filter

Builtin index · Collections & strings

result = filter(df, mask)

Input: df, mask · Output: result

What it is

df is a Table; mask is a boolean Vec/Mask the same length as df's rows (typically built from a comparison, e.g. df.x > 0).

Overloads

One call shape — this function is not overloaded.

Code	Input	Output	Description
`result = filter(df, mask)`	`df` Table`df` is a `Table`. `mask` boolean, Vec, or Mask`mask` is a boolean `Vec`/`Mask` the same length as `df`'s rows (typically built from a comparison, e.g.	`result` Table	Returns a `Table` containing only the rows where `mask` is `true`.

Examples

From the documentation, and run by the build -- the output and the figure below are what the engine actually produced. Simplest first.

function is_even(n)
    return n mod 2 == 0
end function

function add2(a, b)
    return a + b
end function

print(filter(acc, "is_even"))     # (4, 16, 36)

print(fold(acc, "add2", 0))       # 91

output

(4, 16, 36)
91

from agent-guide

df = table(x=[1,2,3,-1,5], y=[10,20,-5,7,2])
pos = filter(df, df.x > 0)
print(pos)

s = sort_by(df, "y")
print(s)

g = table(grp=[1,1,2,2], v=[10,20,30,40])
agg = group_by_agg(g, "grp", "v", "mean")
print(agg)

output

x   y
-  --
1  10
2  20
3  -5
5   2
 x   y
--  --
 3  -5
 5   2
-1   7
 1  10
 2  20
grp  mean_v
    ... 3 more lines

from tour

df = DataFrame(x=[1, 2, 3, 4, 5], y=[10, 20, 30, 40, 50], label="sample")
print(nrow(df))            # 5

print(ncol(df))             # 3

tall = filter(df, df.x > 2)
xy = select(tall, "x", "y")
print(xy)
print(describe(df))

output

5
3
x   y
-  --
3  30
4  40
5  50
stat          x          y
-----  --------  ---------
count         5          5
mean          3         30
std    1.581139  15.811388
min           1         10
25%           2         20
    ... 3 more lines

from collections-strings

measurements = table(
    x = [-1.5, 0.5, 2.0, -0.25, 3.75],
    sensor = ["a", "b", "a", "c", "b"]
)
write_csv(measurements, "measurements.csv")

df = read_csv("measurements.csv")
print(nrow(df))
print(describe(df))
write_csv(filter(df, df.x > 0), "positive_only.csv")
print(read_all_text("positive_only.csv"))

output

5
stat         x
-----  -------
count        5
mean       0.9
std    2.03562
min       -1.5
25%      -0.25
50%        0.5
75%          2
max       3.75
x,sensor
0.5,b
2,a
    ... 1 more lines

from file-io