ENH: allow rolling with non-numerical (eg string) data

Hi the Pandas dream team.

I think it would be nice if rolling could accept strings as well (see https://stackoverflow.com/questions/52657429/rolling-with-string-variables)

With the abundance of textual data nowadays, we want Pandas to stay at the top of the curve!

import pandas as pd
import numpy as np

df = pd.DataFrame({'mytime' : [pd.to_datetime('2018-01-01 14:34:12.340'),
                             pd.to_datetime('2018-01-01 14:34:13.0'),
                             pd.to_datetime('2018-01-01 14:34:15.342'),
                             pd.to_datetime('2018-01-01 14:34:16.42'),
                             pd.to_datetime('2018-01-01 14:34:28.742')],
                    'myvalue' : [1,2,np.NaN,3,1],
                    'mychart' : ['a','b','c','d','e']})

df.set_index('mytime', inplace = True)

df
Out[15]: 
                        mychart  myvalue
mytime                                  
2018-01-01 14:34:12.340       a      1.0
2018-01-01 14:34:13.000       b      2.0
2018-01-01 14:34:15.342       c      NaN
2018-01-01 14:34:16.420       d      3.0
2018-01-01 14:34:28.742       e      1.0

Here I want to concatenate the strings in mychart using the values in the last 2 seconds (not the last two observations).

Unfortunately, both attempts below fail miserably

df.mychart.rolling(window = '2s', closed = 'right').apply(lambda x: ' '.join(x), raw = False)
df.mychart.rolling(window = '2s', closed = 'right').apply(lambda x: (x + ' ').cumsum(), raw = False)

TypeError: cannot handle this type -> object

What do you think? Thanks!

[alfonsomhc]

+1 to this!

Even if most ops require a cast to float, apply should work on strings. I have a table with timestamps and strings, and I was hoping to group records with time windows and process the strings using apply and a custom function. While there may be a workaround, this seems to me the most natural way of doing it.

Hopefully this can be fixed at some point 😃

[mrucker]

@alexanderfrey Thanks! For what it is worth, we ended up finding that support is a little better than we realized. For example, this works:

df.groupby("group")['vector'].apply(lambda x: np.mean(x, axis=0))

I know it’s not quite the same, but just pointing it out in case it helps.

For our “work-around” we just ended up not using rolling. We wanted to use rolling to apply a simple noise-reduction filter but ultimately decided the benefit of doing was going to be marginal at best because of additional post-processing that was also happening.