pandas 计算与熊猫的滚动相关性
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Calculate rolling correlation with pandas
提问by Wade Bratz
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.
我有一份由 PERMNO 区分的 10 只股票的清单。我想按 PERMNO 对这些股票进行分组，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。
CRSP['rollingcorr'] = CRSP.groupby('PERMNO').rolling_corr(CRSP['RET'],CRSP['vwretd'],10)
The error I am getting is below.
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'
please help!
请帮忙！
Thanks
谢谢
回答by Parikshit Bhinde
Running rolling.corr()on Python 3.5 generates a warning the function is deprecated and may stop working in future. Using Series.rolling(window=<period>).corr(other=series)instead is recommended.
E.g.
rolling.corr()在 Python 3.5 上运行会生成警告，该函数已弃用，将来可能会停止工作。使用Series.rolling(window=<period>).corr(other=series)建议来代替。例如
data['scrip1DailyReturn'].rolling(window=90).corr(other=data['scrip2DailyReturn'])
回答by Jerome Montino
Use pandas.rolling_corr, not DataFrame.rolling_corr. Besides, groupbyreturns a generator. See below code.
使用pandas.rolling_corr，不是DataFrame.rolling_corr。此外，groupby返回一个生成器。见下面的代码。
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)
Output:
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
You can change the loop part to the following to view the original dataframe post-grouping with a new column as well.
您可以将循环部分更改为以下内容，以查看原始数据框后分组以及新列。
for key, value in df_gen:
value["ROLL_CORR"] = pd.rolling_corr(value["Value1"],value["Value2"], 3)
print value
Output:
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
If you want to join them all together after processing (this might be confusing to others, by the way), just use concatafter processing groups.
如果您想在处理后将它们全部连接在一起（顺便说一下，这可能会让其他人感到困惑），只需使用concat后处理组。
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
Output:
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
Hope this helps.
希望这可以帮助。
回答by Wade Bratz
I found an efficient solution. Fairly simple.
我找到了一个有效的解决方案。相当简单。
def roll_corr_groupby(x,i):
x['Z'] = rolling_corr(x['col 1'], x['col 2'],i)
return x
x.groupby(['key']).apply(roll_corr_groupby)
x.head()
