describe

Builtin index · Collections & strings

result = describe(df)

Input: df · Output: result

What it is

df is a Table. Pandas-style describe(): returns a Table with one row per summary statistic (count, mean, std, min, 25%, 50%, 75%, max) and one column per NUMERIC column of df (text columns are skipped).

Overloads

One call shape — this function is not overloaded.

Examples

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

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

print("add(2,3) = {add(2,3)}")

square(x) := x^2
print("square(5) = {square(5)}")

function describe(x: num)
    return "num: {x}"
end function

function describe(x: str)
    return "str: {x}"
end function

print(describe(5))
print(describe("hi"))

add(2,3) = 5
square(5) = 25
num: 5
str: hi

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))

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"))

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

How it works

describe(df) — pandas' DataFrame.describe(): one row per summary statistic (count, mean, std, min, 25%, 50%, 75%, max), one column per NUMERIC column of df (text columns are skipped, same filter corr_heatmap already applies — a mean/quantile of a text column isn't meaningful). Returned as a genuine Value::Table (not a Model handle): a table-of-stats reads and prints naturally with the existing table machinery (head, select, print, ...) and keeps the per-column layout pandas users expect, whereas a Model handle's flat named fields would force one field per column instead of one row-labeled table. Every number is computed via the existing free std_dev/quantile_of helpers (the same ones std/quantile themselves call) — no separate statistics implementation.

From the note above this builtin in the interpreter, where the reasoning was written down as it was built.