Pandas基础学习入门级别

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

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]