Method: Polars::Expr#value_counts

Defined in:

lib/polars/expr.rb

#value_counts(sort: false, parallel: false, name: nil, normalize: false) ⇒ Expr

Count all unique values and create a struct mapping value to count.

Examples:

df = Polars::DataFrame.new(
  {
    "id" => ["a", "b", "b", "c", "c", "c"]
  }
)
df.select(
  [
    Polars.col("id").value_counts(sort: true),
  ]
)
# =>
# shape: (3, 1)
# ┌───────────┐
# │ id        │
# │ ---       │
# │ struct[2] │
# ╞═══════════╡
# │ {"c",3}   │
# │ {"b",2}   │
# │ {"a",1}   │
# └───────────┘

Parameters:

• sort (Boolean) (defaults to: false) —

  Sort the output by count in descending order. If set to false (default), the order of the output is random.

• parallel (Boolean) (defaults to: false) —

  Execute the computation in parallel.

• name (String) (defaults to: nil) —

  Give the resulting count column a specific name; if normalize is true defaults to "count", otherwise defaults to "proportion".

• normalize (Boolean) (defaults to: false) —

  If true gives relative frequencies of the unique values

Returns:

• (Expr)

# File 'lib/polars/expr.rb', line 7503

def value_counts(
  sort: false,
  parallel: false,
  name: nil,
  normalize: false
)
  if name.nil?
    if normalize
      name = "proportion"
    else
      name = "count"
    end
  end
  wrap_expr(
    _rbexpr.value_counts(sort, parallel, name, normalize)
  )
end