pandas学习笔记

Posted 2020-10-10 bean_sprouts

#构造一行数据
>>> s = pd.Series([1,3,6,np.nan,44,1])
>>> s
0 1.0
1 3.0
2 6.0
3 NaN
4 44.0
5 1.0
dtype: float64

#创建一个索引列
>>> dates = pd.date_range(‘20160101‘,periods=6)
>>> dates
DatetimeIndex([‘2016-01-01‘, ‘2016-01-02‘, ‘2016-01-03‘, ‘2016-01-04‘,
‘2016-01-05‘, ‘2016-01-06‘],
dtype=‘datetime64[ns]‘, freq=‘D‘)
>>> df = pd.DataFrame(np.random.randn(6,4),index=dates,columns=[‘a‘,‘b‘,‘c‘,‘d‘])
>>> df
a b c d
2016-01-01 0.607993 -0.937166 -0.112296 -0.072984
2016-01-02 -0.040631 0.535248 0.872500 0.043600
2016-01-03 -2.345816 0.433454 0.845263 -1.181626
2016-01-04 -0.021304 0.119520 -0.827935 0.347086
2016-01-05 -1.626559 0.272060 0.854826 -0.972919
2016-01-06 -1.213062 -0.980244 -1.011220 -0.119660
>>> df.index
DatetimeIndex([‘2016-01-01‘, ‘2016-01-02‘, ‘2016-01-03‘, ‘2016-01-04‘,
‘2016-01-05‘, ‘2016-01-06‘],
dtype=‘datetime64[ns]‘, freq=‘D‘)
>>> df.columns
Index([u‘a‘, u‘b‘, u‘c‘, u‘d‘], dtype=‘object‘)
>>> df.values
array([[ 0.60799315, -0.93716631, -0.11229586, -0.07298433],
[-0.04063099, 0.53524821, 0.87249982, 0.04360029],
[-2.34581586, 0.43345385, 0.8452626 , -1.18162629],
[-0.02130354, 0.11952033, -0.827935 , 0.34708601],
[-1.62655855, 0.27206008, 0.85482623, -0.97291868],
[-1.21306154, -0.98024449, -1.01122044, -0.11965997]])
>>> df.describe()
a b c d
count 6.000000 6.000000 6.000000 6.000000
mean -0.773230 -0.092855 0.103523 -0.326084
std 1.131770 0.685580 0.878954 0.607757
min -2.345816 -0.980244 -1.011220 -1.181626
25% -1.523184 -0.672995 -0.649025 -0.759604
50% -0.626846 0.195790 0.366483 -0.096322
75% -0.026135 0.393105 0.852435 0.014454
max 0.607993 0.535248 0.872500 0.347086
>>> df.T
2016-01-01 2016-01-02 2016-01-03 2016-01-04 2016-01-05 2016-01-06
a 0.607993 -0.040631 -2.345816 -0.021304 -1.626559 -1.213062
b -0.937166 0.535248 0.433454 0.119520 0.272060 -0.980244
c -0.112296 0.872500 0.845263 -0.827935 0.854826 -1.011220
d -0.072984 0.043600 -1.181626 0.347086 -0.972919 -0.119660

#给列的先后顺序排序
>>> df.sort_index(axis=1,ascending=False)
d c b a
2016-01-01 -0.072984 -0.112296 -0.937166 0.607993
2016-01-02 0.043600 0.872500 0.535248 -0.040631
2016-01-03 -1.181626 0.845263 0.433454 -2.345816
2016-01-04 0.347086 -0.827935 0.119520 -0.021304
2016-01-05 -0.972919 0.854826 0.272060 -1.626559
2016-01-06 -0.119660 -1.011220 -0.980244 -1.213062

#根据索引值排序
>>> df.sort_index(axis=0,ascending=False)
a b c d
2016-01-06 -1.213062 -0.980244 -1.011220 -0.119660
2016-01-05 -1.626559 0.272060 0.854826 -0.972919
2016-01-04 -0.021304 0.119520 -0.827935 0.347086
2016-01-03 -2.345816 0.433454 0.845263 -1.181626
2016-01-02 -0.040631 0.535248 0.872500 0.043600
2016-01-01 0.607993 -0.937166 -0.112296 -0.072984

#根据a列的值排序
>>> df.sort_values(by=‘a‘)
a b c d
2016-01-03 -2.345816 0.433454 0.845263 -1.181626
2016-01-05 -1.626559 0.272060 0.854826 -0.972919
2016-01-06 -1.213062 -0.980244 -1.011220 -0.119660
2016-01-02 -0.040631 0.535248 0.872500 0.043600
2016-01-04 -0.021304 0.119520 -0.827935 0.347086
2016-01-01 0.607993 -0.937166 -0.112296 -0.072984

#pandas选择数据
>>> df.a #或者df[‘a‘]
2016-01-01 0.607993
2016-01-02 -0.040631
2016-01-03 -2.345816
2016-01-04 -0.021304
2016-01-05 -1.626559
2016-01-06 -1.213062
Freq: D, Name: a, dtype: float64
>>> df[0:3]
a b c d
2016-01-01 0.607993 -0.937166 -0.112296 -0.072984
2016-01-02 -0.040631 0.535248 0.872500 0.043600
2016-01-03 -2.345816 0.433454 0.845263 -1.181626

#select by label: loc 根据索引的值选择行
>>> df.loc[‘2016-01-01‘]
a 0.607993
b -0.937166
c -0.112296
d -0.072984
Name: 2016-01-01 00:00:00, dtype: float64

#根据切片选择行
>>> df[:1]
a b c d
2016-01-01 0.607993 -0.937166 -0.112296 -0.072984

#根据索引值和列名选择行和列
>>> df.loc[‘2016-01-01‘,[‘a‘,‘b‘]]
a 0.607993
b -0.937166
Name: 2016-01-01 00:00:00, dtype: float64

#select by position: iloc
#选择第3行
>>> df.iloc[3]
a -0.021304
b 0.119520
c -0.827935
d 0.347086
Name: 2016-01-04 00:00:00, dtype: float64

#选择第3行第1列
>>> df.iloc[3,1]
0.11952032779945752

#选择第1,3,4行,第1到3列
>>> df.iloc[[1,3,4],1:3]
b c
2016-01-02 0.535248 0.872500
2016-01-04 0.119520 -0.827935
2016-01-05 0.272060 0.854826

#根据切片和列名选择数据
#选择前面3行,第a列和c列的值
>>> df.ix[:3,[‘a‘,‘c‘]]
a c
2016-01-01 0.607993 -0.112296
2016-01-02 -0.040631 0.872500
2016-01-03 -2.345816 0.845263

#选择a列中值小于0的数据
>>> df[df.a<0]
a b c d
2016-01-02 -0.040631 0.535248 0.872500 0.043600
2016-01-03 -2.345816 0.433454 0.845263 -1.181626
2016-01-04 -0.021304 0.119520 -0.827935 0.347086
2016-01-05 -1.626559 0.272060 0.854826 -0.972919
2016-01-06 -1.213062 -0.980244 -1.011220 -0.119660

 

#pandas设置值
>>> df.iloc[2,2]=111
>>> df
a b c d
2016-01-01 0.607993 -0.937166 -0.112296 -0.072984
2016-01-02 -0.040631 0.535248 0.872500 0.043600
2016-01-03 -2.345816 0.433454 111.000000 -1.181626
2016-01-04 -0.021304 0.119520 -0.827935 0.347086
2016-01-05 -1.626559 0.272060 0.854826 -0.972919
2016-01-06 -1.213062 -0.980244 -1.011220 -0.119660
>>> df.loc[‘2016-01-01‘,‘b‘] = 22222
>>> df
a b c d
2016-01-01 0.607993 22222.000000 -0.112296 -0.072984
2016-01-02 -0.040631 0.535248 0.872500 0.043600
2016-01-03 -2.345816 0.433454 111.000000 -1.181626
2016-01-04 -0.021304 0.119520 -0.827935 0.347086
2016-01-05 -1.626559 0.272060 0.854826 -0.972919
2016-01-06 -1.213062 -0.980244 -1.011220 -0.119660
>>> df[df.a<0] = 0
>>> df
a b c d
2016-01-01 0.607993 22222.0 -0.112296 -0.072984
2016-01-02 0.000000 0.0 0.000000 0.000000
2016-01-03 0.000000 0.0 0.000000 0.000000
2016-01-04 0.000000 0.0 0.000000 0.000000
2016-01-05 0.000000 0.0 0.000000 0.000000
2016-01-06 0.000000 0.0 0.000000 0.000000
>>> df.c[df.c==0]=111
>>> df
a b c d
2016-01-01 0.607993 22222.0 -0.112296 -0.072984
2016-01-02 0.000000 0.0 111.000000 0.000000
2016-01-03 0.000000 0.0 111.000000 0.000000
2016-01-04 0.000000 0.0 111.000000 0.000000
2016-01-05 0.000000 0.0 111.000000 0.000000
2016-01-06 0.000000 0.0 111.000000 0.000000

>>> df[‘e‘]=np.nan
>>> df
a b c d e
2016-01-01 0.607993 22222.0 -0.112296 -0.072984 NaN
2016-01-02 0.000000 0.0 111.000000 0.000000 NaN
2016-01-03 0.000000 0.0 111.000000 0.000000 NaN
2016-01-04 0.000000 0.0 111.000000 0.000000 NaN
2016-01-05 0.000000 0.0 111.000000 0.000000 NaN
2016-01-06 0.000000 0.0 111.000000 0.000000 NaN

>>> df[‘f‘] = pd.Series([1,2,3,4,5,6],index=pd.date_range(‘2016-01-01‘,periods=6))
>>> df
a b c d e f
2016-01-01 0.607993 22222.0 -0.112296 -0.072984 NaN 1
2016-01-02 0.000000 0.0 111.000000 0.000000 NaN 2
2016-01-03 0.000000 0.0 111.000000 0.000000 NaN 3
2016-01-04 0.000000 0.0 111.000000 0.000000 NaN 4
2016-01-05 0.000000 0.0 111.000000 0.000000 NaN 5
2016-01-06 0.000000 0.0 111.000000 0.000000 NaN 6

 

#处理丢失数据
axis=1时丢掉任何一列中有nan的数据,axis=0时丢掉有nan的那一行数据
>>> df.dropna(axis=1,how=‘any‘) #how={‘any‘,‘all‘}
a b c d f
2016-01-01 0.607993 22222.0 -0.112296 -0.072984 1
2016-01-02 0.000000 0.0 111.000000 0.000000 2
2016-01-03 0.000000 0.0 111.000000 0.000000 3
2016-01-04 0.000000 0.0 111.000000 0.000000 4
2016-01-05 0.000000 0.0 111.000000 0.000000 5
2016-01-06 0.000000 0.0 111.000000 0.000000 6

#将数据中为nan的数据填充为0
>>> df.fillna(value=0)
a b c d e f
2016-01-01 0.607993 22222.0 -0.112296 -0.072984 0.0 1
2016-01-02 0.000000 0.0 111.000000 0.000000 0.0 2
2016-01-03 0.000000 0.0 111.000000 0.000000 0.0 3
2016-01-04 0.000000 0.0 111.000000 0.000000 0.0 4
2016-01-05 0.000000 0.0 111.000000 0.000000 0.0 5
2016-01-06 0.000000 0.0 111.000000 0.000000 0.0 6

#判断是否有缺失数据
>>> df.isnull()
a b c d e f
2016-01-01 False False False False True False
2016-01-02 False False False False True False
2016-01-03 False False False False True False
2016-01-04 False False False False True False
2016-01-05 False False False False True False
2016-01-06 False False False False True False

#判断整个表格中是否有丢失的数据
>>> np.any(df.isnull())==True
True

#pandas数据导入导出
>>> pd.read_csv(‘/Users/lijie/Downloads/student.csv‘)
Student ID name age gender
0 1100 Kelly 22 Female
1 1101 Clo 21 Female
2 1102 Tilly 22 Female
3 1103 Tony 24 Male
4 1104 David 20 Male
5 1105 Catty 22 Female
6 1106 M 3 Female
7 1107 N 43 Male
8 1108 A 13 Male
9 1109 S 12 Male
10 1110 David 33 Male
11 1111 Dw 3 Female
12 1112 Q 23 Male
13 1113 W 21 Female

#pandas数据合并
#contact
>>> df1 = pd.DataFrame(np.ones((3,4))*0,columns=[‘a‘,‘b‘,‘c‘,‘d‘]
... )
>>> df2 = pd.DataFrame(np.ones((3,4))*1,columns=[‘a‘,‘b‘,‘c‘,‘d‘])
>>> df3 = pd.DataFrame(np.ones((3,4))*2,columns=[‘a‘,‘b‘,‘c‘,‘d‘])
>>> df1
a b c d
0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
>>> df2
a b c d
0 1.0 1.0 1.0 1.0
1 1.0 1.0 1.0 1.0
2 1.0 1.0 1.0 1.0
>>> df3
a b c d
0 2.0 2.0 2.0 2.0
1 2.0 2.0 2.0 2.0
2 2.0 2.0 2.0 2.0
>>> res = pd.concat([df1,df2,df3],axis=0)
>>> res
a b c d
0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
0 1.0 1.0 1.0 1.0
1 1.0 1.0 1.0 1.0
2 1.0 1.0 1.0 1.0
0 2.0 2.0 2.0 2.0
1 2.0 2.0 2.0 2.0
2 2.0 2.0 2.0 2.0
>>> res = pd.concat([df1,df2,df3],axis=0,ignore_index=True)
>>> res
a b c d
0 0.0 0.0 0.0 0.0
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
3 1.0 1.0 1.0 1.0
4 1.0 1.0 1.0 1.0
5 1.0 1.0 1.0 1.0
6 2.0 2.0 2.0 2.0
7 2.0 2.0 2.0 2.0
8 2.0 2.0 2.0 2.0

 

>>> df4=pd.DataFrame(np.ones((3,4))*0,columns=[‘a‘,‘b‘,‘c‘,‘d‘],index=[1,2,3])
>>> df5=pd.DataFrame(np.ones((3,4))*1,columns=[‘b‘,‘c‘,‘d‘,‘e‘],index=[2,3,4])
>>> df4
a b c d
1 0.0 0.0 0.0 0.0
2 0.0 0.0 0.0 0.0
3 0.0 0.0 0.0 0.0
>>> df5
b c d e
2 1.0 1.0 1.0 1.0
3 1.0 1.0 1.0 1.0
4 1.0 1.0 1.0 1.0
>>> res = pd.concat([df4,df5])
>>> res
a b c d e
1 0.0 0.0 0.0 0.0 NaN
2 0.0 0.0 0.0 0.0 NaN
3 0.0 0.0 0.0 0.0 NaN
2 NaN 1.0 1.0 1.0 1.0
3 NaN 1.0 1.0 1.0 1.0
4 NaN 1.0 1.0 1.0 1.0
>>> res = pd.concat([df4,df5],join=‘inner‘)
>>> res
b c d
1 0.0 0.0 0.0
2 0.0 0.0 0.0
3 0.0 0.0 0.0
2 1.0 1.0 1.0
3 1.0 1.0 1.0
4 1.0 1.0 1.0

 

>>> left = pd.DataFrame({‘key‘: [‘K0‘, ‘K1‘, ‘K2‘, ‘K3‘],
... ‘A‘: [‘A0‘, ‘A1‘, ‘A2‘, ‘A3‘],
... ‘B‘: [‘B0‘, ‘B1‘, ‘B2‘, ‘B3‘]})
>>> right = pd.DataFrame({‘key‘: [‘K0‘, ‘K1‘, ‘K2‘, ‘K3‘],
... ‘C‘: [‘C0‘, ‘C1‘, ‘C2‘, ‘C3‘],
... ‘D‘: [‘D0‘, ‘D1‘, ‘D2‘, ‘D3‘]})
>>> left
A B key
0 A0 B0 K0
1 A1 B1 K1
2 A2 B2 K2
3 A3 B3 K3
>>> right
C D key
0 C0 D0 K0
1 C1 D1 K1
2 C2 D2 K2
3 C3 D3 K3
>>> res = pd.merge(left,right,on=‘key‘)
>>> res
A B key C D
0 A0 B0 K0 C0 D0
1 A1 B1 K1 C1 D1
2 A2 B2 K2 C2 D2
3 A3 B3 K3 C3 D3

>>> left = pd.DataFrame({‘key1‘: [‘K0‘, ‘K0‘, ‘K1‘, ‘K2‘],
... ‘key2‘: [‘K0‘, ‘K1‘, ‘K0‘, ‘K1‘],
... ‘A‘: [‘A0‘, ‘A1‘, ‘A2‘, ‘A3‘],
... ‘B‘: [‘B0‘, ‘B1‘, ‘B2‘, ‘B3‘]})
>>> right = pd.DataFrame({‘key1‘: [‘K0‘, ‘K1‘, ‘K1‘, ‘K2‘],
... ‘key2‘: [‘K0‘, ‘K0‘, ‘K0‘, ‘K0‘],
... ‘C‘: [‘C0‘, ‘C1‘, ‘C2‘, ‘C3‘],
... ‘D‘: [‘D0‘, ‘D1‘, ‘D2‘, ‘D3‘]})
>>> res = pd.merge(left,right,on=[‘key1‘,‘key2‘])
>>> res
A B key1 key2 C D
0 A0 B0 K0 K0 C0 D0
1 A2 B2 K1 K0 C1 D1
2 A2 B2 K1 K0 C2 D2

 

>>> import matplotlib.pyplot as plt
>>> data = pd.Series(np.random.randn(1000),index=np.arange(1000))
>>> data=data.cumsum()
>>> data.plot()
<matplotlib.axes.AxesSubplot object at 0x10409ffd0>
>>> plt.show()

 

>>> data = pd.DataFrame(np.random.randn(1000,4),index=np.arange(1000),columns=list(‘ABCD‘))
>>> data.head()
A B C D
0 -0.219043 -0.116109 -0.227378 -1.246710
1 0.603295 -2.291828 -0.245817 0.178349
2 -0.661455 1.234543 1.193432 0.145587
3 2.185926 -1.254439 0.029333 -0.475892
4 -0.282924 -0.127020 0.359198 -0.719617
>>> data=data.cumsum()
>>> data.plot()
<matplotlib.axes.AxesSubplot object at 0x107d8f490>
>>> plt.show()

>>> ax = data.plot.scatter(x=‘A‘,y=‘B‘,color=‘DarkBlue‘,label=‘Class1‘)
>>> data.plot.scatter(x=‘A‘,y=‘C‘,color=‘DarkGreen‘,label=‘Class2‘,ax=ax)
<matplotlib.axes.AxesSubplot object at 0x103c5fc10>
>>> plt.show()