Apply the function in the cropping window in the Dataframe where the entire data file is passed to the function
I have a dataframe of 5 columns indexed by YearMo:
yearmo = np.repeat(np.arange(2000, 2010) * 100, 12) + [x for x in range(1,13)] * 10
rates = pd.DataFrame(data=np.random.random(120, 5)),
index=pd.Series(data=yearmo, name='YearMo'),
columns=['A', 'B','C', 'D', 'E'])
rates.head()
YearMo A B C D E
200411 0.237696 0.341937 0.258713 0.569689 0.470776
200412 0.601713 0.313006 0.221821 0.720162 0.889891
200501 0.024379 0.761315 0.225032 0.293682 0.302431
200502 0.996778 0.388783 0.026448 0.056188 0.744850
200503 0.942024 0.768416 0.484236 0.102904 0.287446
What I would like to do is be able to apply a navigation window and pass all five columns to a function - something like:
rates.rolling(window=60, min_periods=60).apply(lambda x: my_func(data=x, param=5)
but this approach applies a function to each column. The task axis=1
does nothing either ...
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1 answer
Question : ... apply unfolding and pass all five columns to the function
This will do what you want min_periods=5, axis=1
. Window .rolling(...
is column "A": "E" or multiplicity 5 .
def f1(data=None):
print('f1(%s, %s) data=%s' % (str(type(data)), param, data))
return data.sum()
subRates = rates.rolling(window=60, min_periods=5, axis=1).apply(lambda x: f1( x ) )
Input
A B C D E
YearMo
200001 0.666744 0.569194 0.546873 0.018696 0.240783
200002 0.035888 0.853077 0.348200 0.921997 0.283177
200003 0.652761 0.076630 0.298076 0.800504 0.041231
200004 0.537397 0.968399 0.211072 0.328157 0.929783
200005 0.759506 0.702220 0.807477 0.886935 0.022587
Output
f1(<class 'numpy.ndarray'>, None) data=[ 0.66674393 0.56919434 0.54687296 0.01869609 0.24078329]
f1(<class 'numpy.ndarray'>, None) data=[ 0.03588751 0.85307707 0.34819965 0.92199698 0.28317727]
f1(<class 'numpy.ndarray'>, None) data=[ 0.65276067 0.07663029 0.29807589 0.80050448 0.04123137]
f1(<class 'numpy.ndarray'>, None) data=[ 0.53739687 0.96839917 0.21107155 0.32815687 0.92978308]
f1(<class 'numpy.ndarray'>, None) data=[ 0.75950632 0.70222034 0.80747698 0.88693524 0.02258685]
A B C D E
YearMo
200001 NaN NaN NaN NaN 2.042291
200002 NaN NaN NaN NaN 2.442338
200003 NaN NaN NaN NaN 1.869203
200004 NaN NaN NaN NaN 2.974808
200005 NaN NaN NaN NaN 3.178726
Tested with Python: 3.4.2 - pandas: 0.19.2
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