我正在尝试使用pandas_datareader.data从雅虎获取股票数据，但我不断收到丢失的数据部分。这是我编码的内容。我现在想做的就是返回开始日期和结束日期之间的所有数据
import pandas as pd
import pandas_datareader.data as web
from datetime import datetime
ibm = web.DataReader('IBM', 'yahoo', datetime(2015,1,1)
datetime(2016,1,1))
现在返回的是:
我很困惑为什么当我尝试创建我的集合时会得到带有所有丢失数据的椭圆。任何帮助将不胜感激!
最佳答案
这就是 pandas 显示结果 (as explained here) 的方式。 pandas 忽略超出 pd.set_option('max_rows', X) 设置的行(我相信 default 是 50)。您可以使用pd.options.display.max_rows查看限制。
尝试 ibm.info()，您应该会看到行数比显示的行数多。
您的查询结果为:
<class 'pandas.core.frame.DataFrame'>
DatetimeIndex: 252 entries, 2015-01-02 to 2015-12-31
Data columns (total 6 columns):
Open 252 non-null float64
High 252 non-null float64
Low 252 non-null float64
Close 252 non-null float64
Volume 252 non-null int64
Adj Close 252 non-null float64
dtypes: float64(5), int64(1)
memory usage: 13.8 KB
None
但显示为(尽管有省略号，但底部不是行x列信息):
Open High Low Close Volume \
Date
2015-01-02 161.309998 163.309998 161.000000 162.059998 5525500
2015-01-05 161.270004 161.270004 159.190002 159.509995 4880400
2015-01-06 159.669998 159.960007 155.169998 156.070007 6146700
2015-01-07 157.199997 157.199997 154.029999 155.050003 4701800
2015-01-08 156.240005 159.039993 155.550003 158.419998 4236800
2015-01-09 158.419998 160.339996 157.250000 159.110001 4484800
2015-01-12 159.000000 159.250000 155.759995 156.440002 4182800
2015-01-13 157.259995 159.970001 155.679993 156.809998 4377500
2015-01-14 154.860001 156.490005 153.740005 155.800003 4690300
2015-01-15 156.690002 156.970001 154.160004 154.570007 4248400
2015-01-16 153.820007 157.630005 153.820007 157.139999 5756000
2015-01-20 156.699997 157.330002 154.029999 156.949997 8392800
2015-01-21 153.029999 154.500000 151.070007 152.089996 11897100
2015-01-22 151.940002 155.720001 151.759995 155.389999 6120100
2015-01-23 155.029999 157.600006 154.889999 155.869995 4834800
2015-01-26 158.259995 159.460007 155.770004 156.360001 7888100
2015-01-27 154.940002 155.089996 152.589996 153.669998 5659600
2015-01-28 154.000000 154.529999 151.550003 151.550003 4495900
2015-01-29 151.380005 155.580002 149.520004 155.479996 8320800
2015-01-30 153.910004 155.240005 153.039993 153.309998 6563600
2015-02-02 154.000000 154.660004 151.509995 154.660004 4712200
2015-02-03 154.750000 158.600006 154.750000 158.470001 5539400
2015-02-04 157.210007 158.710007 156.699997 156.960007 3678500
2015-02-05 157.289993 158.589996 157.149994 157.910004 5253600
2015-02-06 157.339996 158.080002 156.229996 156.720001 3225000
2015-02-09 156.000000 157.500000 155.399994 155.750000 3057700
2015-02-10 156.740005 158.559998 155.080002 158.559998 4440600
2015-02-11 157.759995 159.089996 157.169998 158.199997 3626700
2015-02-12 158.720001 159.500000 158.089996 158.520004 3333100
2015-02-13 158.779999 160.800003 158.639999 160.399994 3706900
... ... ... ... ... ...
2015-11-18 134.789993 135.910004 134.259995 135.820007 4149200
2015-11-19 136.210007 137.740005 136.009995 136.740005 4753600
2015-11-20 137.369995 138.919998 137.250000 138.500000 5176400
2015-11-23 138.529999 138.869995 137.119995 138.460007 5137900
2015-11-24 137.649994 139.339996 137.309998 138.600006 3407700
2015-11-25 138.369995 138.429993 137.380005 138.000000 3238200
2015-11-27 138.000000 138.809998 137.210007 138.460007 1415800
2015-11-30 138.610001 139.899994 138.520004 139.419998 4545600
2015-12-01 139.580002 141.399994 139.580002 141.279999 4195100
2015-12-02 140.929993 141.210007 139.500000 139.699997 3725400
2015-12-03 140.100006 140.729996 138.190002 138.919998 5909600
2015-12-04 138.089996 141.020004 137.990005 140.429993 4571600
2015-12-07 140.160004 140.410004 138.809998 139.550003 3279400
2015-12-08 138.279999 139.059998 137.529999 138.050003 3905200
2015-12-09 137.380005 139.839996 136.229996 136.610001 4615000
2015-12-10 137.029999 137.850006 135.720001 136.779999 4222300
2015-12-11 135.229996 135.440002 133.910004 134.570007 5333800
2015-12-14 135.309998 136.139999 134.020004 135.929993 5143800
2015-12-15 137.399994 138.970001 137.279999 137.789993 4207900
2015-12-16 139.119995 139.649994 137.789993 139.289993 4345500
2015-12-17 139.350006 139.500000 136.309998 136.750000 4089500
2015-12-18 136.410004 136.960007 134.270004 134.899994 10026100
2015-12-21 135.830002 135.830002 134.020004 135.500000 5617500
2015-12-22 135.880005 138.190002 135.649994 137.929993 4263800
2015-12-23 138.300003 139.309998 138.110001 138.539993 5164900
2015-12-24 138.429993 138.880005 138.110001 138.250000 1495200
2015-12-28 137.740005 138.039993 136.539993 137.610001 3143400
2015-12-29 138.250000 140.059998 138.199997 139.779999 3943700
2015-12-30 139.580002 140.440002 139.220001 139.339996 2989400
2015-12-31 139.070007 139.100006 137.570007 137.619995 3462100
Adj Close
Date
2015-01-02 153.863588
2015-01-05 151.442555
2015-01-06 148.176550
2015-01-07 147.208134
2015-01-08 150.407687
2015-01-09 151.062791
2015-01-12 148.527832
2015-01-13 148.879114
2015-01-14 147.920202
2015-01-15 146.752415
2015-01-16 149.192426
2015-01-20 149.012033
2015-01-21 144.397834
2015-01-22 147.530934
2015-01-23 147.986654
2015-01-26 148.451876
2015-01-27 145.897925
2015-01-28 143.885151
2015-01-29 147.616379
2015-01-30 145.556132
2015-02-02 146.837859
2015-02-03 150.455161
2015-02-04 149.021536
2015-02-05 149.923486
2015-02-06 149.837432
2015-02-09 148.910029
2015-02-10 151.596622
2015-02-11 151.252431
2015-02-12 151.558385
2015-02-13 153.355812
... ...
2015-11-18 133.161622
2015-11-19 134.063613
2015-11-20 135.789160
2015-11-23 135.749949
2015-11-24 135.887208
2015-11-25 135.298946
2015-11-27 135.749949
2015-11-30 136.691151
2015-12-01 138.514746
2015-12-02 136.965669
2015-12-03 136.200937
2015-12-04 137.681377
2015-12-07 136.818611
2015-12-08 135.347970
2015-12-09 133.936153
2015-12-10 134.102824
2015-12-11 131.936088
2015-12-14 133.269455
2015-12-15 135.093050
2015-12-16 136.563691
2015-12-17 134.073412
2015-12-18 132.259616
2015-12-21 132.847878
2015-12-22 135.230309
2015-12-23 135.828370
2015-12-24 135.544053
2015-12-28 134.916580
2015-12-29 137.044105
2015-12-30 136.612715
2015-12-31 134.926379
[252 rows x 6 columns]
关于python - pandas_datareader.data 不返回从开始日期到结束日期的所有股票值，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/37174237/