1. 基础：数据导入，读取，索引

1.1 数据导入，读取

import pandas as pa#导入库

titan=pa.read_csv('./titanic_train911.csv')#读取文件
titan.head(5)#查看前5行
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 
titan.info()#信息

<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

titan.columns#查看表格的列名

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

titan.dtypes#查看数据类型

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

titan.values#查看数据

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)

titan.index#查看索引

RangeIndex(start=0, stop=891, step=1)

titan=titan.set_index('PassengerId')#自己定义索引（可取列名）
titan.head(8)
Survived Pclass Age SibSp Parch Ticket Cabin Embarked
PassengerId
1 0 3 22.0 1 0 A/5 21171 NaN S
2 1 1 38.0 1 0 PC 17599 C85 C
3 1 3 26.0 0 0 STON/O2. 3101282 NaN S
4 1 1 35.0 1 0 113803 C123 S
5 0 3 35.0 0 0 373450 NaN S
6 0 3 NaN 0 0 330877 NaN Q
7 0 1 54.0 0 0 17463 E46 S
8 0 3 2.0 3 1 349909 NaN S 
titan.describe()#查看基本统计特性
Survived Pclass Age SibSp Parch
count 891.000000 891.000000 714.000000 891.000000 891.000000
mean 0.383838 2.308642 29.699118 0.523008 0.381594
std 0.486592 0.836071 14.526497 1.102743 0.806057
min 0.000000 1.000000 0.420000 0.000000 0.000000
25% 0.000000 2.000000 20.125000 0.000000 0.000000
50% 0.000000 3.000000 28.000000 0.000000 0.000000
75% 1.000000 3.000000 38.000000 1.000000 0.000000
max 1.000000 3.000000 80.000000 8.000000 6.000000

1.2 索引

titan['Survived'][:8]#通过索引查看0-8行的Survived 属性

PassengerId
1    0
2    1
3    1
4    1
5    0
6    0
7    0
8    0
Name: Survived, dtype: int64

findlist=['Survived','Age']
titan[findlist][:8]#将要查询的属性列名保存到list中传入进行查看
Survived Age
PassengerId
1 0 22.0
2 1 38.0
3 1 26.0
4 1 35.0
5 0 35.0
6 0 NaN
7 0 54.0
8 0 2.0 
titan=titan.set_index('Age')#先指定索引
titan.loc[38]#loc方法定位
Survived Pclass SibSp Parch Ticket Cabin Embarked
Age
38.0 1 1 1 0 PC 17599 C85 C
38.0 1 3 1 5 347077 NaN S
38.0 1 1 0 0 113572 B28 NaN
38.0 0 3 0 0 349249 NaN S
38.0 1 1 1 0 19943 C93 S
38.0 0 1 0 1 PC 17582 C91 S
38.0 0 2 0 0 237671 NaN S
38.0 0 3 0 0 SOTON/O.Q. 3101306 NaN S
38.0 0 3 0 0 315089 NaN S
38.0 1 1 0 0 PC 17757 C45 C
38.0 0 1 0 0 19972 NaN S 
titan.iloc[666]#iloc方法查找定位

Survived         0
Pclass           2
SibSp            0
Parch            0
Ticket      234686
Cabin          NaN
Embarked         S
Name: 25.0, dtype: object

titan.iloc[666:668,2:5]
SibSp Parch Ticket
Age
25.0 0 0 234686
NaN 0 0 312993 
titan=pa.read_csv('./titanic_train911.csv')#读取文件
titan.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 
titan=titan.set_index('Name')
titan.head()
PassengerId Survived Pclass Sex Age SibSp Parch Ticket Fare Cabin Embarked
Name
Braund, Mr. Owen Harris 1 0 3 male 22.0 1 0 A/5 21171 7.2500 NaN S
Cumings, Mrs. John Bradley (Florence Briggs Thayer) 2 1 1 female 38.0 1 0 PC 17599 71.2833 C85 C
Heikkinen, Miss. Laina 3 1 3 female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S
Futrelle, Mrs. Jacques Heath (Lily May Peel) 4 1 1 female 35.0 1 0 113803 53.1000 C123 S
Allen, Mr. William Henry 5 0 3 male 35.0 0 0 373450 8.0500 NaN S 
titan[titan['Age']>18][2:8]#bool查询
PassengerId Survived Pclass Sex Age SibSp Parch Ticket Fare Cabin Embarked
Name
Heikkinen, Miss. Laina 3 1 3 female 26.0 0 0 STON/O2. 3101282 7.9250 NaN S
Futrelle, Mrs. Jacques Heath (Lily May Peel) 4 1 1 female 35.0 1 0 113803 53.1000 C123 S
Allen, Mr. William Henry 5 0 3 male 35.0 0 0 373450 8.0500 NaN S
McCarthy, Mr. Timothy J 7 0 1 male 54.0 0 0 17463 51.8625 E46 S
Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg) 9 1 3 female 27.0 0 2 347742 11.1333 NaN S
Bonnell, Miss. Elizabeth 12 1 1 female 58.0 0 0 113783 26.5500 C103 S 
titan[titan['Age']==18][:8]
PassengerId Survived Pclass Sex Age SibSp Parch Ticket Fare Cabin Embarked
Name
Vander Planke, Miss. Augusta Maria 39 0 3 female 18.0 2 0 345764 18.0000 NaN S
Arnold-Franchi, Mrs. Josef (Josefine Franchi) 50 0 3 female 18.0 1 0 349237 17.8000 NaN S
Andrew, Mr. Edgardo Samuel 145 0 2 male 18.0 0 0 231945 11.5000 NaN S
Klasen, Mr. Klas Albin 176 0 3 male 18.0 1 1 350404 7.8542 NaN S
Cohen, Mr. Gurshon "Gus" 205 1 3 male 18.0 0 0 A/5 3540 8.0500 NaN S
Fahlstrom, Mr. Arne Jonas 229 0 2 male 18.0 0 0 236171 13.0000 NaN S
Ryerson, Miss. Emily Borie 312 1 1 female 18.0 2 2 PC 17608 262.3750 B57 B59 B63 B66 C
Wiklund, Mr. Jakob Alfred 372 0 3 male 18.0 1 0 3101267 6.4958 NaN S

1.3 排序

titan.groupby('Sex').sum()#按照年龄分组后统计各组的sum和
PassengerId Survived Pclass Age SibSp Parch Fare
Sex
female 135343 233 678 7286.00 218 204 13966.6628
male 262043 109 1379 13919.17 248 136 14727.2865 
titan.groupby('Sex')['Age'].mean()#按照性别分组后求各组中年龄的均值

Sex
female    27.915709
male      30.726645
Name: Age, dtype: float64

2 统计计算

mac=pa.read_csv('macrodata.csv')
mac.head()
year quarter realgdp realcons realinv realgovt realdpi cpi m1 tbilrate unemp pop infl realint
0 1959.0 1.0 2710.349 1707.4 286.898 470.045 1886.9 28.98 139.7 2.82 5.8 177.146 0.00 0.00
1 1959.0 2.0 2778.801 1733.7 310.859 481.301 1919.7 29.15 141.7 3.08 5.1 177.830 2.34 0.74
2 1959.0 3.0 2775.488 1751.8 289.226 491.260 1916.4 29.35 140.5 3.82 5.3 178.657 2.74 1.09
3 1959.0 4.0 2785.204 1753.7 299.356 484.052 1931.3 29.37 140.0 4.33 5.6 179.386 0.27 4.06
4 1960.0 1.0 2847.699 1770.5 331.722 462.199 1955.5 29.54 139.6 3.50 5.2 180.007 2.31 1.19 
mac['realinv'].sum()#

205611.364

mac.sum(axis=0)#按列求和

year         402727.000
quarter         506.000
realgdp     1465897.896
realcons     979534.500
realinv      205611.364
realgovt     134655.714
realdpi     1078039.800
cpi           21330.385
m1           135589.300
tbilrate       1078.290
unemp          1194.600
pop           48664.003
infl            804.150
realint         271.310
dtype: float64

mac.mean(axis=1)#按行求均值

0       669.717000
1       681.807214
2       681.973643
3       684.259143
4       692.140500
5       691.066643
6       692.083643
7       687.265786
8       690.154643
9       699.882857
10      708.679857
11      719.592571
12      728.865429
13      734.630857
14      740.307857
15      743.272714
16      748.771071
17      754.663357
18      765.761786
19      770.859429
20      783.005000
21      792.656357
22      801.510857
23      804.556643
24      818.246500
25      825.562357
26      841.191571
27      857.239786
28      871.669929
29      873.893786...
173    2461.527500
174    2469.052643
175    2475.373643
176    2485.468214
177    2513.350214
178    2550.004357
179    2570.842071
180    2588.743786
181    2611.854143
182    2632.526786
183    2659.255071
184    2667.557429
185    2680.025786
186    2699.507571
187    2714.498857
188    2750.110786
189    2761.561000
190    2766.796214
191    2785.227357
192    2794.254500
193    2807.221143
194    2824.258929
195    2828.893143
196    2820.595929
197    2840.309786
198    2816.360214
199    2802.594286
200    2767.597071
201    2774.576071
202    2788.397500
Length: 203, dtype: float64

#mac.sum(axis ='columns')
mac.sum(axis=1)#按列求和

0       9376.038
1       9545.301
2       9547.631
3       9579.628
4       9689.967
5       9674.933
6       9689.171
7       9621.721
8       9662.165
9       9798.360
10      9921.518
11     10074.296
12     10204.116
13     10284.832
14     10364.310
15     10405.818
16     10482.795
17     10565.287
18     10720.665
19     10792.032
20     10962.070
21     11097.189
22     11221.152
23     11263.793
24     11455.451
25     11557.873
26     11776.682
27     12001.357
28     12203.379
29     12234.513...
173    34461.385
174    34566.737
175    34655.231
176    34796.555
177    35186.903
178    35700.061
179    35991.789
180    36242.413
181    36565.958
182    36855.375
183    37229.571
184    37345.804
185    37520.361
186    37793.106
187    38002.984
188    38501.551
189    38661.854
190    38735.147
191    38993.183
192    39119.563
193    39301.096
194    39539.625
195    39604.504
196    39488.343
197    39764.337
198    39429.043
199    39236.320
200    38746.359
201    38844.065
202    39037.565
Length: 203, dtype: float64

mac.cov()#协方差
year quarter realgdp realcons realinv realgovt realdpi cpi m1 tbilrate unemp pop infl realint
year 215.702580 -0.186558 4.647762e+04 3.329587e+04 8.096633e+03 1782.201530 3.510097e+04 889.385028 6.510849e+03 -7.164087 0.914942 547.621880 -6.729093 -0.093007
quarter -0.186558 1.251183 2.187058e+01 1.421728e+01 4.628355e+00 1.913510 1.622233e+01 0.345575 2.667532e+00 0.035261 -0.006797 0.348573 -0.008926 0.065246
realgdp 46477.622176 21.870583 1.033594e+07 7.431573e+06 1.839145e+06 393386.359633 7.783769e+06 194353.924345 1.432731e+06 -2484.948926 -301.503958 119402.877291 -2006.862002 -416.061514
realcons 33295.865483 14.217276 7.431573e+06 5.351571e+06 1.321452e+06 283735.957690 5.601336e+06 139453.560568 1.029089e+06 -1906.458928 -189.504759 85662.398005 -1496.161105 -367.448608
realinv 8096.633424 4.628355 1.839145e+06 1.321452e+06 3.423447e+05 65506.893003 1.377683e+06 33957.136254 2.486486e+05 -448.762616 -158.177573 20952.267936 -366.256662 -72.505352
realgovt 1782.201530 1.913510 3.933864e+05 2.837360e+05 6.550689e+04 19842.569221 2.977704e+05 7619.553691 5.711414e+04 -119.590326 -5.058225 4539.121687 -135.480431 18.526286
realdpi 35100.969912 16.222333 7.783769e+06 5.601336e+06 1.377683e+06 297770.416433 5.873430e+06 146538.249776 1.078988e+06 -1834.742182 -108.863828 90094.189290 -1471.848862 -315.850267
cpi 889.385028 0.345575 1.943539e+05 1.394536e+05 3.395714e+04 7619.553691 1.465382e+05 3755.100856 2.759093e+04 -40.682744 2.210069 2268.750456 -44.328471 4.689722
m1 6510.849454 2.667532 1.432731e+06 1.029089e+06 2.486486e+05 57114.142869 1.078988e+06 27590.930981 2.073403e+05 -421.744940 -10.890270 16668.489012 -395.866750 -18.638259
tbilrate -7.164087 0.035261 -2.484949e+03 -1.906459e+03 -4.487626e+02 -119.590326 -1.834742e+03 -40.682744 -4.217449e+02 7.857206 0.940225 -22.802668 5.658413 2.233145
unemp 0.914942 -0.006797 -3.015040e+02 -1.895048e+02 -1.581776e+02 -5.058225 -1.088638e+02 2.210069 -1.089027e+01 0.940225 2.127439 0.487778 0.308501 0.632511
pop 547.621880 0.348573 1.194029e+05 8.566240e+04 2.095227e+04 4539.121687 9.009419e+04 2268.750456 1.666849e+04 -22.802668 0.487778 1398.045724 -19.926368 -2.014960
infl -6.729093 -0.008926 -2.006862e+03 -1.496161e+03 -3.662567e+02 -135.480431 -1.471849e+03 -44.328471 -3.958667e+02 5.658413 0.308501 -19.926368 10.583418 -4.870398
realint -0.093007 0.065246 -4.160615e+02 -3.674486e+02 -7.250535e+01 18.526286 -3.158503e+02 4.689722 -1.863826e+01 2.233145 0.632511 -2.014960 -4.870398 7.122486 
mac.corr()#相关系数
year quarter realgdp realcons realinv realgovt realdpi cpi m1 tbilrate unemp pop infl realint
year 1.000000 -0.011356 0.984331 0.979991 0.942204 0.861450 0.986156 0.988215 0.973572 -0.174020 0.042711 0.997223 -0.140837 -0.002373
quarter -0.011356 1.000000 0.006082 0.005494 0.007072 0.012144 0.005984 0.005042 0.005237 0.011246 -0.004166 0.008334 -0.002453 0.021856
realgdp 0.984331 0.006082 1.000000 0.999229 0.977708 0.868651 0.999008 0.986524 0.978696 -0.275745 -0.064297 0.993297 -0.191880 -0.048492
realcons 0.979991 0.005494 0.999229 1.000000 0.976290 0.870713 0.999091 0.983735 0.976946 -0.294004 -0.056163 0.990350 -0.198804 -0.059517
realinv 0.942204 0.007072 0.977708 0.976290 1.000000 0.794797 0.971564 0.947084 0.933281 -0.273622 -0.185347 0.957720 -0.192416 -0.046433
realgovt 0.861450 0.012144 0.868651 0.870713 0.794797 1.000000 0.872241 0.882714 0.890436 -0.302875 -0.024619 0.861811 -0.295641 0.049280
realdpi 0.986156 0.005984 0.999008 0.999091 0.971564 0.872241 1.000000 0.986721 0.977752 -0.270082 -0.030797 0.994238 -0.186683 -0.048834
cpi 0.988215 0.005042 0.986524 0.983735 0.947084 0.882714 0.986721 1.000000 0.988812 -0.236846 0.024727 0.990182 -0.222361 0.028676
m1 0.973572 0.005237 0.978696 0.976946 0.933281 0.890436 0.977752 0.988812 1.000000 -0.330426 -0.016397 0.979025 -0.267236 -0.015337
tbilrate -0.174020 0.011246 -0.275745 -0.294004 -0.273622 -0.302875 -0.270082 -0.236846 -0.330426 1.000000 0.229969 -0.217566 0.620508 0.298516
unemp 0.042711 -0.004166 -0.064297 -0.056163 -0.185347 -0.024619 -0.030797 0.024727 -0.016397 0.229969 1.000000 0.008944 0.065015 0.162489
pop 0.997223 0.008334 0.993297 0.990350 0.957720 0.861811 0.994238 0.990182 0.979025 -0.217566 0.008944 1.000000 -0.163815 -0.020192
infl -0.140837 -0.002453 -0.191880 -0.198804 -0.192416 -0.295641 -0.186683 -0.222361 -0.267236 0.620508 0.065015 -0.163815 1.000000 -0.560965
realint -0.002373 0.021856 -0.048492 -0.059517 -0.046433 0.049280 -0.048834 0.028676 -0.015337 0.298516 0.162489 -0.020192 -0.560965 1.000000 
mac['cpi'].value_counts(ascending = True,bins=4)#升序,bins:平均分成4份

(76.388, 123.795]               38
(171.203, 218.61]               38
(123.795, 171.203]              44
(28.788999999999998, 76.388]    83
Name: cpi, dtype: int64

3. 对象操作

3.1 Dataframe结构

import pandas as pa#导入库
#Dataframe结构
data = [[1,2,3],[4,5,6]]
index = ['a','b']
columns = ['A','B','C']df = pa.DataFrame(data=data,index=index,columns = columns)
df
A B C
a 1 2 3
b 4 5 6

增：df.loc()/pd.concat()(练接两个dataframe)/直接添加；删：df.drop()/del df[]/；改：通过loc和index重新定义；查：iloc/loc/索引；

df.loc['c']=[7,8,9]#增加行
df
A B C
a 1 2 3
b 4 5 6
D 7 7 7
c 7 8 9 
df.drop(['D'],axis=0,inplace=True)#删除行
df
A B C
a 1 2 3
b 4 5 6
c 7 8 9 
df.loc['a']['B']=20#改
df
A B C
a 1 20 3
b 4 5 6
c 7 8 9 
df.loc['b2']#查行

B      5
C      6
D    200
Name: b2, dtype: int64

df.iloc[2]

B      8
C      9
D    300
Name: c3, dtype: int64

df['D']=[100,200,300]#增加列
df
A B C D
a 1 20 3 100
b 4 5 6 200
c 7 8 9 300 
del df['A']#删除列
df
B C D
a 20 3 100
b 5 6 200
c 8 9 300 
df.index=['a1','b2','c3']#修改索引
df
B C D
a1 20 3 100
b2 5 6 200
c3 8 9 300 
df['D']#查列

a1    100
b2    200
c3    300
Name: D, dtype: int64

data = [[10,20,30],[40,50,60],[70,80,90]]
index = ['j','k','l']
columns = ['B','C','D']df1 = pa.DataFrame(data=data,index=index,columns = columns)
df1
B C D
j 10 20 30
k 40 50 60
l 70 80 90 
df2=pa.concat([df,df1])#拼接
df2
B C D
a1 20 3 100
b2 5 6 200
c3 8 9 300
j 10 20 30
k 40 50 60
l 70 80 90 
df2=pa.concat([df,df1],axis=1)
df2

C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version
of pandas will change to not sort by default.To accept the future behavior, pass 'sort=False'.To retain the current behavior and silence the warning, pass 'sort=True'."""Entry point for launching an IPython kernel.
B C D B C D
a1 20.0 3.0 100.0 NaN NaN NaN
b2 5.0 6.0 200.0 NaN NaN NaN
c3 8.0 9.0 300.0 NaN NaN NaN
j NaN NaN NaN 10.0 20.0 30.0
k NaN NaN NaN 40.0 50.0 60.0
l NaN NaN NaN 70.0 80.0 90.0 
left = pa.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],'A': ['A0', 'A1', 'A2', 'A3'], 'B': ['B0', 'B1', 'B2', 'B3']})
right = pa.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],'C': ['C0', 'C1', 'C2', 'C3'], 'D': ['D0', 'D1', 'D2', 'D3']})
key A B
0 K0 A0 B0
1 K1 A1 B1
2 K2 A2 B2
3 K3 A3 B3 
merge=pa.merge(left,right)
merge
key A B C D
0 K0 A0 B0 C0 D0
1 K1 A1 B1 C1 D1
2 K2 A2 B2 C2 D2
3 K3 A3 B3 C3 D3 
merge=pa.merge(left,right,on='key')
merge
key A B C D
0 K0 A0 B0 C0 D0
1 K1 A1 B1 C1 D1
2 K2 A2 B2 C2 D2
3 K3 A3 B3 C3 D3

3.2 series结构

data = [10,11,12]
index = ['a','b','c']
s = pa.Series(data = data,index = index)#Sersies结构
s

a    10
b    11
c    12
dtype: int64

data = [100,110]
index = ['h','k']
s2 = pa.Series(data = data,index = index)
s3=s.append(s2)#增：append（）
s3

a     10
b     11
c     12
h    100
k    110
dtype: int64

s3['L']=900#直接添加
s3

a     10
b     11
c     12
h    100
k    110
L    900
dtype: int64

del s2['h']#删除一行
s2

k    110
dtype: int64

s.drop(['b','a'],inplace = True)#删除多行用drop()
s

c    12
dtype: int64

s3.replace(to_replace = 100,value = 101,inplace = True)#改，replace（）、修改索引rename（）
s3

a     10
b     11
c     12
h    101
k    110
L    900
dtype: int64

s3.rename(index = {'a':'A'},inplace = True)
s3

A     10
b     11
c     12
h    101
k    110
L    900
dtype: int64

s3.loc['b']#查：可用索引/loc/Iloc等查找定位

11

s3.iloc[2]

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