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There are a few similar questions in this site, but I couldn't find out a solution to my particular question.
I have a dataframe that I want to process with a custom function (the real function has a bit more pre-procesing, but the gist is contained in the toy example fun).
import statsmodels.api as sm
import numpy as np
import pandas as pd
mtcars=pd.DataFrame(sm.datasets.get_rdataset("mtcars", "datasets", cache=True).data)
def fun(col1, col2, w1=10, w2=2):
return(np.mean(w1 * col1 + w2 * col2))
# This is the behavior I would expect for the full dataset, currently working
mtcars.apply(lambda x: fun(x.cyl, x.mpg), axis=1)
# This was my approach to do the same with a rolling function
mtcars.rolling(3).apply(lambda x: fun(x.cyl, x.mpg))
The rolling version returns this error:
AttributeError: 'Series' object has no attribute 'cyl'
I figured I don't fully understand how rolling works, since adding a print statement to the beginning of my function shows that fun is not getting the full dataset but an unnamed series of 3. What is the approach to apply this rolling function in pandas?
Just in case, I am running
>>> pd.__version__
'1.5.2'
Looks like there is a very similar question here which might partially overlap with what I'm trying to do.
For completeness, here's how I would do this in R with the expected output.
library(dplyr)
fun <- function(col1, col2, w1=10, w2=2){
return(mean(w1*col1 + w2*col2))
}
mtcars %>%
mutate(roll = slider::slide2(.x = cyl,
.y = mpg,
.f = fun,
.before = 1,
.after = 1))
mpg cyl disp hp drat wt qsec vs am gear carb roll
Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 102
Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 96.53333
Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 96.8
Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 101.9333
Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 105.4667
Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 107.4
Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 97.86667
Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 94.33333
Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 90.93333
Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 93.2
Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 102.2667
Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 107.6667
Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 112.6
Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 108.6
Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 104
Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 103.6667
Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 105
Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 105
Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 104.4667
Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 97.2
Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 100.6
Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 101.4667
AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 109.3333
Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 111.8
Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 106.5333
Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 101.6667
Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 95.8
Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 101.4667
Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 103.9333
Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 107
Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 97.4
Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 96.4
320
There is no really elegant way to do this. Here is a suggestion:
First install numpy_ext (use pip install numpy_ext or pip install numpy_ext --user).
Second, you'll need to compute your column separatly and concat it to your ariginal dataframe:
import statsmodels.api as sm
import pandas as pd
from numpy_ext import rolling_apply as rolling_apply_ext
import numpy as np
mtcars=pd.DataFrame(sm.datasets.get_rdataset("mtcars", "datasets", cache=True).data).reset_index()
def fun(col1, col2, w1=10, w2=2):
return(w1 * col1 + w2 * col2)
Col= pd.DataFrame(rolling_apply_ext(fun, 3, mtcars.cyl.values, mtcars.mpg.values)).rename(columns={2:'rolling'})
mtcars.join(Col["rolling"])
to get:
index mpg cyl disp hp drat wt qsec vs am \
0 Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1
1 Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1
2 Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1
3 Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0
4 Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0
5 Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0
6 Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0
7 Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0
8 Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0
9 Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0
10 Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0
11 Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0
12 Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0
13 Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0
14 Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0
15 Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0
16 Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0
17 Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1
18 Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1
19 Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1
20 Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0
21 Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0
22 AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0
23 Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0
24 Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0
25 Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1
26 Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1
27 Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1
28 Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1
29 Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1
30 Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1
31 Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1
gear carb rolling
0 4 4 NaN
1 4 4 NaN
2 4 1 85.6
3 3 1 102.8
4 3 2 117.4
5 3 1 96.2
6 3 4 108.6
7 4 2 88.8
8 4 2 85.6
9 4 4 98.4
10 4 4 95.6
11 3 3 112.8
12 3 3 114.6
13 3 3 110.4
14 3 4 100.8
15 3 4 100.8
16 3 4 109.4
17 4 1 104.8
18 4 2 100.8
19 4 1 107.8
20 3 1 83.0
21 3 2 111.0
22 3 2 110.4
23 3 4 106.6
24 3 2 118.4
25 4 1 94.6
26 5 2 92.0
27 5 2 100.8
28 5 4 111.6
29 5 6 99.4
30 5 8 110.0
31 4 2 82.8
You can use the below function for rolling apply. It might be slow compared to pandas inbuild rolling in certain situations but has additional functionality.
Function argument win_size, min_periods (similar to pandas and takes only integer input). In addition, after parameter is also used to control to window, it shifts the windows to include after observation.
def roll_apply(df, fn, win_size, min_periods=None, after=None):
if min_periods is None:
min_periods = win_size
else:
assert min_periods >= 1
if after is None:
after = 0
before = win_size - 1 - after
i = np.arange(df.shape[0])
s = np.maximum(i - before, 0)
e = np.minimum(i + after, df.shape[0]) + 1
res = [fn(df.iloc[si:ei]) for si, ei in zip(s, e) if (ei-si) >= min_periods]
idx = df.index[(e-s) >= min_periods]
types = {type(ri) for ri in res}
if len(types) != 1:
return pd.Series(res, index=idx)
t = list(types)[0]
if t == pd.Series:
return pd.DataFrame(res, index=idx)
elif t == pd.DataFrame:
return pd.concat(res, keys=idx)
else:
return pd.Series(res, index=idx)
mtcars['roll'] = roll_apply(mtcars, lambda x: fun(x.cyl, x.mpg), win_size=3, min_periods=1, after=1)
| index | mpg | cyl | disp | hp | drat | wt | qsec | vs | am | gear | carb | roll |
|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Mazda RX4 | 21.0 | 6 | 160.0 | 110 | 3.9 | 2.62 | 16.46 | 0 | 1 | 4 | 4 | 102.0 |
| Mazda RX4 Wag | 21.0 | 6 | 160.0 | 110 | 3.9 | 2.875 | 17.02 | 0 | 1 | 4 | 4 | 96.53333333333335 |
| Datsun 710 | 22.8 | 4 | 108.0 | 93 | 3.85 | 2.32 | 18.61 | 1 | 1 | 4 | 1 | 96.8 |
| Hornet 4 Drive | 21.4 | 6 | 258.0 | 110 | 3.08 | 3.215 | 19.44 | 1 | 0 | 3 | 1 | 101.93333333333332 |
| Hornet Sportabout | 18.7 | 8 | 360.0 | 175 | 3.15 | 3.44 | 17.02 | 0 | 0 | 3 | 2 | 105.46666666666665 |
| Valiant | 18.1 | 6 | 225.0 | 105 | 2.76 | 3.46 | 20.22 | 1 | 0 | 3 | 1 | 107.40000000000002 |
| Duster 360 | 14.3 | 8 | 360.0 | 245 | 3.21 | 3.57 | 15.84 | 0 | 0 | 3 | 4 | 97.86666666666667 |
| Merc 240D | 24.4 | 4 | 146.7 | 62 | 3.69 | 3.19 | 20.0 | 1 | 0 | 4 | 2 | 94.33333333333333 |
| Merc 230 | 22.8 | 4 | 140.8 | 95 | 3.92 | 3.15 | 22.9 | 1 | 0 | 4 | 2 | 90.93333333333332 |
| Merc 280 | 19.2 | 6 | 167.6 | 123 | 3.92 | 3.44 | 18.3 | 1 | 0 | 4 | 4 | 93.2 |
| Merc 280C | 17.8 | 6 | 167.6 | 123 | 3.92 | 3.44 | 18.9 | 1 | 0 | 4 | 4 | 102.26666666666667 |
| Merc 450SE | 16.4 | 8 | 275.8 | 180 | 3.07 | 4.07 | 17.4 | 0 | 0 | 3 | 3 | 107.66666666666667 |
| Merc 450SL | 17.3 | 8 | 275.8 | 180 | 3.07 | 3.73 | 17.6 | 0 | 0 | 3 | 3 | 112.59999999999998 |
| Merc 450SLC | 15.2 | 8 | 275.8 | 180 | 3.07 | 3.78 | 18.0 | 0 | 0 | 3 | 3 | 108.60000000000001 |
| Cadillac Fleetwood | 10.4 | 8 | 472.0 | 205 | 2.93 | 5.25 | 17.98 | 0 | 0 | 3 | 4 | 104.0 |
| Lincoln Continental | 10.4 | 8 | 460.0 | 215 | 3.0 | 5.424 | 17.82 | 0 | 0 | 3 | 4 | 103.66666666666667 |
| Chrysler Imperial | 14.7 | 8 | 440.0 | 230 | 3.23 | 5.345 | 17.42 | 0 | 0 | 3 | 4 | 105.0 |
| Fiat 128 | 32.4 | 4 | 78.7 | 66 | 4.08 | 2.2 | 19.47 | 1 | 1 | 4 | 1 | 105.0 |
| Honda Civic | 30.4 | 4 | 75.7 | 52 | 4.93 | 1.615 | 18.52 | 1 | 1 | 4 | 2 | 104.46666666666665 |
| Toyota Corolla | 33.9 | 4 | 71.1 | 65 | 4.22 | 1.835 | 19.9 | 1 | 1 | 4 | 1 | 97.2 |
| Toyota Corona | 21.5 | 4 | 120.1 | 97 | 3.7 | 2.465 | 20.01 | 1 | 0 | 3 | 1 | 100.60000000000001 |
| Dodge Challenger | 15.5 | 8 | 318.0 | 150 | 2.76 | 3.52 | 16.87 | 0 | 0 | 3 | 2 | 101.46666666666665 |
| AMC Javelin | 15.2 | 8 | 304.0 | 150 | 3.15 | 3.435 | 17.3 | 0 | 0 | 3 | 2 | 109.33333333333333 |
| Camaro Z28 | 13.3 | 8 | 350.0 | 245 | 3.73 | 3.84 | 15.41 | 0 | 0 | 3 | 4 | 111.8 |
| Pontiac Firebird | 19.2 | 8 | 400.0 | 175 | 3.08 | 3.845 | 17.05 | 0 | 0 | 3 | 2 | 106.53333333333335 |
| Fiat X1-9 | 27.3 | 4 | 79.0 | 66 | 4.08 | 1.935 | 18.9 | 1 | 1 | 4 | 1 | 101.66666666666667 |
| Porsche 914-2 | 26.0 | 4 | 120.3 | 91 | 4.43 | 2.14 | 16.7 | 0 | 1 | 5 | 2 | 95.8 |
| Lotus Europa | 30.4 | 4 | 95.1 | 113 | 3.77 | 1.513 | 16.9 | 1 | 1 | 5 | 2 | 101.46666666666665 |
| Ford Pantera L | 15.8 | 8 | 351.0 | 264 | 4.22 | 3.17 | 14.5 | 0 | 1 | 5 | 4 | 103.93333333333332 |
| Ferrari Dino | 19.7 | 6 | 145.0 | 175 | 3.62 | 2.77 | 15.5 | 0 | 1 | 5 | 6 | 107.0 |
| Maserati Bora | 15.0 | 8 | 301.0 | 335 | 3.54 | 3.57 | 14.6 | 0 | 1 | 5 | 8 | 97.39999999999999 |
| Volvo 142E | 21.4 | 4 | 121.0 | 109 | 4.11 | 2.78 | 18.6 | 1 | 1 | 4 | 2 | 96.4 |
You can pass more complex function in roll_apply function. Below are few example
roll_apply(mtcars, lambda d: pd.Series({'A': d.sum().sum(), 'B': d.std().std()}), win_size=3, min_periods=1, after=1) # Simple example to illustrate use case
roll_apply(mtcars, lambda d: d, win_size=3, min_periods=3, after=1) # This will return rolling dataframe
answered Oct 20 '25 22:10
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