python pandas模块的功能_Python数据分析模块pandas用法详解

本文实例讲述了Python数据分析模块pandas用法。分享给大家供大家参考，具体如下：

一 介绍

pandas(Python Data Analysis Library)是基于numpy的数据分析模块，提供了大量标准数据模型和高效操作大型数据集所需要的工具，可以说pandas是使得Python能够成为高效且强大的数据分析环境的重要因素之一。

pandas主要提供了3种数据结构：

1)Series，带标签的一维数组。

2)DataFrame，带标签且大小可变的二维表格结构。

3)Panel，带标签且大小可变的三维数组。

二 代码

1、生成一维数组

>>>import pandas as pd

>>>import numpy as np

>>> x = pd.Series([1,3,5, np.nan])

>>>print(x)

01.0

13.0

25.0

3NaN

dtype: float64

2、生成二维数组

>>> dates = pd.date_range(start='20170101', end='20171231', freq='D')#间隔为天

>>>print(dates)

DatetimeIndex(['2017-01-01','2017-01-02','2017-01-03','2017-01-04',

'2017-01-05','2017-01-06','2017-01-07','2017-01-08',

'2017-01-09','2017-01-10',

...

'2017-12-22','2017-12-23','2017-12-24','2017-12-25',

'2017-12-26','2017-12-27','2017-12-28','2017-12-29',

'2017-12-30','2017-12-31'],

dtype='datetime64[ns]', length=365, freq='D')

>>> dates = pd.date_range(start='20170101', end='20171231', freq='M')#间隔为月

>>>print(dates)

DatetimeIndex(['2017-01-31','2017-02-28','2017-03-31','2017-04-30',

'2017-05-31','2017-06-30','2017-07-31','2017-08-31',

'2017-09-30','2017-10-31','2017-11-30','2017-12-31'],

dtype='datetime64[ns]', freq='M')

>>> df = pd.DataFrame(np.random.randn(12,4), index=dates, columns=list('ABCD'))

>>>print(df)

A B C D

2017-01-31-0.6825560.2441020.4508550.236475

2017-02-28-0.6300600.5906670.4824380.225697

2017-03-311.0669890.3193391.0949531.716053

2017-04-300.334944-0.053049-1.009493-1.039470

2017-05-31-0.380778-0.0444290.0756470.931243

2017-06-300.8675400.872197-0.738974-1.114596

2017-07-310.423371-1.0863860.183820-0.438921

2017-08-311.2851630.634134-0.4729731.281057

2017-09-30-1.002832-0.888122-1.316014-0.070637

2017-10-311.735617-0.2538150.5544031.536211

2017-11-302.0303840.6675561.0126980.239479

2017-12-312.059718-0.0890501.4205170.224578

>>> df = pd.DataFrame([[np.random.randint(1,100)for j in range(4)]for i in range(12)], index=dates, columns=list('ABCD'))

>>>print(df)

A B C D

2017-01-317532522

2017-02-2870997098

2017-03-3199477567

2017-04-3033701749

2017-05-3162886891

2017-06-3019751844

2017-07-3150856582

2017-08-315628776

2017-09-306173111

2017-10-318296692

2017-11-306359194

2017-12-3179586933

>>> df = pd.DataFrame({'A':[np.random.randint(1,100)for i in range(4)],

'B':pd.date_range(start='20130101', periods=4, freq='D'),

'C':pd.Series([1,2,3,4],index=list(range(4)),dtype='float32'),

'D':np.array([3]*4,dtype='int32'),

'E':pd.Categorical(["test","train","test","train"]),

'F':'foo'})

>>>print(df)

A B C D E F

0152013-01-011.03 test foo

1112013-01-022.03 train foo

2912013-01-033.03 test foo

3912013-01-044.03 train foo

>>> df = pd.DataFrame({'A':[np.random.randint(1,100)for i in range(4)],

'B':pd.date_range(start='20130101', periods=4, freq='D'),

'C':pd.Series([1,2,3,4],index=['zhang','li','zhou','wang'],dtype='float32'),

'D':np.array([3]*4,dtype='int32'),

'E':pd.Categorical(["test","train","test","train"]),

'F':'foo'})

>>>print(df)

A B C D E F

zhang 362013-01-011.03 test foo

li 862013-01-022.03 train foo

zhou 102013-01-033.03 test foo

wang 792013-01-044.03 train foo

>>>

3、二维数据查看

>>> df.head() #默认显示前5行

A B C D E F

zhang 362013-01-011.03 test foo

li 862013-01-022.03 train foo

zhou 102013-01-033.03 test foo

wang 792013-01-044.03 train foo

>>> df.head(3) #查看前3行

A B C D E F

zhang 362013-01-011.03 test foo

li 862013-01-022.03 train foo

zhou 102013-01-033.03 test foo

>>> df.tail(2) #查看最后2行

A B C D E F

zhou 102013-01-033.03 test foo

wang 792013-01-044.03 train foo

4、查看二维数据的索引、列名和数据

>>> df.index

Index(['zhang','li','zhou','wang'], dtype='object')

>>> df.columns

Index(['A','B','C','D','E','F'], dtype='object')

>>> df.values

array([[36,Timestamp('2013-01-01 00:00:00'),1.0,3,'test','foo'],

[86,Timestamp('2013-01-02 00:00:00'),2.0,3,'train','foo'],

[10,Timestamp('2013-01-03 00:00:00'),3.0,3,'test','foo'],

[79,Timestamp('2013-01-04 00:00:00'),4.0,3,'train','foo']], dtype=object)

5、查看数据的统计信息

>>> df.describe() #平均值、标准差、最小值、最大值等信息

A C D

count 4.0000004.0000004.0

mean 52.7500002.5000003.0

std 36.0682221.2909940.0

min 10.0000001.0000003.0

25%29.5000001.7500003.0

50%57.5000002.5000003.0

75%80.7500003.2500003.0

max 86.0000004.0000003.0

6、二维数据转置

>>> df.T

zhang li zhou \

A 368610

B 2013-01-0100:00:002013-01-0200:00:002013-01-0300:00:00

C 123

D 333

E test train test

F foo foo foo

wang

A 79

B 2013-01-0400:00:00

C 4

D 3

E train

F foo

7、排序

>>> df.sort_index(axis=0, ascending=False)#对轴进行排序

A B C D E F

zhou 102013-01-033.03 test foo

zhang 362013-01-011.03 test foo

wang 792013-01-044.03 train foo

li 862013-01-022.03 train foo

>>> df.sort_index(axis=1, ascending=False)

F E D C B A

zhang foo test 31.02013-01-0136

li foo train 32.02013-01-0286

zhou foo test 33.02013-01-0310

wang foo train 34.02013-01-0479

>>> df.sort_index(axis=0, ascending=True)

A B C D E F

li 862013-01-022.03 train foo

wang 792013-01-044.03 train foo

zhang 362013-01-011.03 test foo

zhou 102013-01-033.03 test foo

>>> df.sort_values(by='A')#对数据进行排序

A B C D E F

zhou 102013-01-033.03 test foo

zhang 362013-01-011.03 test foo

wang 792013-01-044.03 train foo

li 862013-01-022.03 train foo

>>> df.sort_values(by='A', ascending=False)#降序排列

A B C D E F

li 862013-01-022.03 train foo

wang 792013-01-044.03 train foo

zhang 362013-01-011.03 test foo

zhou 102013-01-033.03 test foo

8、数据选择

>>> df['A']#选择列

zhang 1

li 1

zhou 60

wang 58

Name: A, dtype: int64

>>> df[0:2]#使用切片选择多行

A B C D E F

zhang 12013-01-011.03 test foo

li 12013-01-022.03 train foo

>>> df.loc[:,['A','C']]#选择多列

A C

zhang 11.0

li 12.0

zhou 603.0

wang 584.0

>>> df.loc[['zhang','zhou'],['A','D','E']]#同时指定多行与多列进行选择

A D E

zhang 13 test

zhou 603 test

>>> df.loc['zhang',['A','D','E']]

A 1

D 3

E test

Name: zhang, dtype: object

9、数据修改和设置

>>> df.iat[0,2]=3#修改指定行、列位置的数据值

>>>print(df)

A B C D E F

zhang 12013-01-013.03 test foo

li 12013-01-022.03 train foo

zhou 602013-01-033.03 test foo

wang 582013-01-044.03 train foo

>>> df.loc[:,'D']=[np.random.randint(50,60)for i in range(4)]#修改某列的值

>>>print(df)

A B C D E F

zhang 12013-01-013.057 test foo

li 12013-01-022.052 train foo

zhou 602013-01-033.057 test foo

wang 582013-01-044.056 train foo

>>> df['C']=-df['C']#对指定列数据取反

>>>print(df)

A B C D E F

zhang 12013-01-01-3.057 test foo

li 12013-01-02-2.052 train foo

zhou 602013-01-03-3.057 test foo

wang 582013-01-04-4.056 train foo

10、缺失值处理

>>> df1 = df.reindex(index=['zhang','li','zhou','wang'], columns=list(df.columns)+['G'])

>>>print(df1)

A B C D E F G

zhang 12013-01-01-3.057 test foo NaN

li 12013-01-02-2.052 train foo NaN

zhou 602013-01-03-3.057 test foo NaN

wang 582013-01-04-4.056 train foo NaN

>>> df1.iat[0,6]=3#修改指定位置元素值，该列其他元素为缺失值NaN

>>>print(df1)

A B C D E F G

zhang 12013-01-01-3.057 test foo 3.0

li 12013-01-02-2.052 train foo NaN

zhou 602013-01-03-3.057 test foo NaN

wang 582013-01-04-4.056 train foo NaN

>>> pd.isnull(df1)#测试缺失值，返回值为True/False阵列

A B C D E F G

zhang FalseFalseFalseFalseFalseFalseFalse

li FalseFalseFalseFalseFalseFalseTrue

zhou FalseFalseFalseFalseFalseFalseTrue

wang FalseFalseFalseFalseFalseFalseTrue

>>> df1.dropna()#返回不包含缺失值的行

A B C D E F G

zhang 12013-01-01-3.057 test foo 3.0

>>> df1['G'].fillna(5, inplace=True)#使用指定值填充缺失值

>>>print(df1)

A B C D E F G

zhang 12013-01-01-3.057 test foo 3.0

li 12013-01-02-2.052 train foo 5.0

zhou 602013-01-03-3.057 test foo 5.0

wang 582013-01-04-4.056 train foo 5.0

11、数据操作

>>> df1.mean()#平均值，自动忽略缺失值

A 30.0

C -3.0

D 55.5

G 4.5

dtype: float64

>>> df.mean(1)#横向计算平均值

zhang 18.333333

li 17.000000

zhou 38.000000

wang 36.666667

dtype: float64

>>> df1.shift(1)#数据移位

A B C D E F G

zhang NaNNaTNaNNaNNaNNaNNaN

li 1.02013-01-01-3.057.0 test foo 3.0

zhou 1.02013-01-02-2.052.0 train foo 5.0

wang 60.02013-01-03-3.057.0 test foo 5.0

>>> df1['D'].value_counts()#直方图统计

572

561

521

Name: D, dtype: int64

>>>print(df1)

A B C D E F G

zhang 12013-01-01-3.057 test foo 3.0

li 12013-01-02-2.052 train foo 5.0

zhou 602013-01-03-3.057 test foo 5.0

wang 582013-01-04-4.056 train foo 5.0

>>> df2 = pd.DataFrame(np.random.randn(10,4))

>>>print(df2)

0123

0-0.939904-1.856658-0.2819650.203624

10.3501620.060674-0.9148080.135735

2-1.031384-1.6112740.341546-0.363671

30.139464-0.050959-0.810610-0.772648

4-1.146810-0.7916081.488790-0.490004

5-0.100707-0.763545-0.071274-0.298142

6-0.2120140.8097090.6931960.980568

7-0.812985-0.000325-0.675101-0.217394

80.066969-0.084609-0.4330990.535616

9-0.319120-0.5328541.321712-1.751913

>>> p1 = df2[:3] >>> print(p1) 0 1 2 3 0 -0.939904 -1.856658 -0.281965 0.203624 1 0.350162 0.060674 -0.914808 0.135735 2 -1.031384 -1.611274 0.341546 -0.363671 >>> p2 = df2[3:7] >>> print(p2) 0 1 2 3 3 0.139464 -0.050959 -0.810610 -0.772648 4 -1.146810 -0.791608 1.488790 -0.490004 5 -0.100707 -0.763545 -0.071274 -0.298142 6 -0.212014 0.809709 0.693196 0.980568 >>> p3 = df2[7:] >>> print(p3) 0 1 2 3 7 -0.812985 -0.000325 -0.675101 -0.217394 8 0.066969 -0.084609 -0.433099 0.535616 9 -0.319120 -0.532854 1.321712 -1.751913 >>> df3 = pd.concat([p1, p2, p3]) #数据行合并 >>> print(df3) 0 1 2 3 0 -0.939904 -1.856658 -0.281965 0.203624 1 0.350162 0.060674 -0.914808 0.135735 2 -1.031384 -1.611274 0.341546 -0.363671 3 0.139464 -0.050959 -0.810610 -0.772648 4 -1.146810 -0.791608 1.488790 -0.490004 5 -0.100707 -0.763545 -0.071274 -0.298142 6 -0.212014 0.809709 0.693196 0.980568 7 -0.812985 -0.000325 -0.675101 -0.217394 8 0.066969 -0.084609 -0.433099 0.535616 9 -0.319120 -0.532854 1.321712 -1.751913 >>> df2 == df3 0 1 2 3 0 True True True True 1 True True True True 2 True True True True 3 True True True True 4 True True True True 5 True True True True 6 True True True True 7 True True True True 8 True True True True 9 True True True True >>> df4 = pd.DataFrame({'A':[np.random.randint(1,5) for i in range(8)], 'B':[np.random.randint(10,15) for i in range(8)], 'C':[np.random.randint(20,30) for i in range(8)], 'D':[np.random.randint(80,100) for i in range(8)]}) >>> print(df4) A B C D 0 4 11 24 91 1 1 13 28 95 2 2 12 27 91 3 1 12 20 87 4 3 11 24 96 5 1 13 21 99 6 3 11 22 95 7 2 13 26 98 >>> >>> df4.groupby('A').sum() #数据分组计算 B C D A 1 38 69 281 2 25 53 189 3 22 46 191 4 11 24 91 >>> >>> df4.groupby(['A','B']).mean() C D A B 1 12 20.0 87.0 13 24.5 97.0 2 12 27.0 91.0 13 26.0 98.0 3 11 23.0 95.5 4 11 24.0 91.0

12、结合matplotlib绘图

>>>import pandas as pd

>>>import numpy as np

>>>import matplotlib.pyplot as plt

>>> df = pd.DataFrame(np.random.randn(1000,2), columns=['B','C']).cumsum()

>>>print(df)

B C

00.0898860.511081

11.3237661.584758

21.489479-0.438671

30.831331-0.398021

4-0.2482330.494418

5-0.0130850.684518

60.666951-1.422161

71.768838-0.658786

82.6610800.648505

91.9517510.836261

103.5387851.657475

113.2540342.052609

124.2486201.568401

134.0771730.055622

143.452590-0.200314

152.627620-0.408829

163.690537-0.210440

173.1849240.365447

183.646556-0.150044

194.164563-0.023405

202.3914470.517872

212.8651530.686649

223.6231830.663927

231.5451170.151044

243.5959240.903619

253.0138041.855083

264.4388011.014572

275.1552160.882628

284.4314570.741509

292.8419490.709991

........

970-7.910646-13.738689

971-7.318091-14.811335

972-9.144376-15.466873

973-9.538658-15.367167

974-9.061114-16.822726

975-9.803798-17.368350

976-10.180575-17.270180

977-10.601352-17.671543

978-10.804909-19.535919

979-10.397964-20.361419

980-10.979640-20.300267

981-8.738223-20.202669

982-9.339929-21.528973

983-9.780686-20.902152

984-11.072655-21.235735

985-10.849717-20.439201

986-10.953247-19.708973

987-13.032707-18.687553

988-12.984567-19.557132

989-13.508836-18.747584

990-13.420713-19.883180

991-11.718125-20.474092

992-11.936512-21.360752

993-14.225655-22.006776

994-13.524940-20.844519

995-14.088767-20.492952

996-14.169056-20.666777

997-14.798708-19.960555

998-15.766568-19.395622

999-17.281143-19.089793

[1000 rows x 2 columns]

>>> df['A']= pd.Series(list(range(len(df))))

>>>print(df)

B C A

00.0898860.5110810

11.3237661.5847581

21.489479-0.4386712

30.831331-0.3980213

4-0.2482330.4944184

5-0.0130850.6845185

60.666951-1.4221616

71.768838-0.6587867

82.6610800.6485058

91.9517510.8362619

103.5387851.65747510

113.2540342.05260911

124.2486201.56840112

134.0771730.05562213

143.452590-0.20031414

152.627620-0.40882915

163.690537-0.21044016

173.1849240.36544717

183.646556-0.15004418

194.164563-0.02340519

202.3914470.51787220

212.8651530.68664921

223.6231830.66392722

231.5451170.15104423

243.5959240.90361924

253.0138041.85508325

264.4388011.01457226

275.1552160.88262827

284.4314570.74150928

292.8419490.70999129

...........

970-7.910646-13.738689970

971-7.318091-14.811335971

972-9.144376-15.466873972

973-9.538658-15.367167973

974-9.061114-16.822726974

975-9.803798-17.368350975

976-10.180575-17.270180976

977-10.601352-17.671543977

978-10.804909-19.535919978

979-10.397964-20.361419979

980-10.979640-20.300267980

981-8.738223-20.202669981

982-9.339929-21.528973982

983-9.780686-20.902152983

984-11.072655-21.235735984

985-10.849717-20.439201985

986-10.953247-19.708973986

987-13.032707-18.687553987

988-12.984567-19.557132988

989-13.508836-18.747584989

990-13.420713-19.883180990

991-11.718125-20.474092991

992-11.936512-21.360752992

993-14.225655-22.006776993

994-13.524940-20.844519994

995-14.088767-20.492952995

996-14.169056-20.666777996

997-14.798708-19.960555997

998-15.766568-19.395622998

999-17.281143-19.089793999

[1000 rows x 3 columns]

>>> plt.figure()

>>> df.plot(x='A')

>>> plt.show()

运行结果

>>> df = pd.DataFrame(np.random.rand(10,4), columns=['a','b','c','d'])

>>>print(df)

a b c d

00.5044340.1908750.0016870.327372

10.4068440.6020290.9120750.815889

20.8285340.9859100.0946620.552089

30.1988430.8187850.7506490.967054

40.4984940.1513780.4175060.264438

50.6552880.6727880.0886160.433270

60.4931270.0092540.1794790.396655

70.4193860.9109860.0200040.229063

80.6714690.6121890.3749200.407093

90.4149780.0334990.7560250.717849

>>> df.plot(kind='bar')

>>> plt.show()

运行结果

>>> df = pd.DataFrame(np.random.rand(10,4), columns=['a','b','c','d'])

>>> df.plot(kind='barh', stacked=True)

>>> plt.show()

希望本文所述对大家Python程序设计有所帮助。