pandas

Series一行，有列名

 1 from pandas import Series
 2 
 3 print('用數組生成Series')
 4 obj=Series([4,7,-5,3])
 5 '''
 6 0    4
 7 1    7
 8 2   -5
 9 3    3
10 '''
11 print(obj) #dtype: int64
12 print(obj.values) #[ 4  7 -5  3]
13 print(obj.index) #RangeIndex(start=0, stop=4, step=1)
14 print('')
15 
16 print('指定Series的Index')
17 obj2=Series(data=[4, 7, -5, 3] , index=['d','b','a','c'])
18 '''
19 d    4
20 b    7
21 a   -5
22 c    3
23 '''
24 print(obj2)
25 print(obj2.index) #Index(['d', 'b', 'a', 'c'], dtype='object')
26 print(obj2['a']) #-5
27 obj2['d']=6
28 print(obj2[['c','a','d']])
29 print(obj2[obj2>0]) #找出大於0的元素
30 print('b' in obj2) #判斷索引是否存在  True
31 print('')
32 
33 print('使用字典生成Series')
34 sdata={'Ohio':45000,'Texas':71000,'Oregon':16000,'Utah':5000}
35 obj3=Series(sdata)
36 print('')
37 
38 states=['Califonia','Ohio','Oregon','Texas']
39 obj4=Series(sdata,index=states)
40 print(obj4)
41 '''
42 Califonia        NaN
43 Ohio         45000.0
44 Oregon       16000.0
45 Texas        71000.0
46 dtype: float6
47 '''
48 print('')
49 
50 print('Series相加，相同索引部分相加')
51 print(obj3+obj4)
52 '''
53 Series相加，相同索引部分相加
54 Califonia         NaN
55 Ohio          90000.0
56 Oregon        32000.0
57 Texas        142000.0
58 Utah              NaN
59 dtype: float64
60 '''
61 print('')

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DataFrame表格：列名，行名

 1 import pandas as pd
 2 import numpy as np
 3 s=pd.Series([1,3,5,np.nan,6,8]) #np.nan =NAN
 4 print(s)
 5 '''
 6 0    1.0
 7 1    3.0
 8 2    5.0
 9 3    NaN
10 4    6.0
11 5    8.0
12 dtype: float64
13 '''
14 print('')
15 
16 dates=pd.date_range('20130101',periods=6)
17 print(dates)
18 '''
19 
20 DatetimeIndex(['2013-01-01', '2013-01-02', '2013-01-03', '2013-01-04',
21                '2013-01-05', '2013-01-06'],
22               dtype='datetime64[ns]', freq='D'
23 '''
24 print('')
25 
26 df=pd.DataFrame(np.random.randn(6,4),index=dates,columns=list('ABCD'))
27 print(df)
28 '''
29 
30                    A         B         C         D
31 2013-01-01  0.736643  1.159705  0.107344  0.565232
32 2013-01-02 -0.673290 -0.050150  0.092945  0.032246
33 2013-01-03  0.523298  1.016662  0.027891  0.709381
34 2013-01-04  0.891428 -1.641901 -0.276879 -1.700767
35 2013-01-05  0.974133  0.780603  0.053150  0.329980
36 2013-01-06 -0.370341 -2.264599  0.302939 -0.148044
37 '''
38 print('')

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