Hi,
Python newbie here. I need help with the following two tasks I need to
accomplish using Python:
------------------------
Creating a matrix of rolling variances
I have a pandas data frame of six columns, I would like to iteratively compute
the variance along each column. Since I am a newbie, I don't really understand
the niceties of the language and common usage patterns. What is the common
Python idiom for achieving the following?
vars = []
for i in range(1, 100000):
v = (data.iloc[range(0, i+1)].var()).values
if len(vars) == 0:
vars = v
else:
vars = np.vstack((vars, v))
Also, when I run this code, it takes a long time to execute. Can anyone suggest
how to improve the running time?
------------------------
Pandas dataframe: sum of exponentially weighted correlation matrices per row
Consider the following dataframe:
df = pd.DataFrame(np.random.random((200,3)))
df['date'] = pd.date_range('2000-1-1', periods=200, freq='D')
df = df.set_index(['date'])
date 0 1 2 3 4 5
2000-01-01 0.101782 0.111237 0.177719 0.229994 0.298786 0.747169
2000-01-02 0.348568 0.916997 0.527036 0.998144 0.544261 0.824907
2000-01-03 0.095015 0.480519 0.493345 0.632072 0.965326 0.244732
2000-01-04 0.502706 0.014287 0.045354 0.461621 0.359125 0.489150
2000-01-05 0.559364 0.337121 0.763715 0.460163 0.515309 0.732979
2000-01-06 0.488153 0.149655 0.015616 0.658693 0.864032 0.425497
2000-01-07 0.266161 0.392923 0.606358 0.286874 0.160191 0.573436
2000-01-08 0.786785 0.770826 0.202838 0.259263 0.732071 0.546918
2000-01-09 0.739847 0.886894 0.094900 0.257210 0.264688 0.005631
2000-01-10 0.615846 0.347249 0.516575 0.886096 0.347741 0.259998
Now, I want to treat each row as a vector and perform a multiplication like
this:
[[0.101782]] [[0.101782 0.111237 0.177719 0.229994 0.298786 0.747169]]
[[0.111237]]
[[0.177719]]
[[0.229994]]
[[0.298786]]
[[0.747169]]
For the i-th row, let's call this X_i. Now I have a parameter alpha and I want
to multiply X_i with alpha^i and sum across all the i's. In the real world, I
can have thousands of rows so I need to do this with reasonably good
performance.
------------------------
Many thanks in advance!
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