 18.000000 1 0 349237 17.8000 NaN S NaN 
#对某列数据进行再拆分
#对泰坦尼克号Name列拆分，拆为姓和名
data_namesplit = pd.DataFrame((x.split('.') for x in data['Name']),index=data.index,columns=['名','姓','s'])
data_namesplit
名 姓 s
0 braund, mr owen harris None
1 cumings, mrs john bradley (florence briggs thayer) None
2 heikkinen, miss laina None
3 futrelle, mrs jacques heath (lily may peel) None
4 allen, mr william henry None
5 moran, mr james None
6 mccarthy, mr timothy j None
7 palsson, master gosta leonard None
8 johnson, mrs oscar w (elisabeth vilhelmina berg) None
9 nasser, mrs nicholas (adele achem) None
10 sandstrom, miss marguerite rut None
11 bonnell, miss elizabeth None
12 saundercock, mr william henry None
13 andersson, mr anders johan None
14 vestrom, miss hulda amanda adolfina None
15 hewlett, mrs (mary d kingcome) None
16 rice, master eugene None
17 williams, mr charles eugene None
18 vander planke, mrs julius (emelia maria vandemoortele) None
19 masselmani, mrs fatima None
20 fynney, mr joseph j None
21 beesley, mr lawrence None
22 mcgowan, miss anna "annie" None
23 sloper, mr william thompson None
24 palsson, miss torborg danira None
25 asplund, mrs carl oscar (selma augusta emilia johansson) None
26 emir, mr farred chehab None
27 fortune, mr charles alexander None
28 o'dwyer, miss ellen "nellie" None
29 todoroff, mr lalio None
... ... ... ...
861 giles, mr frederick edward None
862 swift, mrs frederick joel (margaret welles barron) None
863 sage, miss dorothy edith "dolly" None
864 gill, mr john william None
865 bystrom, mrs (karolina) None
866 duran y more, miss asuncion None
867 roebling, mr washington augustus ii None
868 van melkebeke, mr philemon None
869 johnson, master harold theodor None
870 balkic, mr cerin None
871 beckwith, mrs richard leonard (sallie monypeny) None
872 carlsson, mr frans olof None
873 vander cruyssen, mr victor None
874 abelson, mrs samuel (hannah wizosky) None
875 najib, miss adele kiamie "jane" None
876 gustafsson, mr alfred ossian None
877 petroff, mr nedelio None
878 laleff, mr kristo None
879 potter, mrs thomas jr (lily alexenia wilson) None
880 shelley, mrs william (imanita parrish hall) None
881 markun, mr johann None
882 dahlberg, miss gerda ulrika None
883 banfield, mr frederick james None
884 sutehall, mr henry jr None
885 rice, mrs william (margaret norton) None
886 montvila, rev juozas None
887 graham, miss margaret edith None
888 johnston, miss catherine helen "carrie" None
889 behr, mr karl howell None
890 dooley, mr patrick None

891 rows × 3 columns

#把新分出来的表合并进原表axis = 1按行合并
data = pd.concat([data_namesplit,data],axis = 1)
data
名 姓 s PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked tip
0 braund, mr owen harris None 1 0 3 braund, mr. owen harris male 22.000000 1 0 A/5 21171 7.2500 NaN S NaN
1 cumings, mrs john bradley (florence briggs thayer) None 2 1 1 cumings, mrs. john bradley (florence briggs th... female 38.000000 1 0 PC 17599 71.2833 C85 C NaN
2 heikkinen, miss laina None 3 1 3 heikkinen, miss. laina female 26.000000 0 0 STON/O2. 3101282 7.9250 NaN S NaN
3 futrelle, mrs jacques heath (lily may peel) None 4 1 1 futrelle, mrs. jacques heath (lily may peel) female 35.000000 1 0 113803 53.1000 C123 S NaN
4 allen, mr william henry None 5 0 3 allen, mr. william henry male 35.000000 0 0 373450 8.0500 NaN S NaN
5 moran, mr james None 6 0 3 moran, mr. james male 29.699118 0 0 330877 8.4583 NaN Q NaN
6 mccarthy, mr timothy j None 7 0 1 mccarthy, mr. timothy j male 54.000000 0 0 17463 51.8625 E46 S NaN
7 palsson, master gosta leonard None 8 0 3 palsson, master. gosta leonard male 2.000000 3 1 349909 21.0750 NaN S 1.0
8 johnson, mrs oscar w (elisabeth vilhelmina berg) None 9 1 3 johnson, mrs. oscar w (elisabeth vilhelmina berg) female 27.000000 0 2 347742 11.1333 NaN S NaN
9 nasser, mrs nicholas (adele achem) None 10 1 2 nasser, mrs. nicholas (adele achem) female 14.000000 1 0 237736 30.0708 NaN C NaN
10 sandstrom, miss marguerite rut None 11 1 3 sandstrom, miss. marguerite rut female 4.000000 1 1 PP 9549 16.7000 G6 S NaN
11 bonnell, miss elizabeth None 12 1 1 bonnell, miss. elizabeth female 58.000000 0 0 113783 26.5500 C103 S NaN
12 saundercock, mr william henry None 13 0 3 saundercock, mr. william henry male 20.000000 0 0 A/5. 2151 8.0500 NaN S NaN
13 andersson, mr anders johan None 14 0 3 andersson, mr. anders johan male 39.000000 1 5 347082 31.2750 NaN S NaN
14 vestrom, miss hulda amanda adolfina None 15 0 3 vestrom, miss. hulda amanda adolfina female 14.000000 0 0 350406 7.8542 NaN S NaN
15 hewlett, mrs (mary d kingcome) None 16 1 2 hewlett, mrs. (mary d kingcome) female 55.000000 0 0 248706 16.0000 NaN S NaN
16 rice, master eugene None 17 0 3 rice, master. eugene male 2.000000 4 1 382652 29.1250 NaN Q 1.0
17 williams, mr charles eugene None 18 1 2 williams, mr. charles eugene male 29.699118 0 0 244373 13.0000 NaN S NaN
18 vander planke, mrs julius (emelia maria vandemoortele) None 19 0 3 vander planke, mrs. julius (emelia maria vande... female 31.000000 1 0 345763 18.0000 NaN S NaN
19 masselmani, mrs fatima None 20 1 3 masselmani, mrs. fatima female 29.699118 0 0 2649 7.2250 NaN C NaN
20 fynney, mr joseph j None 21 0 2 fynney, mr. joseph j male 35.000000 0 0 239865 26.0000 NaN S NaN
21 beesley, mr lawrence None 22 1 2 beesley, mr. lawrence male 34.000000 0 0 248698 13.0000 D56 S NaN
22 mcgowan, miss anna "annie" None 23 1 3 mcgowan, miss. anna "annie" female 15.000000 0 0 330923 8.0292 NaN Q NaN
23 sloper, mr william thompson None 24 1 1 sloper, mr. william thompson male 28.000000 0 0 113788 35.5000 A6 S NaN
24 palsson, miss torborg danira None 25 0 3 palsson, miss. torborg danira female 8.000000 3 1 349909 21.0750 NaN S NaN
25 asplund, mrs carl oscar (selma augusta emilia johansson) None 26 1 3 asplund, mrs. carl oscar (selma augusta emilia... female 38.000000 1 5 347077 31.3875 NaN S NaN
26 emir, mr farred chehab None 27 0 3 emir, mr. farred chehab male 29.699118 0 0 2631 7.2250 NaN C NaN
27 fortune, mr charles alexander None 28 0 1 fortune, mr. charles alexander male 19.000000 3 2 19950 263.0000 C23 C25 C27 S NaN
28 o'dwyer, miss ellen "nellie" None 29 1 3 o'dwyer, miss. ellen "nellie" female 29.699118 0 0 330959 7.8792 NaN Q NaN
29 todoroff, mr lalio None 30 0 3 todoroff, mr. lalio male 29.699118 0 0 349216 7.8958 NaN S NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
861 giles, mr frederick edward None 862 0 2 giles, mr. frederick edward male 21.000000 1 0 28134 11.5000 NaN S NaN
862 swift, mrs frederick joel (margaret welles barron) None 863 1 1 swift, mrs. frederick joel (margaret welles ba... female 48.000000 0 0 17466 25.9292 D17 S NaN
863 sage, miss dorothy edith "dolly" None 864 0 3 sage, miss. dorothy edith "dolly" female 29.699118 8 2 CA. 2343 69.5500 NaN S NaN
864 gill, mr john william None 865 0 2 gill, mr. john william male 24.000000 0 0 233866 13.0000 NaN S NaN
865 bystrom, mrs (karolina) None 866 1 2 bystrom, mrs. (karolina) female 42.000000 0 0 236852 13.0000 NaN S NaN
866 duran y more, miss asuncion None 867 1 2 duran y more, miss. asuncion female 27.000000 1 0 SC/PARIS 2149 13.8583 NaN C NaN
867 roebling, mr washington augustus ii None 868 0 1 roebling, mr. washington augustus ii male 31.000000 0 0 PC 17590 50.4958 A24 S NaN
868 van melkebeke, mr philemon None 869 0 3 van melkebeke, mr. philemon male 29.699118 0 0 345777 9.5000 NaN S NaN
869 johnson, master harold theodor None 870 1 3 johnson, master. harold theodor male 4.000000 1 1 347742 11.1333 NaN S 1.0
870 balkic, mr cerin None 871 0 3 balkic, mr. cerin male 26.000000 0 0 349248 7.8958 NaN S NaN
871 beckwith, mrs richard leonard (sallie monypeny) None 872 1 1 beckwith, mrs. richard leonard (sallie monypeny) female 47.000000 1 1 11751 52.5542 D35 S NaN
872 carlsson, mr frans olof None 873 0 1 carlsson, mr. frans olof male 33.000000 0 0 695 5.0000 B51 B53 B55 S NaN
873 vander cruyssen, mr victor None 874 0 3 vander cruyssen, mr. victor male 47.000000 0 0 345765 9.0000 NaN S NaN
874 abelson, mrs samuel (hannah wizosky) None 875 1 2 abelson, mrs. samuel (hannah wizosky) female 28.000000 1 0 P/PP 3381 24.0000 NaN C NaN
875 najib, miss adele kiamie "jane" None 876 1 3 najib, miss. adele kiamie "jane" female 15.000000 0 0 2667 7.2250 NaN C NaN
876 gustafsson, mr alfred ossian None 877 0 3 gustafsson, mr. alfred ossian male 20.000000 0 0 7534 9.8458 NaN S NaN
877 petroff, mr nedelio None 878 0 3 petroff, mr. nedelio male 19.000000 0 0 349212 7.8958 NaN S NaN
878 laleff, mr kristo None 879 0 3 laleff, mr. kristo male 29.699118 0 0 349217 7.8958 NaN S NaN
879 potter, mrs thomas jr (lily alexenia wilson) None 880 1 1 potter, mrs. thomas jr (lily alexenia wilson) female 56.000000 0 1 11767 83.1583 C50 C NaN
880 shelley, mrs william (imanita parrish hall) None 881 1 2 shelley, mrs. william (imanita parrish hall) female 25.000000 0 1 230433 26.0000 NaN S NaN
881 markun, mr johann None 882 0 3 markun, mr. johann male 33.000000 0 0 349257 7.8958 NaN S NaN
882 dahlberg, miss gerda ulrika None 883 0 3 dahlberg, miss. gerda ulrika female 22.000000 0 0 7552 10.5167 NaN S NaN
883 banfield, mr frederick james None 884 0 2 banfield, mr. frederick james male 28.000000 0 0 C.A./SOTON 34068 10.5000 NaN S NaN
884 sutehall, mr henry jr None 885 0 3 sutehall, mr. henry jr male 25.000000 0 0 SOTON/OQ 392076 7.0500 NaN S NaN
885 rice, mrs william (margaret norton) None 886 0 3 rice, mrs. william (margaret norton) female 39.000000 0 5 382652 29.1250 NaN Q NaN
886 montvila, rev juozas None 887 0 2 montvila, rev. juozas male 27.000000 0 0 211536 13.0000 NaN S NaN
887 graham, miss margaret edith None 888 1 1 graham, miss. margaret edith female 19.000000 0 0 112053 30.0000 B42 S NaN
888 johnston, miss catherine helen "carrie" None 889 0 3 johnston, miss. catherine helen "carrie" female 29.699118 1 2 W./C. 6607 23.4500 NaN S NaN
889 behr, mr karl howell None 890 1 1 behr, mr. karl howell male 26.000000 0 0 111369 30.0000 C148 C NaN
890 dooley, mr patrick None 891 0 3 dooley, mr. patrick male 32.000000 0 0 370376 7.7500 NaN Q NaN

891 rows × 16 columns

5.提取数据

#----------------------------------------------------------数据提取

#按索引行提取单行的值
data.loc[3]

名                                             futrelle, mrs
姓                             jacques heath (lily may peel)
s                                                      None
PassengerId                                               4
Survived                                                  1
Pclass                                                    1
Name           futrelle, mrs. jacques heath (lily may peel)
Sex                                                  female
Age                                                      35
SibSp                                                     1
Parch                                                     0
Ticket                                               113803
Fare                                                   53.1
Cabin                                                  C123
Embarked                                                  S
tip                                                     NaN
Name: 3, dtype: object

#按照索引提取多行的值,左闭右开
data.iloc[0:3]
名 姓 s PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked tip
0 braund, mr owen harris None 1 0 3 braund, mr. owen harris male 22.0 1 0 A/5 21171 7.2500 NaN S NaN
1 cumings, mrs john bradley (florence briggs thayer) None 2 1 1 cumings, mrs. john bradley (florence briggs th... female 38.0 1 0 PC 17599 71.2833 C85 C NaN
2 heikkinen, miss laina None 3 1 3 heikkinen, miss. laina female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S NaN 
#按照要求提取行值
data.loc[data['Age']<3]
名 姓 s PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked tip
7 palsson, master gosta leonard None 8 0 3 palsson, master. gosta leonard male 2.00 3 1 349909 21.0750 NaN S 1.0
16 rice, master eugene None 17 0 3 rice, master. eugene male 2.00 4 1 382652 29.1250 NaN Q 1.0
78 caldwell, master alden gates None 79 1 2 caldwell, master. alden gates male 0.83 0 2 248738 29.0000 NaN S 1.0
119 andersson, miss ellis anna maria None 120 0 3 andersson, miss. ellis anna maria female 2.00 4 2 347082 31.2750 NaN S NaN
164 panula, master eino viljami None 165 0 3 panula, master. eino viljami male 1.00 4 1 3101295 39.6875 NaN S 1.0
172 johnson, miss eleanor ileen None 173 1 3 johnson, miss. eleanor ileen female 1.00 1 1 347742 11.1333 NaN S NaN
183 becker, master richard f None 184 1 2 becker, master. richard f male 1.00 2 1 230136 39.0000 F4 S 1.0
205 strom, miss telma matilda None 206 0 3 strom, miss. telma matilda female 2.00 0 1 347054 10.4625 G6 S NaN
297 allison, miss helen loraine None 298 0 1 allison, miss. helen loraine female 2.00 1 2 113781 151.5500 C22 C26 S NaN
305 allison, master hudson trevor None 306 1 1 allison, master. hudson trevor male 0.92 1 2 113781 151.5500 C22 C26 S 1.0
340 navratil, master edmond roger None 341 1 2 navratil, master. edmond roger male 2.00 1 1 230080 26.0000 F2 S 1.0
381 nakid, miss maria ("mary") None 382 1 3 nakid, miss. maria ("mary") female 1.00 0 2 2653 15.7417 NaN C NaN
386 goodwin, master sidney leonard None 387 0 3 goodwin, master. sidney leonard male 1.00 5 2 CA 2144 46.9000 NaN S 1.0
469 baclini, miss helene barbara None 470 1 3 baclini, miss. helene barbara female 0.75 2 1 2666 19.2583 NaN C NaN
479 hirvonen, miss hildur e None 480 1 3 hirvonen, miss. hildur e female 2.00 0 1 3101298 12.2875 NaN S NaN
530 quick, miss phyllis may None 531 1 2 quick, miss. phyllis may female 2.00 1 1 26360 26.0000 NaN S NaN
642 skoog, miss margit elizabeth None 643 0 3 skoog, miss. margit elizabeth female 2.00 3 2 347088 27.9000 NaN S NaN
644 baclini, miss eugenie None 645 1 3 baclini, miss. eugenie female 0.75 2 1 2666 19.2583 NaN C NaN
755 hamalainen, master viljo None 756 1 2 hamalainen, master. viljo male 0.67 1 1 250649 14.5000 NaN S 1.0
788 dean, master bertram vere None 789 1 3 dean, master. bertram vere male 1.00 1 2 C.A. 2315 20.5750 NaN S 1.0
803 thomas, master assad alexander None 804 1 3 thomas, master. assad alexander male 0.42 0 1 2625 8.5167 NaN C 1.0
824 panula, master urho abraham None 825 0 3 panula, master. urho abraham male 2.00 4 1 3101295 39.6875 NaN S 1.0
827 mallet, master andre None 828 1 2 mallet, master. andre male 1.00 0 2 S.C./PARIS 2079 37.0042 NaN C 1.0
831 richards, master george sibley None 832 1 2 richards, master. george sibley male 0.83 1 1 29106 18.7500 NaN S 1.0 
#按位置提取数据
data.iloc[:3,:4]
名 姓 s PassengerId
0 braund, mr owen harris None 1
1 cumings, mrs john bradley (florence briggs thayer) None 2
2 heikkinen, miss laina None 3 
data.iloc[1:3,3:4]
PassengerId
1 2
2 3 
#判断某列值是否为某值
data['SibSp'].isin([1,2])

0       True
1       True
2      False
3       True
4      False
5      False
6      False
7      False
8      False
9       True
10      True
11     False
12     False
13      True
14     False
15     False
16     False
17     False
18      True
19     False
20     False
21     False
22     False
23     False
24     False
25      True
26     False
27     False
28     False
29     False...
861     True
862    False
863    False
864    False
865    False
866     True
867    False
868    False
869     True
870    False
871     True
872    False
873    False
874     True
875    False
876    False
877    False
878    False
879    False
880    False
881    False
882    False
883    False
884    False
885    False
886    False
887    False
888     True
889    False
890    False
Name: SibSp, Length: 891, dtype: bool

#提取符合条件的行
data.loc[data['SibSp'].isin([1,2])]
名 姓 s PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked tip
0 braund, mr owen harris None 1 0 3 braund, mr. owen harris male 22.000000 1 0 A/5 21171 7.2500 NaN S NaN
1 cumings, mrs john bradley (florence briggs thayer) None 2 1 1 cumings, mrs. john bradley (florence briggs th... female 38.000000 1 0 PC 17599 71.2833 C85 C NaN
3 futrelle, mrs jacques heath (lily may peel) None 4 1 1 futrelle, mrs. jacques heath (lily may peel) female 35.000000 1 0 113803 53.1000 C123 S NaN
9 nasser, mrs nicholas (adele achem) None 10 1 2 nasser, mrs. nicholas (adele achem) female 14.000000 1 0 237736 30.0708 NaN C NaN
10 sandstrom, miss marguerite rut None 11 1 3 sandstrom, miss. marguerite rut female 4.000000 1 1 PP 9549 16.7000 G6 S NaN
13 andersson, mr anders johan None 14 0 3 andersson, mr. anders johan male 39.000000 1 5 347082 31.2750 NaN S NaN
18 vander planke, mrs julius (emelia maria vandemoortele) None 19 0 3 vander planke, mrs. julius (emelia maria vande... female 31.000000 1 0 345763 18.0000 NaN S NaN
25 asplund, mrs carl oscar (selma augusta emilia johansson) None 26 1 3 asplund, mrs. carl oscar (selma augusta emilia... female 38.000000 1 5 347077 31.3875 NaN S NaN
31 spencer, mrs william augustus (marie eugenie) None 32 1 1 spencer, mrs. william augustus (marie eugenie) female 29.699118 1 0 PC 17569 146.5208 B78 C NaN
34 meyer, mr edgar joseph None 35 0 1 meyer, mr. edgar joseph male 28.000000 1 0 PC 17604 82.1708 NaN C NaN
35 holverson, mr alexander oskar None 36 0 1 holverson, mr. alexander oskar male 42.000000 1 0 113789 52.0000 NaN S NaN
38 vander planke, miss augusta maria None 39 0 3 vander planke, miss. augusta maria female 18.000000 2 0 345764 18.0000 NaN S NaN
39 nicola-yarred, miss jamila None 40 1 3 nicola-yarred, miss. jamila female 14.000000 1 0 2651 11.2417 NaN C NaN
40 ahlin, mrs johan (johanna persdotter larsson) None 41 0 3 ahlin, mrs. johan (johanna persdotter larsson) female 40.000000 1 0 7546 9.4750 NaN S NaN
41 turpin, mrs william john robert (dorothy ann wonnacott) None 42 0 2 turpin, mrs. william john robert (dorothy ann ... female 27.000000 1 0 11668 21.0000 NaN S NaN
43 laroche, miss simonne marie anne andree None 44 1 2 laroche, miss. simonne marie anne andree female 3.000000 1 2 SC/Paris 2123 41.5792 NaN C NaN
46 lennon, mr denis None 47 0 3 lennon, mr. denis male 29.699118 1 0 370371 15.5000 NaN Q NaN
48 samaan, mr youssef None 49 0 3 samaan, mr. youssef male 29.699118 2 0 2662 21.6792 NaN C NaN
49 arnold-franchi, mrs josef (josefine franchi) None 50 0 3 arnold-franchi, mrs. josef (josefine franchi) female 18.000000 1 0 349237 17.8000 NaN S NaN
52 harper, mrs henry sleeper (myna haxtun) None 53 1 1 harper, mrs. henry sleeper (myna haxtun) female 49.000000 1 0 PC 17572 76.7292 D33 C NaN
53 faunthorpe, mrs lizzie (elizabeth anne wilkinson) None 54 1 2 faunthorpe, mrs. lizzie (elizabeth anne wilkin... female 29.000000 1 0 2926 26.0000 NaN S NaN
58 west, miss constance mirium None 59 1 2 west, miss. constance mirium female 5.000000 1 2 C.A. 34651 27.7500 NaN S NaN
62 harris, mr henry birkhardt None 63 0 1 harris, mr. henry birkhardt male 45.000000 1 0 36973 83.4750 C83 S NaN
65 moubarek, master gerios None 66 1 3 moubarek, master. gerios male 29.699118 1 1 2661 15.2458 NaN C NaN
69 kink, mr vincenz None 70 0 3 kink, mr. vincenz male 26.000000 2 0 315151 8.6625 NaN S NaN
73 chronopoulos, mr apostolos None 74 0 3 chronopoulos, mr. apostolos male 26.000000 1 0 2680 14.4542 NaN C NaN
86 ford, mr william neal None 87 0 3 ford, mr. william neal male 16.000000 1 3 W./C. 6608 34.3750 NaN S NaN
92 chaffee, mr herbert fuller None 93 0 1 chaffee, mr. herbert fuller male 46.000000 1 0 W.E.P. 5734 61.1750 E31 S NaN
93 dean, mr bertram frank None 94 0 3 dean, mr. bertram frank male 26.000000 1 2 C.A. 2315 20.5750 NaN S NaN
99 kantor, mr sinai None 100 0 2 kantor, mr. sinai male 34.000000 1 0 244367 26.0000 NaN S NaN
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
750 wells, miss joan None 751 1 2 wells, miss. joan female 4.000000 1 1 29103 23.0000 NaN S NaN
754 herman, mrs samuel (jane laver) None 755 1 2 herman, mrs. samuel (jane laver) female 48.000000 1 2 220845 65.0000 NaN S NaN
755 hamalainen, master viljo None 756 1 2 hamalainen, master. viljo male 0.670000 1 1 250649 14.5000 NaN S 1.0
763 carter, mrs william ernest (lucile polk) None 764 1 1 carter, mrs. william ernest (lucile polk) female 36.000000 1 2 113760 120.0000 B96 B98 S NaN
765 hogeboom, mrs john c (anna andrews) None 766 1 1 hogeboom, mrs. john c (anna andrews) female 51.000000 1 0 13502 77.9583 D11 S NaN
768 moran, mr daniel j None 769 0 3 moran, mr. daniel j male 29.699118 1 0 371110 24.1500 NaN Q NaN
774 hocking, mrs elizabeth (eliza needs) None 775 1 2 hocking, mrs. elizabeth (eliza needs) female 54.000000 1 3 29105 23.0000 NaN S NaN
781 dick, mrs albert adrian (vera gillespie) None 782 1 1 dick, mrs. albert adrian (vera gillespie) female 17.000000 1 0 17474 57.0000 B20 S NaN
783 johnston, mr andrew g None 784 0 3 johnston, mr. andrew g male 29.699118 1 2 W./C. 6607 23.4500 NaN S NaN
788 dean, master bertram vere None 789 1 3 dean, master. bertram vere male 1.000000 1 2 C.A. 2315 20.5750 NaN S 1.0
799 van impe, mrs jean baptiste (rosalie paula govaert) None 800 0 3 van impe, mrs. jean baptiste (rosalie paula go... female 30.000000 1 1 345773 24.1500 NaN S NaN
801 collyer, mrs harvey (charlotte annie tate) None 802 1 2 collyer, mrs. harvey (charlotte annie tate) female 31.000000 1 1 C.A. 31921 26.2500 NaN S NaN
802 carter, master william thornton ii None 803 1 1 carter, master. william thornton ii male 11.000000 1 2 113760 120.0000 B96 B98 S NaN
809 chambers, mrs norman campbell (bertha griggs) None 810 1 1 chambers, mrs. norman campbell (bertha griggs) female 33.000000 1 0 113806 53.1000 E8 S NaN
817 mallet, mr albert None 818 0 2 mallet, mr. albert male 31.000000 1 1 S.C./PARIS 2079 37.0042 NaN C NaN
820 hays, mrs charles melville (clara jennings gregg) None 821 1 1 hays, mrs. charles melville (clara jennings gr... female 52.000000 1 1 12749 93.5000 B69 S NaN
830 yasbeck, mrs antoni (selini alexander) None 831 1 3 yasbeck, mrs. antoni (selini alexander) female 15.000000 1 0 2659 14.4542 NaN C NaN
831 richards, master george sibley None 832 1 2 richards, master. george sibley male 0.830000 1 1 29106 18.7500 NaN S 1.0
835 compton, miss sara rebecca None 836 1 1 compton, miss. sara rebecca female 39.000000 1 1 PC 17756 83.1583 E49 C NaN
849 goldenberg, mrs samuel l (edwiga grabowska) None 850 1 1 goldenberg, mrs. samuel l (edwiga grabowska) female 29.699118 1 0 17453 89.1042 C92 C NaN
852 boulos, miss nourelain None 853 0 3 boulos, miss. nourelain female 9.000000 1 1 2678 15.2458 NaN C NaN
854 carter, mrs ernest courtenay (lilian hughes) None 855 0 2 carter, mrs. ernest courtenay (lilian hughes) female 44.000000 1 0 244252 26.0000 NaN S NaN
856 wick, mrs george dennick (mary hitchcock) None 857 1 1 wick, mrs. george dennick (mary hitchcock) female 45.000000 1 1 36928 164.8667 NaN S NaN
860 hansen, mr claus peter None 861 0 3 hansen, mr. claus peter male 41.000000 2 0 350026 14.1083 NaN S NaN
861 giles, mr frederick edward None 862 0 2 giles, mr. frederick edward male 21.000000 1 0 28134 11.5000 NaN S NaN
866 duran y more, miss asuncion None 867 1 2 duran y more, miss. asuncion female 27.000000 1 0 SC/PARIS 2149 13.8583 NaN C NaN
869 johnson, master harold theodor None 870 1 3 johnson, master. harold theodor male 4.000000 1 1 347742 11.1333 NaN S 1.0
871 beckwith, mrs richard leonard (sallie monypeny) None 872 1 1 beckwith, mrs. richard leonard (sallie monypeny) female 47.000000 1 1 11751 52.5542 D35 S NaN
874 abelson, mrs samuel (hannah wizosky) None 875 1 2 abelson, mrs. samuel (hannah wizosky) female 28.000000 1 0 P/PP 3381 24.0000 NaN C NaN
888 johnston, miss catherine helen "carrie" None 889 0 3 johnston, miss. catherine helen "carrie" female 29.699118 1 2 W./C. 6607 23.4500 NaN S NaN

237 rows × 16 columns

6.筛选数据

#--------------------------------------------------------数据筛选

#使用'与'进行筛选
data.loc[(data['Age']<5) & (data['Sex']=='male')]
名 姓 s PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked tip
7 palsson, master gosta leonard None 8 0 3 palsson, master. gosta leonard male 2.00 3 1 349909 21.0750 NaN S 1.0
16 rice, master eugene None 17 0 3 rice, master. eugene male 2.00 4 1 382652 29.1250 NaN Q 1.0
63 skoog, master harald None 64 0 3 skoog, master. harald male 4.00 3 2 347088 27.9000 NaN S 1.0
78 caldwell, master alden gates None 79 1 2 caldwell, master. alden gates male 0.83 0 2 248738 29.0000 NaN S 1.0
164 panula, master eino viljami None 165 0 3 panula, master. eino viljami male 1.00 4 1 3101295 39.6875 NaN S 1.0
171 rice, master arthur None 172 0 3 rice, master. arthur male 4.00 4 1 382652 29.1250 NaN Q 1.0
183 becker, master richard f None 184 1 2 becker, master. richard f male 1.00 2 1 230136 39.0000 F4 S 1.0
193 navratil, master michel m None 194 1 2 navratil, master. michel m male 3.00 1 1 230080 26.0000 F2 S 1.0
261 asplund, master edvin rojj felix None 262 1 3 asplund, master. edvin rojj felix male 3.00 4 2 347077 31.3875 NaN S 1.0
305 allison, master hudson trevor None 306 1 1 allison, master. hudson trevor male 0.92 1 2 113781 151.5500 C22 C26 S 1.0
340 navratil, master edmond roger None 341 1 2 navratil, master. edmond roger male 2.00 1 1 230080 26.0000 F2 S 1.0
348 coutts, master william loch "william" None 349 1 3 coutts, master. william loch "william" male 3.00 1 1 C.A. 37671 15.9000 NaN S 1.0
386 goodwin, master sidney leonard None 387 0 3 goodwin, master. sidney leonard male 1.00 5 2 CA 2144 46.9000 NaN S 1.0
407 richards, master william rowe None 408 1 2 richards, master. william rowe male 3.00 1 1 29106 18.7500 NaN S 1.0
445 dodge, master washington None 446 1 1 dodge, master. washington male 4.00 0 2 33638 81.8583 A34 S 1.0
755 hamalainen, master viljo None 756 1 2 hamalainen, master. viljo male 0.67 1 1 250649 14.5000 NaN S 1.0
788 dean, master bertram vere None 789 1 3 dean, master. bertram vere male 1.00 1 2 C.A. 2315 20.5750 NaN S 1.0
803 thomas, master assad alexander None 804 1 3 thomas, master. assad alexander male 0.42 0 1 2625 8.5167 NaN C 1.0
824 panula, master urho abraham None 825 0 3 panula, master. urho abraham male 2.00 4 1 3101295 39.6875 NaN S 1.0
827 mallet, master andre None 828 1 2 mallet, master. andre male 1.00 0 2 S.C./PARIS 2079 37.0042 NaN C 1.0
831 richards, master george sibley None 832 1 2 richards, master. george sibley male 0.83 1 1 29106 18.7500 NaN S 1.0
850 andersson, master sigvard harald elias None 851 0 3 andersson, master. sigvard harald elias male 4.00 4 2 347082 31.2750 NaN S 1.0
869 johnson, master harold theodor None 870 1 3 johnson, master. harold theodor male 4.00 1 1 347742 11.1333 NaN S 1.0 
#使用'或'进行筛选
data.loc[(data['Age']==5) | (data['Age']==2)]
名 姓 s PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked tip
7 palsson, master gosta leonard None 8 0 3 palsson, master. gosta leonard male 2.0 3 1 349909 21.0750 NaN S 1.0
16 rice, master eugene None 17 0 3 rice, master. eugene male 2.0 4 1 382652 29.1250 NaN Q 1.0
58 west, miss constance mirium None 59 1 2 west, miss. constance mirium female 5.0 1 2 C.A. 34651 27.7500 NaN S NaN
119 andersson, miss ellis anna maria None 120 0 3 andersson, miss. ellis anna maria female 2.0 4 2 347082 31.2750 NaN S NaN
205 strom, miss telma matilda None 206 0 3 strom, miss. telma matilda female 2.0 0 1 347054 10.4625 G6 S NaN
233 asplund, miss lillian gertrud None 234 1 3 asplund, miss. lillian gertrud female 5.0 4 2 347077 31.3875 NaN S NaN
297 allison, miss helen loraine None 298 0 1 allison, miss. helen loraine female 2.0 1 2 113781 151.5500 C22 C26 S NaN
340 navratil, master edmond roger None 341 1 2 navratil, master. edmond roger male 2.0 1 1 230080 26.0000 F2 S 1.0
448 baclini, miss marie catherine None 449 1 3 baclini, miss. marie catherine female 5.0 2 1 2666 19.2583 NaN C NaN
479 hirvonen, miss hildur e None 480 1 3 hirvonen, miss. hildur e female 2.0 0 1 3101298 12.2875 NaN S NaN
530 quick, miss phyllis may None 531 1 2 quick, miss. phyllis may female 2.0 1 1 26360 26.0000 NaN S NaN
642 skoog, miss margit elizabeth None 643 0 3 skoog, miss. margit elizabeth female 2.0 3 2 347088 27.9000 NaN S NaN
777 emanuel, miss virginia ethel None 778 1 3 emanuel, miss. virginia ethel female 5.0 0 0 364516 12.4750 NaN S NaN
824 panula, master urho abraham None 825 0 3 panula, master. urho abraham male 2.0 4 1 3101295 39.6875 NaN S 1.0 
#对筛选后的数据按某列计数
data.loc[(data['Age']==5) | (data['Age']==2)].Parch.count()  #计算年龄为5或者1的乘客中船舱等级为1的个数

14

#使用query函数进行筛选
data.query('Age==[5,2]')
名 姓 s PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked tip
7 palsson, master gosta leonard None 8 0 3 palsson, master. gosta leonard male 2.0 3 1 349909 21.0750 NaN S 1.0
16 rice, master eugene None 17 0 3 rice, master. eugene male 2.0 4 1 382652 29.1250 NaN Q 1.0
58 west, miss constance mirium None 59 1 2 west, miss. constance mirium female 5.0 1 2 C.A. 34651 27.7500 NaN S NaN
119 andersson, miss ellis anna maria None 120 0 3 andersson, miss. ellis anna maria female 2.0 4 2 347082 31.2750 NaN S NaN
205 strom, miss telma matilda None 206 0 3 strom, miss. telma matilda female 2.0 0 1 347054 10.4625 G6 S NaN
233 asplund, miss lillian gertrud None 234 1 3 asplund, miss. lillian gertrud female 5.0 4 2 347077 31.3875 NaN S NaN
297 allison, miss helen loraine None 298 0 1 allison, miss. helen loraine female 2.0 1 2 113781 151.5500 C22 C26 S NaN
340 navratil, master edmond roger None 341 1 2 navratil, master. edmond roger male 2.0 1 1 230080 26.0000 F2 S 1.0
448 baclini, miss marie catherine None 449 1 3 baclini, miss. marie catherine female 5.0 2 1 2666 19.2583 NaN C NaN
479 hirvonen, miss hildur e None 480 1 3 hirvonen, miss. hildur e female 2.0 0 1 3101298 12.2875 NaN S NaN
530 quick, miss phyllis may None 531 1 2 quick, miss. phyllis may female 2.0 1 1 26360 26.0000 NaN S NaN
642 skoog, miss margit elizabeth None 643 0 3 skoog, miss. margit elizabeth female 2.0 3 2 347088 27.9000 NaN S NaN
777 emanuel, miss virginia ethel None 778 1 3 emanuel, miss. virginia ethel female 5.0 0 0 364516 12.4750 NaN S NaN
824 panula, master urho abraham None 825 0 3 panula, master. urho abraham male 2.0 4 1 3101295 39.6875 NaN S 1.0 
#对筛选后的数据求和
data.query('Age==[5,2]').SibSp.sum()

28

7.汇总数据

#-------------------------------------------------------------数据汇总

#对某列的不同类进行分组计数汇总
data.groupby('Pclass').count()
名 姓 s PassengerId Survived Name Sex Age SibSp Parch Ticket Fare Cabin Embarked tip
Pclass
1 216 216 1 216 216 216 216 216 216 216 216 216 176 214 2
2 184 184 0 184 184 184 184 184 184 184 184 184 16 184 9
3 491 491 0 491 491 491 491 491 491 491 491 491 12 491 22 
#对某列的不同类进行分组，只计数某一列
data.groupby('Pclass')['Sex'].count()

Pclass
1    216
2    184
3    491
Name: Sex, dtype: int64

#对两个字段进行汇总
data.groupby(['Pclass','Sex'])['PassengerId'].count()

Pclass  Sex
1       female     94male      122
2       female     76male      108
3       female    144male      347
Name: PassengerId, dtype: int64

#对某列数据进行汇总，并分析其组内的各值(agg是聚合)
data.groupby(['Pclass','Sex'])['Age'].agg([len,np.sum,np.mean])
len sum mean
Pclass Sex
1 female 94.0 3209.292059 34.141405
male 122.0 4793.101471 39.287717
2 female 76.0 2184.898235 28.748661
male 108.0 3310.622059 30.653908
3 female 144.0 3465.862941 24.068493
male 347.0 9498.137059 27.372153

8.统计数据

#--------------------------------------------------------数据统计

#数据采样，随机抽取数据样本
data.sample(n=10)
名 姓 s PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked tip
70 jenkin, mr stephen curnow None 71 0 2 jenkin, mr. stephen curnow male 32.000000 0 0 C.A. 33111 10.5000 NaN S NaN
92 chaffee, mr herbert fuller None 93 0 1 chaffee, mr. herbert fuller male 46.000000 1 0 W.E.P. 5734 61.1750 E31 S NaN
700 astor, mrs john jacob (madeleine talmadge force) None 701 1 1 astor, mrs. john jacob (madeleine talmadge force) female 18.000000 1 0 PC 17757 227.5250 C62 C64 C NaN
626 kirkland, rev charles leonard None 627 0 2 kirkland, rev. charles leonard male 57.000000 0 0 219533 12.3500 NaN Q NaN
722 gillespie, mr william henry None 723 0 2 gillespie, mr. william henry male 34.000000 0 0 12233 13.0000 NaN S NaN
371 wiklund, mr jakob alfred None 372 0 3 wiklund, mr. jakob alfred male 18.000000 1 0 3101267 6.4958 NaN S NaN
248 beckwith, mr richard leonard None 249 1 1 beckwith, mr. richard leonard male 37.000000 1 1 11751 52.5542 D35 S NaN
795 otter, mr richard None 796 0 2 otter, mr. richard male 39.000000 0 0 28213 13.0000 NaN S NaN
567 palsson, mrs nils (alma cornelia berglund) None 568 0 3 palsson, mrs. nils (alma cornelia berglund) female 29.000000 0 4 349909 21.0750 NaN S NaN
837 sirota, mr maurice None 838 0 3 sirota, mr. maurice male 29.699118 0 0 392092 8.0500 NaN S NaN 
#采样后不放回
data.sample(n=10,replace=False)
名 姓 s PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked tip
345 brown, miss amelia "mildred" None 346 1 2 brown, miss. amelia "mildred" female 24.000000 0 0 248733 13.0000 F33 S NaN
304 williams, mr howard hugh "harry" None 305 0 3 williams, mr. howard hugh "harry" male 29.699118 0 0 A/5 2466 8.0500 NaN S NaN
697 mullens, miss katherine "katie" None 698 1 3 mullens, miss. katherine "katie" female 29.699118 0 0 35852 7.7333 NaN Q NaN
493 artagaveytia, mr ramon None 494 0 1 artagaveytia, mr. ramon male 71.000000 0 0 PC 17609 49.5042 NaN C NaN
226 mellors, mr william john None 227 1 2 mellors, mr. william john male 19.000000 0 0 SW/PP 751 10.5000 NaN S NaN
506 quick, mrs frederick charles (jane richards) None 507 1 2 quick, mrs. frederick charles (jane richards) female 33.000000 0 2 26360 26.0000 NaN S NaN
806 andrews, mr thomas jr None 807 0 1 andrews, mr. thomas jr male 39.000000 0 0 112050 0.0000 A36 S NaN
335 denkoff, mr mitto None 336 0 3 denkoff, mr. mitto male 29.699118 0 0 349225 7.8958 NaN S NaN
560 morrow, mr thomas rowan None 561 0 3 morrow, mr. thomas rowan male 29.699118 0 0 372622 7.7500 NaN Q NaN
492 molson, mr harry markland None 493 0 1 molson, mr. harry markland male 55.000000 0 0 113787 30.5000 C30 S NaN 
#采样后放回
data.sample(n=10,replace=True)  #这里数据太多，看不出区别，放回和不放回的区别在于放回会取到重复值，不放回不会
名 姓 s PassengerId Survived Pclass Name Sex Age SibSp Parch Ticket Fare Cabin Embarked tip
703 gallagher, mr martin None 704 0 3 gallagher, mr. martin male 25.000000 0 0 36864 7.7417 NaN Q NaN
398 pain, dr alfred None 399 0 2 pain, dr. alfred male 23.000000 0 0 244278 10.5000 NaN S NaN
721 jensen, mr svend lauritz None 722 0 3 jensen, mr. svend lauritz male 17.000000 1 0 350048 7.0542 NaN S NaN
708 cleaver, miss alice None 709 1 1 cleaver, miss. alice female 22.000000 0 0 113781 151.5500 NaN S NaN
805 johansson, mr karl johan None 806 0 3 johansson, mr. karl johan male 31.000000 0 0 347063 7.7750 NaN S NaN
262 taussig, mr emil None 263 0 1 taussig, mr. emil male 52.000000 1 1 110413 79.6500 E67 S NaN
52 harper, mrs henry sleeper (myna haxtun) None 53 1 1 harper, mrs. henry sleeper (myna haxtun) female 49.000000 1 0 PC 17572 76.7292 D33 C NaN
659 newell, mr arthur webster None 660 0 1 newell, mr. arthur webster male 58.000000 0 2 35273 113.2750 D48 C NaN
692 lam, mr ali None 693 1 3 lam, mr. ali male 29.699118 0 0 1601 56.4958 NaN S NaN
597 johnson, mr alfred None 598 0 3 johnson, mr. alfred male 49.000000 0 0 LINE 0.0000 NaN S NaN 
#数据表描述性统计
data.describe().round(2).T  #可以一步一步执行，先获得描述性信息，再四舍五入保留两位小数，再转置
count mean std min 25% 50% 75% max
PassengerId 891.0 446.00 257.35 1.00 223.50 446.00 668.5 891.00
Survived 891.0 0.38 0.49 0.00 0.00 0.00 1.0 1.00
Pclass 891.0 2.31 0.84 1.00 2.00 3.00 3.0 3.00
Age 891.0 29.70 13.00 0.42 22.00 29.70 35.0 80.00
SibSp 891.0 0.52 1.10 0.00 0.00 0.00 1.0 8.00
Parch 891.0 0.38 0.81 0.00 0.00 0.00 0.0 6.00
Fare 891.0 32.20 49.69 0.00 7.91 14.45 31.0 512.33
tip 33.0 1.00 0.00 1.00 1.00 1.00 1.0 1.00 
#计算列的标准差
data['Age'].std()

13.002015226002884

#计算两个字段的协方差
data['Age'].cov(data['SibSp'])

-3.3353450099140782

#计算所有字段的协方差
data.cov()   #我们能发现只能计算数字
PassengerId Survived Pclass Age SibSp Parch Fare tip
PassengerId 66231.000000 -0.626966 -7.561798 111.113042 -16.325843 -0.342697 161.883369 0.0
Survived -0.626966 0.236772 -0.137703 -0.441656 -0.018954 0.032017 6.221787 0.0
Pclass -7.561798 -0.137703 0.699015 -3.601855 0.076599 0.012429 -22.830196 0.0
Age 111.113042 -0.441656 -3.601855 169.052400 -3.335345 -1.877987 59.162200 0.0
SibSp -16.325843 -0.018954 0.076599 -3.335345 1.216043 0.368739 8.748734 0.0
Parch -0.342697 0.032017 0.012429 -1.877987 0.368739 0.649728 8.661052 0.0
Fare 161.883369 6.221787 -22.830196 59.162200 8.748734 8.661052 2469.436846 0.0
tip 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.000000 0.0 
#两个字段的相关性分析
data['Age'].corr(data['Survived'])

-0.06980851528714313

#计算所有字段的相关性
data.corr()
PassengerId Survived Pclass Age SibSp Parch Fare tip
PassengerId 1.000000 -0.005007 -0.035144 0.033207 -0.057527 -0.001652 0.012658 NaN
Survived -0.005007 1.000000 -0.338481 -0.069809 -0.035322 0.081629 0.257307 NaN
Pclass -0.035144 -0.338481 1.000000 -0.331339 0.083081 0.018443 -0.549500 NaN
Age 0.033207 -0.069809 -0.331339 1.000000 -0.232625 -0.179191 0.091566 NaN
SibSp -0.057527 -0.035322 0.083081 -0.232625 1.000000 0.414838 0.159651 NaN
Parch -0.001652 0.081629 0.018443 -0.179191 0.414838 1.000000 0.216225 NaN
Fare 0.012658 0.257307 -0.549500 0.091566 0.159651 0.216225 1.000000 NaN
tip NaN NaN NaN NaN NaN NaN NaN NaN

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