group_by

In [1]:

# https://dplyr.tidyverse.org/reference/group_by.html
%run nb_helpers.py

from datar.data import mtcars
from datar.all import *

nb_header(group_by, ungroup)

# https://dplyr.tidyverse.org/reference/group_by.html %run nb_helpers.py from datar.data import mtcars from datar.all import * nb_header(group_by, ungroup)

Try this notebook on binder.

★ group_by

¶

Create a grouped frame¶

The original API:
https://dplyr.tidyverse.org/reference/group_by.html

Args:¶

  _data: A data frame
  *args: A variable or function of variables to group by.
  _add: If True, add grouping variables to an existing group.
  _drop: If True, drop grouping variables from the output.

Returns:¶

  A grouped frame

★ ungroup

¶

Remove grouping variables¶

The original API:
https://dplyr.tidyverse.org/reference/ungroup.html

Args:¶

  _data: A grouped frame
  *cols: Columns to remove grouping variables from.

Returns:¶

  A data frame

In [2]:

by_cyl = mtcars >> group_by(f.cyl) 
by_cyl

by_cyl = mtcars >> group_by(f.cyl) by_cyl

Out[2]:

	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
	<float64>	<int64>	<float64>	<int64>	<float64>	<float64>	<float64>	<int64>	<int64>	<int64>	<int64>
0	21.0	6	160.0	110	3.90	2.620	16.46	0	1	4	4
1	21.0	6	160.0	110	3.90	2.875	17.02	0	1	4	4
2	22.8	4	108.0	93	3.85	2.320	18.61	1	1	4	1
3	21.4	6	258.0	110	3.08	3.215	19.44	1	0	3	1
4	18.7	8	360.0	175	3.15	3.440	17.02	0	0	3	2
5	18.1	6	225.0	105	2.76	3.460	20.22	1	0	3	1
6	14.3	8	360.0	245	3.21	3.570	15.84	0	0	3	4
7	24.4	4	146.7	62	3.69	3.190	20.00	1	0	4	2
8	22.8	4	140.8	95	3.92	3.150	22.90	1	0	4	2
9	19.2	6	167.6	123	3.92	3.440	18.30	1	0	4	4
10	17.8	6	167.6	123	3.92	3.440	18.90	1	0	4	4
11	16.4	8	275.8	180	3.07	4.070	17.40	0	0	3	3
12	17.3	8	275.8	180	3.07	3.730	17.60	0	0	3	3
13	15.2	8	275.8	180	3.07	3.780	18.00	0	0	3	3
14	10.4	8	472.0	205	2.93	5.250	17.98	0	0	3	4
15	10.4	8	460.0	215	3.00	5.424	17.82	0	0	3	4
16	14.7	8	440.0	230	3.23	5.345	17.42	0	0	3	4
17	32.4	4	78.7	66	4.08	2.200	19.47	1	1	4	1
18	30.4	4	75.7	52	4.93	1.615	18.52	1	1	4	2
19	33.9	4	71.1	65	4.22	1.835	19.90	1	1	4	1
20	21.5	4	120.1	97	3.70	2.465	20.01	1	0	3	1
21	15.5	8	318.0	150	2.76	3.520	16.87	0	0	3	2
22	15.2	8	304.0	150	3.15	3.435	17.30	0	0	3	2
23	13.3	8	350.0	245	3.73	3.840	15.41	0	0	3	4
24	19.2	8	400.0	175	3.08	3.845	17.05	0	0	3	2
25	27.3	4	79.0	66	4.08	1.935	18.90	1	1	4	1
26	26.0	4	120.3	91	4.43	2.140	16.70	0	1	5	2
27	30.4	4	95.1	113	3.77	1.513	16.90	1	1	5	2
28	15.8	8	351.0	264	4.22	3.170	14.50	0	1	5	4
29	19.7	6	145.0	175	3.62	2.770	15.50	0	1	5	6
30	15.0	8	301.0	335	3.54	3.570	14.60	0	1	5	8
31	21.4	4	121.0	109	4.11	2.780	18.60	1	1	4	2

TibbleGrouped: cyl (n=3)

In [3]:

by_cyl >> group_vars()

by_cyl >> group_vars()

Out[3]:

['cyl']

In [4]:

by_cyl >> summarise(
  disp = mean(f.disp),
  hp = mean(f.hp)
)

by_cyl >> summarise( disp = mean(f.disp), hp = mean(f.hp) )

Out[4]:

	cyl	disp	hp
	<int64>	<float64>	<float64>
0	6	183.314286	122.285714
1	4	105.136364	82.636364
2	8	353.100000	209.214286

In [5]:

by_cyl >> summarise(
  disp = f.disp.mean(),
  hp = f.hp.mean()
)

by_cyl >> summarise( disp = f.disp.mean(), hp = f.hp.mean() )

Out[5]:

	cyl	disp	hp
	<int64>	<float64>	<float64>
0	6	183.314286	122.285714
1	4	105.136364	82.636364
2	8	353.100000	209.214286

In [6]:

by_cyl >> filter(f.disp == max(f.disp))

by_cyl >> filter(f.disp == max(f.disp))

Out[6]:

	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb
	<float64>	<int64>	<float64>	<int64>	<float64>	<float64>	<float64>	<int64>	<int64>	<int64>	<int64>
0	21.4	6	258.0	110	3.08	3.215	19.44	1	0	3	1
1	24.4	4	146.7	62	3.69	3.190	20.00	1	0	4	2
2	10.4	8	472.0	205	2.93	5.250	17.98	0	0	3	4

TibbleGrouped: cyl (n=3)

In [7]:

by_vs_am = mtcars >> group_by(f.vs, f.am)
by_vs = by_vs_am >> summarise(n=n())
by_vs

by_vs_am = mtcars >> group_by(f.vs, f.am) by_vs = by_vs_am >> summarise(n=n()) by_vs

[2022-12-02 14:03:28][datar][   INFO] `summarise()` has grouped output by ['vs'] (override with `_groups` argument)

Out[7]:

	vs	am	n
	<int64>	<int64>	<int64>
0	0	1	6
1	1	1	7
2	1	0	7
3	0	0	12

TibbleGrouped: vs (n=2)

In [8]:

by_vs >> summarise(n=sum(f.n))

by_vs >> summarise(n=sum(f.n))

Out[8]:

	vs	n
	<int64>	<int64>
0	0	18
1	1	14

In [9]:

by_vs >> \
  ungroup() >> \
  summarise(n = sum(f.n))

by_vs >> \ ungroup() >> \ summarise(n = sum(f.n))

Out[9]:

	n
	<int64>
0	32

In [10]:

mtcars_vsam = mtcars >> group_by(vsam=f.vs + f.am) 
mtcars_vsam

mtcars_vsam = mtcars >> group_by(vsam=f.vs + f.am) mtcars_vsam

Out[10]:

	mpg	cyl	disp	hp	drat	wt	qsec	vs	am	gear	carb	vsam
	<float64>	<int64>	<float64>	<int64>	<float64>	<float64>	<float64>	<int64>	<int64>	<int64>	<int64>	<int64>
0	21.0	6	160.0	110	3.90	2.620	16.46	0	1	4	4	1
1	21.0	6	160.0	110	3.90	2.875	17.02	0	1	4	4	1
2	22.8	4	108.0	93	3.85	2.320	18.61	1	1	4	1	2
3	21.4	6	258.0	110	3.08	3.215	19.44	1	0	3	1	1
4	18.7	8	360.0	175	3.15	3.440	17.02	0	0	3	2	0
5	18.1	6	225.0	105	2.76	3.460	20.22	1	0	3	1	1
6	14.3	8	360.0	245	3.21	3.570	15.84	0	0	3	4	0
7	24.4	4	146.7	62	3.69	3.190	20.00	1	0	4	2	1
8	22.8	4	140.8	95	3.92	3.150	22.90	1	0	4	2	1
9	19.2	6	167.6	123	3.92	3.440	18.30	1	0	4	4	1
10	17.8	6	167.6	123	3.92	3.440	18.90	1	0	4	4	1
11	16.4	8	275.8	180	3.07	4.070	17.40	0	0	3	3	0
12	17.3	8	275.8	180	3.07	3.730	17.60	0	0	3	3	0
13	15.2	8	275.8	180	3.07	3.780	18.00	0	0	3	3	0
14	10.4	8	472.0	205	2.93	5.250	17.98	0	0	3	4	0
15	10.4	8	460.0	215	3.00	5.424	17.82	0	0	3	4	0
16	14.7	8	440.0	230	3.23	5.345	17.42	0	0	3	4	0
17	32.4	4	78.7	66	4.08	2.200	19.47	1	1	4	1	2
18	30.4	4	75.7	52	4.93	1.615	18.52	1	1	4	2	2
19	33.9	4	71.1	65	4.22	1.835	19.90	1	1	4	1	2
20	21.5	4	120.1	97	3.70	2.465	20.01	1	0	3	1	1
21	15.5	8	318.0	150	2.76	3.520	16.87	0	0	3	2	0
22	15.2	8	304.0	150	3.15	3.435	17.30	0	0	3	2	0
23	13.3	8	350.0	245	3.73	3.840	15.41	0	0	3	4	0
24	19.2	8	400.0	175	3.08	3.845	17.05	0	0	3	2	0
25	27.3	4	79.0	66	4.08	1.935	18.90	1	1	4	1	2
26	26.0	4	120.3	91	4.43	2.140	16.70	0	1	5	2	1
27	30.4	4	95.1	113	3.77	1.513	16.90	1	1	5	2	2
28	15.8	8	351.0	264	4.22	3.170	14.50	0	1	5	4	1
29	19.7	6	145.0	175	3.62	2.770	15.50	0	1	5	6	1
30	15.0	8	301.0	335	3.54	3.570	14.60	0	1	5	8	1
31	21.4	4	121.0	109	4.11	2.780	18.60	1	1	4	2	2

TibbleGrouped: vsam (n=3)

In [11]:

by_cyl >> \
  group_by(f.vs, f.am) >> \
  group_vars()

by_cyl >> \ group_by(f.vs, f.am) >> \ group_vars()

Out[11]:

['vs', 'am']

In [12]:

by_cyl >> \
  group_by(f.vs, f.am, _add=True) >> \
  group_vars()

by_cyl >> \ group_by(f.vs, f.am, _add=True) >> \ group_vars()

Out[12]:

['cyl', 'vs', 'am']

In [ ]: