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Apply rolling custom function with pandas

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'
Update
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

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

I'm not aware of a way to do this calculation easily and efficiently by apply a single function to a pandas dataframe because you're calculating values across multiple rows and columns. An efficient way is to first calculate the column you want to calculate the rolling average for, then calculate the rolling average:
import statsmodels.api as sm
import pandas as pd
mtcars=pd.DataFrame(sm.datasets.get_rdataset("mtcars", "datasets", cache=True).data)
# Create column
def df_fun(df, col1, col2, w1=10, w2=2):
return w1 * df[col1] + w2 * df[col2]
mtcars['fun_val'] = df_fun(mtcars, 'cyl', 'mpg')
# Calculate rolling average
mtcars['fun_val_r3m'] = mtcars['fun_val'].rolling(3, center=True, min_periods=0).mean()
This gives the correct answer, and is efficient since each step should be optimized for performance. I found that separating the row and column calculations like this is about 10 times faster than the latest approach you proposed and no need to import numpy. If you don't want to keep the intermediate calculation, fun_val, you can overwrite it with the rolling average value, fun_val_r3m.
If you really need to do this in one line with apply, I'm not aware of another way other than what you've done in your latest post. numpy array based approaches may be able to perform better, though less readable.

After much searching and fighting against arguments. I found an approach inspired by this answer
def fun(series, w1=10, w2=2):
col1 = mtcars.loc[series.index, 'cyl']
col2 = mtcars.loc[series.index, 'mpg']
return(np.mean(w1 * col1 + w2 * col2))
mtcars['roll'] = mtcars.rolling(3, center=True, min_periods=0)['mpg'] \
.apply(fun, raw=False)
mtcars
mpg cyl disp hp ... am gear carb roll
Mazda RX4 21.0 6 160.0 110 ... 1 4 4 102.000000
Mazda RX4 Wag 21.0 6 160.0 110 ... 1 4 4 96.533333
Datsun 710 22.8 4 108.0 93 ... 1 4 1 96.800000
Hornet 4 Drive 21.4 6 258.0 110 ... 0 3 1 101.933333
Hornet Sportabout 18.7 8 360.0 175 ... 0 3 2 105.466667
Valiant 18.1 6 225.0 105 ... 0 3 1 107.400000
Duster 360 14.3 8 360.0 245 ... 0 3 4 97.866667
Merc 240D 24.4 4 146.7 62 ... 0 4 2 94.333333
Merc 230 22.8 4 140.8 95 ... 0 4 2 90.933333
Merc 280 19.2 6 167.6 123 ... 0 4 4 93.200000
Merc 280C 17.8 6 167.6 123 ... 0 4 4 102.266667
Merc 450SE 16.4 8 275.8 180 ... 0 3 3 107.666667
Merc 450SL 17.3 8 275.8 180 ... 0 3 3 112.600000
Merc 450SLC 15.2 8 275.8 180 ... 0 3 3 108.600000
Cadillac Fleetwood 10.4 8 472.0 205 ... 0 3 4 104.000000
Lincoln Continental 10.4 8 460.0 215 ... 0 3 4 103.666667
Chrysler Imperial 14.7 8 440.0 230 ... 0 3 4 105.000000
Fiat 128 32.4 4 78.7 66 ... 1 4 1 105.000000
Honda Civic 30.4 4 75.7 52 ... 1 4 2 104.466667
Toyota Corolla 33.9 4 71.1 65 ... 1 4 1 97.200000
Toyota Corona 21.5 4 120.1 97 ... 0 3 1 100.600000
Dodge Challenger 15.5 8 318.0 150 ... 0 3 2 101.466667
AMC Javelin 15.2 8 304.0 150 ... 0 3 2 109.333333
Camaro Z28 13.3 8 350.0 245 ... 0 3 4 111.800000
Pontiac Firebird 19.2 8 400.0 175 ... 0 3 2 106.533333
Fiat X1-9 27.3 4 79.0 66 ... 1 4 1 101.666667
Porsche 914-2 26.0 4 120.3 91 ... 1 5 2 95.800000
Lotus Europa 30.4 4 95.1 113 ... 1 5 2 101.466667
Ford Pantera L 15.8 8 351.0 264 ... 1 5 4 103.933333
Ferrari Dino 19.7 6 145.0 175 ... 1 5 6 107.000000
Maserati Bora 15.0 8 301.0 335 ... 1 5 8 97.400000
Volvo 142E 21.4 4 121.0 109 ... 1 4 2 96.400000
[32 rows x 12 columns]
There are several things that are needed for this to perform as I wanted. raw=False will give fun access to the series if only to call .index (False : passes each row or column as a Series to the function.). This is dumb and inefficient, but it works. I needed my window center=True. I also needed the NaN filled with available info, so I set min_periods=0.
There are a few things that I don't like about this approach:
It seems to me that calling mtcars from outside the fun scope is potentially dangerous and might cause bugs.
Multiple indexing with .loc line by line does not scale well and probably has worse performance (doing the rolling more times than needed)

What's wrong with this code to conditionally count Pandas dataframe columns?

I have the following data:
Data:
ObjectID,Date,Price,Vol,Mx
101,2017-01-01,,145,203
101,2017-01-02,,155,163
101,2017-01-03,67.0,140,234
101,2017-01-04,78.0,130,182
101,2017-01-05,58.0,178,202
101,2017-01-06,53.0,134,204
101,2017-01-07,52.0,134,183
101,2017-01-08,62.0,148,176
101,2017-01-09,42.0,152,193
101,2017-01-10,80.0,137,150
I want to add a new column called CheckCount counting the values in the Vol and Mx columns IF they are greater than 150. I have written the following code:
Code:
import pandas as pd
Observations = pd.read_csv("C:\\Users\\Observations.csv", parse_dates=['Date'], index_col=['ObjectID', 'Date'])
Observations['CheckCount'] = (Observations[['Vol', 'Mx']]>150).count(axis=1)
print(Observations)
However, unfortunately it is counting every value (result is always 2) rather than only where the values are >150 - what is wrong with my code?
Current Result:
ObjectID,Date,Price,Vol,Mx,CheckCount
101,2017-01-01,,145,203,2
101,2017-01-02,,155,163,2
101,2017-01-03,67.0,140,234,2
101,2017-01-04,78.0,130,182,2
101,2017-01-05,58.0,178,202,2
101,2017-01-06,53.0,134,204,2
101,2017-01-07,52.0,134,183,2
101,2017-01-08,62.0,148,176,2
101,2017-01-09,42.0,152,193,2
101,2017-01-10,80.0,137,150,2
Desired Result:
ObjectID,Date,Price,Vol,Mx,CheckCount
101,2017-01-01,,145,203,1
101,2017-01-02,,155,163,2
101,2017-01-03,67.0,140,234,1
101,2017-01-04,78.0,130,182,1
101,2017-01-05,58.0,178,202,2
101,2017-01-06,53.0,134,204,1
101,2017-01-07,52.0,134,183,1
101,2017-01-08,62.0,148,176,1
101,2017-01-09,42.0,152,193,2
101,2017-01-10,80.0,137,150,0

Are you looking for:
df['CheckCount'] = df[['Vol','Mx']].gt(150).sum(1)
Output:
ObjectID Date Price Vol Mx CheckCount
0 101 2017-01-01 NaN 145 203 1
1 101 2017-01-02 NaN 155 163 2
2 101 2017-01-03 67.0 140 234 1
3 101 2017-01-04 78.0 130 182 1
4 101 2017-01-05 58.0 178 202 2
5 101 2017-01-06 53.0 134 204 1
6 101 2017-01-07 52.0 134 183 1
7 101 2017-01-08 62.0 148 176 1
8 101 2017-01-09 42.0 152 193 2
9 101 2017-01-10 80.0 137 150 0

get value from pandas segment and subtract in place

i have a table with values similar to
val1 val2 val3 segVal
0 12.3 88.2
20 0 0
50 14.5 88.7
70 0 0
85 0 0
90 18.2 88.9
for my segVal, i need to use the differences from my val1 columns where val2 is known. so my first segment would be zero to 50, i'm subtracting from 0 and applying that to all segVal rows. my next segment is at 90 so i would subtract that from 50 and apply that.
So my output table would be
val1 val2 val3 segVal
0 12.3 88.2 50
20 0 0 50
50 14.5 88.7 50
70 0 0 40
85 0 0 40
90 18.2 88.9 40
my current somewhat working method is
df1 = df[df.val2 != 0]
df1 = df1.copy()
df1.segVal=(df1['val1'].diff(-1))*1
so i'm creating a additional df and calculating the values this way, then merging back the values with the original df.
It seems there has got to be a better way to do this, I mean, my method works, but doesn't' seem too efficient creating additional df's

Here's one way:
df['segVal'] = df.where(df.val2.ne(0)).val1.dropna().diff().reindex(df.index).bfill()
val1 val2 val3 segVal
0 0 12.3 88.2 50.0
1 20 0.0 0.0 50.0
2 50 14.5 88.7 50.0
3 70 0.0 0.0 40.0
4 85 0.0 0.0 40.0
5 90 18.2 88.9 40.0

Create histogram from panda frame

I am trying to created bar histogram that will show the mean of subjects by groups
my data looks like this -
week 8 exp
Subject Group 1 2 3 Mean
0 255 WT 0 101.8 75.6 84.1 87.166667
1 157 HD 0 92.6 87.8 82.3 87.566667
2 418 WT 0 54.5 47.0 50.8 50.766667
3 300 WT 0 48.1 73.1 72.2 64.466667
4 299 HD 0 71.8 86.0 93.4 83.733333
5 258 WT 0 88.0 98.5 50.2 78.900000
6 173 WT 0 75.4 70.5 83.9 76.600000
7 273 HD 0 103.6 94.2 108.3 102.033333
8 175 WT 0 36.7 30.7 42.2 36.533333
9 172 HD 0 82.6 91.6 73.4 82.533333
10 263 WT 0 110.7 102.4 105.5 106.200000
11 304 1 90.4 90.1 103.4 94.633333
12 305 1 128.6 141.5 123.1 131.066667
13 306 1 52.0 45.6 57.2 51.600000
14 309 0.1 41.3 52.6 79.9 57.933333
15 317 0.1 86.2 95.8 77.1 86.366667
My code is -
frame_data = pd.read_csv('final results.csv', header=[0,1])
data_avg = df.iloc[:, -3:].mean(axis=1)
frame_data[('exp', 'Mean')] = frame_data.iloc[:, -3:].mean(axis=1)
grouped_by_group = frame_data.groupby(['Group',
'Mean']).size().unstack('Mean')
grouped_by_group.plot.bar(title='Grip')
I am getting an error
KeyError: 'Group'
i checked many times and it is the way it is written... I do not know what is wrong...

I think need reshape DataFrame by melt, aggregate mean and then then Series.plot:
frame_data = pd.read_csv('final results.csv', header=[0,1])
frame_data[('exp', 'Mean')] = frame_data.iloc[:, -3:].mean(axis=1)
#flatten MultiIndex to columns
frame_data.columns = frame_data.columns.map('_'.join)
grouped_by_group = frame_data.groupby('8_Group')['exp_Mean'].mean()
print (grouped_by_group)
8_Group
0.1 72.150000
1 92.433333
HD 0 88.966667
WT 0 71.519048
Name: value, dtype: float64
grouped_by_group.plot.bar(title='Grip')

shifting down rows of specific columns from a specific index in python

I am scraping multiple tables from multiple pages of a website. The issue is there is a row missing from the initial table. Basically, this is how the dataframe looks.
mar2018 feb2018 jan2018 dec2017 nov2017
oct2017 sep2017 aug2017
balls faced 345 561 295 0 645 balls faced 200 58 0
runs scored 156 281 183 0 389 runs scored 50 20 0
strike rate 52.3 42.6 61.1 0 52.2 strike rate 25 34 0
dot balls 223 387 173 0 476 dot balls 125 34 0
fours 8 12 19 0 22 sixes 2 0 0
doubles 20 38 16 0 36 fours 4 2 0
notout 2 0 0 0 4 doubles 2 0 0
notout 4 2 0
the column 'sixes' is missing in the first page and present in the subsequent pages. So, I am trying to move the rows starting from 'fours' to 'not out' to a position down and leave nan's in row 4 for first 5 columns starting from mar2018 to nov2017.
I tried the following code but it isn't working. This is moving the values horizontally but not vertically downward.
df.iloc[4][0:6] = df.iloc[4][0:6].shift(1)
and also
df2 = pd.DataFrame(index = 4)
df = pd.concat([df.iloc[:], df2, df.iloc[4:]]).reset_index(drop=True)
did not work.
df['mar2018'] = df['mar2018'].shift(1)
But this moves all the values of that column down by 1 row.
So, I was wondering if it is possible to shift down rows of specific columns from a specific index?

I think need reindex by union by numpy.union1d of all index values:
idx = np.union1d(df1.index, df2.index)
df1 = df1.reindex(idx)
df2 = df2.reindex(idx)
print (df1)
mar2018 feb2018 jan2018 dec2017 nov2017
balls faced 345.0 561.0 295.0 0.0 645.0
dot balls 223.0 387.0 173.0 0.0 476.0
doubles 20.0 38.0 16.0 0.0 36.0
fours 8.0 12.0 19.0 0.0 22.0
notout 2.0 0.0 0.0 0.0 4.0
runs scored 156.0 281.0 183.0 0.0 389.0
sixes NaN NaN NaN NaN NaN
strike rate 52.3 42.6 61.1 0.0 52.2
print (df2)
oct2017 sep2017 aug2017
balls faced 200 58 0
dot balls 125 34 0
doubles 2 0 0
fours 4 2 0
notout 4 2 0
runs scored 50 20 0
sixes 2 0 0
strike rate 25 34 0
If multiple DataFrames in list is possible use list comprehension:
from functools import reduce
dfs = [df1, df2]
idx = reduce(np.union1d, [x.index for x in dfs])
dfs1 = [df.reindex(idx) for df in dfs]
print (dfs1)
[ mar2018 feb2018 jan2018 dec2017 nov2017
balls faced 345.0 561.0 295.0 0.0 645.0
dot balls 223.0 387.0 173.0 0.0 476.0
doubles 20.0 38.0 16.0 0.0 36.0
fours 8.0 12.0 19.0 0.0 22.0
notout 2.0 0.0 0.0 0.0 4.0
runs scored 156.0 281.0 183.0 0.0 389.0
sixes NaN NaN NaN NaN NaN
strike rate 52.3 42.6 61.1 0.0 52.2, oct2017 sep2017 aug2017
balls faced 200 58 0
dot balls 125 34 0
doubles 2 0 0
fours 4 2 0
notout 4 2 0
runs scored 50 20 0
sixes 2 0 0
strike rate 25 34 0]