I am trying to downsample a pandas dataframe in order to reduce granularity. In example, I want to reduce this dataframe:
1 2 3 4
2 4 3 3
2 2 1 3
3 1 3 2
to this (downsampling to obtain a 2x2 dataframe using mean):
2.25 3.25
2 2.25
Is there a builtin way or efficient way to do it or I have to write it on my own?
Thanks
One option is to use groupby twice. Once for the index:
In [11]: df.groupby(lambda x: x/2).mean()
Out[11]:
0 1 2 3
0 1.5 3.0 3 3.5
1 2.5 1.5 2 2.5
and once for the columns:
In [12]: df.groupby(lambda x: x/2).mean().groupby(lambda y: y/2, axis=1).mean()
Out[12]:
0 1
0 2.25 3.25
1 2.00 2.25
Note: A solution which only calculated the mean once might be preferable... one option is to stack, groupby, mean, and unstack, but atm this is a little fiddly.
This seems significantly faster than Vicktor's solution:
In [21]: df = pd.DataFrame(np.random.randn(100, 100))
In [22]: %timeit df.groupby(lambda x: x/2).mean().groupby(lambda y: y/2, axis=1).mean()
1000 loops, best of 3: 1.64 ms per loop
In [23]: %timeit viktor()
1 loops, best of 3: 822 ms per loop
In fact, Viktor's solution crashes my (underpowered) laptop for larger DataFrames:
In [31]: df = pd.DataFrame(np.random.randn(1000, 1000))
In [32]: %timeit df.groupby(lambda x: x/2).mean().groupby(lambda y: y/2, axis=1).mean()
10 loops, best of 3: 42.9 ms per loop
In [33]: %timeit viktor()
# crashes
As Viktor points out, this doesn't work with non-integer index, if this was wanted, you could just store them as temp variables and feed them back in after:
df_index, df_cols, df.index, df.columns = df.index, df.columns, np.arange(len(df.index)), np.arange(len(df.columns))
res = df.groupby(...
res.index, res.columns = df_index[::2], df_cols[::2]
You can use the rolling_mean function applied twice, first on the columns and then on the rows, and then slice the results:
rbs = 2 # row block size
cbs = 2 # column block size
pd.rolling_mean(pd.rolling_mean(df.T, cbs, center=True)[cbs-1::cbs].T,
rbs)[rbs-1::rbs]
Which gives the same result you want, except the index will be different (but you can fix this using .reset_index(drop=True)):
1 3
1 2.25 3.25
3 2.00 2.25
Timing info:
In [11]: df = pd.DataFrame(np.random.randn(100, 100))
In [12]: %%timeit
pd.rolling_mean(pd.rolling_mean(df.T, 2, center=True)[1::2].T, 2)[1::2]
100 loops, best of 3: 4.75 ms per loop
In [13]: %%timeit
df.groupby(lambda x: x/2).mean().groupby(lambda y: y/2, axis=1).mean()
100 loops, best of 3: 932 µs per loop
So it's around 5x slower than the groupby not 800x :)
