python之pandas基础知识以及练习题
####pandas数据分析与处理库
import pandas as pd
ad_csv(‘E:\pyhon\pandas\Pandas%E4%BB%A3%E7%A0%81\data\titanic.csv’)
df
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
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
#head()可以读取前⼏条数据，可以指定前⾯的任意⼏条数据
df.head(6)
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
()#info()返回当前信息
<class ‘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
finishedAge 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
df.index
RangeIndex(start=0, stop=891, step=1)
Index([‘PassengerId’, ‘Survived’, ‘Pclass’, ‘Name’, ‘Sex’, ‘Age’, ‘SibSp’,‘Parch’, ‘Ticket’, ‘Fare’, ‘Cabin’, ‘Embarked’],
dtype=‘object’)
df.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
df.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)
#创建⼀个dataFrame结构
#创建pandas
data={‘country’:[‘aaa’,‘bbb’,‘ccc’],#指定列名相当于字典结构
'population':[10,12,24]}
data
{‘country’: [‘aaa’, ‘bbb’, ‘ccc’], ‘population’: [10, 12, 24]}
df_data=pd.DataFrame(data)
df_data
country  population
手机后台怎么设置0 aaa 10
1 bbb 12
2 ccc 24
df_data.info
<bound method of country population
0 aaa 10
1 bbb 12
2 ccc 24>
#取指定的数据
age=df[‘Age’]
age[:5]
0 22.0
1 38.0
2 26.0
3 35.0
4 35.0
Name: Age, dtype: float64
#series:dataframe中的⼀⾏/列
age.index
RangeIndex(start=0, stop=891, step=1)
age.values[:5]
pipeline masters什么意思
array([ 22., 38., 26., 35., 35.])
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
df[‘Age’][:5]
0 22.0
1 38.0
2 26.0
3 35.0
4 35.0
python基础知识整理
Name: Age, dtype: float64
age=df[‘Age’]
age[:5]
Name
Braund, Mr. Owen Harris 22.0
Cumings, Mrs. John Bradley (Florence Briggs Thayer) 38.0
Heikkinen, Miss. Laina 26.0
Futrelle, Mrs. Jacques Heath (Lily May Peel) 35.0
Allen, Mr. William Henry 35.0
Name: Age, dtype: float64
#加减法
age[:5]+10
Name
Braund, Mr. Owen Harris 32.0
Cumings, Mrs. John Bradley (Florence Briggs Thayer) 48.0 Heikkinen, Miss. Laina 36.0
Futrelle, Mrs. Jacques Heath (Lily May Peel) 45.0
Allen, Mr. William Henry 45.0
Name: Age, dtype: float64
age*10
Name
Braund, Mr. Owen Harris 220.0
Cumings, Mrs. John Bradley (Florence Briggs Thayer) 380.0 Heikkinen, Miss. Laina 260.0
Futrelle, Mrs. Jacques Heath (Lily May Peel) 350.0
Allen, Mr. William Henry 350.0
Moran, Mr. James NaN
请简述 spring框架的优点
McCarthy, Mr. Timothy J 540.0
Palsson, Master. Gosta Leonard 20.0
Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) 270.0
Nasser, Mrs. Nicholas (Adele Achem) 140.0
Sandstrom, Miss. Marguerite Rut 40.0
Bonnell, Miss. Elizabeth 580.0
Saundercock, Mr. William Henry 200.0
Andersson, Mr. Anders Johan 390.0
Vestrom, Miss. Hulda Amanda Adolfina 140.0
Hewlett, Mrs. (Mary D Kingcome) 550.0
Rice, Master. Eugene 20.0
Williams, Mr. Charles Eugene NaN
Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele) 310.0 Masselmani, Mrs. Fatima NaN
Fynney, Mr. Joseph J 350.0
Beesley, Mr. Lawrence 340.0
McGowan, Miss. Anna “Annie” 150.0
Sloper, Mr. William Thompson 280.0
Palsson, Miss. Torborg Danira 80.0
Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson) 380.0 Emir, Mr. Farred Chehab NaN
Fortune, Mr. Charles Alexander 190.0
O’Dwyer, Miss. Ellen “Nellie” NaN
Todoroff, Mr. Lalio NaN
…
Giles, Mr. Frederick Edward 210.0
Swift, Mrs. Frederick Joel (Margaret Welles Barron) 480.0输入数组回车结束
Sage, Miss. Dorothy Edith “Dolly” NaN
Gill, Mr. John William 240.0
Bystrom, Mrs. (Karolina) 420.0
Duran y More, Miss. Asuncion 270.0
Roebling, Mr. Washington Augustus II 310.0
van Melkebeke, Mr. Philemon NaN
Johnson, Master. Harold Theodor 40.0
Balkic, Mr. Cerin 260.0
Beckwith, Mrs. Richard Leonard (Sallie Monypeny) 470.0 Carlsson, Mr. Frans Olof 330.0
Vander Cruyssen, Mr. Victor 470.0
Abelson, Mrs. Samuel (Hannah Wizosky) 280.0

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