cumall

In [1]:

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

import numpy

from datar.all import *

nb_header(cummean, cumsum, cumall, cumany, cumany, book='cumall')

# https://dplyr.tidyverse.org/reference/cumall.html %run nb_helpers.py import numpy from datar.all import * nb_header(cummean, cumsum, cumall, cumany, cumany, book='cumall')

Try this notebook on binder.

★ cummean

¶

Cumulative mean¶

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

Args:¶

  x: A numeric vector
  na_rm: If True, remove missing values before computing.

Returns:¶

  An array of cumulative means

★ cumsum

¶

Cumulative sums¶

Args:¶

  x: A numeric vector

Returns:¶

  The cumulative sums

★ cumall

¶

Get cumulative bool. All cases after first False¶

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

Args:¶

  x: A logical vector

Returns:¶

  An array of cumulative conjunctions

★ cumany

¶

Get cumulative bool. All cases after first True¶

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

Args:¶

  x: A logical vector

Returns:¶

  An array of cumulative disjunctions

★ cumany

¶

Get cumulative bool. All cases after first True¶

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

Args:¶

  x: A logical vector

Returns:¶

  An array of cumulative disjunctions

In [2]:

x = [1, 3, 5, 2, 2]
cummean(x)

x = [1, 3, 5, 2, 2] cummean(x)

Out[2]:

0    1.00
1    2.00
2    3.00
3    2.75
4    2.60
dtype: float64

In [3]:

cumsum(x) / seq_along(x)

cumsum(x) / seq_along(x)

Out[3]:

array([1.  , 2.  , 3.  , 2.75, 2.6 ])

In [4]:

cumall(numpy.array(x) < 5)

cumall(numpy.array(x) < 5)

Out[4]:

0     True
1     True
2    False
3    False
4    False
dtype: bool

In [5]:

cumany(numpy.array(x) == 3)

cumany(numpy.array(x) == 3)

Out[5]:

0    False
1     True
2     True
3     True
4     True
dtype: bool

In [6]:

df = tibble(
  date = as_date([f"2020-01-0{i+1}" for i in range(7)]),
  balance = c(100, 50, 25, -25, -50, 30, 120)
)
df >> filter(cumany(f.balance < 0))

df = tibble( date = as_date([f"2020-01-0{i+1}" for i in range(7)]), balance = c(100, 50, 25, -25, -50, 30, 120) ) df >> filter(cumany(f.balance < 0))

Out[6]:

	date	balance
	<object>	<int64>
3	2020-01-04	-25
4	2020-01-05	-50
5	2020-01-06	30
6	2020-01-07	120

In [7]:

df >> filter(cumall(~(f.balance < 0)))

df >> filter(cumall(~(f.balance < 0)))

Out[7]:

	date	balance
	<object>	<int64>
0	2020-01-01	100
1	2020-01-02	50
2	2020-01-03	25