In [4]:
import pandas as pd
# conda install pandas-datareader
from pandas_datareader import data, wb
In [19]:
pd.datetime(2016, 4, 27)
Out[19]:
datetime.datetime(2016, 4, 27, 0, 0)
In [28]:
aapl.loc[pd.date_range(start="01/01/1990", end="01/01/2000")]
Out[28]:
Open
High
Low
Close
Volume
Adj Close
1990-01-01
NaN
NaN
NaN
NaN
NaN
NaN
1990-01-02
35.249999
37.500000
35.000000
37.250001
45799600.0
1.151017
1990-01-03
37.999999
37.999999
37.500000
37.500000
51998800.0
1.158742
1990-01-04
38.250001
38.749999
37.250001
37.625000
55378400.0
1.162604
1990-01-05
37.750000
38.250001
36.999999
37.750000
30828000.0
1.166467
1990-01-06
NaN
NaN
NaN
NaN
NaN
NaN
1990-01-07
NaN
NaN
NaN
NaN
NaN
NaN
1990-01-08
37.500000
37.999999
36.999999
37.999999
25393200.0
1.174191
1990-01-09
37.999999
37.999999
36.999999
37.625000
21534800.0
1.162604
1990-01-10
37.625000
37.625000
35.750000
36.000000
49929600.0
1.112392
1990-01-11
36.249999
36.249999
34.499999
34.499999
52763200.0
1.066042
1990-01-12
34.250000
34.750001
33.750001
34.499999
42974400.0
1.066042
1990-01-13
NaN
NaN
NaN
NaN
NaN
NaN
1990-01-14
NaN
NaN
NaN
NaN
NaN
NaN
1990-01-15
34.499999
35.750000
34.250000
34.250000
40434800.0
1.058317
1990-01-16
33.499999
35.000000
32.749999
34.875000
53561200.0
1.077630
1990-01-17
34.750001
34.750001
33.000001
33.250000
49324800.0
1.027418
1990-01-18
33.000001
33.499999
32.250000
32.375000
68322800.0
1.000380
1990-01-19
33.750001
34.499999
33.499999
34.250000
66284400.0
1.058317
1990-01-20
NaN
NaN
NaN
NaN
NaN
NaN
1990-01-21
NaN
NaN
NaN
NaN
NaN
NaN
1990-01-22
34.000000
34.499999
33.250000
33.250000
36402800.0
1.027418
1990-01-23
33.750001
34.250000
33.000001
33.750001
35218400.0
1.042867
1990-01-24
32.500000
34.250000
32.250000
34.000000
42448000.0
1.050592
1990-01-25
34.250000
34.750001
34.000000
34.125000
27885200.0
1.054455
1990-01-26
34.000000
34.000000
32.250000
32.749999
45312400.0
1.011968
1990-01-27
NaN
NaN
NaN
NaN
NaN
NaN
1990-01-28
NaN
NaN
NaN
NaN
NaN
NaN
1990-01-29
33.000001
33.499999
32.125001
33.250000
29982400.0
1.027418
1990-01-30
33.250000
34.499999
33.000001
34.000000
29111600.0
1.050592
...
...
...
...
...
...
...
1999-12-03
112.187494
115.562498
111.874997
115.000002
161980000.0
3.803581
1999-12-04
NaN
NaN
NaN
NaN
NaN
NaN
1999-12-05
NaN
NaN
NaN
NaN
NaN
NaN
1999-12-06
114.562502
117.312498
111.437497
115.999998
116695600.0
3.836656
1999-12-07
116.562494
118.000004
114.000006
117.812496
111255200.0
3.896603
1999-12-08
116.250004
117.874994
109.500003
110.062499
103087600.0
3.640275
1999-12-09
110.999997
110.999997
100.874999
105.249999
213799600.0
3.481103
1999-12-10
105.312497
109.249997
99.000003
103.000001
159440400.0
3.406686
1999-12-11
NaN
NaN
NaN
NaN
NaN
NaN
1999-12-12
NaN
NaN
NaN
NaN
NaN
NaN
1999-12-13
102.390601
102.500003
98.937498
99.000003
132490400.0
3.274387
1999-12-14
98.375002
99.750000
94.749999
94.875002
108967600.0
3.137954
1999-12-15
93.249998
97.250003
91.062498
96.999997
155744400.0
3.208238
1999-12-16
98.000000
98.375002
94.000002
98.312497
115956400.0
3.251648
1999-12-17
100.874999
101.999998
98.499998
99.999999
123751600.0
3.307462
1999-12-18
NaN
NaN
NaN
NaN
NaN
NaN
1999-12-19
NaN
NaN
NaN
NaN
NaN
NaN
1999-12-20
99.562499
99.624997
96.625002
98.000000
70996800.0
3.241313
1999-12-21
98.187501
103.062499
97.937502
102.500003
76899200.0
3.390148
1999-12-22
102.874998
104.562500
98.749997
99.937501
81768400.0
3.305395
1999-12-23
101.812497
104.250003
101.062500
103.499999
57383200.0
3.423223
1999-12-24
NaN
NaN
NaN
NaN
NaN
NaN
1999-12-25
NaN
NaN
NaN
NaN
NaN
NaN
1999-12-26
NaN
NaN
NaN
NaN
NaN
NaN
1999-12-27
104.374999
104.437497
99.250002
99.312500
42098000.0
3.284723
1999-12-28
99.124999
99.624997
94.999998
98.187501
61894000.0
3.247514
1999-12-29
96.812503
102.187499
95.500003
100.687498
71125600.0
3.330200
1999-12-30
102.187499
104.125000
99.624997
100.312503
51786000.0
3.317798
1999-12-31
100.937497
102.874998
99.500001
102.812500
40952800.0
3.400484
2000-01-01
NaN
NaN
NaN
NaN
NaN
NaN
3653 rows × 6 columns
In [24]:
aapl = data.DataReader("AAPL", "yahoo", start="01/01/1990", end="01/01/2016")
In [29]:
aapl.head()
Out[29]:
Open
High
Low
Close
Volume
Adj Close
Date
1990-01-02
35.249999
37.500000
35.000000
37.250001
45799600
1.151017
1990-01-03
37.999999
37.999999
37.500000
37.500000
51998800
1.158742
1990-01-04
38.250001
38.749999
37.250001
37.625000
55378400
1.162604
1990-01-05
37.750000
38.250001
36.999999
37.750000
30828000
1.166467
1990-01-08
37.500000
37.999999
36.999999
37.999999
25393200
1.174191
In [30]:
aapl.index[0]
Out[30]:
Timestamp('1990-01-02 00:00:00')
In [36]:
aapl.resample("3D").head()
C:\Users\dbackus\AppData\Local\Continuum\Anaconda3\lib\site-packages\ipykernel\__main__.py:1: FutureWarning: .resample() is now a deferred operation
use .resample(...).mean() instead of .resample(...)
if __name__ == '__main__':
Out[36]:
Open
High
Low
Close
Volume
Adj Close
Date
1990-01-02
37.166666
38.083333
36.583334
37.458334
5.105893e+07
1.157454
1990-01-05
37.750000
38.250001
36.999999
37.750000
3.082800e+07
1.166467
1990-01-08
37.708333
37.874999
36.583333
37.208333
3.228587e+07
1.149729
1990-01-11
35.250000
35.500000
34.125000
34.499999
4.786880e+07
1.066042
1990-01-14
33.999999
35.375000
33.500000
34.562500
4.699800e+07
1.067973
In [ ]:
Content source: DaveBackus/Data_Bootcamp
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