rowwise

In [1]:

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

from datar.all import *

nb_header(rowwise)

# https://dplyr.tidyverse.org/reference/rowwise.html %run nb_helpers.py from datar.all import * nb_header(rowwise)

Try this notebook on binder.

★ rowwise

¶

Create a rowwise frame¶

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

Args:¶

  _data: A data frame
  *cols: Columns to make rowwise.

Returns:¶

  A rowwise frame

In [2]:

df = tibble(x=runif(6), y=runif(6), z=runif(6))

df >> rowwise() >> mutate(m=mean(c_across(c(f.x, f.y, f.z))))

df = tibble(x=runif(6), y=runif(6), z=runif(6)) df >> rowwise() >> mutate(m=mean(c_across(c(f.x, f.y, f.z))))

Out[2]:

	x	y	z	m
	<float64>	<float64>	<float64>	<float64>
0	0.740427	0.722062	0.796442	0.752977
1	0.420291	0.016988	0.681690	0.372989
2	0.983908	0.507227	0.508663	0.666599
3	0.059662	0.483942	0.047045	0.196883
4	0.288083	0.735485	0.868933	0.630834
5	0.184138	0.502597	0.424734	0.370489

TibbleRowwise: (n=6)

In [3]:

df >> rowwise() >> mutate(m=mean(c_across(f[f.x:])))

df >> rowwise() >> mutate(m=mean(c_across(f[f.x:])))

Out[3]:

	x	y	z	m
	<float64>	<float64>	<float64>	<float64>
0	0.740427	0.722062	0.796442	0.752977
1	0.420291	0.016988	0.681690	0.372989
2	0.983908	0.507227	0.508663	0.666599
3	0.059662	0.483942	0.047045	0.196883
4	0.288083	0.735485	0.868933	0.630834
5	0.184138	0.502597	0.424734	0.370489

TibbleRowwise: (n=6)

In [4]:

df >> rowwise() >> mutate(m=min(c_across([f.x, f.y, f.z])))

df >> rowwise() >> mutate(m=min(c_across([f.x, f.y, f.z])))

Out[4]:

	x	y	z	m
	<float64>	<float64>	<float64>	<float64>
0	0.740427	0.722062	0.796442	0.722062
1	0.420291	0.016988	0.681690	0.016988
2	0.983908	0.507227	0.508663	0.507227
3	0.059662	0.483942	0.047045	0.047045
4	0.288083	0.735485	0.868933	0.288083
5	0.184138	0.502597	0.424734	0.184138

TibbleRowwise: (n=6)

In [5]:

df >> rowwise() >> mutate(m=min(c_across(f[f.x:])))

df >> rowwise() >> mutate(m=min(c_across(f[f.x:])))

Out[5]:

	x	y	z	m
	<float64>	<float64>	<float64>	<float64>
0	0.740427	0.722062	0.796442	0.722062
1	0.420291	0.016988	0.681690	0.016988
2	0.983908	0.507227	0.508663	0.507227
3	0.059662	0.483942	0.047045	0.047045
4	0.288083	0.735485	0.868933	0.288083
5	0.184138	0.502597	0.424734	0.184138

TibbleRowwise: (n=6)

In [6]:

df >> mutate(m = pmin(f.x, f.y, f.z))

df >> mutate(m = pmin(f.x, f.y, f.z))

Out[6]:

	x	y	z	m
	<float64>	<float64>	<float64>	<float64>
0	0.740427	0.722062	0.796442	0.722062
1	0.420291	0.016988	0.681690	0.016988
2	0.983908	0.507227	0.508663	0.507227
3	0.059662	0.483942	0.047045	0.047045
4	0.288083	0.735485	0.868933	0.288083
5	0.184138	0.502597	0.424734	0.184138

In [7]:

params = tibble(
    sim=[1, 2, 3],
    n=[1, 2, 3],
    mean=[1, 2, 1],
    sd=[1, 4, 2]
)

params >> rowwise(f.sim) >> mutate(z=rnorm(f.n, f.mean, f.sd))

params = tibble( sim=[1, 2, 3], n=[1, 2, 3], mean=[1, 2, 1], sd=[1, 4, 2] ) params >> rowwise(f.sim) >> mutate(z=rnorm(f.n, f.mean, f.sd))

Out[7]:

	sim	n	mean	sd	z
	<int64>	<int64>	<int64>	<int64>	<object>
0	1	1	1	1	[1.8805605045050253]
1	2	2	2	4	[-2.39100601732882, 2.6882535131773917]
2	3	3	1	2	[-0.8909366235397611, 2.2500642263497337, 4.00...

TibbleRowwise: sim (n=3)

In [ ]: