问题描述
我列出了由PERMNO区分的10只股票.我想将这些股票按PERMNO分组，并计算每个PERMNO的股票收益(RET)与市场收益(vwretd)之间的滚动相关性.我正在尝试的代码如下.
I have a list of 10 stocks differentiated by PERMNO. I would like to group those stocks by PERMNO and calculate the rolling correlation between the stock return (RET) for each PERMNO with the market return (vwretd). The code I am trying is below.
CRSP['rollingcorr'] = CRSP.groupby('PERMNO').rolling_corr(CRSP['RET'],CRSP['vwretd'],10)
我得到的错误如下.
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-32-c18e1ce01302> in <module>()
1 #CRSP['rollingcorr'] = CRSP.rolling_corr(CRSP['vwretd'],CRSP['RET'],120)
----> 2 CRSP['rollingmean'] = CRSP.groupby('PERMNO').rolling_corr(CRSP['vwretd'],10)
3 CRSP.head(20)
C:\Users\rebortz\Anaconda\lib\site-packages\pandas\core\groupby.pyc in __getattr__(self, attr)
296
297 raise AttributeError("%r object has no attribute %r" %
--> 298 (type(self).__name__, attr))
299
300 def __getitem__(self, key):
AttributeError: 'DataFrameGroupBy' object has no attribute 'rolling_corr'
请帮助！
谢谢
推荐答案
使用pandas.rolling_corr，而不是DataFrame.rolling_corr.此外，groupby返回一个生成器.参见下面的代码.
Use pandas.rolling_corr, not DataFrame.rolling_corr. Besides, groupby returns a generator. See below code.
代码:
import pandas as pd
df = pd.read_csv("color.csv")
df_gen = df.copy().groupby("Color")
for key, value in df_gen:
print "key: {}".format(key)
print value.rolling_corr(value["Value1"],value["Value2"], 3)
输出:
key: Blue
1 NaN
3 NaN
6 0.931673
8 0.865066
10 0.089304
12 -0.998656
15 -0.971373
17 -0.667316
dtype: float64
key: Red
0 NaN
2 NaN
5 -0.911357
9 -0.152221
11 -0.971153
14 0.438697
18 -0.550727
dtype: float64
key: Yellow
4 NaN
7 NaN
13 -0.040330
16 0.879371
dtype: float64
您可以将循环部分更改为以下内容，以查看原始数据帧的后分组以及新的列.
You can change the loop part to the following to view the original dataframe post-grouping with a new column as well.
for key, value in df_gen:
value["ROLL_CORR"] = pd.rolling_corr(value["Value1"],value["Value2"], 3)
print value
输出:
Color Value1 Value2 ROLL_CORR
1 Blue 0.951227 0.514999 NaN
3 Blue 0.649112 0.513052 NaN
6 Blue 0.148165 0.342205 0.931673
8 Blue 0.626883 0.421530 0.865066
10 Blue 0.286738 0.583811 0.089304
12 Blue 0.966779 0.227340 -0.998656
15 Blue 0.065493 0.887640 -0.971373
17 Blue 0.757932 0.900103 -0.667316
key: Red
Color Value1 Value2 ROLL_CORR
0 Red 0.201435 0.981871 NaN
2 Red 0.522955 0.357239 NaN
5 Red 0.806326 0.310039 -0.911357
9 Red 0.656126 0.678047 -0.152221
11 Red 0.435898 0.908388 -0.971153
14 Red 0.116419 0.555821 0.438697
18 Red 0.793102 0.168033 -0.550727
key: Yellow
Color Value1 Value2 ROLL_CORR
4 Yellow 0.099474 0.143293 NaN
7 Yellow 0.073128 0.749297 NaN
13 Yellow 0.006777 0.318383 -0.040330
16 Yellow 0.345647 0.993382 0.879371
如果要在处理后将它们全部结合在一起(顺便说一句，这可能会使其他人困惑)，只需在处理组后使用concat即可.
If you want to join them all together after processing (this might be confusing to others, by the way), just use concat after processing groups.
import pandas as pd
df = pd.read_csv("color.csv")
df_gen = df.copy().groupby("Color")
dfs = [] # Container for dataframes.
for key, value in df_gen:
value["ROLL_CORR"] = pd.rolling_corr(value["Value1"],value["Value2"], 3)
print value
dfs.append(value)
df_final = pd.concat(dfs)
print df_final
输出:
Color Value1 Value2 ROLL_CORR
1 Blue 0.951227 0.514999 NaN
3 Blue 0.649112 0.513052 NaN
6 Blue 0.148165 0.342205 0.931673
8 Blue 0.626883 0.421530 0.865066
10 Blue 0.286738 0.583811 0.089304
12 Blue 0.966779 0.227340 -0.998656
15 Blue 0.065493 0.887640 -0.971373
17 Blue 0.757932 0.900103 -0.667316
0 Red 0.201435 0.981871 NaN
2 Red 0.522955 0.357239 NaN
5 Red 0.806326 0.310039 -0.911357
9 Red 0.656126 0.678047 -0.152221
11 Red 0.435898 0.908388 -0.971153
14 Red 0.116419 0.555821 0.438697
18 Red 0.793102 0.168033 -0.550727
4 Yellow 0.099474 0.143293 NaN
7 Yellow 0.073128 0.749297 NaN
13 Yellow 0.006777 0.318383 -0.040330
16 Yellow 0.345647 0.993382 0.879371
希望这会有所帮助.
这篇关于计算与 pandas 的滚动相关性的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持！