Class: Polars::DataFrame

Inherits:
Object
  • Object
show all
Defined in:
lib/polars/data_frame.rb

Overview

Two-dimensional data structure representing data as a table with rows and columns.

Class Method Summary collapse

Instance Method Summary collapse

Constructor Details

#initialize(data = nil, schema: nil, schema_overrides: nil, strict: true, orient: nil, infer_schema_length: N_INFER_DEFAULT, nan_to_null: false, height: nil) ⇒ DataFrame

Create a new DataFrame.

Parameters:

  • data (Object) (defaults to: nil) —

    Two-dimensional data in various forms; hash input must contain arrays or a range. Arrays may contain Series or other arrays.

  • schema (Object) (defaults to: nil) —

    The schema of the resulting DataFrame. The schema may be declared in several ways:

    • As a hash of \{name:type} pairs; if type is nil, it will be auto-inferred.
    • As an array of column names; in this case types are automatically inferred.
    • As an array of (name,type) pairs; this is equivalent to the hash form.

    If you supply an array of column names that does not match the names in the underlying data, the names given here will overwrite them. The number of names given in the schema should match the underlying data dimensions.

    If set to nil (default), the schema is inferred from the data.

  • schema_overrides (Hash) (defaults to: nil) —

    Support type specification or override of one or more columns; note that any dtypes inferred from the schema param will be overridden.

    The number of entries in the schema should match the underlying data dimensions, unless an array of hashes is being passed, in which case a partial schema can be declared to prevent specific fields from being loaded.

  • strict (Boolean) (defaults to: true) —

    Throw an error if any data value does not exactly match the given or inferred data type for that column. If set to false, values that do not match the data type are cast to that data type or, if casting is not possible, set to null instead.

  • orient ("col", "row") (defaults to: nil) —

    Whether to interpret two-dimensional data as columns or as rows. If nil, the orientation is inferred by matching the columns and data dimensions. If this does not yield conclusive results, column orientation is used.

  • infer_schema_length (Integer) (defaults to: N_INFER_DEFAULT) —

    The maximum number of rows to scan for schema inference. If set to nil, the full data may be scanned (this can be slow). This parameter only applies if the input data is an array or generator of rows; other input is read as-is.

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

    If the data comes from one or more Numo arrays, can optionally convert input data NaN values to null instead. This is a no-op for all other input data.



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# File 'lib/polars/data_frame.rb', line 48

def initialize(
  data = nil,
  schema: nil,
  schema_overrides: nil,
  strict: true,
  orient: nil,
  infer_schema_length: N_INFER_DEFAULT,
  nan_to_null: false,
  height: nil
)
  if defined?(ActiveRecord) && (data.is_a?(ActiveRecord::Relation) || data.is_a?(ActiveRecord::Result))
    raise ArgumentError, "Use read_database instead"
  end

  if !height.nil?
    msg = "the `height` parameter of `DataFrame` is considered unstable."
    Utils.issue_unstable_warning(msg)

    raise Todo
  end

  if data.nil?
    self._df = Utils.hash_to_rbdf({}, schema: schema, schema_overrides: schema_overrides)
  elsif data.is_a?(Hash)
    data = data.transform_keys { |v| v.is_a?(Symbol) ? v.to_s : v }
    self._df = Utils.hash_to_rbdf(data, schema: schema, schema_overrides: schema_overrides, strict: strict, nan_to_null: nan_to_null)
  elsif data.is_a?(::Array)
    self._df = Utils.sequence_to_rbdf(data, schema: schema, schema_overrides: schema_overrides, strict: strict, orient: orient, infer_schema_length: infer_schema_length)
  elsif data.is_a?(Series)
    self._df = Utils.series_to_rbdf(data, schema: schema, schema_overrides: schema_overrides, strict: strict)
  elsif data.respond_to?(:arrow_c_stream)
    # This uses the fact that RbSeries.from_arrow_c_stream will create a
    # struct-typed Series. Then we unpack that to a DataFrame.
    tmp_col_name = ""
    s = Utils.wrap_s(RbSeries.from_arrow_c_stream(data))
    self._df = s.to_frame(tmp_col_name).unnest(tmp_col_name)._df
  else
    raise ArgumentError, "DataFrame constructor called with unsupported type; got #{data.class.name}"
  end
end

Class Method Details

.deserialize(source) ⇒ DataFrame

Note:

Serialization is not stable across Polars versions: a LazyFrame serialized in one Polars version may not be deserializable in another Polars version.

Read a serialized DataFrame from a file.

Examples:

df = Polars::DataFrame.new({"a" => [1, 2, 3], "b" => [4.0, 5.0, 6.0]})
bytes = df.serialize
Polars::DataFrame.deserialize(StringIO.new(bytes))
# =>
# shape: (3, 2)
# ┌─────┬─────┐
# │ a   ┆ b   │
# │ --- ┆ --- │
# │ i64 ┆ f64 │
# ╞═════╪═════╡
# │ 1   ┆ 4.0 │
# │ 2   ┆ 5.0 │
# │ 3   ┆ 6.0 │
# └─────┴─────┘

Parameters:

  • source (Object) —

    Path to a file or a file-like object (by file-like object, we refer to objects that have a read method, such as a file handler or StringIO).

Returns:



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# File 'lib/polars/data_frame.rb', line 116

def self.deserialize(source)
  if Utils.pathlike?(source)
    source = Utils.normalize_filepath(source)
  end

  deserializer = RbDataFrame.method(:deserialize_binary)

  _from_rbdf(deserializer.(source))
end

Instance Method Details

#!=(other) ⇒ DataFrame

Not equal.

Returns:



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# File 'lib/polars/data_frame.rb', line 315

def !=(other)
  _comp(other, "neq")
end

#%(other) ⇒ DataFrame

Returns the modulo.

Returns:



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# File 'lib/polars/data_frame.rb', line 398

def %(other)
  if other.is_a?(DataFrame)
    return _from_rbdf(_df.rem_df(other._df))
  end

  other = _prepare_other_arg(other)
  _from_rbdf(_df.rem(other._s))
end

#*(other) ⇒ DataFrame

Performs multiplication.

Returns:



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# File 'lib/polars/data_frame.rb', line 350

def *(other)
  if other.is_a?(DataFrame)
    return _from_rbdf(_df.mul_df(other._df))
  end

  other = _prepare_other_arg(other)
  _from_rbdf(_df.mul(other._s))
end

#+(other) ⇒ DataFrame

Performs addition.

Returns:



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# File 'lib/polars/data_frame.rb', line 374

def +(other)
  if other.is_a?(DataFrame)
    return _from_rbdf(_df.add_df(other._df))
  end

  other = _prepare_other_arg(other)
  _from_rbdf(_df.add(other._s))
end

#-(other) ⇒ DataFrame

Performs subtraction.

Returns:



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# File 'lib/polars/data_frame.rb', line 386

def -(other)
  if other.is_a?(DataFrame)
    return _from_rbdf(_df.sub_df(other._df))
  end

  other = _prepare_other_arg(other)
  _from_rbdf(_df.sub(other._s))
end

#/(other) ⇒ DataFrame

Performs division.

Returns:



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# File 'lib/polars/data_frame.rb', line 362

def /(other)
  if other.is_a?(DataFrame)
    return _from_rbdf(_df.div_df(other._df))
  end

  other = _prepare_other_arg(other)
  _from_rbdf(_df.div(other._s))
end

#<(other) ⇒ DataFrame

Less than.

Returns:



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# File 'lib/polars/data_frame.rb', line 329

def <(other)
  _comp(other, "lt")
end

#<=(other) ⇒ DataFrame

Less than or equal.

Returns:



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# File 'lib/polars/data_frame.rb', line 343

def <=(other)
  _comp(other, "lt_eq")
end

#==(other) ⇒ DataFrame

Equal.

Returns:



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# File 'lib/polars/data_frame.rb', line 308

def ==(other)
  _comp(other, "eq")
end

#>(other) ⇒ DataFrame

Greater than.

Returns:



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# File 'lib/polars/data_frame.rb', line 322

def >(other)
  _comp(other, "gt")
end

#>=(other) ⇒ DataFrame

Greater than or equal.

Returns:



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# File 'lib/polars/data_frame.rb', line 336

def >=(other)
  _comp(other, "gt_eq")
end

#[](*key) ⇒ Object

Returns subset of the DataFrame.

Examples:

df = Polars::DataFrame.new(
  {"a" => [1, 2, 3], "d" => [4, 5, 6], "c" => [1, 3, 2], "b" => [7, 8, 9]}
)
df[0]
# =>
# shape: (1, 4)
# ┌─────┬─────┬─────┬─────┐
# │ a   ┆ d   ┆ c   ┆ b   │
# │ --- ┆ --- ┆ --- ┆ --- │
# │ i64 ┆ i64 ┆ i64 ┆ i64 │
# ╞═════╪═════╪═════╪═════╡
# │ 1   ┆ 4   ┆ 1   ┆ 7   │
# └─────┴─────┴─────┴─────┘
df[0, "a"]
# => 1
df["a"]
# =>
# shape: (3,)
# Series: 'a' [i64]
# [
#         1
#         2
#         3
# ]
df[0..1]
# =>
# shape: (2, 4)
# ┌─────┬─────┬─────┬─────┐
# │ a   ┆ d   ┆ c   ┆ b   │
# │ --- ┆ --- ┆ --- ┆ --- │
# │ i64 ┆ i64 ┆ i64 ┆ i64 │
# ╞═════╪═════╪═════╪═════╡
# │ 1   ┆ 4   ┆ 1   ┆ 7   │
# │ 2   ┆ 5   ┆ 3   ┆ 8   │
# └─────┴─────┴─────┴─────┘
df[0..1, "a"]
# =>
# shape: (2,)
# Series: 'a' [i64]
# [
#         1
#         2
# ]
df[0..1, 0]
# =>
# shape: (2,)
# Series: 'a' [i64]
# [
#         1
#         2
# ]
df[[0, 1], [0, 1, 2]]
# =>
# shape: (2, 3)
# ┌─────┬─────┬─────┐
# │ a   ┆ d   ┆ c   │
# │ --- ┆ --- ┆ --- │
# │ i64 ┆ i64 ┆ i64 │
# ╞═════╪═════╪═════╡
# │ 1   ┆ 4   ┆ 1   │
# │ 2   ┆ 5   ┆ 3   │
# └─────┴─────┴─────┘
df[0..1, ["a", "c"]]
# =>
# shape: (2, 2)
# ┌─────┬─────┐
# │ a   ┆ c   │
# │ --- ┆ --- │
# │ i64 ┆ i64 │
# ╞═════╪═════╡
# │ 1   ┆ 1   │
# │ 2   ┆ 3   │
# └─────┴─────┘
df[0.., 0..1]
# =>
# shape: (3, 2)
# ┌─────┬─────┐
# │ a   ┆ d   │
# │ --- ┆ --- │
# │ i64 ┆ i64 │
# ╞═════╪═════╡
# │ 1   ┆ 4   │
# │ 2   ┆ 5   │
# │ 3   ┆ 6   │
# └─────┴─────┘
df[0.., "a".."c"]
# =>
# shape: (3, 3)
# ┌─────┬─────┬─────┐
# │ a   ┆ d   ┆ c   │
# │ --- ┆ --- ┆ --- │
# │ i64 ┆ i64 ┆ i64 │
# ╞═════╪═════╪═════╡
# │ 1   ┆ 4   ┆ 1   │
# │ 2   ┆ 5   ┆ 3   │
# │ 3   ┆ 6   ┆ 2   │
# └─────┴─────┴─────┘

Returns:



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# File 'lib/polars/data_frame.rb', line 556

def [](*key)
  get_df_item_by_key(self, key)
end

#[]=(*key, value) ⇒ Object

Set item.

Examples:

df[["a", "b"]] = value:

df = Polars::DataFrame.new({"a" => [1, 2, 3], "b" => [4, 5, 6]})
df[["a", "b"]] = [[10, 40], [20, 50], [30, 60]]
df
# =>
# shape: (3, 2)
# ┌─────┬─────┐
# │ a   ┆ b   │
# │ --- ┆ --- │
# │ i64 ┆ i64 │
# ╞═════╪═════╡
# │ 10  ┆ 40  │
# │ 20  ┆ 50  │
# │ 30  ┆ 60  │
# └─────┴─────┘

df[row_idx, "a"] = value:

df[1, "a"] = 100
df
# =>
# shape: (3, 2)
# ┌─────┬─────┐
# │ a   ┆ b   │
# │ --- ┆ --- │
# │ i64 ┆ i64 │
# ╞═════╪═════╡
# │ 10  ┆ 40  │
# │ 100 ┆ 50  │
# │ 30  ┆ 60  │
# └─────┴─────┘

df[row_idx, col_idx] = value:

df[0, 1] = 30
df
# =>
# shape: (3, 2)
# ┌─────┬─────┐
# │ a   ┆ b   │
# │ --- ┆ --- │
# │ i64 ┆ i64 │
# ╞═════╪═════╡
# │ 10  ┆ 30  │
# │ 100 ┆ 50  │
# │ 30  ┆ 60  │
# └─────┴─────┘

Returns:



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# File 'lib/polars/data_frame.rb', line 609

def []=(*key, value)
  if key.empty? || key.length > 2
    raise ArgumentError, "wrong number of arguments (given #{key.length + 1}, expected 2..3)"
  end

  if key.length == 1 && Utils.strlike?(key[0])
    key = key[0]

    if value.is_a?(::Array) || (defined?(Numo::NArray) && value.is_a?(Numo::NArray))
      value = Series.new(value)
    elsif !value.is_a?(Series)
      value = Polars.lit(value)
    end
    self._df = with_columns(value.alias(key.to_s))._df

  # df[["C", "D"]]
  elsif key.length == 1 && key[0].is_a?(::Array)
    key = key[0]

    if !value.is_a?(::Array) || !value.all? { |v| v.is_a?(::Array) }
      msg = "can only set multiple columns with 2D matrix"
      raise ArgumentError, msg
    end
    if value.any? { |v| v.size != key.length }
      msg = "matrix columns should be equal to list used to determine column names"
      raise ArgumentError, msg
    end

    columns = []
    key.each_with_index do |name, i|
      columns << Series.new(name, value.map { |v| v[i] })
    end
    self._df = with_columns(columns)._df

  # df[a, b]
  else
    row_selection, col_selection = key

    if (row_selection.is_a?(Series) && row_selection.dtype == Boolean) || Utils.is_bool_sequence(row_selection)
      msg = (
        "not allowed to set DataFrame by boolean mask in the row position" +
        "\n\nConsider using `DataFrame.with_columns`."
      )
      raise TypeError, msg
    end

    # get series column selection
    if Utils.strlike?(col_selection)
      s = self[col_selection]
    elsif col_selection.is_a?(Integer)
      s = self[0.., col_selection]
    else
      msg = "unexpected column selection #{col_selection.inspect}"
      raise TypeError, msg
    end

    # dispatch to []= of Series to do modification
    s[row_selection] = value

    # now find the location to place series
    # df[idx]
    if col_selection.is_a?(Integer)
      replace_column(col_selection, s)
    # df["foo"]
    elsif Utils.strlike?(col_selection)
      _replace(col_selection.to_s, s)
    end
  end
end

#bottom_k(k, by:, reverse: false) ⇒ DataFrame

Return the k smallest rows.

Non-null elements are always preferred over null elements, regardless of the value of reverse. The output is not guaranteed to be in any particular order, call sort after this function if you wish the output to be sorted.

Examples:

Get the rows which contain the 4 smallest values in column b.

df = Polars::DataFrame.new(
  {
    "a" => ["a", "b", "a", "b", "b", "c"],
    "b" => [2, 1, 1, 3, 2, 1]
  }
)
df.bottom_k(4, by: "b")
# =>
# shape: (4, 2)
# ┌─────┬─────┐
# │ a   ┆ b   │
# │ --- ┆ --- │
# │ str ┆ i64 │
# ╞═════╪═════╡
# │ b   ┆ 1   │
# │ a   ┆ 1   │
# │ c   ┆ 1   │
# │ a   ┆ 2   │
# └─────┴─────┘

Get the rows which contain the 4 smallest values when sorting on column a and b.

df.bottom_k(4, by: ["a", "b"])
# =>
# shape: (4, 2)
# ┌─────┬─────┐
# │ a   ┆ b   │
# │ --- ┆ --- │
# │ str ┆ i64 │
# ╞═════╪═════╡
# │ a   ┆ 1   │
# │ a   ┆ 2   │
# │ b   ┆ 1   │
# │ b   ┆ 2   │
# └─────┴─────┘

Parameters:

  • k (Integer) —

    Number of rows to return.

  • by (Object) —

    Column(s) used to determine the bottom rows. Accepts expression input. Strings are parsed as column names.

  • reverse (Object) (defaults to: false) —

    Consider the k largest elements of the by column(s) (instead of the k smallest). This can be specified per column by passing an array of booleans.

Returns:



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# File 'lib/polars/data_frame.rb', line 2430

def bottom_k(
  k,
  by:,
  reverse: false
)
  lazy
  .bottom_k(k, by: by, reverse: reverse)
  .collect(
    optimizations: QueryOptFlags.new(
      projection_pushdown: false,
      predicate_pushdown: false,
      comm_subplan_elim: false,
      slice_pushdown: true
    )
  )
end

#cast(dtypes, strict: true) ⇒ DataFrame

Cast DataFrame column(s) to the specified dtype(s).

Examples:

Cast specific frame columns to the specified dtypes:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6.0, 7.0, 8.0],
    "ham" => [Date.new(2020, 1, 2), Date.new(2021, 3, 4), Date.new(2022, 5, 6)]
  }
)
df.cast({"foo" => Polars::Float32, "bar" => Polars::UInt8})
# =>
# shape: (3, 3)
# ┌─────┬─────┬────────────┐
# │ foo ┆ bar ┆ ham        │
# │ --- ┆ --- ┆ ---        │
# │ f32 ┆ u8  ┆ date       │
# ╞═════╪═════╪════════════╡
# │ 1.0 ┆ 6   ┆ 2020-01-02 │
# │ 2.0 ┆ 7   ┆ 2021-03-04 │
# │ 3.0 ┆ 8   ┆ 2022-05-06 │
# └─────┴─────┴────────────┘

Cast all frame columns matching one dtype (or dtype group) to another dtype:

df.cast({Polars::Date => Polars::Datetime})
# =>
# shape: (3, 3)
# ┌─────┬─────┬─────────────────────┐
# │ foo ┆ bar ┆ ham                 │
# │ --- ┆ --- ┆ ---                 │
# │ i64 ┆ f64 ┆ datetime[μs]        │
# ╞═════╪═════╪═════════════════════╡
# │ 1   ┆ 6.0 ┆ 2020-01-02 00:00:00 │
# │ 2   ┆ 7.0 ┆ 2021-03-04 00:00:00 │
# │ 3   ┆ 8.0 ┆ 2022-05-06 00:00:00 │
# └─────┴─────┴─────────────────────┘

Cast all frame columns to the specified dtype:

df.cast(Polars::String).to_h(as_series: false)
# => {"foo"=>["1", "2", "3"], "bar"=>["6.0", "7.0", "8.0"], "ham"=>["2020-01-02", "2021-03-04", "2022-05-06"]}

Parameters:

  • dtypes (Object) —

    Mapping of column names (or selector) to dtypes, or a single dtype to which all columns will be cast.

  • strict (Boolean) (defaults to: true) —

    Throw an error if a cast could not be done (for instance, due to an overflow).

Returns:



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# File 'lib/polars/data_frame.rb', line 4184

def cast(dtypes, strict: true)
  lazy.cast(dtypes, strict: strict).collect(optimizations: QueryOptFlags._eager)
end

#clear(n = 0) ⇒ DataFrame

Create an empty copy of the current DataFrame.

Returns a DataFrame with identical schema but no data.

Examples:

df = Polars::DataFrame.new(
  {
    "a" => [nil, 2, 3, 4],
    "b" => [0.5, nil, 2.5, 13],
    "c" => [true, true, false, nil]
  }
)
df.clear
# =>
# shape: (0, 3)
# ┌─────┬─────┬──────┐
# │ a   ┆ b   ┆ c    │
# │ --- ┆ --- ┆ ---  │
# │ i64 ┆ f64 ┆ bool │
# ╞═════╪═════╪══════╡
# └─────┴─────┴──────┘
df.clear(2)
# =>
# shape: (2, 3)
# ┌──────┬──────┬──────┐
# │ a    ┆ b    ┆ c    │
# │ ---  ┆ ---  ┆ ---  │
# │ i64  ┆ f64  ┆ bool │
# ╞══════╪══════╪══════╡
# │ null ┆ null ┆ null │
# │ null ┆ null ┆ null │
# └──────┴──────┴──────┘

Returns:



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# File 'lib/polars/data_frame.rb', line 4224

def clear(n = 0)
  if n == 0
    _from_rbdf(_df.clear)
  elsif n > 0 || len > 0
    self.class.new(
      schema.to_h { |nm, tp| [nm, Series.new(nm, [], dtype: tp).extend_constant(nil, n)] }
    )
  else
    clone
  end
end

#collect_schema ⇒ Schema

Note:

This method is included to facilitate writing code that is generic for both DataFrame and LazyFrame.

Get an ordered mapping of column names to their data type.

Examples:

Determine the schema.

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6.0, 7.0, 8.0],
    "ham" => ["a", "b", "c"]
  }
)
df.collect_schema
# => Polars::Schema({"foo"=>Polars::Int64, "bar"=>Polars::Float64, "ham"=>Polars::String})

Access various properties of the schema using the Schema object.

schema = df.collect_schema
schema["bar"]
# => Polars::Float64
schema.names
# => ["foo", "bar", "ham"]
schema.dtypes
# => [Polars::Int64, Polars::Float64, Polars::String]
schema.length
# => 3

Returns:



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# File 'lib/polars/data_frame.rb', line 719

def collect_schema
  Schema.new(columns.zip(dtypes), check_dtypes: false)
end

#columns ⇒ Array

Get column names.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6, 7, 8],
    "ham" => ["a", "b", "c"]
  }
)
df.columns
# => ["foo", "bar", "ham"]

Returns:



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# File 'lib/polars/data_frame.rb', line 225

def columns
  _df.columns
end

#columns=(columns) ⇒ Object

Change the column names of the DataFrame.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6, 7, 8],
    "ham" => ["a", "b", "c"]
  }
)
df.columns = ["apple", "banana", "orange"]
df
# =>
# shape: (3, 3)
# ┌───────┬────────┬────────┐
# │ apple ┆ banana ┆ orange │
# │ ---   ┆ ---    ┆ ---    │
# │ i64   ┆ i64    ┆ str    │
# ╞═══════╪════════╪════════╡
# │ 1     ┆ 6      ┆ a      │
# │ 2     ┆ 7      ┆ b      │
# │ 3     ┆ 8      ┆ c      │
# └───────┴────────┴────────┘

Parameters:

  • columns (Array) —

    A list with new names for the DataFrame. The length of the list should be equal to the width of the DataFrame.

Returns:



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# File 'lib/polars/data_frame.rb', line 258

def columns=(columns)
  _df.set_column_names(columns)
end

#delete(name) ⇒ Series

Drop in place if exists.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6, 7, 8],
    "ham" => ["a", "b", "c"]
  }
)
df.delete("ham")
# =>
# shape: (3,)
# Series: 'ham' [str]
# [
#         "a"
#         "b"
#         "c"
# ]
df.delete("missing")
# => nil

Parameters:

  • name (Object) —

    Column to drop.

Returns:



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# File 'lib/polars/data_frame.rb', line 4131

def delete(name)
  drop_in_place(name) if include?(name)
end

#describe(percentiles: [0.25, 0.5, 0.75], interpolation: "nearest") ⇒ DataFrame

Summary statistics for a DataFrame.

Examples:

Show default frame statistics:

df = Polars::DataFrame.new(
  {
    "float" => [1.0, 2.8, 3.0],
    "int" => [40, 50, nil],
    "bool" => [true, false, true],
    "str" => ["zz", "xx", "yy"],
    "date" => [Date.new(2020, 1, 1), Date.new(2021, 7, 5), Date.new(2022, 12, 31)]
  }
)
df.describe
# =>
# shape: (9, 6)
# ┌────────────┬──────────┬──────────┬──────────┬──────┬─────────────────────────┐
# │ statistic  ┆ float    ┆ int      ┆ bool     ┆ str  ┆ date                    │
# │ ---        ┆ ---      ┆ ---      ┆ ---      ┆ ---  ┆ ---                     │
# │ str        ┆ f64      ┆ f64      ┆ f64      ┆ str  ┆ str                     │
# ╞════════════╪══════════╪══════════╪══════════╪══════╪═════════════════════════╡
# │ count      ┆ 3.0      ┆ 2.0      ┆ 3.0      ┆ 3    ┆ 3                       │
# │ null_count ┆ 0.0      ┆ 1.0      ┆ 0.0      ┆ 0    ┆ 0                       │
# │ mean       ┆ 2.266667 ┆ 45.0     ┆ 0.666667 ┆ null ┆ 2021-07-02 16:00:00 UTC │
# │ std        ┆ 1.101514 ┆ 7.071068 ┆ null     ┆ null ┆ null                    │
# │ min        ┆ 1.0      ┆ 40.0     ┆ 0.0      ┆ xx   ┆ 2020-01-01              │
# │ 25%        ┆ 2.8      ┆ 40.0     ┆ null     ┆ null ┆ 2021-07-05              │
# │ 50%        ┆ 2.8      ┆ 50.0     ┆ null     ┆ null ┆ 2021-07-05              │
# │ 75%        ┆ 3.0      ┆ 50.0     ┆ null     ┆ null ┆ 2022-12-31              │
# │ max        ┆ 3.0      ┆ 50.0     ┆ 1.0      ┆ zz   ┆ 2022-12-31              │
# └────────────┴──────────┴──────────┴──────────┴──────┴─────────────────────────┘

Customize which percentiles are displayed, applying linear interpolation:

df.describe(
  percentiles: [0.1, 0.3, 0.5, 0.7, 0.9],
  interpolation: "linear"
)
# =>
# shape: (11, 6)
# ┌────────────┬──────────┬──────────┬──────────┬──────┬─────────────────────────┐
# │ statistic  ┆ float    ┆ int      ┆ bool     ┆ str  ┆ date                    │
# │ ---        ┆ ---      ┆ ---      ┆ ---      ┆ ---  ┆ ---                     │
# │ str        ┆ f64      ┆ f64      ┆ f64      ┆ str  ┆ str                     │
# ╞════════════╪══════════╪══════════╪══════════╪══════╪═════════════════════════╡
# │ count      ┆ 3.0      ┆ 2.0      ┆ 3.0      ┆ 3    ┆ 3                       │
# │ null_count ┆ 0.0      ┆ 1.0      ┆ 0.0      ┆ 0    ┆ 0                       │
# │ mean       ┆ 2.266667 ┆ 45.0     ┆ 0.666667 ┆ null ┆ 2021-07-02 16:00:00 UTC │
# │ std        ┆ 1.101514 ┆ 7.071068 ┆ null     ┆ null ┆ null                    │
# │ min        ┆ 1.0      ┆ 40.0     ┆ 0.0      ┆ xx   ┆ 2020-01-01              │
# │ …          ┆ …        ┆ …        ┆ …        ┆ …    ┆ …                       │
# │ 30%        ┆ 2.08     ┆ 43.0     ┆ null     ┆ null ┆ 2020-11-26              │
# │ 50%        ┆ 2.8      ┆ 45.0     ┆ null     ┆ null ┆ 2021-07-05              │
# │ 70%        ┆ 2.88     ┆ 47.0     ┆ null     ┆ null ┆ 2022-02-07              │
# │ 90%        ┆ 2.96     ┆ 49.0     ┆ null     ┆ null ┆ 2022-09-13              │
# │ max        ┆ 3.0      ┆ 50.0     ┆ 1.0      ┆ zz   ┆ 2022-12-31              │
# └────────────┴──────────┴──────────┴──────────┴──────┴─────────────────────────┘

Parameters:

  • percentiles (Array) (defaults to: [0.25, 0.5, 0.75]) —

    One or more percentiles to include in the summary statistics. All values must be in the range [0, 1].

  • interpolation ('nearest', 'higher', 'lower', 'midpoint', 'linear', 'equiprobable') (defaults to: "nearest") —

    Interpolation method used when calculating percentiles.

Returns:



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# File 'lib/polars/data_frame.rb', line 2070

def describe(
  percentiles: [0.25, 0.5, 0.75],
  interpolation: "nearest"
)
  if columns.empty?
    msg = "cannot describe a DataFrame that has no columns"
    raise TypeError, msg
  end

  lazy.describe(
    percentiles: percentiles, interpolation: interpolation
  )
end

#drop(*columns, strict: true) ⇒ DataFrame

Remove column from DataFrame and return as new.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6.0, 7.0, 8.0],
    "ham" => ["a", "b", "c"]
  }
)
df.drop("ham")
# =>
# shape: (3, 2)
# ┌─────┬─────┐
# │ foo ┆ bar │
# │ --- ┆ --- │
# │ i64 ┆ f64 │
# ╞═════╪═════╡
# │ 1   ┆ 6.0 │
# │ 2   ┆ 7.0 │
# │ 3   ┆ 8.0 │
# └─────┴─────┘

Drop multiple columns by passing a list of column names.

df.drop(["bar", "ham"])
# =>
# shape: (3, 1)
# ┌─────┐
# │ foo │
# │ --- │
# │ i64 │
# ╞═════╡
# │ 1   │
# │ 2   │
# │ 3   │
# └─────┘

Use positional arguments to drop multiple columns.

df.drop("foo", "ham")
# =>
# shape: (3, 1)
# ┌─────┐
# │ bar │
# │ --- │
# │ f64 │
# ╞═════╡
# │ 6.0 │
# │ 7.0 │
# │ 8.0 │
# └─────┘

Parameters:

  • columns (Object) —

    Column(s) to drop.

  • strict (Boolean) (defaults to: true) —

    Validate that all column names exist in the current schema, and throw an exception if any do not.

Returns:



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# File 'lib/polars/data_frame.rb', line 4071

def drop(*columns, strict: true)
  lazy.drop(*columns, strict: strict).collect(optimizations: QueryOptFlags._eager)
end

#drop_in_place(name) ⇒ Series

Drop in place.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6, 7, 8],
    "ham" => ["a", "b", "c"]
  }
)
df.drop_in_place("ham")
# =>
# shape: (3,)
# Series: 'ham' [str]
# [
#         "a"
#         "b"
#         "c"
# ]

Parameters:

  • name (Object) —

    Column to drop.

Returns:



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# File 'lib/polars/data_frame.rb', line 4099

def drop_in_place(name)
  Utils.wrap_s(_df.drop_in_place(name))
end

#drop_nans(subset: nil) ⇒ DataFrame

Drop all rows that contain one or more NaN values.

The original order of the remaining rows is preserved.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [-20.5, Float::NAN, 80.0],
    "bar" => [Float::NAN, 110.0, 25.5],
    "ham" => ["xxx", "yyy", nil]
  }
)
df.drop_nans
# =>
# shape: (1, 3)
# ┌──────┬──────┬──────┐
# │ foo  ┆ bar  ┆ ham  │
# │ ---  ┆ ---  ┆ ---  │
# │ f64  ┆ f64  ┆ str  │
# ╞══════╪══════╪══════╡
# │ 80.0 ┆ 25.5 ┆ null │
# └──────┴──────┴──────┘
df.drop_nans(subset: ["bar"])
# =>
# shape: (2, 3)
# ┌──────┬───────┬──────┐
# │ foo  ┆ bar   ┆ ham  │
# │ ---  ┆ ---   ┆ ---  │
# │ f64  ┆ f64   ┆ str  │
# ╞══════╪═══════╪══════╡
# │ NaN  ┆ 110.0 ┆ yyy  │
# │ 80.0 ┆ 25.5  ┆ null │
# └──────┴───────┴──────┘

Parameters:

  • subset (Object) (defaults to: nil) —

    Column name(s) for which NaN values are considered; if set to nil (default), use all columns (note that only floating-point columns can contain NaNs).

Returns:



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# File 'lib/polars/data_frame.rb', line 2649

def drop_nans(subset: nil)
  lazy.drop_nans(subset: subset).collect(optimizations: QueryOptFlags._eager)
end

#drop_nulls(subset: nil) ⇒ DataFrame

Drop all rows that contain one or more null values.

The original order of the remaining rows is preserved.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6, nil, 8],
    "ham" => ["a", "b", nil]
  }
)
df.drop_nulls
# =>
# shape: (1, 3)
# ┌─────┬─────┬─────┐
# │ foo ┆ bar ┆ ham │
# │ --- ┆ --- ┆ --- │
# │ i64 ┆ i64 ┆ str │
# ╞═════╪═════╪═════╡
# │ 1   ┆ 6   ┆ a   │
# └─────┴─────┴─────┘
df.drop_nulls(subset: Polars.cs.integer)
# =>
# shape: (2, 3)
# ┌─────┬─────┬──────┐
# │ foo ┆ bar ┆ ham  │
# │ --- ┆ --- ┆ ---  │
# │ i64 ┆ i64 ┆ str  │
# ╞═════╪═════╪══════╡
# │ 1   ┆ 6   ┆ a    │
# │ 3   ┆ 8   ┆ null │
# └─────┴─────┴──────┘

Parameters:

  • subset (Object) (defaults to: nil) —

    Column name(s) for which null values are considered. If set to nil (default), use all columns.

Returns:



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# File 'lib/polars/data_frame.rb', line 2694

def drop_nulls(subset: nil)
  lazy.drop_nulls(subset: subset).collect(optimizations: QueryOptFlags._eager)
end

#dtypes ⇒ Array

Get dtypes of columns in DataFrame. Dtypes can also be found in column headers when printing the DataFrame.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6.0, 7.0, 8.0],
    "ham" => ["a", "b", "c"]
  }
)
df.dtypes
# => [Polars::Int64, Polars::Float64, Polars::String]

Returns:



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# File 'lib/polars/data_frame.rb', line 276

def dtypes
  _df.dtypes
end

#each(&block) ⇒ Object

Returns an enumerator.

Returns:



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# File 'lib/polars/data_frame.rb', line 432

def each(&block)
  get_columns.each(&block)
end

#each_row(named: true, buffer_size: 500, &block) ⇒ Object

Returns an iterator over the DataFrame of rows of Ruby-native values.

Parameters:

  • named (Boolean) (defaults to: true) —

    Return hashes instead of arrays. The hashes are a mapping of column name to row value. This is more expensive than returning an array, but allows for accessing values by column name.

  • buffer_size (Integer) (defaults to: 500) —

    Determines the number of rows that are buffered internally while iterating over the data; you should only modify this in very specific cases where the default value is determined not to be a good fit to your access pattern, as the speedup from using the buffer is significant (~2-4x). Setting this value to zero disables row buffering.

Returns:



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# File 'lib/polars/data_frame.rb', line 6216

def each_row(named: true, buffer_size: 500, &block)
  iter_rows(named: named, buffer_size: buffer_size, &block)
end

#equals(other, null_equal: true) ⇒ Boolean

Check if DataFrame is equal to other.

Examples:

df1 = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6.0, 7.0, 8.0],
    "ham" => ["a", "b", "c"]
  }
)
df2 = Polars::DataFrame.new(
  {
    "foo" => [3, 2, 1],
    "bar" => [8.0, 7.0, 6.0],
    "ham" => ["c", "b", "a"]
  }
)
df1.equals(df1)
# => true
df1.equals(df2)
# => false

Parameters:

  • other (DataFrame) —

    DataFrame to compare with.

  • null_equal (Boolean) (defaults to: true) —

    Consider null values as equal.

Returns:



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# File 'lib/polars/data_frame.rb', line 2475

def equals(other, null_equal: true)
  _df.equals(other._df, null_equal)
end

#estimated_size(unit = "b") ⇒ Numeric

Return an estimation of the total (heap) allocated size of the DataFrame.

Estimated size is given in the specified unit (bytes by default).

This estimation is the sum of the size of its buffers, validity, including nested arrays. Multiple arrays may share buffers and bitmaps. Therefore, the size of 2 arrays is not the sum of the sizes computed from this function. In particular, StructArray's size is an upper bound.

When an array is sliced, its allocated size remains constant because the buffer unchanged. However, this function will yield a smaller number. This is because this function returns the visible size of the buffer, not its total capacity.

FFI buffers are included in this estimation.

Examples:

df = Polars::DataFrame.new(
  {
    "x" => 1_000_000.times.to_a.reverse,
    "y" => 1_000_000.times.map { |v| v / 1000.0 },
    "z" => 1_000_000.times.map(&:to_s)
  },
  schema: {"x" => Polars::UInt32, "y" => Polars::Float64, "z" => Polars::String}
)
df.estimated_size
# => 25888898
df.estimated_size("mb")
# => 17.0601749420166

Parameters:

  • unit ("b", "kb", "mb", "gb", "tb") (defaults to: "b") —

    Scale the returned size to the given unit.

Returns:

  • (Numeric) 


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# File 'lib/polars/data_frame.rb', line 1563

def estimated_size(unit = "b")
  sz = _df.estimated_size
  Utils.scale_bytes(sz, to: unit)
end

#explode(columns, *more_columns, empty_as_null: true, keep_nulls: true) ⇒ DataFrame

Explode DataFrame to long format by exploding a column with Lists.

Examples:

df = Polars::DataFrame.new(
  {
    "letters" => ["a", "a", "b", "c"],
    "numbers" => [[1], [2, 3], [4, 5], [6, 7, 8]]
  }
)
df.explode("numbers")
# =>
# shape: (8, 2)
# ┌─────────┬─────────┐
# │ letters ┆ numbers │
# │ ---     ┆ ---     │
# │ str     ┆ i64     │
# ╞═════════╪═════════╡
# │ a       ┆ 1       │
# │ a       ┆ 2       │
# │ a       ┆ 3       │
# │ b       ┆ 4       │
# │ b       ┆ 5       │
# │ c       ┆ 6       │
# │ c       ┆ 7       │
# │ c       ┆ 8       │
# └─────────┴─────────┘

Parameters:

  • columns (Object) —

    Column of LargeList type.

  • more_columns (Array) —

    Additional names of columns to explode, specified as positional arguments.

  • empty_as_null (Boolean) (defaults to: true) —

    Explode an empty list/array into a null.

  • keep_nulls (Boolean) (defaults to: true) —

    Explode a null list/array into a null.

Returns:



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# File 'lib/polars/data_frame.rb', line 4495

def explode(
  columns,
  *more_columns,
  empty_as_null: true,
  keep_nulls: true
)
  lazy
  .explode(
    columns,
    *more_columns,
    empty_as_null: empty_as_null,
    keep_nulls: keep_nulls
  ).collect(optimizations: QueryOptFlags._eager)
end

#extend(other) ⇒ DataFrame

Extend the memory backed by this DataFrame with the values from other.

Different from vstack which adds the chunks from other to the chunks of this DataFrame extend appends the data from other to the underlying memory locations and thus may cause a reallocation.

If this does not cause a reallocation, the resulting data structure will not have any extra chunks and thus will yield faster queries.

Prefer extend over vstack when you want to do a query after a single append. For instance during online operations where you add n rows and rerun a query.

Prefer vstack over extend when you want to append many times before doing a query. For instance when you read in multiple files and when to store them in a single DataFrame. In the latter case, finish the sequence of vstack operations with a rechunk.

Examples:

df1 = Polars::DataFrame.new({"foo" => [1, 2, 3], "bar" => [4, 5, 6]})
df2 = Polars::DataFrame.new({"foo" => [10, 20, 30], "bar" => [40, 50, 60]})
df1.extend(df2)
# =>
# shape: (6, 2)
# ┌─────┬─────┐
# │ foo ┆ bar │
# │ --- ┆ --- │
# │ i64 ┆ i64 │
# ╞═════╪═════╡
# │ 1   ┆ 4   │
# │ 2   ┆ 5   │
# │ 3   ┆ 6   │
# │ 10  ┆ 40  │
# │ 20  ┆ 50  │
# │ 30  ┆ 60  │
# └─────┴─────┘

Parameters:

  • other (DataFrame) —

    DataFrame to vertically add.

Returns:



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# File 'lib/polars/data_frame.rb', line 4008

def extend(other)
  _df.extend(other._df)
  self
end

#fill_nan(value) ⇒ DataFrame

Note:

Note that floating point NaNs (Not a Number) are not missing values! To replace missing values, use fill_null.

Fill floating point NaN values by an Expression evaluation.

Examples:

df = Polars::DataFrame.new(
  {
    "a" => [1.5, 2, Float::NAN, 4],
    "b" => [0.5, 4, Float::NAN, 13]
  }
)
df.fill_nan(99)
# =>
# shape: (4, 2)
# ┌──────┬──────┐
# │ a    ┆ b    │
# │ ---  ┆ ---  │
# │ f64  ┆ f64  │
# ╞══════╪══════╡
# │ 1.5  ┆ 0.5  │
# │ 2.0  ┆ 4.0  │
# │ 99.0 ┆ 99.0 │
# │ 4.0  ┆ 13.0 │
# └──────┴──────┘

Parameters:

  • value (Object) —

    Value to fill NaN with.

Returns:



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# File 'lib/polars/data_frame.rb', line 4454

def fill_nan(value)
  lazy.fill_nan(value).collect(optimizations: QueryOptFlags._eager)
end

#fill_null(value = nil, strategy: nil, limit: nil, matches_supertype: true) ⇒ DataFrame

Fill null values using the specified value or strategy.

Examples:

df = Polars::DataFrame.new(
  {
    "a" => [1, 2, nil, 4],
    "b" => [0.5, 4, nil, 13]
  }
)
df.fill_null(99)
# =>
# shape: (4, 2)
# ┌─────┬──────┐
# │ a   ┆ b    │
# │ --- ┆ ---  │
# │ i64 ┆ f64  │
# ╞═════╪══════╡
# │ 1   ┆ 0.5  │
# │ 2   ┆ 4.0  │
# │ 99  ┆ 99.0 │
# │ 4   ┆ 13.0 │
# └─────┴──────┘
df.fill_null(strategy: "forward")
# =>
# shape: (4, 2)
# ┌─────┬──────┐
# │ a   ┆ b    │
# │ --- ┆ ---  │
# │ i64 ┆ f64  │
# ╞═════╪══════╡
# │ 1   ┆ 0.5  │
# │ 2   ┆ 4.0  │
# │ 2   ┆ 4.0  │
# │ 4   ┆ 13.0 │
# └─────┴──────┘
df.fill_null(strategy: "max")
# =>
# shape: (4, 2)
# ┌─────┬──────┐
# │ a   ┆ b    │
# │ --- ┆ ---  │
# │ i64 ┆ f64  │
# ╞═════╪══════╡
# │ 1   ┆ 0.5  │
# │ 2   ┆ 4.0  │
# │ 4   ┆ 13.0 │
# │ 4   ┆ 13.0 │
# └─────┴──────┘
df.fill_null(strategy: "zero")
# =>
# shape: (4, 2)
# ┌─────┬──────┐
# │ a   ┆ b    │
# │ --- ┆ ---  │
# │ i64 ┆ f64  │
# ╞═════╪══════╡
# │ 1   ┆ 0.5  │
# │ 2   ┆ 4.0  │
# │ 0   ┆ 0.0  │
# │ 4   ┆ 13.0 │
# └─────┴──────┘

Parameters:

  • value (Numeric) (defaults to: nil) —

    Value used to fill null values.

  • strategy (nil, "forward", "backward", "min", "max", "mean", "zero", "one") (defaults to: nil) —

    Strategy used to fill null values.

  • limit (Integer) (defaults to: nil) —

    Number of consecutive null values to fill when using the 'forward' or 'backward' strategy.

  • matches_supertype (Boolean) (defaults to: true) —

    Fill all matching supertype of the fill value.

Returns:



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# File 'lib/polars/data_frame.rb', line 4414

def fill_null(value = nil, strategy: nil, limit: nil, matches_supertype: true)
  _from_rbdf(
    lazy
      .fill_null(value, strategy: strategy, limit: limit, matches_supertype: matches_supertype)
      .collect(optimizations: QueryOptFlags._eager)
      ._df
  )
end

#filter(*predicates, **constraints) ⇒ DataFrame

Filter the rows in the DataFrame based on a predicate expression.

Examples:

Filter on one condition:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6, 7, 8],
    "ham" => ["a", "b", "c"]
  }
)
df.filter(Polars.col("foo") < 3)
# =>
# shape: (2, 3)
# ┌─────┬─────┬─────┐
# │ foo ┆ bar ┆ ham │
# │ --- ┆ --- ┆ --- │
# │ i64 ┆ i64 ┆ str │
# ╞═════╪═════╪═════╡
# │ 1   ┆ 6   ┆ a   │
# │ 2   ┆ 7   ┆ b   │
# └─────┴─────┴─────┘

Filter on multiple conditions:

df.filter((Polars.col("foo") < 3) & (Polars.col("ham") == "a"))
# =>
# shape: (1, 3)
# ┌─────┬─────┬─────┐
# │ foo ┆ bar ┆ ham │
# │ --- ┆ --- ┆ --- │
# │ i64 ┆ i64 ┆ str │
# ╞═════╪═════╪═════╡
# │ 1   ┆ 6   ┆ a   │
# └─────┴─────┴─────┘

Parameters:

  • predicates (Array) —

    Expression(s) that evaluate to a boolean Series.

  • constraints (Hash) —

    Column filters; use name = value to filter columns by the supplied value. Each constraint will behave the same as Polars.col(name).eq(value), and be implicitly joined with the other filter conditions using &.

Returns:



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# File 'lib/polars/data_frame.rb', line 1789

def filter(*predicates, **constraints)
  lazy.filter(*predicates, **constraints).collect(optimizations: QueryOptFlags._eager)
end

#flags ⇒ Hash

Get flags that are set on the columns of this DataFrame.

Returns:

  • (Hash) 


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# File 'lib/polars/data_frame.rb', line 283

def flags
  columns.to_h { |name| [name, self[name].flags] }
end

#fold ⇒ Series

Apply a horizontal reduction on a DataFrame.

This can be used to effectively determine aggregations on a row level, and can be applied to any DataType that can be supercasted (casted to a similar parent type).

An example of the supercast rules when applying an arithmetic operation on two DataTypes are for instance:

i8 + str = str f32 + i64 = f32 f32 + f64 = f64

Examples:

A horizontal sum operation:

df = Polars::DataFrame.new(
  {
    "a" => [2, 1, 3],
    "b" => [1, 2, 3],
    "c" => [1.0, 2.0, 3.0]
  }
)
df.fold { |s1, s2| s1 + s2 }
# =>
# shape: (3,)
# Series: 'a' [f64]
# [
#         4.0
#         5.0
#         9.0
# ]

A horizontal minimum operation:

df = Polars::DataFrame.new({"a" => [2, 1, 3], "b" => [1, 2, 3], "c" => [1.0, 2.0, 3.0]})
df.fold { |s1, s2| s1.zip_with(s1 < s2, s2) }
# =>
# shape: (3,)
# Series: 'a' [f64]
# [
#         1.0
#         1.0
#         3.0
# ]

A horizontal string concatenation:

df = Polars::DataFrame.new(
  {
    "a" => ["foo", "bar", nil],
    "b" => [1, 2, 3],
    "c" => [1.0, 2.0, 3.0]
  }
)
df.fold { |s1, s2| s1 + s2 }
# =>
# shape: (3,)
# Series: 'a' [str]
# [
#         "foo11.0"
#         "bar22.0"
#         null
# ]

A horizontal boolean or, similar to a row-wise .any:

df = Polars::DataFrame.new(
  {
    "a" => [false, false, true],
    "b" => [false, true, false]
  }
)
df.fold { |s1, s2| s1 | s2 }
# =>
# shape: (3,)
# Series: 'a' [bool]
# [
#         false
#         true
#         true
# ]

Returns:



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# File 'lib/polars/data_frame.rb', line 5946

def fold
  acc = to_series(0)

  1.upto(width - 1) do |i|
    acc = yield(acc, to_series(i))
  end
  acc
end

#gather_every(n, offset = 0) ⇒ DataFrame

Take every nth row in the DataFrame and return as a new DataFrame.

Examples:

s = Polars::DataFrame.new({"a" => [1, 2, 3, 4], "b" => [5, 6, 7, 8]})
s.gather_every(2)
# =>
# shape: (2, 2)
# ┌─────┬─────┐
# │ a   ┆ b   │
# │ --- ┆ --- │
# │ i64 ┆ i64 │
# ╞═════╪═════╡
# │ 1   ┆ 5   │
# │ 3   ┆ 7   │
# └─────┴─────┘

Returns:



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# File 'lib/polars/data_frame.rb', line 6337

def gather_every(n, offset = 0)
  select(F.col("*").gather_every(n, offset))
end

#get_column(name, default: NO_DEFAULT) ⇒ Series

Get a single column by name.

Examples:

df = Polars::DataFrame.new({"foo" => [1, 2, 3], "bar" => [4, 5, 6]})
df.get_column("foo")
# =>
# shape: (3,)
# Series: 'foo' [i64]
# [
#         1
#         2
#         3
# ]
df.get_column("baz", default: Polars::Series.new("baz", ["?", "?", "?"]))
# =>
# shape: (3,)
# Series: 'baz' [str]
# [
#         "?"
#         "?"
#         "?"
# ]

Parameters:

  • name (String) —

    Name of the column to retrieve.

  • default (Object) (defaults to: NO_DEFAULT) —

    Value to return if the column does not exist; if not explicitly set and the column is not present a ColumnNotFoundError exception is raised.

Returns:



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# File 'lib/polars/data_frame.rb', line 4328

def get_column(name, default: NO_DEFAULT)
  Utils.wrap_s(_df.get_column(name.to_s))
rescue ColumnNotFoundError
  raise if default.eql?(NO_DEFAULT)
  default
end

#get_column_index(name) ⇒ Series

Find the index of a column by name.

Examples:

df = Polars::DataFrame.new(
  {"foo" => [1, 2, 3], "bar" => [6, 7, 8], "ham" => ["a", "b", "c"]}
)
df.get_column_index("ham")
# => 2

Parameters:

  • name (String) —

    Name of the column to find.

Returns:



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# File 'lib/polars/data_frame.rb', line 2097

def get_column_index(name)
  _df.get_column_index(name)
end

#get_columns ⇒ Array

Get the DataFrame as a Array of Series.

Examples:

df = Polars::DataFrame.new({"foo" => [1, 2, 3], "bar" => [4, 5, 6]})
df.get_columns
# =>
# [shape: (3,)
# Series: 'foo' [i64]
# [
#         1
#         2
#         3
# ], shape: (3,)
# Series: 'bar' [i64]
# [
#         4
#         5
#         6
# ]]
df = Polars::DataFrame.new(
  {
    "a" => [1, 2, 3, 4],
    "b" => [0.5, 4, 10, 13],
    "c" => [true, true, false, true]
  }
)
df.get_columns
# =>
# [shape: (4,)
# Series: 'a' [i64]
# [
#         1
#         2
#         3
#         4
# ], shape: (4,)
# Series: 'b' [f64]
# [
#         0.5
#         4.0
#         10.0
#         13.0
# ], shape: (4,)
# Series: 'c' [bool]
# [
#         true
#         true
#         false
#         true
# ]]

Returns:



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# File 'lib/polars/data_frame.rb', line 4292

def get_columns
  _df.get_columns.map { |s| Utils.wrap_s(s) }
end

#glimpse(max_items_per_column: 10, max_colname_length: 50, return_type: nil) ⇒ Object

Return a dense preview of the DataFrame.

The formatting shows one line per column so that wide dataframes display cleanly. Each line shows the column name, the data type, and the first few values.

Examples:

Return the glimpse output as a DataFrame:

df = Polars::DataFrame.new(
  {
    "a" => [1.0, 2.8, 3.0],
    "b" => [4, 5, nil],
    "c" => [true, false, true],
    "d" => [nil, "b", "c"],
    "e" => ["usd", "eur", nil],
    "f" => [Date.new(2020, 1, 1), Date.new(2021, 1, 2), Date.new(2022, 1, 1)]
  }
)
df.glimpse(return_type: "frame")
# =>
# shape: (6, 3)
# ┌────────┬───────┬─────────────────────────────────┐
# │ column ┆ dtype ┆ values                          │
# │ ---    ┆ ---   ┆ ---                             │
# │ str    ┆ str   ┆ list[str]                       │
# ╞════════╪═══════╪═════════════════════════════════╡
# │ a      ┆ f64   ┆ ["1.0", "2.8", "3.0"]           │
# │ b      ┆ i64   ┆ ["4", "5", null]                │
# │ c      ┆ bool  ┆ ["true", "false", "true"]       │
# │ d      ┆ str   ┆ [null, ""b"", ""c""]            │
# │ e      ┆ str   ┆ [""usd"", ""eur"", null]        │
# │ f      ┆ date  ┆ ["2020-01-01", "2021-01-02", "… │
# └────────┴───────┴─────────────────────────────────┘

Parameters:

  • max_items_per_column (Integer) (defaults to: 10) —

    Maximum number of items to show per column.

  • max_colname_length (Integer) (defaults to: 50) —

    Maximum length of the displayed column names; values that exceed this value are truncated with a trailing ellipsis.

  • return_type (nil, 'self', 'frame', 'string') (defaults to: nil) —

    Modify the return format:

    • nil (default): Print the glimpse output to stdout, returning nil.
    • "self": Print the glimpse output to stdout, returning the original frame.
    • "frame": Return the glimpse output as a new DataFrame.
    • "string": Return the glimpse output as a string.

Returns:



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# File 'lib/polars/data_frame.rb', line 1960

def glimpse(
  max_items_per_column: 10,
  max_colname_length: 50,
  return_type: nil
)
  if return_type.nil?
    return_frame = false
  else
    return_frame = return_type == "frame"
    if !return_frame && !["self", "string"].include?(return_type)
      msg = "invalid `return_type`; found #{return_type.inspect}, expected one of 'string', 'frame', 'self', or nil"
      raise ArgumentError, msg
    end
  end

  # always print at most this number of values (mainly ensures that
  # we do not cast long arrays to strings, which would be slow)
  max_n_values = [max_items_per_column, height].min
  schema = self.schema

  _column_to_row_output = lambda do |col_name, dtype|
    fn = schema[col_name] == String ? :inspect : :to_s
    values = self[0...max_n_values, col_name].to_a
    if col_name.length > max_colname_length
      col_name = col_name[0...(max_colname_length - 1)] + "…"
    end
    dtype_str = Plr.dtype_str_repr(dtype)
    if !return_frame
      dtype_str = "<#{dtype_str}>"
    end
    [col_name, dtype_str, values.map { |v| !v.nil? ? v.send(fn) : nil }]
  end

  data = self.schema.map { |s, dtype| _column_to_row_output.(s, dtype) }

  # output one row per column
  if return_frame
    DataFrame.new(
      data,
      orient: "row",
      schema: {"column" => String, "dtype" => String, "values" => List.new(String)}
    )
  else
    raise Todo
  end
end

#group_by(*by, maintain_order: false, **named_by) ⇒ GroupBy

Start a group by operation.

Examples:

Group by one column and call agg to compute the grouped sum of another column.

df = Polars::DataFrame.new(
  {
    "a" => ["a", "b", "a", "b", "c"],
    "b" => [1, 2, 1, 3, 3],
    "c" => [5, 4, 3, 2, 1]
  }
)
df.group_by("a").agg(Polars.col("b").sum)
# =>
# shape: (3, 2)
# ┌─────┬─────┐
# │ a   ┆ b   │
# │ --- ┆ --- │
# │ str ┆ i64 │
# ╞═════╪═════╡
# │ a   ┆ 2   │
# │ b   ┆ 5   │
# │ c   ┆ 3   │
# └─────┴─────┘

Set maintain_order: true to ensure the order of the groups is consistent with the input.

df.group_by("a", maintain_order: true).agg(Polars.col("c"))
# =>
# shape: (3, 2)
# ┌─────┬───────────┐
# │ a   ┆ c         │
# │ --- ┆ ---       │
# │ str ┆ list[i64] │
# ╞═════╪═══════════╡
# │ a   ┆ [5, 3]    │
# │ b   ┆ [4, 2]    │
# │ c   ┆ [1]       │
# └─────┴───────────┘

Group by multiple columns by passing a list of column names.

df.group_by(["a", "b"]).agg(Polars.max("c"))
# =>
# shape: (4, 3)
# ┌─────┬─────┬─────┐
# │ a   ┆ b   ┆ c   │
# │ --- ┆ --- ┆ --- │
# │ str ┆ i64 ┆ i64 │
# ╞═════╪═════╪═════╡
# │ a   ┆ 1   ┆ 5   │
# │ b   ┆ 2   ┆ 4   │
# │ b   ┆ 3   ┆ 2   │
# │ c   ┆ 3   ┆ 1   │
# └─────┴─────┴─────┘

Or use positional arguments to group by multiple columns in the same way. Expressions are also accepted.

df.group_by("a", Polars.col("b") / 2).agg(Polars.col("c").mean)
# =>
# shape: (3, 3)
# ┌─────┬─────┬─────┐
# │ a   ┆ b   ┆ c   │
# │ --- ┆ --- ┆ --- │
# │ str ┆ i64 ┆ f64 │
# ╞═════╪═════╪═════╡
# │ a   ┆ 0   ┆ 4.0 │
# │ b   ┆ 1   ┆ 3.0 │
# │ c   ┆ 1   ┆ 1.0 │
# └─────┴─────┴─────┘

The GroupBy object returned by this method is iterable, returning the name and data of each group.

df.group_by("a").each do |name, data|
   # ...
end

Parameters:

  • by (Array) —

    Column(s) to group by.

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

    Make sure that the order of the groups remain consistent. This is more expensive than a default group by. Note that this only works in expression aggregations.

  • named_by (Hash) —

    Additional columns to group by, specified as keyword arguments. The columns will be renamed to the keyword used.

Returns:



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# File 'lib/polars/data_frame.rb', line 2938

def group_by(*by, maintain_order: false, **named_by)
  named_by.each do |_, value|
    if !(value.is_a?(::String) || value.is_a?(Expr) || value.is_a?(Series))
      msg = "Expected Polars expression or object convertible to one, got #{value.class.name}."
      raise TypeError, msg
    end
  end
  GroupBy.new(
    self,
    *by,
    **named_by,
    maintain_order: maintain_order,
    predicates: nil
  )
end

#group_by_dynamic(index_column, every:, period: nil, offset: nil, include_boundaries: false, closed: "left", label: "left", group_by: nil, start_by: "window") ⇒ DataFrame

Group based on a time value (or index value of type Int32, Int64).

Time windows are calculated and rows are assigned to windows. Different from a normal group by is that a row can be member of multiple groups. The time/index window could be seen as a rolling window, with a window size determined by dates/times/values instead of slots in the DataFrame.

A window is defined by:

  • every: interval of the window
  • period: length of the window
  • offset: offset of the window

The every, period and offset arguments are created with the following string language:

  • 1ns (1 nanosecond)
  • 1us (1 microsecond)
  • 1ms (1 millisecond)
  • 1s (1 second)
  • 1m (1 minute)
  • 1h (1 hour)
  • 1d (1 day)
  • 1w (1 week)
  • 1mo (1 calendar month)
  • 1y (1 calendar year)
  • 1i (1 index count)

Or combine them: "3d12h4m25s" # 3 days, 12 hours, 4 minutes, and 25 seconds

In case of a group_by_dynamic on an integer column, the windows are defined by:

  • "1i" # length 1
  • "10i" # length 10

Examples:

df = Polars::DataFrame.new(
  {
    "time" => Polars.datetime_range(
      DateTime.new(2021, 12, 16),
      DateTime.new(2021, 12, 16, 3),
      "30m",
      time_unit: "us",
      eager: true
    ),
    "n" => 0..6
  }
)
# =>
# shape: (7, 2)
# ┌─────────────────────┬─────┐
# │ time                ┆ n   │
# │ ---                 ┆ --- │
# │ datetime[μs]        ┆ i64 │
# ╞═════════════════════╪═════╡
# │ 2021-12-16 00:00:00 ┆ 0   │
# │ 2021-12-16 00:30:00 ┆ 1   │
# │ 2021-12-16 01:00:00 ┆ 2   │
# │ 2021-12-16 01:30:00 ┆ 3   │
# │ 2021-12-16 02:00:00 ┆ 4   │
# │ 2021-12-16 02:30:00 ┆ 5   │
# │ 2021-12-16 03:00:00 ┆ 6   │
# └─────────────────────┴─────┘

Group by windows of 1 hour starting at 2021-12-16 00:00:00.

df.group_by_dynamic("time", every: "1h", closed: "right").agg(
  [
    Polars.col("time").min.alias("time_min"),
    Polars.col("time").max.alias("time_max")
  ]
)
# =>
# shape: (4, 3)
# ┌─────────────────────┬─────────────────────┬─────────────────────┐
# │ time                ┆ time_min            ┆ time_max            │
# │ ---                 ┆ ---                 ┆ ---                 │
# │ datetime[μs]        ┆ datetime[μs]        ┆ datetime[μs]        │
# ╞═════════════════════╪═════════════════════╪═════════════════════╡
# │ 2021-12-15 23:00:00 ┆ 2021-12-16 00:00:00 ┆ 2021-12-16 00:00:00 │
# │ 2021-12-16 00:00:00 ┆ 2021-12-16 00:30:00 ┆ 2021-12-16 01:00:00 │
# │ 2021-12-16 01:00:00 ┆ 2021-12-16 01:30:00 ┆ 2021-12-16 02:00:00 │
# │ 2021-12-16 02:00:00 ┆ 2021-12-16 02:30:00 ┆ 2021-12-16 03:00:00 │
# └─────────────────────┴─────────────────────┴─────────────────────┘

The window boundaries can also be added to the aggregation result.

df.group_by_dynamic(
  "time", every: "1h", include_boundaries: true, closed: "right"
).agg([Polars.col("time").count.alias("time_count")])
# =>
# shape: (4, 4)
# ┌─────────────────────┬─────────────────────┬─────────────────────┬────────────┐
# │ _lower_boundary     ┆ _upper_boundary     ┆ time                ┆ time_count │
# │ ---                 ┆ ---                 ┆ ---                 ┆ ---        │
# │ datetime[μs]        ┆ datetime[μs]        ┆ datetime[μs]        ┆ u32        │
# ╞═════════════════════╪═════════════════════╪═════════════════════╪════════════╡
# │ 2021-12-15 23:00:00 ┆ 2021-12-16 00:00:00 ┆ 2021-12-15 23:00:00 ┆ 1          │
# │ 2021-12-16 00:00:00 ┆ 2021-12-16 01:00:00 ┆ 2021-12-16 00:00:00 ┆ 2          │
# │ 2021-12-16 01:00:00 ┆ 2021-12-16 02:00:00 ┆ 2021-12-16 01:00:00 ┆ 2          │
# │ 2021-12-16 02:00:00 ┆ 2021-12-16 03:00:00 ┆ 2021-12-16 02:00:00 ┆ 2          │
# └─────────────────────┴─────────────────────┴─────────────────────┴────────────┘

When closed="left", should not include right end of interval.

df.group_by_dynamic("time", every: "1h", closed: "left").agg(
  [
    Polars.col("time").count.alias("time_count"),
    Polars.col("time").alias("time_agg_list")
  ]
)
# =>
# shape: (4, 3)
# ┌─────────────────────┬────────────┬─────────────────────────────────┐
# │ time                ┆ time_count ┆ time_agg_list                   │
# │ ---                 ┆ ---        ┆ ---                             │
# │ datetime[μs]        ┆ u32        ┆ list[datetime[μs]]              │
# ╞═════════════════════╪════════════╪═════════════════════════════════╡
# │ 2021-12-16 00:00:00 ┆ 2          ┆ [2021-12-16 00:00:00, 2021-12-… │
# │ 2021-12-16 01:00:00 ┆ 2          ┆ [2021-12-16 01:00:00, 2021-12-… │
# │ 2021-12-16 02:00:00 ┆ 2          ┆ [2021-12-16 02:00:00, 2021-12-… │
# │ 2021-12-16 03:00:00 ┆ 1          ┆ [2021-12-16 03:00:00]           │
# └─────────────────────┴────────────┴─────────────────────────────────┘

When closed="both" the time values at the window boundaries belong to 2 groups.

df.group_by_dynamic("time", every: "1h", closed: "both").agg(
  [Polars.col("time").count.alias("time_count")]
)
# =>
# shape: (5, 2)
# ┌─────────────────────┬────────────┐
# │ time                ┆ time_count │
# │ ---                 ┆ ---        │
# │ datetime[μs]        ┆ u32        │
# ╞═════════════════════╪════════════╡
# │ 2021-12-15 23:00:00 ┆ 1          │
# │ 2021-12-16 00:00:00 ┆ 3          │
# │ 2021-12-16 01:00:00 ┆ 3          │
# │ 2021-12-16 02:00:00 ┆ 3          │
# │ 2021-12-16 03:00:00 ┆ 1          │
# └─────────────────────┴────────────┘

Dynamic group bys can also be combined with grouping on normal keys.

df = Polars::DataFrame.new(
  {
    "time" => Polars.datetime_range(
      DateTime.new(2021, 12, 16),
      DateTime.new(2021, 12, 16, 3),
      "30m",
      time_unit: "us",
      eager: true
    ),
    "groups" => ["a", "a", "a", "b", "b", "a", "a"]
  }
)
df.group_by_dynamic(
  "time",
  every: "1h",
  closed: "both",
  group_by: "groups",
  include_boundaries: true
).agg([Polars.col("time").count.alias("time_count")])
# =>
# shape: (7, 5)
# ┌────────┬─────────────────────┬─────────────────────┬─────────────────────┬────────────┐
# │ groups ┆ _lower_boundary     ┆ _upper_boundary     ┆ time                ┆ time_count │
# │ ---    ┆ ---                 ┆ ---                 ┆ ---                 ┆ ---        │
# │ str    ┆ datetime[μs]        ┆ datetime[μs]        ┆ datetime[μs]        ┆ u32        │
# ╞════════╪═════════════════════╪═════════════════════╪═════════════════════╪════════════╡
# │ a      ┆ 2021-12-15 23:00:00 ┆ 2021-12-16 00:00:00 ┆ 2021-12-15 23:00:00 ┆ 1          │
# │ a      ┆ 2021-12-16 00:00:00 ┆ 2021-12-16 01:00:00 ┆ 2021-12-16 00:00:00 ┆ 3          │
# │ a      ┆ 2021-12-16 01:00:00 ┆ 2021-12-16 02:00:00 ┆ 2021-12-16 01:00:00 ┆ 1          │
# │ a      ┆ 2021-12-16 02:00:00 ┆ 2021-12-16 03:00:00 ┆ 2021-12-16 02:00:00 ┆ 2          │
# │ a      ┆ 2021-12-16 03:00:00 ┆ 2021-12-16 04:00:00 ┆ 2021-12-16 03:00:00 ┆ 1          │
# │ b      ┆ 2021-12-16 01:00:00 ┆ 2021-12-16 02:00:00 ┆ 2021-12-16 01:00:00 ┆ 2          │
# │ b      ┆ 2021-12-16 02:00:00 ┆ 2021-12-16 03:00:00 ┆ 2021-12-16 02:00:00 ┆ 1          │
# └────────┴─────────────────────┴─────────────────────┴─────────────────────┴────────────┘

Dynamic group by on an index column.

df = Polars::DataFrame.new(
  {
    "idx" => Polars.arange(0, 6, eager: true),
    "A" => ["A", "A", "B", "B", "B", "C"]
  }
)
df.group_by_dynamic(
  "idx",
  every: "2i",
  period: "3i",
  include_boundaries: true,
  closed: "right"
).agg(Polars.col("A").alias("A_agg_list"))
# =>
# shape: (4, 4)
# ┌─────────────────┬─────────────────┬─────┬─────────────────┐
# │ _lower_boundary ┆ _upper_boundary ┆ idx ┆ A_agg_list      │
# │ ---             ┆ ---             ┆ --- ┆ ---             │
# │ i64             ┆ i64             ┆ i64 ┆ list[str]       │
# ╞═════════════════╪═════════════════╪═════╪═════════════════╡
# │ -2              ┆ 1               ┆ -2  ┆ ["A", "A"]      │
# │ 0               ┆ 3               ┆ 0   ┆ ["A", "B", "B"] │
# │ 2               ┆ 5               ┆ 2   ┆ ["B", "B", "C"] │
# │ 4               ┆ 7               ┆ 4   ┆ ["C"]           │
# └─────────────────┴─────────────────┴─────┴─────────────────┘

Parameters:

  • index_column —

    Column used to group based on the time window. Often to type Date/Datetime This column must be sorted in ascending order. If not the output will not make sense.

    In case of a dynamic group by on indices, dtype needs to be one of \{Int32, Int64}. Note that Int32 gets temporarily cast to Int64, so if performance matters use an Int64 column.

  • every —

    Interval of the window.

  • period (defaults to: nil) —

    Length of the window, if nil it is equal to 'every'.

  • offset (defaults to: nil) —

    Offset of the window if nil and period is nil it will be equal to negative every.

  • include_boundaries (defaults to: false) —

    Add the lower and upper bound of the window to the "_lower_bound" and "_upper_bound" columns. This will impact performance because it's harder to parallelize

  • closed ("right", "left", "both", "none") (defaults to: "left") —

    Define whether the temporal window interval is closed or not.

  • label ('left', 'right', 'datapoint') (defaults to: "left") —

    Define which label to use for the window:

    • 'left': lower boundary of the window
    • 'right': upper boundary of the window
    • 'datapoint': the first value of the index column in the given window. If you don't need the label to be at one of the boundaries, choose this option for maximum performance
  • group_by (defaults to: nil) —

    Also group by this column/these columns

  • start_by ('window', 'datapoint', 'monday', 'tuesday', 'wednesday', 'thursday', 'friday', 'saturday', 'sunday') (defaults to: "window") —

    The strategy to determine the start of the first window by.

    • 'window': Start by taking the earliest timestamp, truncating it with every, and then adding offset. Note that weekly windows start on Monday.
    • 'datapoint': Start from the first encountered data point.

    • a day of the week (only takes effect if every contains 'w'):

      • 'monday': Start the window on the Monday before the first data point.
      • 'tuesday': Start the window on the Tuesday before the first data point.
      • ...
      • 'sunday': Start the window on the Sunday before the first data point.

    The resulting window is then shifted back until the earliest datapoint is in or in front of it.

Returns:



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# File 'lib/polars/data_frame.rb', line 3299

def group_by_dynamic(
  index_column,
  every:,
  period: nil,
  offset: nil,
  include_boundaries: false,
  closed: "left",
  label: "left",
  group_by: nil,
  start_by: "window"
)
  DynamicGroupBy.new(
    self,
    index_column,
    every,
    period,
    offset,
    include_boundaries,
    closed,
    label,
    group_by,
    start_by,
    nil
  )
end

#hash_rows(seed: 0, seed_1: nil, seed_2: nil, seed_3: nil) ⇒ Series

Hash and combine the rows in this DataFrame.

The hash value is of type UInt64.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, nil, 3, 4],
    "ham" => ["a", "b", nil, "d"]
  }
)
df.hash_rows(seed: 42)
# =>
# shape: (4,)
# Series: '' [u64]
# [
#         4238614331852490969
#         17976148875586754089
#         4702262519505526977
#         18144177983981041107
# ]

Parameters:

  • seed (Integer) (defaults to: 0) —

    Random seed parameter. Defaults to 0.

  • seed_1 (Integer) (defaults to: nil) —

    Random seed parameter. Defaults to seed if not set.

  • seed_2 (Integer) (defaults to: nil) —

    Random seed parameter. Defaults to seed if not set.

  • seed_3 (Integer) (defaults to: nil) —

    Random seed parameter. Defaults to seed if not set.

Returns:



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# File 'lib/polars/data_frame.rb', line 6373

def hash_rows(seed: 0, seed_1: nil, seed_2: nil, seed_3: nil)
  k0 = seed
  k1 = seed_1.nil? ? seed : seed_1
  k2 = seed_2.nil? ? seed : seed_2
  k3 = seed_3.nil? ? seed : seed_3
  Utils.wrap_s(_df.hash_rows(k0, k1, k2, k3))
end

#head(n = 5) ⇒ DataFrame

Get the first n rows.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3, 4, 5],
    "bar" => [6, 7, 8, 9, 10],
    "ham" => ["a", "b", "c", "d", "e"]
  }
)
df.head(3)
# =>
# shape: (3, 3)
# ┌─────┬─────┬─────┐
# │ foo ┆ bar ┆ ham │
# │ --- ┆ --- ┆ --- │
# │ i64 ┆ i64 ┆ str │
# ╞═════╪═════╪═════╡
# │ 1   ┆ 6   ┆ a   │
# │ 2   ┆ 7   ┆ b   │
# │ 3   ┆ 8   ┆ c   │
# └─────┴─────┴─────┘

Parameters:

  • n (Integer) (defaults to: 5) —

    Number of rows to return.

Returns:



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# File 'lib/polars/data_frame.rb', line 2572

def head(n = 5)
  _from_rbdf(_df.head(n))
end

#height ⇒ Integer Also known as: count, length, size

Get the height of the DataFrame.

Examples:

df = Polars::DataFrame.new({"foo" => [1, 2, 3, 4, 5]})
df.height
# => 5

Returns:

  • (Integer) 


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# File 'lib/polars/data_frame.rb', line 192

def height
  _df.height
end

#hstack(columns, in_place: false) ⇒ DataFrame

Return a new DataFrame grown horizontally by stacking multiple Series to it.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6, 7, 8],
    "ham" => ["a", "b", "c"]
  }
)
x = Polars::Series.new("apple", [10, 20, 30])
df.hstack([x])
# =>
# shape: (3, 4)
# ┌─────┬─────┬─────┬───────┐
# │ foo ┆ bar ┆ ham ┆ apple │
# │ --- ┆ --- ┆ --- ┆ ---   │
# │ i64 ┆ i64 ┆ str ┆ i64   │
# ╞═════╪═════╪═════╪═══════╡
# │ 1   ┆ 6   ┆ a   ┆ 10    │
# │ 2   ┆ 7   ┆ b   ┆ 20    │
# │ 3   ┆ 8   ┆ c   ┆ 30    │
# └─────┴─────┴─────┴───────┘

Parameters:

  • columns (Object) —

    Series to stack.

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

    Modify in place.

Returns:



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# File 'lib/polars/data_frame.rb', line 3910

def hstack(columns, in_place: false)
  if !columns.is_a?(::Array)
    columns = columns.get_columns
  end
  if in_place
    _df.hstack_mut(columns.map(&:_s))
    self
  else
    _from_rbdf(_df.hstack(columns.map(&:_s)))
  end
end

#include?(name) ⇒ Boolean

Check if DataFrame includes column.

Returns:



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# File 'lib/polars/data_frame.rb', line 425

def include?(name)
  columns.include?(name)
end

#insert_column(index, column) ⇒ DataFrame

Insert a Series at a certain column index. This operation is in place.

Examples:

df = Polars::DataFrame.new({"foo" => [1, 2, 3], "bar" => [4, 5, 6]})
s = Polars::Series.new("baz", [97, 98, 99])
df.insert_column(1, s)
# =>
# shape: (3, 3)
# ┌─────┬─────┬─────┐
# │ foo ┆ baz ┆ bar │
# │ --- ┆ --- ┆ --- │
# │ i64 ┆ i64 ┆ i64 │
# ╞═════╪═════╪═════╡
# │ 1   ┆ 97  ┆ 4   │
# │ 2   ┆ 98  ┆ 5   │
# │ 3   ┆ 99  ┆ 6   │
# └─────┴─────┴─────┘
df = Polars::DataFrame.new(
  {
    "a" => [1, 2, 3, 4],
    "b" => [0.5, 4, 10, 13],
    "c" => [true, true, false, true]
  }
)
s = Polars::Series.new("d", [-2.5, 15, 20.5, 0])
df.insert_column(3, s)
# =>
# shape: (4, 4)
# ┌─────┬──────┬───────┬──────┐
# │ a   ┆ b    ┆ c     ┆ d    │
# │ --- ┆ ---  ┆ ---   ┆ ---  │
# │ i64 ┆ f64  ┆ bool  ┆ f64  │
# ╞═════╪══════╪═══════╪══════╡
# │ 1   ┆ 0.5  ┆ true  ┆ -2.5 │
# │ 2   ┆ 4.0  ┆ true  ┆ 15.0 │
# │ 3   ┆ 10.0 ┆ false ┆ 20.5 │
# │ 4   ┆ 13.0 ┆ true  ┆ 0.0  │
# └─────┴──────┴───────┴──────┘

Parameters:

  • index (Integer) —

    Column to insert the new Series column.

  • column (Series) —

    Series to insert.

Returns:



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# File 'lib/polars/data_frame.rb', line 1739

def insert_column(index, column)
  if index < 0
    index = width + index
  end
  _df.insert_column(index, column._s)
  self
end

#interpolate ⇒ DataFrame

Interpolate intermediate values. The interpolation method is linear.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, nil, 9, 10],
    "bar" => [6, 7, 9, nil],
    "baz" => [1, nil, nil, 9]
  }
)
df.interpolate
# =>
# shape: (4, 3)
# ┌──────┬──────┬──────────┐
# │ foo  ┆ bar  ┆ baz      │
# │ ---  ┆ ---  ┆ ---      │
# │ f64  ┆ f64  ┆ f64      │
# ╞══════╪══════╪══════════╡
# │ 1.0  ┆ 6.0  ┆ 1.0      │
# │ 5.0  ┆ 7.0  ┆ 3.666667 │
# │ 9.0  ┆ 9.0  ┆ 6.333333 │
# │ 10.0 ┆ null ┆ 9.0      │
# └──────┴──────┴──────────┘

Returns:



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# File 'lib/polars/data_frame.rb', line 6406

def interpolate
  select(F.col("*").interpolate)
end

#is_duplicated ⇒ Series

Get a mask of all duplicated rows in this DataFrame.

Examples:

df = Polars::DataFrame.new(
  {
    "a" => [1, 2, 3, 1],
    "b" => ["x", "y", "z", "x"],
  }
)
df.is_duplicated
# =>
# shape: (4,)
# Series: '' [bool]
# [
#         true
#         false
#         false
#         true
# ]

Returns:



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# File 'lib/polars/data_frame.rb', line 4929

def is_duplicated
  Utils.wrap_s(_df.is_duplicated)
end

#is_empty ⇒ Boolean Also known as: empty?

Check if the dataframe is empty.

Examples:

df = Polars::DataFrame.new({"foo" => [1, 2, 3], "bar" => [4, 5, 6]})
df.is_empty
# => false
df.filter(Polars.col("foo") > 99).is_empty
# => true

Returns:



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# File 'lib/polars/data_frame.rb', line 6420

def is_empty
  height == 0
end

#is_unique ⇒ Series

Get a mask of all unique rows in this DataFrame.

Examples:

df = Polars::DataFrame.new(
  {
    "a" => [1, 2, 3, 1],
    "b" => ["x", "y", "z", "x"]
  }
)
df.is_unique
# =>
# shape: (4,)
# Series: '' [bool]
# [
#         false
#         true
#         true
#         false
# ]

Returns:



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# File 'lib/polars/data_frame.rb', line 4954

def is_unique
  Utils.wrap_s(_df.is_unique)
end

#item(row = nil, column = nil) ⇒ Object

Note:

If row/col not provided, this is equivalent to df[0,0], with a check that the shape is (1,1). With row/col, this is equivalent to df[row,col].

Return the DataFrame as a scalar, or return the element at the given row/column.

Examples:

df = Polars::DataFrame.new({"a" => [1, 2, 3], "b" => [4, 5, 6]})
df.select((Polars.col("a") * Polars.col("b")).sum).item
# => 32
df.item(1, 1)
# => 5
df.item(2, "b")
# => 6

Parameters:

  • row (Integer) (defaults to: nil) —

    Optional row index.

  • column (Integer, String) (defaults to: nil) —

    Optional column index or name.

Returns:



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# File 'lib/polars/data_frame.rb', line 748

def item(row = nil, column = nil)
  if row.nil? && column.nil?
    if shape != [1, 1]
      msg = (
        "can only call `.item()` if the dataframe is of shape (1, 1)," +
        " or if explicit row/col values are provided;" +
        " frame has shape #{shape.inspect}"
      )
      raise ArgumentError, msg
    end
    return _df.to_series(0).get_index(0)

  elsif row.nil? || column.nil?
    msg = "cannot call `.item()` with only one of `row` or `column`"
    raise ArgumentError, msg
  end

  s =
    if column.is_a?(Integer)
      _df.to_series(column)
    else
      _df.get_column(column)
    end
  s.get_index_signed(row)
end

#iter_columns ⇒ Object

Note:

Consider whether you can use all instead. If you can, it will be more efficient.

Returns an iterator over the columns of this DataFrame.

Examples:

df = Polars::DataFrame.new(
  {
    "a" => [1, 3, 5],
    "b" => [2, 4, 6]
  }
)
df.iter_columns.map { |s| s.name }
# => ["a", "b"]

If you're using this to modify a dataframe's columns, e.g.

# Do NOT do this
Polars::DataFrame.new(df.iter_columns.map { |column| column * 2 })
# =>
# shape: (3, 2)
# ┌─────┬─────┐
# │ a   ┆ b   │
# │ --- ┆ --- │
# │ i64 ┆ i64 │
# ╞═════╪═════╡
# │ 2   ┆ 4   │
# │ 6   ┆ 8   │
# │ 10  ┆ 12  │
# └─────┴─────┘

then consider whether you can use all instead:

df.select(Polars.all * 2)
# =>
# shape: (3, 2)
# ┌─────┬─────┐
# │ a   ┆ b   │
# │ --- ┆ --- │
# │ i64 ┆ i64 │
# ╞═════╪═════╡
# │ 2   ┆ 4   │
# │ 6   ┆ 8   │
# │ 10  ┆ 12  │
# └─────┴─────┘

Returns:



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# File 'lib/polars/data_frame.rb', line 6266

def iter_columns
  return to_enum(:iter_columns) unless block_given?

  _df.get_columns.each do |s|
    yield Utils.wrap_s(s)
  end
end

#iter_rows(named: false, buffer_size: 512, &block) ⇒ Object

Returns an iterator over the DataFrame of rows of Ruby-native values.

Examples:

df = Polars::DataFrame.new(
  {
    "a" => [1, 3, 5],
    "b" => [2, 4, 6]
  }
)
df.iter_rows.map { |row| row[0] }
# => [1, 3, 5]
df.iter_rows(named: true).map { |row| row["b"] }
# => [2, 4, 6]

Parameters:

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

    Return hashes instead of arrays. The hashes are a mapping of column name to row value. This is more expensive than returning an array, but allows for accessing values by column name.

  • buffer_size (Integer) (defaults to: 512) —

    Determines the number of rows that are buffered internally while iterating over the data; you should only modify this in very specific cases where the default value is determined not to be a good fit to your access pattern, as the speedup from using the buffer is significant (~2-4x). Setting this value to zero disables row buffering.

Returns:



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# File 'lib/polars/data_frame.rb', line 6169

def iter_rows(named: false, buffer_size: 512, &block)
  return to_enum(:iter_rows, named: named, buffer_size: buffer_size) unless block_given?

  # load into the local namespace for a modest performance boost in the hot loops
  columns = self.columns

  # note: buffering rows results in a 2-4x speedup over individual calls
  # to ".row(i)", so it should only be disabled in extremely specific cases.
  if buffer_size
    offset = 0
    while offset < height
      zerocopy_slice = slice(offset, buffer_size)
      rows_chunk = zerocopy_slice.rows(named: false)
      if named
        rows_chunk.each do |row|
          yield columns.zip(row).to_h
        end
      else
        rows_chunk.each(&block)
      end
      offset += buffer_size
    end
  elsif named
    height.times do |i|
      yield columns.zip(row(i)).to_h
    end
  else
    height.times do |i|
      yield row(i)
    end
  end
end

#iter_slices(n_rows: 10_000) ⇒ Object

Returns a non-copying iterator of slices over the underlying DataFrame.

Examples:

df = Polars::DataFrame.new(
  {
    "a" => 0...17_500,
    "b" => Date.new(2023, 1, 1),
    "c" => "klmnoopqrstuvwxyz"
  },
  schema_overrides: {"a" => Polars::Int32}
)
df.iter_slices.map.with_index do |frame, idx|
  "#{frame.class.name}:[#{idx}]:#{frame.length}"
end
# => ["Polars::DataFrame:[0]:10000", "Polars::DataFrame:[1]:7500"]

Parameters:

  • n_rows (Integer) (defaults to: 10_000) —

    Determines the number of rows contained in each DataFrame slice.

Returns:



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# File 'lib/polars/data_frame.rb', line 6294

def iter_slices(n_rows: 10_000)
  return to_enum(:iter_slices, n_rows: n_rows) unless block_given?

  offset = 0
  while offset < height
    yield slice(offset, n_rows)
    offset += n_rows
  end
end

#join(other, left_on: nil, right_on: nil, on: nil, how: "inner", suffix: "_right", validate: "m:m", nulls_equal: false, coalesce: nil, maintain_order: nil) ⇒ DataFrame

Join in SQL-like fashion.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6.0, 7.0, 8.0],
    "ham" => ["a", "b", "c"]
  }
)
other_df = Polars::DataFrame.new(
  {
    "apple" => ["x", "y", "z"],
    "ham" => ["a", "b", "d"]
  }
)
df.join(other_df, on: "ham")
# =>
# shape: (2, 4)
# ┌─────┬─────┬─────┬───────┐
# │ foo ┆ bar ┆ ham ┆ apple │
# │ --- ┆ --- ┆ --- ┆ ---   │
# │ i64 ┆ f64 ┆ str ┆ str   │
# ╞═════╪═════╪═════╪═══════╡
# │ 1   ┆ 6.0 ┆ a   ┆ x     │
# │ 2   ┆ 7.0 ┆ b   ┆ y     │
# └─────┴─────┴─────┴───────┘
df.join(other_df, on: "ham", how: "full")
# =>
# shape: (4, 5)
# ┌──────┬──────┬──────┬───────┬───────────┐
# │ foo  ┆ bar  ┆ ham  ┆ apple ┆ ham_right │
# │ ---  ┆ ---  ┆ ---  ┆ ---   ┆ ---       │
# │ i64  ┆ f64  ┆ str  ┆ str   ┆ str       │
# ╞══════╪══════╪══════╪═══════╪═══════════╡
# │ 1    ┆ 6.0  ┆ a    ┆ x     ┆ a         │
# │ 2    ┆ 7.0  ┆ b    ┆ y     ┆ b         │
# │ null ┆ null ┆ null ┆ z     ┆ d         │
# │ 3    ┆ 8.0  ┆ c    ┆ null  ┆ null      │
# └──────┴──────┴──────┴───────┴───────────┘
df.join(other_df, on: "ham", how: "left")
# =>
# shape: (3, 4)
# ┌─────┬─────┬─────┬───────┐
# │ foo ┆ bar ┆ ham ┆ apple │
# │ --- ┆ --- ┆ --- ┆ ---   │
# │ i64 ┆ f64 ┆ str ┆ str   │
# ╞═════╪═════╪═════╪═══════╡
# │ 1   ┆ 6.0 ┆ a   ┆ x     │
# │ 2   ┆ 7.0 ┆ b   ┆ y     │
# │ 3   ┆ 8.0 ┆ c   ┆ null  │
# └─────┴─────┴─────┴───────┘
df.join(other_df, on: "ham", how: "semi")
# =>
# shape: (2, 3)
# ┌─────┬─────┬─────┐
# │ foo ┆ bar ┆ ham │
# │ --- ┆ --- ┆ --- │
# │ i64 ┆ f64 ┆ str │
# ╞═════╪═════╪═════╡
# │ 1   ┆ 6.0 ┆ a   │
# │ 2   ┆ 7.0 ┆ b   │
# └─────┴─────┴─────┘
df.join(other_df, on: "ham", how: "anti")
# =>
# shape: (1, 3)
# ┌─────┬─────┬─────┐
# │ foo ┆ bar ┆ ham │
# │ --- ┆ --- ┆ --- │
# │ i64 ┆ f64 ┆ str │
# ╞═════╪═════╪═════╡
# │ 3   ┆ 8.0 ┆ c   │
# └─────┴─────┴─────┘

Parameters:

  • other (DataFrame) —

    DataFrame to join with.

  • left_on (Object) (defaults to: nil) —

    Name(s) of the left join column(s).

  • right_on (Object) (defaults to: nil) —

    Name(s) of the right join column(s).

  • on (Object) (defaults to: nil) —

    Name(s) of the join columns in both DataFrames.

  • how ("inner", "left", "full", "semi", "anti", "cross") (defaults to: "inner") —

    Join strategy.

  • suffix (String) (defaults to: "_right") —

    Suffix to append to columns with a duplicate name.

  • validate ('m:m', 'm:1', '1:m', '1:1') (defaults to: "m:m") —

    Checks if join is of specified type.

    • many_to_many - “m:m”: default, does not result in checks
    • one_to_one - “1:1”: check if join keys are unique in both left and right datasets
    • one_to_many - “1:m”: check if join keys are unique in left dataset
    • many_to_one - “m:1”: check if join keys are unique in right dataset
  • nulls_equal (Boolean) (defaults to: false) —

    Join on null values. By default null values will never produce matches.

  • coalesce (Boolean) (defaults to: nil) —

    Coalescing behavior (merging of join columns).

    • nil: -> join specific.
    • true: -> Always coalesce join columns.
    • false: -> Never coalesce join columns. Note that joining on any other expressions than col will turn off coalescing.
  • maintain_order ('none', 'left', 'right', 'left_right', 'right_left') (defaults to: nil) —

    Which DataFrame row order to preserve, if any. Do not rely on any observed ordering without explicitly setting this parameter, as your code may break in a future release. Not specifying any ordering can improve performance Supported for inner, left, right and full joins

    • none No specific ordering is desired. The ordering might differ across Polars versions or even between different runs.
    • left Preserves the order of the left DataFrame.
    • right Preserves the order of the right DataFrame.
    • left_right First preserves the order of the left DataFrame, then the right.
    • right_left First preserves the order of the right DataFrame, then the left.

Returns:



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# File 'lib/polars/data_frame.rb', line 3688

def join(
  other,
  left_on: nil,
  right_on: nil,
  on: nil,
  how: "inner",
  suffix: "_right",
  validate: "m:m",
  nulls_equal: false,
  coalesce: nil,
  maintain_order: nil
)
  lazy
    .join(
      other.lazy,
      left_on: left_on,
      right_on: right_on,
      on: on,
      how: how,
      suffix: suffix,
      validate: validate,
      nulls_equal: nulls_equal,
      coalesce: coalesce,
      maintain_order: maintain_order
    )
    .collect(optimizations: QueryOptFlags._eager)
end

#join_asof(other, left_on: nil, right_on: nil, on: nil, by_left: nil, by_right: nil, by: nil, strategy: "backward", suffix: "_right", tolerance: nil, allow_parallel: true, force_parallel: false, coalesce: true, allow_exact_matches: true, check_sortedness: true) ⇒ DataFrame

Perform an asof join.

This is similar to a left-join except that we match on nearest key rather than equal keys.

Both DataFrames must be sorted by the asof_join key.

For each row in the left DataFrame:

  • A "backward" search selects the last row in the right DataFrame whose 'on' key is less than or equal to the left's key.
  • A "forward" search selects the first row in the right DataFrame whose 'on' key is greater than or equal to the left's key.

The default is "backward".

Examples:

gdp = Polars::DataFrame.new(
  {
    "date" => [
      DateTime.new(2016, 1, 1),
      DateTime.new(2017, 1, 1),
      DateTime.new(2018, 1, 1),
      DateTime.new(2019, 1, 1),
    ],  # note record date: Jan 1st (sorted!)
    "gdp" => [4164, 4411, 4566, 4696]
  }
).set_sorted("date")
population = Polars::DataFrame.new(
  {
    "date" => [
      DateTime.new(2016, 5, 12),
      DateTime.new(2017, 5, 12),
      DateTime.new(2018, 5, 12),
      DateTime.new(2019, 5, 12),
    ],  # note record date: May 12th (sorted!)
    "population" => [82.19, 82.66, 83.12, 83.52]
  }
).set_sorted("date")
population.join_asof(
  gdp, left_on: "date", right_on: "date", strategy: "backward"
)
# =>
# shape: (4, 3)
# ┌─────────────────────┬────────────┬──────┐
# │ date                ┆ population ┆ gdp  │
# │ ---                 ┆ ---        ┆ ---  │
# │ datetime[ns]        ┆ f64        ┆ i64  │
# ╞═════════════════════╪════════════╪══════╡
# │ 2016-05-12 00:00:00 ┆ 82.19      ┆ 4164 │
# │ 2017-05-12 00:00:00 ┆ 82.66      ┆ 4411 │
# │ 2018-05-12 00:00:00 ┆ 83.12      ┆ 4566 │
# │ 2019-05-12 00:00:00 ┆ 83.52      ┆ 4696 │
# └─────────────────────┴────────────┴──────┘

Parameters:

  • other (DataFrame) —

    DataFrame to join with.

  • left_on (String) (defaults to: nil) —

    Join column of the left DataFrame.

  • right_on (String) (defaults to: nil) —

    Join column of the right DataFrame.

  • on (String) (defaults to: nil) —

    Join column of both DataFrames. If set, left_on and right_on should be nil.

  • by_left (Object) (defaults to: nil) —

    join on these columns before doing asof join

  • by_right (Object) (defaults to: nil) —

    join on these columns before doing asof join

  • by (Object) (defaults to: nil) —

    join on these columns before doing asof join

  • strategy ("backward", "forward") (defaults to: "backward") —

    Join strategy.

  • suffix (String) (defaults to: "_right") —

    Suffix to append to columns with a duplicate name.

  • tolerance (Object) (defaults to: nil) —

    Numeric tolerance. By setting this the join will only be done if the near keys are within this distance. If an asof join is done on columns of dtype "Date", "Datetime", "Duration" or "Time" you use the following string language:

    • 1ns (1 nanosecond)
    • 1us (1 microsecond)
    • 1ms (1 millisecond)
    • 1s (1 second)
    • 1m (1 minute)
    • 1h (1 hour)
    • 1d (1 day)
    • 1w (1 week)
    • 1mo (1 calendar month)
    • 1y (1 calendar year)
    • 1i (1 index count)

    Or combine them: "3d12h4m25s" # 3 days, 12 hours, 4 minutes, and 25 seconds

  • allow_parallel (Boolean) (defaults to: true) —

    Allow the physical plan to optionally evaluate the computation of both DataFrames up to the join in parallel.

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

    Force the physical plan to evaluate the computation of both DataFrames up to the join in parallel.

  • coalesce (Boolean) (defaults to: true) —

    Coalescing behavior (merging of join columns).

    • true: -> Always coalesce join columns.
    • false: -> Never coalesce join columns. Note that joining on any other expressions than col will turn off coalescing.
  • allow_exact_matches (Boolean) (defaults to: true) —

    Whether exact matches are valid join predicates.

    • If true, allow matching with the same on value (i.e. less-than-or-equal-to / greater-than-or-equal-to).
    • If false, don't match the same on value (i.e., strictly less-than / strictly greater-than).
  • check_sortedness (Boolean) (defaults to: true) —

    Check the sortedness of the asof keys. If the keys are not sorted Polars will error, or in case of 'by' argument raise a warning. This might become a hard error in the future.

Returns:



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# File 'lib/polars/data_frame.rb', line 3522

def join_asof(
  other,
  left_on: nil,
  right_on: nil,
  on: nil,
  by_left: nil,
  by_right: nil,
  by: nil,
  strategy: "backward",
  suffix: "_right",
  tolerance: nil,
  allow_parallel: true,
  force_parallel: false,
  coalesce: true,
  allow_exact_matches: true,
  check_sortedness: true
)
  lazy
    .join_asof(
      other.lazy,
      left_on: left_on,
      right_on: right_on,
      on: on,
      by_left: by_left,
      by_right: by_right,
      by: by,
      strategy: strategy,
      suffix: suffix,
      tolerance: tolerance,
      allow_parallel: allow_parallel,
      force_parallel: force_parallel,
      coalesce: coalesce,
      allow_exact_matches: allow_exact_matches,
      check_sortedness: check_sortedness
    )
    .collect(optimizations: QueryOptFlags._eager)
end

#join_where(other, *predicates, suffix: "_right") ⇒ DataFrame

Note:

The row order of the input DataFrames is not preserved.

Note:

This functionality is experimental. It may be changed at any point without it being considered a breaking change.

Perform a join based on one or multiple (in)equality predicates.

This performs an inner join, so only rows where all predicates are true are included in the result, and a row from either DataFrame may be included multiple times in the result.

Examples:

Join two dataframes together based on two predicates which get AND-ed together.

east = Polars::DataFrame.new(
  {
    "id": [100, 101, 102],
    "dur": [120, 140, 160],
    "rev": [12, 14, 16],
    "cores": [2, 8, 4]
  }
)
west = Polars::DataFrame.new(
  {
    "t_id": [404, 498, 676, 742],
    "time": [90, 130, 150, 170],
    "cost": [9, 13, 15, 16],
    "cores": [4, 2, 1, 4]
  }
)
east.join_where(
  west,
  Polars.col("dur") < Polars.col("time"),
  Polars.col("rev") < Polars.col("cost")
)
# =>
# shape: (5, 8)
# ┌─────┬─────┬─────┬───────┬──────┬──────┬──────┬─────────────┐
# │ id  ┆ dur ┆ rev ┆ cores ┆ t_id ┆ time ┆ cost ┆ cores_right │
# │ --- ┆ --- ┆ --- ┆ ---   ┆ ---  ┆ ---  ┆ ---  ┆ ---         │
# │ i64 ┆ i64 ┆ i64 ┆ i64   ┆ i64  ┆ i64  ┆ i64  ┆ i64         │
# ╞═════╪═════╪═════╪═══════╪══════╪══════╪══════╪═════════════╡
# │ 100 ┆ 120 ┆ 12  ┆ 2     ┆ 498  ┆ 130  ┆ 13   ┆ 2           │
# │ 100 ┆ 120 ┆ 12  ┆ 2     ┆ 676  ┆ 150  ┆ 15   ┆ 1           │
# │ 100 ┆ 120 ┆ 12  ┆ 2     ┆ 742  ┆ 170  ┆ 16   ┆ 4           │
# │ 101 ┆ 140 ┆ 14  ┆ 8     ┆ 676  ┆ 150  ┆ 15   ┆ 1           │
# │ 101 ┆ 140 ┆ 14  ┆ 8     ┆ 742  ┆ 170  ┆ 16   ┆ 4           │
# └─────┴─────┴─────┴───────┴──────┴──────┴──────┴─────────────┘

To OR them together, use a single expression and the | operator.

east.join_where(
  west,
  (Polars.col("dur") < Polars.col("time")) | (Polars.col("rev") < Polars.col("cost"))
)
# =>
# shape: (6, 8)
# ┌─────┬─────┬─────┬───────┬──────┬──────┬──────┬─────────────┐
# │ id  ┆ dur ┆ rev ┆ cores ┆ t_id ┆ time ┆ cost ┆ cores_right │
# │ --- ┆ --- ┆ --- ┆ ---   ┆ ---  ┆ ---  ┆ ---  ┆ ---         │
# │ i64 ┆ i64 ┆ i64 ┆ i64   ┆ i64  ┆ i64  ┆ i64  ┆ i64         │
# ╞═════╪═════╪═════╪═══════╪══════╪══════╪══════╪═════════════╡
# │ 100 ┆ 120 ┆ 12  ┆ 2     ┆ 498  ┆ 130  ┆ 13   ┆ 2           │
# │ 100 ┆ 120 ┆ 12  ┆ 2     ┆ 676  ┆ 150  ┆ 15   ┆ 1           │
# │ 100 ┆ 120 ┆ 12  ┆ 2     ┆ 742  ┆ 170  ┆ 16   ┆ 4           │
# │ 101 ┆ 140 ┆ 14  ┆ 8     ┆ 676  ┆ 150  ┆ 15   ┆ 1           │
# │ 101 ┆ 140 ┆ 14  ┆ 8     ┆ 742  ┆ 170  ┆ 16   ┆ 4           │
# │ 102 ┆ 160 ┆ 16  ┆ 4     ┆ 742  ┆ 170  ┆ 16   ┆ 4           │
# └─────┴─────┴─────┴───────┴──────┴──────┴──────┴─────────────┘

Parameters:

  • other (DataFrame) —

    DataFrame to join with.

  • predicates (Array) —

    (In)Equality condition to join the two tables on. When a column name occurs in both tables, the proper suffix must be applied in the predicate.

  • suffix (String) (defaults to: "_right") —

    Suffix to append to columns with a duplicate name.

Returns:



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# File 'lib/polars/data_frame.rb', line 3795

def join_where(
  other,
  *predicates,
  suffix: "_right"
)
  Utils.require_same_type(self, other)

  lazy
  .join_where(
    other.lazy,
    *predicates,
    suffix: suffix
  )
  .collect(optimizations: QueryOptFlags._eager)
end

#lazy ⇒ LazyFrame

Start a lazy query from this point.

Examples:

df = Polars::DataFrame.new(
  {
    "a" => [nil, 2, 3, 4],
    "b" => [0.5, nil, 2.5, 13],
    "c" => [true, true, false, nil]
  }
)
df.lazy

Returns:



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# File 'lib/polars/data_frame.rb', line 4971

def lazy
  wrap_ldf(_df.lazy)
end

#limit(n = 5) ⇒ DataFrame

Get the first n rows.

Alias for #head.

Examples:

df = Polars::DataFrame.new(
  {"foo" => [1, 2, 3, 4, 5, 6], "bar" => ["a", "b", "c", "d", "e", "f"]}
)
df.limit(4)
# =>
# shape: (4, 2)
# ┌─────┬─────┐
# │ foo ┆ bar │
# │ --- ┆ --- │
# │ i64 ┆ str │
# ╞═════╪═════╡
# │ 1   ┆ a   │
# │ 2   ┆ b   │
# │ 3   ┆ c   │
# │ 4   ┆ d   │
# └─────┴─────┘

Parameters:

  • n (Integer) (defaults to: 5) —

    Number of rows to return.

Returns:



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# File 'lib/polars/data_frame.rb', line 2541

def limit(n = 5)
  head(n)
end

#map_columns(column_names, *args, **kwargs, &function) ⇒ DataFrame

Apply eager functions to columns of a DataFrame.

Users should always prefer :meth:with_columns unless they are using expressions that are only possible on Series and not on Expr. This is almost never the case, except for a very select few functions that cannot know the output datatype without looking at the data.

Examples:

df = Polars::DataFrame.new({"a" => [1, 2, 3, 4], "b" => ["10", "20", "30", "40"]})
df.map_columns("a") { |s| s.shrink_dtype }
# =>
# shape: (4, 2)
# ┌─────┬─────┐
# │ a   ┆ b   │
# │ --- ┆ --- │
# │ i8  ┆ str │
# ╞═════╪═════╡
# │ 1   ┆ 10  │
# │ 2   ┆ 20  │
# │ 3   ┆ 30  │
# │ 4   ┆ 40  │
# └─────┴─────┘
df = Polars::DataFrame.new(
  {
    "a" => ['{"x":"a"}', nil, '{"x":"b"}', nil],
    "b" => ['{"a":1, "b": true}', nil, '{"a":2, "b": false}', nil]
  }
)
df.map_columns(["a", "b"]) { |s| s.str.json_decode }
# =>
# shape: (4, 2)
# ┌───────────┬───────────┐
# │ a         ┆ b         │
# │ ---       ┆ ---       │
# │ struct[1] ┆ struct[2] │
# ╞═══════════╪═══════════╡
# │ {"a"}     ┆ {1,true}  │
# │ null      ┆ null      │
# │ {"b"}     ┆ {2,false} │
# │ null      ┆ null      │
# └───────────┴───────────┘
df.map_columns(Polars.cs.all) { |s| s.str.json_decode }
# =>
# shape: (4, 2)
# ┌───────────┬───────────┐
# │ a         ┆ b         │
# │ ---       ┆ ---       │
# │ struct[1] ┆ struct[2] │
# ╞═══════════╪═══════════╡
# │ {"a"}     ┆ {1,true}  │
# │ null      ┆ null      │
# │ {"b"}     ┆ {2,false} │
# │ null      ┆ null      │
# └───────────┴───────────┘

Parameters:

  • column_names (Object) —

    The columns to apply the UDF to.

  • args (Array) —

    Arguments to pass to the UDF.

  • kwargs (Hash) —

    Keyword arguments to pass to the UDF.

Returns:



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# File 'lib/polars/data_frame.rb', line 2805

def map_columns(
  column_names,
  *args,
  **kwargs,
  &function
)
  if column_names.is_a?(Selector) || column_names.is_a?(Expr)
    c_names = Array(Utils.expand_selector(self, column_names))
  elsif Utils.strlike?(column_names)
    c_names = [column_names]
  else
    c_names = Array(column_names)
  end

  with_columns(
    c_names.map { |c| function.call(self[c], *args, **kwargs) }
  )
end

#map_rows(return_dtype: nil, inference_size: 256, &function) ⇒ Object

Note:

The frame-level apply cannot track column names (as the UDF is a black-box that may arbitrarily drop, rearrange, transform, or add new columns); if you want to apply a UDF such that column names are preserved, you should use the expression-level apply syntax instead.

Apply a custom/user-defined function (UDF) over the rows of the DataFrame.

The UDF will receive each row as a tuple of values: udf(row).

Implementing logic using a Ruby function is almost always significantly slower and more memory intensive than implementing the same logic using the native expression API because:

  • The native expression engine runs in Rust; UDFs run in Ruby.
  • Use of Ruby UDFs forces the DataFrame to be materialized in memory.
  • Polars-native expressions can be parallelised (UDFs cannot).
  • Polars-native expressions can be logically optimised (UDFs cannot).

Wherever possible you should strongly prefer the native expression API to achieve the best performance.

Examples:

df = Polars::DataFrame.new({"foo" => [1, 2, 3], "bar" => [-1, 5, 8]})

Return a DataFrame by mapping each row to a tuple:

df.map_rows { |t| [t[0] * 2, t[1] * 3] }
# =>
# shape: (3, 2)
# ┌──────────┬──────────┐
# │ column_0 ┆ column_1 │
# │ ---      ┆ ---      │
# │ i64      ┆ i64      │
# ╞══════════╪══════════╡
# │ 2        ┆ -3       │
# │ 4        ┆ 15       │
# │ 6        ┆ 24       │
# └──────────┴──────────┘

Return a Series by mapping each row to a scalar:

df.map_rows { |t| t[0] * 2 + t[1] }
# =>
# shape: (3, 1)
# ┌─────┐
# │ map │
# │ --- │
# │ i64 │
# ╞═════╡
# │ 1   │
# │ 9   │
# │ 14  │
# └─────┘

Parameters:

  • return_dtype (Symbol) (defaults to: nil) —

    Output type of the operation. If none given, Polars tries to infer the type.

  • inference_size (Integer) (defaults to: 256) —

    Only used in the case when the custom function returns rows. This uses the first n rows to determine the output schema

Returns:



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# File 'lib/polars/data_frame.rb', line 3871

def map_rows(return_dtype: nil, inference_size: 256, &function)
  out, is_df = _df.map_rows(function, return_dtype, inference_size)
  if is_df
    _from_rbdf(out)
  else
    _from_rbdf(Utils.wrap_s(out).to_frame._df)
  end
end

#max ⇒ DataFrame

Aggregate the columns of this DataFrame to their maximum value.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6, 7, 8],
    "ham" => ["a", "b", "c"]
  }
)
df.max
# =>
# shape: (1, 3)
# ┌─────┬─────┬─────┐
# │ foo ┆ bar ┆ ham │
# │ --- ┆ --- ┆ --- │
# │ i64 ┆ i64 ┆ str │
# ╞═════╪═════╪═════╡
# │ 3   ┆ 8   ┆ c   │
# └─────┴─────┴─────┘

Returns:



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# File 'lib/polars/data_frame.rb', line 5276

def max
  lazy.max.collect(optimizations: QueryOptFlags._eager)
end

#max_horizontal ⇒ Series

Get the maximum value horizontally across columns.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [4.0, 5.0, 6.0]
  }
)
df.max_horizontal
# =>
# shape: (3,)
# Series: 'max' [f64]
# [
#         4.0
#         5.0
#         6.0
# ]

Returns:



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# File 'lib/polars/data_frame.rb', line 5300

def max_horizontal
  select(max: F.max_horizontal(F.all)).to_series
end

#mean ⇒ DataFrame

Aggregate the columns of this DataFrame to their mean value.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6, 7, 8],
    "ham" => ["a", "b", "c"]
  }
)
df.mean
# =>
# shape: (1, 3)
# ┌─────┬─────┬──────┐
# │ foo ┆ bar ┆ ham  │
# │ --- ┆ --- ┆ ---  │
# │ f64 ┆ f64 ┆ str  │
# ╞═════╪═════╪══════╡
# │ 2.0 ┆ 7.0 ┆ null │
# └─────┴─────┴──────┘

Returns:



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# File 'lib/polars/data_frame.rb', line 5432

def mean
  lazy.mean.collect(optimizations: QueryOptFlags._eager)
end

#mean_horizontal(ignore_nulls: true) ⇒ Series

Take the mean of all values horizontally across columns.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [4.0, 5.0, 6.0]
  }
)
df.mean_horizontal
# =>
# shape: (3,)
# Series: 'mean' [f64]
# [
#         2.5
#         3.5
#         4.5
# ]

Parameters:

  • ignore_nulls (Boolean) (defaults to: true) —

    Ignore null values (default). If set to false, any null value in the input will lead to a null output.

Returns:



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# File 'lib/polars/data_frame.rb', line 5460

def mean_horizontal(ignore_nulls: true)
  select(
    mean: F.mean_horizontal(F.all, ignore_nulls: ignore_nulls)
  ).to_series
end

#median ⇒ DataFrame

Aggregate the columns of this DataFrame to their median value.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6, 7, 8],
    "ham" => ["a", "b", "c"]
  }
)
df.median
# =>
# shape: (1, 3)
# ┌─────┬─────┬──────┐
# │ foo ┆ bar ┆ ham  │
# │ --- ┆ --- ┆ ---  │
# │ f64 ┆ f64 ┆ str  │
# ╞═════╪═════╪══════╡
# │ 2.0 ┆ 7.0 ┆ null │
# └─────┴─────┴──────┘

Returns:



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# File 'lib/polars/data_frame.rb', line 5570

def median
  lazy.median.collect(optimizations: QueryOptFlags._eager)
end

#merge_sorted(other, key) ⇒ DataFrame

Take two sorted DataFrames and merge them by the sorted key.

The output of this operation will also be sorted. It is the callers responsibility that the frames are sorted by that key otherwise the output will not make sense.

The schemas of both DataFrames must be equal.

Examples:

df0 = Polars::DataFrame.new(
  {"name" => ["steve", "elise", "bob"], "age" => [42, 44, 18]}
).sort("age")
df1 = Polars::DataFrame.new(
  {"name" => ["anna", "megan", "steve", "thomas"], "age" => [21, 33, 42, 20]}
).sort("age")
df0.merge_sorted(df1, "age")
# =>
# shape: (7, 2)
# ┌────────┬─────┐
# │ name   ┆ age │
# │ ---    ┆ --- │
# │ str    ┆ i64 │
# ╞════════╪═════╡
# │ bob    ┆ 18  │
# │ thomas ┆ 20  │
# │ anna   ┆ 21  │
# │ megan  ┆ 33  │
# │ steve  ┆ 42  │
# │ steve  ┆ 42  │
# │ elise  ┆ 44  │
# └────────┴─────┘

Parameters:

  • other (DataFrame) —

    Other DataFrame that must be merged

  • key (String) —

    Key that is sorted.

Returns:



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# File 'lib/polars/data_frame.rb', line 6533

def merge_sorted(other, key)
  lazy.merge_sorted(other.lazy, key).collect(optimizations: QueryOptFlags._eager)
end

#min ⇒ DataFrame

Aggregate the columns of this DataFrame to their minimum value.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6, 7, 8],
    "ham" => ["a", "b", "c"]
  }
)
df.min
# =>
# shape: (1, 3)
# ┌─────┬─────┬─────┐
# │ foo ┆ bar ┆ ham │
# │ --- ┆ --- ┆ --- │
# │ i64 ┆ i64 ┆ str │
# ╞═════╪═════╪═════╡
# │ 1   ┆ 6   ┆ a   │
# └─────┴─────┴─────┘

Returns:



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# File 'lib/polars/data_frame.rb', line 5326

def min
  lazy.min.collect(optimizations: QueryOptFlags._eager)
end

#min_horizontal ⇒ Series

Get the minimum value horizontally across columns.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [4.0, 5.0, 6.0]
  }
)
df.min_horizontal
# =>
# shape: (3,)
# Series: 'min' [f64]
# [
#         1.0
#         2.0
#         3.0
# ]

Returns:



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# File 'lib/polars/data_frame.rb', line 5350

def min_horizontal
  select(min: F.min_horizontal(F.all)).to_series
end

#n_chunks(strategy: "first") ⇒ Object

Get number of chunks used by the ChunkedArrays of this DataFrame.

Examples:

df = Polars::DataFrame.new(
  {
    "a" => [1, 2, 3, 4],
    "b" => [0.5, 4, 10, 13],
    "c" => [true, true, false, true]
  }
)
df.n_chunks
# => 1
df.n_chunks(strategy: "all")
# => [1, 1, 1]

Parameters:

  • strategy ("first", "all") (defaults to: "first") —

    Return the number of chunks of the 'first' column, or 'all' columns in this DataFrame.

Returns:



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# File 'lib/polars/data_frame.rb', line 5244

def n_chunks(strategy: "first")
  if strategy == "first"
    _df.n_chunks
  elsif strategy == "all"
    get_columns.map(&:n_chunks)
  else
    raise ArgumentError, "Strategy: '{strategy}' not understood. Choose one of {{'first',  'all'}}"
  end
end

#n_unique(subset: nil) ⇒ DataFrame

Return the number of unique rows, or the number of unique row-subsets.

Examples:

df = Polars::DataFrame.new(
  {
    "a" => [1, 1, 2, 3, 4, 5],
    "b" => [0.5, 0.5, 1.0, 2.0, 3.0, 3.0],
    "c" => [true, true, true, false, true, true]
  }
)
df.n_unique
# => 5

Simple columns subset

df.n_unique(subset: ["b", "c"])
# => 4

Expression subset

df.n_unique(
  subset: [
    (Polars.col("a").floordiv(2)),
    (Polars.col("c") | (Polars.col("b") >= 2))
  ]
)
# => 3

Parameters:

  • subset (Object) (defaults to: nil) —

    One or more columns/expressions that define what to count; omit to return the count of unique rows.

Returns:



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# File 'lib/polars/data_frame.rb', line 5749

def n_unique(subset: nil)
  if subset.is_a?(StringIO)
    subset = [Polars.col(subset)]
  elsif subset.is_a?(Expr)
    subset = [subset]
  end

  if subset.is_a?(::Array) && subset.length == 1
    expr = Utils.wrap_expr(Utils.parse_into_expression(subset[0], str_as_lit: false))
  else
    struct_fields = subset.nil? ? Polars.all : subset
    expr = Polars.struct(struct_fields)
  end

  df = lazy.select(expr.n_unique).collect
  df.is_empty ? 0 : df.row(0)[0]
end

#null_count ⇒ DataFrame

Create a new DataFrame that shows the null counts per column.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, nil, 3],
    "bar" => [6, 7, nil],
    "ham" => ["a", "b", "c"]
  }
)
df.null_count
# =>
# shape: (1, 3)
# ┌─────┬─────┬─────┐
# │ foo ┆ bar ┆ ham │
# │ --- ┆ --- ┆ --- │
# │ u32 ┆ u32 ┆ u32 │
# ╞═════╪═════╪═════╡
# │ 1   ┆ 1   ┆ 0   │
# └─────┴─────┴─────┘

Returns:



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# File 'lib/polars/data_frame.rb', line 5799

def null_count
  _from_rbdf(_df.null_count)
end

#partition_by(by, *more_by, maintain_order: true, include_key: true, as_dict: false) ⇒ Object

Split into multiple DataFrames partitioned by groups.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => ["A", "A", "B", "B", "C"],
    "N" => [1, 2, 2, 4, 2],
    "bar" => ["k", "l", "m", "m", "l"]
  }
)
df.partition_by("foo", maintain_order: true)
# =>
# [shape: (2, 3)
# ┌─────┬─────┬─────┐
# │ foo ┆ N   ┆ bar │
# │ --- ┆ --- ┆ --- │
# │ str ┆ i64 ┆ str │
# ╞═════╪═════╪═════╡
# │ A   ┆ 1   ┆ k   │
# │ A   ┆ 2   ┆ l   │
# └─────┴─────┴─────┘, shape: (2, 3)
# ┌─────┬─────┬─────┐
# │ foo ┆ N   ┆ bar │
# │ --- ┆ --- ┆ --- │
# │ str ┆ i64 ┆ str │
# ╞═════╪═════╪═════╡
# │ B   ┆ 2   ┆ m   │
# │ B   ┆ 4   ┆ m   │
# └─────┴─────┴─────┘, shape: (1, 3)
# ┌─────┬─────┬─────┐
# │ foo ┆ N   ┆ bar │
# │ --- ┆ --- ┆ --- │
# │ str ┆ i64 ┆ str │
# ╞═════╪═════╪═════╡
# │ C   ┆ 2   ┆ l   │
# └─────┴─────┴─────┘]
df.partition_by("foo", maintain_order: true, as_dict: true)
# =>
# {["A"]=>shape: (2, 3)
# ┌─────┬─────┬─────┐
# │ foo ┆ N   ┆ bar │
# │ --- ┆ --- ┆ --- │
# │ str ┆ i64 ┆ str │
# ╞═════╪═════╪═════╡
# │ A   ┆ 1   ┆ k   │
# │ A   ┆ 2   ┆ l   │
# └─────┴─────┴─────┘, ["B"]=>shape: (2, 3)
# ┌─────┬─────┬─────┐
# │ foo ┆ N   ┆ bar │
# │ --- ┆ --- ┆ --- │
# │ str ┆ i64 ┆ str │
# ╞═════╪═════╪═════╡
# │ B   ┆ 2   ┆ m   │
# │ B   ┆ 4   ┆ m   │
# └─────┴─────┴─────┘, ["C"]=>shape: (1, 3)
# ┌─────┬─────┬─────┐
# │ foo ┆ N   ┆ bar │
# │ --- ┆ --- ┆ --- │
# │ str ┆ i64 ┆ str │
# ╞═════╪═════╪═════╡
# │ C   ┆ 2   ┆ l   │
# └─────┴─────┴─────┘}

Parameters:

  • by (Object) —

    Groups to partition by.

  • more_by (Array) —

    Additional names of columns to group by, specified as positional arguments.

  • maintain_order (Boolean) (defaults to: true) —

    Keep predictable output order. This is slower as it requires an extra sort operation.

  • include_key (Boolean) (defaults to: true) —

    Include the columns used to partition the DataFrame in the output.

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

    If true, return the partitions in a hash keyed by the distinct group values instead of an array.

Returns:



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# File 'lib/polars/data_frame.rb', line 4839

def partition_by(by, *more_by, maintain_order: true, include_key: true, as_dict: false)
  by_parsed = Utils._expand_selectors(self, by, *more_by)

  partitions = _df.partition_by(by_parsed, maintain_order, include_key).map { |df| _from_rbdf(df) }

  if as_dict
    if include_key
      names = partitions.map { |p| p.select(by_parsed).row(0) }
    else
      if !maintain_order
        msg = "cannot use `partition_by` with `maintain_order: false, include_key: false, as_dict: true`"
        raise ArgumentError, msg
      end
      names = select(by_parsed).unique(maintain_order: true).rows
    end

    return names.zip(partitions).to_h
  end

  partitions
end

#pipe(function, *args, **kwargs, &block) ⇒ Object

Note:

It is recommended to use LazyFrame when piping operations, in order to fully take advantage of query optimization and parallelization. See #lazy.

Offers a structured way to apply a sequence of user-defined functions (UDFs).

Examples:

cast_str_to_int = lambda do |data, col_name:|
  data.with_columns(Polars.col(col_name).cast(Polars::Int64))
end

df = Polars::DataFrame.new({"a" => [1, 2, 3, 4], "b" => ["10", "20", "30", "40"]})
df.pipe(cast_str_to_int, col_name: "b")
# =>
# shape: (4, 2)
# ┌─────┬─────┐
# │ a   ┆ b   │
# │ --- ┆ --- │
# │ i64 ┆ i64 │
# ╞═════╪═════╡
# │ 1   ┆ 10  │
# │ 2   ┆ 20  │
# │ 3   ┆ 30  │
# │ 4   ┆ 40  │
# └─────┴─────┘

Parameters:

  • function (Object) —

    Callable; will receive the frame as the first parameter, followed by any given args/kwargs.

  • args (Object) —

    Arguments to pass to the UDF.

  • kwargs (Object) —

    Keyword arguments to pass to the UDF.

Returns:



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# File 'lib/polars/data_frame.rb', line 2734

def pipe(function, *args, **kwargs, &block)
  function.(self, *args, **kwargs, &block)
end

#pivot(on, on_columns: nil, index: nil, values: nil, aggregate_function: nil, maintain_order: true, sort_columns: false, separator: "_") ⇒ DataFrame

Create a spreadsheet-style pivot table as a DataFrame.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => ["one", "one", "two", "two", "one", "two"],
    "bar" => ["y", "y", "y", "x", "x", "x"],
    "baz" => [1, 2, 3, 4, 5, 6]
  }
)
df.pivot("bar", index: "foo", values: "baz", aggregate_function: "sum")
# =>
# shape: (2, 3)
# ┌─────┬─────┬─────┐
# │ foo ┆ y   ┆ x   │
# │ --- ┆ --- ┆ --- │
# │ str ┆ i64 ┆ i64 │
# ╞═════╪═════╪═════╡
# │ one ┆ 3   ┆ 5   │
# │ two ┆ 3   ┆ 10  │
# └─────┴─────┴─────┘

Parameters:

  • on (Object) —

    Columns whose values will be used as the header of the output DataFrame

  • on_columns (Object) (defaults to: nil) —

    What value combinations will be considered for the output table.

  • index (Object) (defaults to: nil) —

    One or multiple keys to group by

  • values (Object) (defaults to: nil) —

    Column values to aggregate. Can be multiple columns if the columns arguments contains multiple columns as well

  • aggregate_function ("first", "sum", "max", "min", "mean", "median", "last", "count") (defaults to: nil) —

    A predefined aggregate function str or an expression.

  • maintain_order (Object) (defaults to: true) —

    Sort the grouped keys so that the output order is predictable.

  • sort_columns (Object) (defaults to: false) —

    Sort the transposed columns by name. Default is by order of discovery.

  • separator (String) (defaults to: "_") —

    Used as separator/delimiter in generated column names in case of multiple values columns.

Returns:



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# File 'lib/polars/data_frame.rb', line 4552

def pivot(
  on,
  on_columns: nil,
  index: nil,
  values: nil,
  aggregate_function: nil,
  maintain_order: true,
  sort_columns: false,
  separator: "_"
)
  if on_columns.nil?
    cols = select(on).unique(maintain_order: true)
    if sort_columns
      cols = cols.sort(on)
    end
    on_cols = cols
  else
    on_cols = on_columns
  end

  lazy
  .pivot(
    on,
    on_columns: on_cols,
    index: index,
    values: values,
    aggregate_function: aggregate_function,
    maintain_order: maintain_order,
    separator: separator
  )
  .collect(optimizations: QueryOptFlags._eager)
end

#plot(x = nil, y = nil, type: nil, group: nil, stacked: nil) ⇒ Object

Plot data.

Returns:

Raises:

  • (ArgumentError) 


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# File 'lib/polars/data_frame.rb', line 136

def plot(x = nil, y = nil, type: nil, group: nil, stacked: nil)
  plot = DataFramePlot.new(self)
  return plot if x.nil? && y.nil?

  raise ArgumentError, "Must specify columns" if x.nil? || y.nil?
  type ||= begin
    if self[x].dtype.numeric? && self[y].dtype.numeric?
      "scatter"
    elsif self[x].dtype == String && self[y].dtype.numeric?
      "column"
    elsif (self[x].dtype == Date || self[x].dtype == Datetime) && self[y].dtype.numeric?
      "line"
    else
      raise "Cannot determine type. Use the type option."
    end
  end

  case type
  when "line"
    plot.line(x, y, color: group)
  when "area"
    plot.area(x, y, color: group)
  when "pie"
    raise ArgumentError, "Cannot use group option with pie chart" unless group.nil?
    plot.pie(x, y)
  when "column"
    plot.column(x, y, color: group, stacked: stacked)
  when "bar"
    plot.bar(x, y, color: group, stacked: stacked)
  when "scatter"
    plot.scatter(x, y, color: group)
  else
    raise ArgumentError, "Invalid type: #{type}"
  end
end

#product ⇒ DataFrame

Aggregate the columns of this DataFrame to their product values.

Examples:

df = Polars::DataFrame.new(
  {
    "a" => [1, 2, 3],
    "b" => [0.5, 4, 10],
    "c" => [true, true, false]
  }
)
df.product
# =>
# shape: (1, 3)
# ┌─────┬──────┬─────┐
# │ a   ┆ b    ┆ c   │
# │ --- ┆ ---  ┆ --- │
# │ i64 ┆ f64  ┆ i64 │
# ╞═════╪══════╪═════╡
# │ 6   ┆ 20.0 ┆ 0   │
# └─────┴──────┴─────┘

Returns:



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# File 'lib/polars/data_frame.rb', line 5596

def product
  select(Polars.all.product)
end

#quantile(quantile, interpolation: "nearest") ⇒ DataFrame

Aggregate the columns of this DataFrame to their quantile value.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6, 7, 8],
    "ham" => ["a", "b", "c"]
  }
)
df.quantile(0.5, interpolation: "nearest")
# =>
# shape: (1, 3)
# ┌─────┬─────┬──────┐
# │ foo ┆ bar ┆ ham  │
# │ --- ┆ --- ┆ ---  │
# │ f64 ┆ f64 ┆ str  │
# ╞═════╪═════╪══════╡
# │ 2.0 ┆ 7.0 ┆ null │
# └─────┴─────┴──────┘

Parameters:

  • quantile (Float) —

    Quantile between 0.0 and 1.0.

  • interpolation ("nearest", "higher", "lower", "midpoint", "linear") (defaults to: "nearest") —

    Interpolation method.

Returns:



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# File 'lib/polars/data_frame.rb', line 5627

def quantile(quantile, interpolation: "nearest")
  lazy.quantile(quantile, interpolation: interpolation).collect(optimizations: QueryOptFlags._eager)
end

#rechunk ⇒ DataFrame

This will make sure all subsequent operations have optimal and predictable performance.

Returns:



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# File 'lib/polars/data_frame.rb', line 5773

def rechunk
  _from_rbdf(_df.rechunk)
end

#remove(*predicates, **constraints) ⇒ DataFrame

Remove rows, dropping those that match the given predicate expression(s).

The original order of the remaining rows is preserved.

Rows where the filter predicate does not evaluate to true are retained (this includes rows where the predicate evaluates as null).

Examples:

Remove rows matching a condition:

df = Polars::DataFrame.new(
  {
    "foo" => [2, 3, nil, 4, 0],
    "bar" => [5, 6, nil, nil, 0],
    "ham" => ["a", "b", nil, "c", "d"]
  }
)
df.remove(Polars.col("bar") >= 5)
# =>
# shape: (3, 3)
# ┌──────┬──────┬──────┐
# │ foo  ┆ bar  ┆ ham  │
# │ ---  ┆ ---  ┆ ---  │
# │ i64  ┆ i64  ┆ str  │
# ╞══════╪══════╪══════╡
# │ null ┆ null ┆ null │
# │ 4    ┆ null ┆ c    │
# │ 0    ┆ 0    ┆ d    │
# └──────┴──────┴──────┘

Discard rows based on multiple conditions, combined with and/or operators:

df.remove(
  (Polars.col("foo") >= 0) & (Polars.col("bar") >= 0),
)
# =>
# shape: (2, 3)
# ┌──────┬──────┬──────┐
# │ foo  ┆ bar  ┆ ham  │
# │ ---  ┆ ---  ┆ ---  │
# │ i64  ┆ i64  ┆ str  │
# ╞══════╪══════╪══════╡
# │ null ┆ null ┆ null │
# │ 4    ┆ null ┆ c    │
# └──────┴──────┴──────┘
df.remove(
  (Polars.col("foo") >= 0) | (Polars.col("bar") >= 0),
)
# =>
# shape: (1, 3)
# ┌──────┬──────┬──────┐
# │ foo  ┆ bar  ┆ ham  │
# │ ---  ┆ ---  ┆ ---  │
# │ i64  ┆ i64  ┆ str  │
# ╞══════╪══════╪══════╡
# │ null ┆ null ┆ null │
# └──────┴──────┴──────┘

Provide multiple constraints using *args syntax:

df.remove(
  Polars.col("ham").is_not_null,
  Polars.col("bar") >= 0
)
# =>
# shape: (2, 3)
# ┌──────┬──────┬──────┐
# │ foo  ┆ bar  ┆ ham  │
# │ ---  ┆ ---  ┆ ---  │
# │ i64  ┆ i64  ┆ str  │
# ╞══════╪══════╪══════╡
# │ null ┆ null ┆ null │
# │ 4    ┆ null ┆ c    │
# └──────┴──────┴──────┘

Provide constraints(s) using **kwargs syntax:

df.remove(foo: 0, bar: 0)
# =>
# shape: (4, 3)
# ┌──────┬──────┬──────┐
# │ foo  ┆ bar  ┆ ham  │
# │ ---  ┆ ---  ┆ ---  │
# │ i64  ┆ i64  ┆ str  │
# ╞══════╪══════╪══════╡
# │ 2    ┆ 5    ┆ a    │
# │ 3    ┆ 6    ┆ b    │
# │ null ┆ null ┆ null │
# │ 4    ┆ null ┆ c    │
# └──────┴──────┴──────┘

Remove rows by comparing two columns against each other:

df.remove(
  Polars.col("foo").ne_missing(Polars.col("bar"))
)
# =>
# shape: (2, 3)
# ┌──────┬──────┬──────┐
# │ foo  ┆ bar  ┆ ham  │
# │ ---  ┆ ---  ┆ ---  │
# │ i64  ┆ i64  ┆ str  │
# ╞══════╪══════╪══════╡
# │ null ┆ null ┆ null │
# │ 0    ┆ 0    ┆ d    │
# └──────┴──────┴──────┘

Parameters:

  • predicates (Array) —

    Expression that evaluates to a boolean Series.

  • constraints (Hash) —

    Column filters; use name = value to filter columns using the supplied value. Each constraint behaves the same as Polars.col(name).eq(value), and is implicitly joined with the other filter conditions using &.

Returns:



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# File 'lib/polars/data_frame.rb', line 1904

def remove(
  *predicates,
  **constraints
)
  lazy
  .remove(*predicates, **constraints)
  .collect(optimizations: QueryOptFlags._eager)
end

#rename(mapping, strict: true) ⇒ DataFrame

Rename column names.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6, 7, 8],
    "ham" => ["a", "b", "c"]
  }
)
df.rename({"foo" => "apple"})
# =>
# shape: (3, 3)
# ┌───────┬─────┬─────┐
# │ apple ┆ bar ┆ ham │
# │ ---   ┆ --- ┆ --- │
# │ i64   ┆ i64 ┆ str │
# ╞═══════╪═════╪═════╡
# │ 1     ┆ 6   ┆ a   │
# │ 2     ┆ 7   ┆ b   │
# │ 3     ┆ 8   ┆ c   │
# └───────┴─────┴─────┘

Parameters:

  • mapping (Hash) —

    Key value pairs that map from old name to new name.

  • strict (Boolean) (defaults to: true) —

    Validate that all column names exist in the current schema, and throw an exception if any do not. (Note that this parameter is a no-op when passing a function to mapping).

Returns:



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# File 'lib/polars/data_frame.rb', line 1688

def rename(mapping, strict: true)
  lazy.rename(mapping, strict: strict).collect(optimizations: QueryOptFlags._eager)
end

#replace_column(index, column) ⇒ DataFrame

Replace a column at an index location.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6, 7, 8],
    "ham" => ["a", "b", "c"]
  }
)
s = Polars::Series.new("apple", [10, 20, 30])
df.replace_column(0, s)
# =>
# shape: (3, 3)
# ┌───────┬─────┬─────┐
# │ apple ┆ bar ┆ ham │
# │ ---   ┆ --- ┆ --- │
# │ i64   ┆ i64 ┆ str │
# ╞═══════╪═════╪═════╡
# │ 10    ┆ 6   ┆ a   │
# │ 20    ┆ 7   ┆ b   │
# │ 30    ┆ 8   ┆ c   │
# └───────┴─────┴─────┘

Parameters:

  • index (Integer) —

    Column index.

  • column (Series) —

    Series that will replace the column.

Returns:



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# File 'lib/polars/data_frame.rb', line 2131

def replace_column(index, column)
  if index < 0
    index = width + index
  end
  _df.replace_column(index, column._s)
  self
end

#reverse ⇒ DataFrame

Reverse the DataFrame.

Examples:

df = Polars::DataFrame.new(
  {
    "key" => ["a", "b", "c"],
    "val" => [1, 2, 3]
  }
)
df.reverse
# =>
# shape: (3, 2)
# ┌─────┬─────┐
# │ key ┆ val │
# │ --- ┆ --- │
# │ str ┆ i64 │
# ╞═════╪═════╡
# │ c   ┆ 3   │
# │ b   ┆ 2   │
# │ a   ┆ 1   │
# └─────┴─────┘

Returns:



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# File 'lib/polars/data_frame.rb', line 1653

def reverse
  select(Polars.col("*").reverse)
end

#rolling(index_column:, period:, offset: nil, closed: "right", group_by: nil) ⇒ RollingGroupBy

Create rolling groups based on a time column.

Different from a dynamic_group_by the windows are now determined by the individual values and are not of constant intervals. For constant intervals use group_by_dynamic

The period and offset arguments are created either from a timedelta, or by using the following string language:

  • 1ns (1 nanosecond)
  • 1us (1 microsecond)
  • 1ms (1 millisecond)
  • 1s (1 second)
  • 1m (1 minute)
  • 1h (1 hour)
  • 1d (1 day)
  • 1w (1 week)
  • 1mo (1 calendar month)
  • 1y (1 calendar year)
  • 1i (1 index count)

Or combine them: "3d12h4m25s" # 3 days, 12 hours, 4 minutes, and 25 seconds

In case of a group_by_rolling on an integer column, the windows are defined by:

  • "1i" # length 1
  • "10i" # length 10

Examples:

dates = [
  "2020-01-01 13:45:48",
  "2020-01-01 16:42:13",
  "2020-01-01 16:45:09",
  "2020-01-02 18:12:48",
  "2020-01-03 19:45:32",
  "2020-01-08 23:16:43"
]
df = Polars::DataFrame.new({"dt" => dates, "a" => [3, 7, 5, 9, 2, 1]}).with_columns(
  Polars.col("dt").str.strptime(Polars::Datetime).set_sorted
)
df.rolling(index_column: "dt", period: "2d").agg(
  [
    Polars.sum("a").alias("sum_a"),
    Polars.min("a").alias("min_a"),
    Polars.max("a").alias("max_a")
  ]
)
# =>
# shape: (6, 4)
# ┌─────────────────────┬───────┬───────┬───────┐
# │ dt                  ┆ sum_a ┆ min_a ┆ max_a │
# │ ---                 ┆ ---   ┆ ---   ┆ ---   │
# │ datetime[μs]        ┆ i64   ┆ i64   ┆ i64   │
# ╞═════════════════════╪═══════╪═══════╪═══════╡
# │ 2020-01-01 13:45:48 ┆ 3     ┆ 3     ┆ 3     │
# │ 2020-01-01 16:42:13 ┆ 10    ┆ 3     ┆ 7     │
# │ 2020-01-01 16:45:09 ┆ 15    ┆ 3     ┆ 7     │
# │ 2020-01-02 18:12:48 ┆ 24    ┆ 3     ┆ 9     │
# │ 2020-01-03 19:45:32 ┆ 11    ┆ 2     ┆ 9     │
# │ 2020-01-08 23:16:43 ┆ 1     ┆ 1     ┆ 1     │
# └─────────────────────┴───────┴───────┴───────┘

Parameters:

  • index_column (Object) —

    Column used to group based on the time window. Often to type Date/Datetime This column must be sorted in ascending order. If not the output will not make sense.

    In case of a rolling operation on indices, dtype needs to be one of \{UInt32, UInt64, Int32, Int64}. Note that the first three get temporarily cast to Int64, so if performance matters use an Int64 column.

  • period (Object) —

    Length of the window.

  • offset (Object) (defaults to: nil) —

    Offset of the window. Default is -period.

  • closed ("right", "left", "both", "none") (defaults to: "right") —

    Define whether the temporal window interval is closed or not.

  • group_by (Object) (defaults to: nil) —

    Also group by this column/these columns.

Returns:



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# File 'lib/polars/data_frame.rb', line 3036

def rolling(
  index_column:,
  period:,
  offset: nil,
  closed: "right",
  group_by: nil
)
  RollingGroupBy.new(self, index_column, period, offset, closed, group_by, nil)
end

#row(index = nil, by_predicate: nil, named: false) ⇒ Object

Note:

The index and by_predicate params are mutually exclusive. Additionally, to ensure clarity, the by_predicate parameter must be supplied by keyword.

When using by_predicate it is an error condition if anything other than one row is returned; more than one row raises TooManyRowsReturned, and zero rows will raise NoRowsReturned (both inherit from RowsException).

Get a row as tuple, either by index or by predicate.

Examples:

Return the row at the given index

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6, 7, 8],
    "ham" => ["a", "b", "c"]
  }
)
df.row(2)
# => [3, 8, "c"]

Get a hash instead with a mapping of column names to row values

df.row(2, named: true)
# => {"foo"=>3, "bar"=>8, "ham"=>"c"}

Return the row that matches the given predicate

df.row(by_predicate: Polars.col("ham") == "b")
# => [2, 7, "b"]

Parameters:

  • index (Object) (defaults to: nil) —

    Row index.

  • by_predicate (Object) (defaults to: nil) —

    Select the row according to a given expression/predicate.

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

    Return a hash instead of an array. The hash is a mapping of column name to row value. This is more expensive than returning an array, but allows for accessing values by column name.

Returns:



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# File 'lib/polars/data_frame.rb', line 5994

def row(index = nil, by_predicate: nil, named: false)
  if !index.nil? && !by_predicate.nil?
    raise ArgumentError, "Cannot set both 'index' and 'by_predicate'; mutually exclusive"
  elsif index.is_a?(Expr)
    raise TypeError, "Expressions should be passed to the 'by_predicate' param"
  end

  if !index.nil?
    row = _df.row_tuple(index)
    if named
      columns.zip(row).to_h
    else
      row
    end
  elsif !by_predicate.nil?
    if !by_predicate.is_a?(Expr)
      raise TypeError, "Expected by_predicate to be an expression; found #{by_predicate.class.name}"
    end
    rows = filter(by_predicate).rows
    n_rows = rows.length
    if n_rows > 1
      raise TooManyRowsReturned, "Predicate #{by_predicate} returned #{n_rows} rows"
    elsif n_rows == 0
      raise NoRowsReturned, "Predicate #{by_predicate} returned no rows"
    end
    row = rows[0]
    if named
      columns.zip(row).to_h
    else
      row
    end
  else
    raise ArgumentError, "One of 'index' or 'by_predicate' must be set"
  end
end

#rows(named: false) ⇒ Array

Convert columnar data to rows as Ruby arrays.

Examples:

df = Polars::DataFrame.new(
  {
    "a" => [1, 3, 5],
    "b" => [2, 4, 6]
  }
)
df.rows
# => [[1, 2], [3, 4], [5, 6]]
df.rows(named: true)
# => [{"a"=>1, "b"=>2}, {"a"=>3, "b"=>4}, {"a"=>5, "b"=>6}]

Parameters:

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

    Return hashes instead of arrays. The hashes are a mapping of column name to row value. This is more expensive than returning an array, but allows for accessing values by column name.

Returns:



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# File 'lib/polars/data_frame.rb', line 6051

def rows(named: false)
  if named
    columns = self.columns
    _df.row_tuples.map do |v|
      columns.zip(v).to_h
    end
  else
    _df.row_tuples
  end
end

#rows_by_key(key, named: false, include_key: false, unique: false) ⇒ Hash

Convert columnar data to rows as Ruby arrays in a hash keyed by some column.

This method is like rows, but instead of returning rows in a flat list, rows are grouped by the values in the key column(s) and returned as a hash.

Note that this method should not be used in place of native operations, due to the high cost of materializing all frame data out into a hash; it should be used only when you need to move the values out into a Ruby data structure or other object that cannot operate directly with Polars/Arrow.

Examples:

Group rows by the given key column(s):

df = Polars::DataFrame.new(
  {
    "w" => ["a", "b", "b", "a"],
    "x" => ["q", "q", "q", "k"],
    "y" => [1.0, 2.5, 3.0, 4.5],
    "z" => [9, 8, 7, 6]
  }
)
df.rows_by_key(["w"])
# => {"a"=>[["q", 1.0, 9], ["k", 4.5, 6]], "b"=>[["q", 2.5, 8], ["q", 3.0, 7]]}

Return the same row groupings as hashes:

df.rows_by_key(["w"], named: true)
# => {"a"=>[{"x"=>"q", "y"=>1.0, "z"=>9}, {"x"=>"k", "y"=>4.5, "z"=>6}], "b"=>[{"x"=>"q", "y"=>2.5, "z"=>8}, {"x"=>"q", "y"=>3.0, "z"=>7}]}

Return row groupings, assuming keys are unique:

df.rows_by_key(["z"], unique: true)
# => {9=>["a", "q", 1.0], 8=>["b", "q", 2.5], 7=>["b", "q", 3.0], 6=>["a", "k", 4.5]}

Return row groupings as hashes, assuming keys are unique:

df.rows_by_key(["z"], named: true, unique: true)
# => {9=>{"w"=>"a", "x"=>"q", "y"=>1.0}, 8=>{"w"=>"b", "x"=>"q", "y"=>2.5}, 7=>{"w"=>"b", "x"=>"q", "y"=>3.0}, 6=>{"w"=>"a", "x"=>"k", "y"=>4.5}}

Return hash rows grouped by a compound key, including key values:

df.rows_by_key(["w", "x"], named: true, include_key: true)
# => {["a", "q"]=>[{"w"=>"a", "x"=>"q", "y"=>1.0, "z"=>9}], ["b", "q"]=>[{"w"=>"b", "x"=>"q", "y"=>2.5, "z"=>8}, {"w"=>"b", "x"=>"q", "y"=>3.0, "z"=>7}], ["a", "k"]=>[{"w"=>"a", "x"=>"k", "y"=>4.5, "z"=>6}]}

Parameters:

  • key (Object) —

    The column(s) to use as the key for the returned hash. If multiple columns are specified, the key will be a tuple of those values, otherwise it will be a string.

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

    Return hashes instead of arrays. The hashes are a mapping of column name to row value. This is more expensive than returning an array, but allows for accessing values by column name.

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

    Include key values inline with the associated data (by default the key values are omitted as a memory/performance optimisation, as they can be reoconstructed from the key).

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

    Indicate that the key is unique; this will result in a 1:1 mapping from key to a single associated row. Note that if the key is not actually unique the last row with the given key will be returned.

Returns:

  • (Hash) 


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# File 'lib/polars/data_frame.rb', line 6118

def rows_by_key(key, named: false, include_key: false, unique: false)
  key = Utils._expand_selectors(self, key)

  keys = key.size == 1 ? get_column(key[0]) : select(key).iter_rows

  if include_key
    values = self
  else
    data_cols = schema.names - key
    values = select(data_cols)
  end

  zipped = keys.each.zip(values.iter_rows(named: named))

  # if unique, we expect to write just one entry per key; otherwise, we're
  # returning a list of rows for each key, so append into a hash of arrays.
  if unique
    zipped.to_h
  else
    zipped.each_with_object({}) { |(key, data), h| (h[key] ||= []) << data }
  end
end

#sample(n: nil, fraction: nil, with_replacement: false, shuffle: false, seed: nil) ⇒ DataFrame

Sample from this DataFrame.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6, 7, 8],
    "ham" => ["a", "b", "c"]
  }
)
df.sample(n: 2, seed: 0)
# =>
# shape: (2, 3)
# ┌─────┬─────┬─────┐
# │ foo ┆ bar ┆ ham │
# │ --- ┆ --- ┆ --- │
# │ i64 ┆ i64 ┆ str │
# ╞═════╪═════╪═════╡
# │ 1   ┆ 6   ┆ a   │
# │ 2   ┆ 7   ┆ b   │
# └─────┴─────┴─────┘

Parameters:

  • n (Integer) (defaults to: nil) —

    Number of items to return. Cannot be used with fraction. Defaults to 1 if fraction is nil.

  • fraction (Float) (defaults to: nil) —

    Fraction of items to return. Cannot be used with n.

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

    Allow values to be sampled more than once.

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

    Shuffle the order of sampled data points.

  • seed (Integer) (defaults to: nil) —

    Seed for the random number generator. If set to nil (default), a random seed is used.

Returns:



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# File 'lib/polars/data_frame.rb', line 5839

def sample(
  n: nil,
  fraction: nil,
  with_replacement: false,
  shuffle: false,
  seed: nil
)
  if !n.nil? && !fraction.nil?
    raise ArgumentError, "cannot specify both `n` and `fraction`"
  end

  if n.nil? && !fraction.nil?
    fraction = Series.new("fraction", [fraction]) unless fraction.is_a?(Series)

    return _from_rbdf(
      _df.sample_frac(fraction._s, with_replacement, shuffle, seed)
    )
  end

  if n.nil?
    n = 1
  end

  n = Series.new("", [n]) unless n.is_a?(Series)

  _from_rbdf(_df.sample_n(n._s, with_replacement, shuffle, seed))
end

#schema ⇒ Hash

Get the schema.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6.0, 7.0, 8.0],
    "ham" => ["a", "b", "c"]
  }
)
df.schema
# => Polars::Schema({"foo"=>Polars::Int64, "bar"=>Polars::Float64, "ham"=>Polars::String})

Returns:

  • (Hash) 


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# File 'lib/polars/data_frame.rb', line 301

def schema
  Schema.new(columns.zip(dtypes).to_h)
end

#select(*exprs, **named_exprs) ⇒ DataFrame

Select columns from this DataFrame.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6, 7, 8],
    "ham" => ["a", "b", "c"]
  }
)
df.select("foo")
# =>
# shape: (3, 1)
# ┌─────┐
# │ foo │
# │ --- │
# │ i64 │
# ╞═════╡
# │ 1   │
# │ 2   │
# │ 3   │
# └─────┘
df.select(["foo", "bar"])
# =>
# shape: (3, 2)
# ┌─────┬─────┐
# │ foo ┆ bar │
# │ --- ┆ --- │
# │ i64 ┆ i64 │
# ╞═════╪═════╡
# │ 1   ┆ 6   │
# │ 2   ┆ 7   │
# │ 3   ┆ 8   │
# └─────┴─────┘
df.select(Polars.col("foo") + 1)
# =>
# shape: (3, 1)
# ┌─────┐
# │ foo │
# │ --- │
# │ i64 │
# ╞═════╡
# │ 2   │
# │ 3   │
# │ 4   │
# └─────┘
df.select([Polars.col("foo") + 1, Polars.col("bar") + 1])
# =>
# shape: (3, 2)
# ┌─────┬─────┐
# │ foo ┆ bar │
# │ --- ┆ --- │
# │ i64 ┆ i64 │
# ╞═════╪═════╡
# │ 2   ┆ 7   │
# │ 3   ┆ 8   │
# │ 4   ┆ 9   │
# └─────┴─────┘
df.select(Polars.when(Polars.col("foo") > 2).then(10).otherwise(0))
# =>
# shape: (3, 1)
# ┌─────────┐
# │ literal │
# │ ---     │
# │ i32     │
# ╞═════════╡
# │ 0       │
# │ 0       │
# │ 10      │
# └─────────┘

Parameters:

  • exprs (Array) —

    Column(s) to select, specified as positional arguments. Accepts expression input. Strings are parsed as column names, other non-expression inputs are parsed as literals.

  • named_exprs (Hash) —

    Additional columns to select, specified as keyword arguments. The columns will be renamed to the keyword used.

Returns:



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# File 'lib/polars/data_frame.rb', line 5063

def select(*exprs, **named_exprs)
  lazy.select(*exprs, **named_exprs).collect(optimizations: QueryOptFlags._eager)
end

#select_seq(*exprs, **named_exprs) ⇒ DataFrame

Select columns from this DataFrame.

This will run all expression sequentially instead of in parallel. Use this when the work per expression is cheap.

Parameters:

  • exprs (Array) —

    Column(s) to select, specified as positional arguments. Accepts expression input. Strings are parsed as column names, other non-expression inputs are parsed as literals.

  • named_exprs (Hash) —

    Additional columns to select, specified as keyword arguments. The columns will be renamed to the keyword used.

Returns:



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# File 'lib/polars/data_frame.rb', line 5081

def select_seq(*exprs, **named_exprs)
  lazy
  .select_seq(*exprs, **named_exprs)
  .collect(optimizations: QueryOptFlags._eager)
end

#serialize(file = nil) ⇒ Object

Note:

Serialization is not stable across Polars versions: a LazyFrame serialized in one Polars version may not be deserializable in another Polars version.

Serialize this DataFrame to a file or string.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6, 7, 8]
  }
)
bytes = df.serialize
Polars::DataFrame.deserialize(StringIO.new(bytes))
# =>
# shape: (3, 2)
# ┌─────┬─────┐
# │ foo ┆ bar │
# │ --- ┆ --- │
# │ i64 ┆ i64 │
# ╞═════╪═════╡
# │ 1   ┆ 6   │
# │ 2   ┆ 7   │
# │ 3   ┆ 8   │
# └─────┴─────┘

Parameters:

  • file (Object) (defaults to: nil) —

    File path or writable file-like object to which the result will be written. If set to nil (default), the output is returned as a string instead.

Returns:



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# File 'lib/polars/data_frame.rb', line 886

def serialize(file = nil)
  serializer = _df.method(:serialize_binary)

  Utils.serialize_polars_object(serializer, file)
end

#set_sorted(column, descending: false) ⇒ DataFrame

Note:

This can lead to incorrect results if the data is NOT sorted! Use with care!

Flag a column as sorted.

This can speed up future operations.

Parameters:

  • column (Object) —

    Column that is sorted.

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

    Whether the column is sorted in descending order.

Returns:



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# File 'lib/polars/data_frame.rb', line 6550

def set_sorted(
  column,
  descending: false
)
  lazy
    .set_sorted(column, descending: descending)
    .collect(optimizations: QueryOptFlags._eager)
end

#shape ⇒ Array

Get the shape of the DataFrame.

Examples:

df = Polars::DataFrame.new({"foo" => [1, 2, 3, 4, 5]})
df.shape
# => [5, 1]

Returns:



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# File 'lib/polars/data_frame.rb', line 180

def shape
  _df.shape
end

#shift(n = 1, fill_value: nil) ⇒ DataFrame

Shift values by the given period.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6, 7, 8],
    "ham" => ["a", "b", "c"]
  }
)
df.shift(1)
# =>
# shape: (3, 3)
# ┌──────┬──────┬──────┐
# │ foo  ┆ bar  ┆ ham  │
# │ ---  ┆ ---  ┆ ---  │
# │ i64  ┆ i64  ┆ str  │
# ╞══════╪══════╪══════╡
# │ null ┆ null ┆ null │
# │ 1    ┆ 6    ┆ a    │
# │ 2    ┆ 7    ┆ b    │
# └──────┴──────┴──────┘
df.shift(-1)
# =>
# shape: (3, 3)
# ┌──────┬──────┬──────┐
# │ foo  ┆ bar  ┆ ham  │
# │ ---  ┆ ---  ┆ ---  │
# │ i64  ┆ i64  ┆ str  │
# ╞══════╪══════╪══════╡
# │ 2    ┆ 7    ┆ b    │
# │ 3    ┆ 8    ┆ c    │
# │ null ┆ null ┆ null │
# └──────┴──────┴──────┘

Parameters:

  • n (Integer) (defaults to: 1) —

    Number of places to shift (may be negative).

  • fill_value (Object) (defaults to: nil) —

    Fill the resulting null values with this value.

Returns:



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# File 'lib/polars/data_frame.rb', line 4904

def shift(n = 1, fill_value: nil)
  lazy.shift(n, fill_value: fill_value).collect(optimizations: QueryOptFlags._eager)
end

#shrink_to_fit(in_place: false) ⇒ DataFrame

Shrink DataFrame memory usage.

Shrinks to fit the exact capacity needed to hold the data.

Returns:



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# File 'lib/polars/data_frame.rb', line 6309

def shrink_to_fit(in_place: false)
  if in_place
    _df.shrink_to_fit
    self
  else
    df = clone
    df._df.shrink_to_fit
    df
  end
end

#slice(offset, length = nil) ⇒ DataFrame

Get a slice of this DataFrame.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6.0, 7.0, 8.0],
    "ham" => ["a", "b", "c"]
  }
)
df.slice(1, 2)
# =>
# shape: (2, 3)
# ┌─────┬─────┬─────┐
# │ foo ┆ bar ┆ ham │
# │ --- ┆ --- ┆ --- │
# │ i64 ┆ f64 ┆ str │
# ╞═════╪═════╪═════╡
# │ 2   ┆ 7.0 ┆ b   │
# │ 3   ┆ 8.0 ┆ c   │
# └─────┴─────┴─────┘

Parameters:

  • offset (Integer) —

    Start index. Negative indexing is supported.

  • length (Integer, nil) (defaults to: nil) —

    Length of the slice. If set to nil, all rows starting at the offset will be selected.

Returns:



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# File 'lib/polars/data_frame.rb', line 2508

def slice(offset, length = nil)
  if !length.nil? && length < 0
    length = height - offset + length
  end
  _from_rbdf(_df.slice(offset, length))
end

#sort(by, *more_by, descending: false, nulls_last: false, multithreaded: true, maintain_order: false) ⇒ DataFrame

Sort the dataframe by the given columns.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6.0, 7.0, 8.0],
    "ham" => ["a", "b", "c"]
  }
)
df.sort("foo", descending: true)
# =>
# shape: (3, 3)
# ┌─────┬─────┬─────┐
# │ foo ┆ bar ┆ ham │
# │ --- ┆ --- ┆ --- │
# │ i64 ┆ f64 ┆ str │
# ╞═════╪═════╪═════╡
# │ 3   ┆ 8.0 ┆ c   │
# │ 2   ┆ 7.0 ┆ b   │
# │ 1   ┆ 6.0 ┆ a   │
# └─────┴─────┴─────┘

Sort by multiple columns.

df.sort(
  [Polars.col("foo"), Polars.col("bar")**2],
  descending: [true, false]
)
# =>
# shape: (3, 3)
# ┌─────┬─────┬─────┐
# │ foo ┆ bar ┆ ham │
# │ --- ┆ --- ┆ --- │
# │ i64 ┆ f64 ┆ str │
# ╞═════╪═════╪═════╡
# │ 3   ┆ 8.0 ┆ c   │
# │ 2   ┆ 7.0 ┆ b   │
# │ 1   ┆ 6.0 ┆ a   │
# └─────┴─────┴─────┘

Parameters:

  • by (Object) —

    Column(s) to sort by. Accepts expression input, including selectors. Strings are parsed as column names.

  • more_by (Array) —

    Additional columns to sort by, specified as positional arguments.

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

    Sort in descending order. When sorting by multiple columns, can be specified per column by passing an array of booleans.

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

    Place null values last; can specify a single boolean applying to all columns or an array of booleans for per-column control.

  • multithreaded (Boolean) (defaults to: true) —

    Sort using multiple threads.

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

    Whether the order should be maintained if elements are equal.

Returns:



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# File 'lib/polars/data_frame.rb', line 2196

def sort(
  by,
  *more_by,
  descending: false,
  nulls_last: false,
  multithreaded: true,
  maintain_order: false
)
  lazy
    .sort(
      by,
      *more_by,
      descending: descending,
      nulls_last: nulls_last,
      multithreaded: multithreaded,
      maintain_order: maintain_order
    )
    .collect(optimizations: QueryOptFlags._eager)
end

#sort!(by, descending: false, nulls_last: false) ⇒ DataFrame

Sort the DataFrame by column in-place.

Parameters:

  • by (String) —

    By which column to sort.

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

    Reverse/descending sort.

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

    Place null values last. Can only be used if sorted by a single column.

Returns:



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# File 'lib/polars/data_frame.rb', line 2226

def sort!(by, descending: false, nulls_last: false)
  self._df = sort(by, descending: descending, nulls_last: nulls_last)._df
end

#sql(query, table_name: "self") ⇒ DataFrame

Note:

This functionality is considered unstable, although it is close to being considered stable. It may be changed at any point without it being considered a breaking change.

Note:
  • The calling frame is automatically registered as a table in the SQL context under the name "self". If you want access to the DataFrames and LazyFrames found in the current globals, use the top-level :meth:pl.sql <polars.sql>.
  • More control over registration and execution behaviour is available by using the :class:SQLContext object.
  • The SQL query executes in lazy mode before being collected and returned as a DataFrame.

Execute a SQL query against the DataFrame.

Examples:

Query the DataFrame using SQL:

df1 = Polars::DataFrame.new(
  {
    "a" => [1, 2, 3],
    "b" => ["zz", "yy", "xx"],
    "c" => [Date.new(1999, 12, 31), Date.new(2010, 10, 10), Date.new(2077, 8, 8)]
  }
)
df1.sql("SELECT c, b FROM self WHERE a > 1")
# =>
# shape: (2, 2)
# ┌────────────┬─────┐
# │ c          ┆ b   │
# │ ---        ┆ --- │
# │ date       ┆ str │
# ╞════════════╪═════╡
# │ 2010-10-10 ┆ yy  │
# │ 2077-08-08 ┆ xx  │
# └────────────┴─────┘

Apply transformations to a DataFrame using SQL, aliasing "self" to "frame".

df1.sql(
  "
    SELECT
        a,
        (a % 2 == 0) AS a_is_even,
        CONCAT_WS(':', b, b) AS b_b,
        EXTRACT(year FROM c) AS year,
        0::float4 AS \"zero\",
    FROM frame
  ",
  table_name: "frame"
)
# =>
# shape: (3, 5)
# ┌─────┬───────────┬───────┬──────┬──────┐
# │ a   ┆ a_is_even ┆ b_b   ┆ year ┆ zero │
# │ --- ┆ ---       ┆ ---   ┆ ---  ┆ ---  │
# │ i64 ┆ bool      ┆ str   ┆ i32  ┆ f32  │
# ╞═════╪═══════════╪═══════╪══════╪══════╡
# │ 1   ┆ false     ┆ zz:zz ┆ 1999 ┆ 0.0  │
# │ 2   ┆ true      ┆ yy:yy ┆ 2010 ┆ 0.0  │
# │ 3   ┆ false     ┆ xx:xx ┆ 2077 ┆ 0.0  │
# └─────┴───────────┴───────┴──────┴──────┘

Parameters:

  • query (String) —

    SQL query to execute.

  • table_name (String) (defaults to: "self") —

    Optionally provide an explicit name for the table that represents the calling frame (defaults to "self").

Returns:



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# File 'lib/polars/data_frame.rb', line 2298

def sql(query, table_name: "self")
  ctx = SQLContext.new(eager: true)
  name = table_name || "self"
  ctx.register(name, self)
  ctx.execute(query)
end

#std(ddof: 1) ⇒ DataFrame

Aggregate the columns of this DataFrame to their standard deviation value.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6, 7, 8],
    "ham" => ["a", "b", "c"]
  }
)
df.std
# =>
# shape: (1, 3)
# ┌─────┬─────┬──────┐
# │ foo ┆ bar ┆ ham  │
# │ --- ┆ --- ┆ ---  │
# │ f64 ┆ f64 ┆ str  │
# ╞═════╪═════╪══════╡
# │ 1.0 ┆ 1.0 ┆ null │
# └─────┴─────┴──────┘
df.std(ddof: 0)
# =>
# shape: (1, 3)
# ┌──────────┬──────────┬──────┐
# │ foo      ┆ bar      ┆ ham  │
# │ ---      ┆ ---      ┆ ---  │
# │ f64      ┆ f64      ┆ str  │
# ╞══════════╪══════════╪══════╡
# │ 0.816497 ┆ 0.816497 ┆ null │
# └──────────┴──────────┴──────┘

Parameters:

  • ddof (Integer) (defaults to: 1) —

    Degrees of freedom

Returns:



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# File 'lib/polars/data_frame.rb', line 5503

def std(ddof: 1)
  lazy.std(ddof: ddof).collect(optimizations: QueryOptFlags._eager)
end

#sum ⇒ DataFrame

Aggregate the columns of this DataFrame to their sum value.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6, 7, 8],
    "ham" => ["a", "b", "c"],
  }
)
df.sum
# =>
# shape: (1, 3)
# ┌─────┬─────┬──────┐
# │ foo ┆ bar ┆ ham  │
# │ --- ┆ --- ┆ ---  │
# │ i64 ┆ i64 ┆ str  │
# ╞═════╪═════╪══════╡
# │ 6   ┆ 21  ┆ null │
# └─────┴─────┴──────┘

Returns:



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# File 'lib/polars/data_frame.rb', line 5376

def sum
  lazy.sum.collect(optimizations: QueryOptFlags._eager)
end

#sum_horizontal(ignore_nulls: true) ⇒ Series

Sum all values horizontally across columns.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [4.0, 5.0, 6.0]
  }
)
df.sum_horizontal
# =>
# shape: (3,)
# Series: 'sum' [f64]
# [
#         5.0
#         7.0
#         9.0
# ]

Parameters:

  • ignore_nulls (Boolean) (defaults to: true) —

    Ignore null values (default). If set to false, any null value in the input will lead to a null output.

Returns:



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# File 'lib/polars/data_frame.rb', line 5404

def sum_horizontal(ignore_nulls: true)
  select(
    sum: F.sum_horizontal(F.all, ignore_nulls: ignore_nulls)
  ).to_series
end

#tail(n = 5) ⇒ DataFrame

Get the last n rows.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3, 4, 5],
    "bar" => [6, 7, 8, 9, 10],
    "ham" => ["a", "b", "c", "d", "e"]
  }
)
df.tail(3)
# =>
# shape: (3, 3)
# ┌─────┬─────┬─────┐
# │ foo ┆ bar ┆ ham │
# │ --- ┆ --- ┆ --- │
# │ i64 ┆ i64 ┆ str │
# ╞═════╪═════╪═════╡
# │ 3   ┆ 8   ┆ c   │
# │ 4   ┆ 9   ┆ d   │
# │ 5   ┆ 10  ┆ e   │
# └─────┴─────┴─────┘

Parameters:

  • n (Integer) (defaults to: 5) —

    Number of rows to return.

Returns:



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# File 'lib/polars/data_frame.rb', line 2603

def tail(n = 5)
  _from_rbdf(_df.tail(n))
end

#to_a ⇒ Array

Returns an array representing the DataFrame

Returns:



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# File 'lib/polars/data_frame.rb', line 418

def to_a
  rows(named: true)
end

#to_csv(**options) ⇒ String

Write to comma-separated values (CSV) string.

Returns:



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# File 'lib/polars/data_frame.rb', line 1099

def to_csv(**options)
  write_csv(**options)
end

#to_dummies(columns: nil, separator: "_", drop_first: false, drop_nulls: false) ⇒ DataFrame

Get one hot encoded dummy variables.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2],
    "bar" => [3, 4],
    "ham" => ["a", "b"]
  }
)
df.to_dummies
# =>
# shape: (2, 6)
# ┌───────┬───────┬───────┬───────┬───────┬───────┐
# │ foo_1 ┆ foo_2 ┆ bar_3 ┆ bar_4 ┆ ham_a ┆ ham_b │
# │ ---   ┆ ---   ┆ ---   ┆ ---   ┆ ---   ┆ ---   │
# │ u8    ┆ u8    ┆ u8    ┆ u8    ┆ u8    ┆ u8    │
# ╞═══════╪═══════╪═══════╪═══════╪═══════╪═══════╡
# │ 1     ┆ 0     ┆ 1     ┆ 0     ┆ 1     ┆ 0     │
# │ 0     ┆ 1     ┆ 0     ┆ 1     ┆ 0     ┆ 1     │
# └───────┴───────┴───────┴───────┴───────┴───────┘

Parameters:

  • columns (Array) (defaults to: nil) —

    A subset of columns to convert to dummy variables. nil means "all columns".

  • separator (String) (defaults to: "_") —

    Separator/delimiter used when generating column names.

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

    Remove the first category from the variables being encoded.

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

    If there are nil values in the series, a null column is not generated

Returns:



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# File 'lib/polars/data_frame.rb', line 5664

def to_dummies(columns: nil, separator: "_", drop_first: false, drop_nulls: false)
  if columns.is_a?(::String)
    columns = [columns]
  end
  _from_rbdf(_df.to_dummies(columns, separator, drop_first, drop_nulls))
end

#to_h(as_series: true) ⇒ Hash

Convert DataFrame to a hash mapping column name to values.

Returns:

  • (Hash) 


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# File 'lib/polars/data_frame.rb', line 779

def to_h(as_series: true)
  if as_series
    get_columns.to_h { |s| [s.name, s] }
  else
    get_columns.to_h { |s| [s.name, s.to_a] }
  end
end

#to_hashes ⇒ Array

Convert every row to a hash.

Examples:

df = Polars::DataFrame.new({"foo" => [1, 2, 3], "bar" => [4, 5, 6]})
df.to_hashes
# =>
# [{"foo"=>1, "bar"=>4}, {"foo"=>2, "bar"=>5}, {"foo"=>3, "bar"=>6}]

Returns:



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# File 'lib/polars/data_frame.rb', line 796

def to_hashes
  rows(named: true)
end

#to_numo ⇒ Numo::NArray

Convert DataFrame to a 2D Numo array.

This operation clones data.

Examples:

df = Polars::DataFrame.new(
  {"foo" => [1, 2, 3], "bar" => [6, 7, 8], "ham" => ["a", "b", "c"]}
)
df.to_numo.class
# => Numo::RObject

Returns:

  • (Numo::NArray) 


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# File 'lib/polars/data_frame.rb', line 812

def to_numo
  out = _df.to_numo
  if out.nil?
    Numo::NArray.vstack(width.times.map { |i| to_series(i).to_numo }).transpose
  else
    out
  end
end

#to_s ⇒ String Also known as: inspect

Returns a string representing the DataFrame.

Returns:



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# File 'lib/polars/data_frame.rb', line 410

def to_s
  _df.to_s
end

#to_series(index = 0) ⇒ Series

Select column as Series at index location.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6, 7, 8],
    "ham" => ["a", "b", "c"]
  }
)
df.to_series(1)
# =>
# shape: (3,)
# Series: 'bar' [i64]
# [
#         6
#         7
#         8
# ]

Parameters:

  • index (Integer) (defaults to: 0) —

    Location of selection.

Returns:



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# File 'lib/polars/data_frame.rb', line 847

def to_series(index = 0)
  if index < 0
    index = columns.length + index
  end
  Utils.wrap_s(_df.to_series(index))
end

#to_struct(name = "") ⇒ Series

Convert a DataFrame to a Series of type Struct.

Examples:

df = Polars::DataFrame.new(
  {
    "a" => [1, 2, 3, 4, 5],
    "b" => ["one", "two", "three", "four", "five"]
  }
)
df.to_struct("nums")
# =>
# shape: (5,)
# Series: 'nums' [struct[2]]
# [
#         {1,"one"}
#         {2,"two"}
#         {3,"three"}
#         {4,"four"}
#         {5,"five"}
# ]

Parameters:

  • name (String) (defaults to: "") —

    Name for the struct Series

Returns:



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# File 'lib/polars/data_frame.rb', line 6450

def to_struct(name = "")
  Utils.wrap_s(_df.to_struct(name))
end

#top_k(k, by:, reverse: false) ⇒ DataFrame

Return the k largest rows.

Non-null elements are always preferred over null elements, regardless of the value of reverse. The output is not guaranteed to be in any particular order, call sort after this function if you wish the output to be sorted.

Examples:

Get the rows which contain the 4 largest values in column b.

df = Polars::DataFrame.new(
  {
    "a" => ["a", "b", "a", "b", "b", "c"],
    "b" => [2, 1, 1, 3, 2, 1]
  }
)
df.top_k(4, by: "b")
# =>
# shape: (4, 2)
# ┌─────┬─────┐
# │ a   ┆ b   │
# │ --- ┆ --- │
# │ str ┆ i64 │
# ╞═════╪═════╡
# │ b   ┆ 3   │
# │ a   ┆ 2   │
# │ b   ┆ 2   │
# │ b   ┆ 1   │
# └─────┴─────┘

Get the rows which contain the 4 largest values when sorting on column b and a.

df.top_k(4, by: ["b", "a"])
# =>
# shape: (4, 2)
# ┌─────┬─────┐
# │ a   ┆ b   │
# │ --- ┆ --- │
# │ str ┆ i64 │
# ╞═════╪═════╡
# │ b   ┆ 3   │
# │ b   ┆ 2   │
# │ a   ┆ 2   │
# │ c   ┆ 1   │
# └─────┴─────┘

Parameters:

  • k (Integer) —

    Number of rows to return.

  • by (Object) —

    Column(s) used to determine the top rows. Accepts expression input. Strings are parsed as column names.

  • reverse (Object) (defaults to: false) —

    Consider the k smallest elements of the by column(s) (instead of the k largest). This can be specified per column by passing an array of booleans.

Returns:



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# File 'lib/polars/data_frame.rb', line 2359

def top_k(
  k,
  by:,
  reverse: false
)
  lazy
  .top_k(k, by: by, reverse: reverse)
  .collect(
    optimizations: QueryOptFlags.new(
      projection_pushdown: false,
      predicate_pushdown: false,
      comm_subplan_elim: false,
      slice_pushdown: true
    )
  )
end

#transpose(include_header: false, header_name: "column", column_names: nil) ⇒ DataFrame

Note:

This is a very expensive operation. Perhaps you can do it differently.

Transpose a DataFrame over the diagonal.

Examples:

df = Polars::DataFrame.new({"a" => [1, 2, 3], "b" => [1, 2, 3]})
df.transpose(include_header: true)
# =>
# shape: (2, 4)
# ┌────────┬──────────┬──────────┬──────────┐
# │ column ┆ column_0 ┆ column_1 ┆ column_2 │
# │ ---    ┆ ---      ┆ ---      ┆ ---      │
# │ str    ┆ i64      ┆ i64      ┆ i64      │
# ╞════════╪══════════╪══════════╪══════════╡
# │ a      ┆ 1        ┆ 2        ┆ 3        │
# │ b      ┆ 1        ┆ 2        ┆ 3        │
# └────────┴──────────┴──────────┴──────────┘

Replace the auto-generated column names with a list

df.transpose(include_header: false, column_names: ["a", "b", "c"])
# =>
# shape: (2, 3)
# ┌─────┬─────┬─────┐
# │ a   ┆ b   ┆ c   │
# │ --- ┆ --- ┆ --- │
# │ i64 ┆ i64 ┆ i64 │
# ╞═════╪═════╪═════╡
# │ 1   ┆ 2   ┆ 3   │
# │ 1   ┆ 2   ┆ 3   │
# └─────┴─────┴─────┘

Include the header as a separate column

df.transpose(
  include_header: true, header_name: "foo", column_names: ["a", "b", "c"]
)
# =>
# shape: (2, 4)
# ┌─────┬─────┬─────┬─────┐
# │ foo ┆ a   ┆ b   ┆ c   │
# │ --- ┆ --- ┆ --- ┆ --- │
# │ str ┆ i64 ┆ i64 ┆ i64 │
# ╞═════╪═════╪═════╪═════╡
# │ a   ┆ 1   ┆ 2   ┆ 3   │
# │ b   ┆ 1   ┆ 2   ┆ 3   │
# └─────┴─────┴─────┴─────┘

Parameters:

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

    If set, the column names will be added as first column.

  • header_name (String) (defaults to: "column") —

    If include_header is set, this determines the name of the column that will be inserted.

  • column_names (Array) (defaults to: nil) —

    Optional generator/iterator that yields column names. Will be used to replace the columns in the DataFrame.

Returns:



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# File 'lib/polars/data_frame.rb', line 1625

def transpose(include_header: false, header_name: "column", column_names: nil)
  keep_names_as = include_header ? header_name : nil
  _from_rbdf(_df.transpose(keep_names_as, column_names))
end

#unique(maintain_order: false, subset: nil, keep: "any") ⇒ DataFrame

Note:

Note that this fails if there is a column of type List in the DataFrame or subset.

Drop duplicate rows from this DataFrame.

Examples:

df = Polars::DataFrame.new(
  {
    "a" => [1, 1, 2, 3, 4, 5],
    "b" => [0.5, 0.5, 1.0, 2.0, 3.0, 3.0],
    "c" => [true, true, true, false, true, true]
  }
)
df.unique(maintain_order: true)
# =>
# shape: (5, 3)
# ┌─────┬─────┬───────┐
# │ a   ┆ b   ┆ c     │
# │ --- ┆ --- ┆ ---   │
# │ i64 ┆ f64 ┆ bool  │
# ╞═════╪═════╪═══════╡
# │ 1   ┆ 0.5 ┆ true  │
# │ 2   ┆ 1.0 ┆ true  │
# │ 3   ┆ 2.0 ┆ false │
# │ 4   ┆ 3.0 ┆ true  │
# │ 5   ┆ 3.0 ┆ true  │
# └─────┴─────┴───────┘

Parameters:

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

    Keep the same order as the original DataFrame. This requires more work to compute.

  • subset (Object) (defaults to: nil) —

    Subset to use to compare rows.

  • keep ("first", "last") (defaults to: "any") —

    Which of the duplicate rows to keep (in conjunction with subset).

Returns:



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# File 'lib/polars/data_frame.rb', line 5709

def unique(maintain_order: false, subset: nil, keep: "any")
  self._from_rbdf(
    lazy
      .unique(maintain_order: maintain_order, subset: subset, keep: keep)
      .collect(optimizations: QueryOptFlags._eager)
      ._df
  )
end

#unnest(columns, *more_columns, separator: nil) ⇒ DataFrame

Decompose a struct into its fields.

The fields will be inserted into the DataFrame on the location of the struct type.

Examples:

df = Polars::DataFrame.new(
  {
    "before" => ["foo", "bar"],
    "t_a" => [1, 2],
    "t_b" => ["a", "b"],
    "t_c" => [true, nil],
    "t_d" => [[1, 2], [3]],
    "after" => ["baz", "womp"]
  }
).select(["before", Polars.struct(Polars.col("^t_.$")).alias("t_struct"), "after"])
df.unnest("t_struct")
# =>
# shape: (2, 6)
# ┌────────┬─────┬─────┬──────┬───────────┬───────┐
# │ before ┆ t_a ┆ t_b ┆ t_c  ┆ t_d       ┆ after │
# │ ---    ┆ --- ┆ --- ┆ ---  ┆ ---       ┆ ---   │
# │ str    ┆ i64 ┆ str ┆ bool ┆ list[i64] ┆ str   │
# ╞════════╪═════╪═════╪══════╪═══════════╪═══════╡
# │ foo    ┆ 1   ┆ a   ┆ true ┆ [1, 2]    ┆ baz   │
# │ bar    ┆ 2   ┆ b   ┆ null ┆ [3]       ┆ womp  │
# └────────┴─────┴─────┴──────┴───────────┴───────┘

Parameters:

  • columns (Object) —

    Name of the struct column(s) that should be unnested.

  • more_columns (Array) —

    Additional columns to unnest, specified as positional arguments.

  • separator (String) (defaults to: nil) —

    Rename output column names as combination of the struct column name, name separator and field name.

Returns:



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# File 'lib/polars/data_frame.rb', line 6491

def unnest(columns, *more_columns, separator: nil)
  lazy.unnest(columns, *more_columns, separator: separator).collect(optimizations: QueryOptFlags._eager)
end

#unpivot(on = nil, index: nil, variable_name: nil, value_name: nil) ⇒ DataFrame

Unpivot a DataFrame from wide to long format.

Optionally leaves identifiers set.

This function is useful to massage a DataFrame into a format where one or more columns are identifier variables (index) while all other columns, considered measured variables (on), are "unpivoted" to the row axis leaving just two non-identifier columns, 'variable' and 'value'.

Examples:

df = Polars::DataFrame.new(
  {
    "a" => ["x", "y", "z"],
    "b" => [1, 3, 5],
    "c" => [2, 4, 6]
  }
)
df.unpivot(Polars.cs.numeric, index: "a")
# =>
# shape: (6, 3)
# ┌─────┬──────────┬───────┐
# │ a   ┆ variable ┆ value │
# │ --- ┆ ---      ┆ ---   │
# │ str ┆ str      ┆ i64   │
# ╞═════╪══════════╪═══════╡
# │ x   ┆ b        ┆ 1     │
# │ y   ┆ b        ┆ 3     │
# │ z   ┆ b        ┆ 5     │
# │ x   ┆ c        ┆ 2     │
# │ y   ┆ c        ┆ 4     │
# │ z   ┆ c        ┆ 6     │
# └─────┴──────────┴───────┘

Parameters:

  • on (Object) (defaults to: nil) —

    Column(s) or selector(s) to use as values variables; if on is empty all columns that are not in index will be used.

  • index (Object) (defaults to: nil) —

    Column(s) or selector(s) to use as identifier variables.

  • variable_name (Object) (defaults to: nil) —

    Name to give to the variable column. Defaults to "variable"

  • value_name (Object) (defaults to: nil) —

    Name to give to the value column. Defaults to "value"

Returns:



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# File 'lib/polars/data_frame.rb', line 4629

def unpivot(on = nil, index: nil, variable_name: nil, value_name: nil)
  on = on.nil? ? [] : Utils._expand_selectors(self, on)
  index = index.nil? ? [] : Utils._expand_selectors(self, index)

  _from_rbdf(_df.unpivot(on, index, value_name, variable_name))
end

#unstack(step:, how: "vertical", columns: nil, fill_values: nil) ⇒ DataFrame

Note:

This functionality is experimental and may be subject to changes without it being considered a breaking change.

Unstack a long table to a wide form without doing an aggregation.

This can be much faster than a pivot, because it can skip the grouping phase.

Examples:

df = Polars::DataFrame.new(
  {
    "col1" => "A".."I",
    "col2" => Polars.arange(0, 9, eager: true)
  }
)
# =>
# shape: (9, 2)
# ┌──────┬──────┐
# │ col1 ┆ col2 │
# │ ---  ┆ ---  │
# │ str  ┆ i64  │
# ╞══════╪══════╡
# │ A    ┆ 0    │
# │ B    ┆ 1    │
# │ C    ┆ 2    │
# │ D    ┆ 3    │
# │ E    ┆ 4    │
# │ F    ┆ 5    │
# │ G    ┆ 6    │
# │ H    ┆ 7    │
# │ I    ┆ 8    │
# └──────┴──────┘
df.unstack(step: 3, how: "vertical")
# =>
# shape: (3, 6)
# ┌────────┬────────┬────────┬────────┬────────┬────────┐
# │ col1_0 ┆ col1_1 ┆ col1_2 ┆ col2_0 ┆ col2_1 ┆ col2_2 │
# │ ---    ┆ ---    ┆ ---    ┆ ---    ┆ ---    ┆ ---    │
# │ str    ┆ str    ┆ str    ┆ i64    ┆ i64    ┆ i64    │
# ╞════════╪════════╪════════╪════════╪════════╪════════╡
# │ A      ┆ D      ┆ G      ┆ 0      ┆ 3      ┆ 6      │
# │ B      ┆ E      ┆ H      ┆ 1      ┆ 4      ┆ 7      │
# │ C      ┆ F      ┆ I      ┆ 2      ┆ 5      ┆ 8      │
# └────────┴────────┴────────┴────────┴────────┴────────┘
df.unstack(step: 3, how: "horizontal")
# =>
# shape: (3, 6)
# ┌────────┬────────┬────────┬────────┬────────┬────────┐
# │ col1_0 ┆ col1_1 ┆ col1_2 ┆ col2_0 ┆ col2_1 ┆ col2_2 │
# │ ---    ┆ ---    ┆ ---    ┆ ---    ┆ ---    ┆ ---    │
# │ str    ┆ str    ┆ str    ┆ i64    ┆ i64    ┆ i64    │
# ╞════════╪════════╪════════╪════════╪════════╪════════╡
# │ A      ┆ B      ┆ C      ┆ 0      ┆ 1      ┆ 2      │
# │ D      ┆ E      ┆ F      ┆ 3      ┆ 4      ┆ 5      │
# │ G      ┆ H      ┆ I      ┆ 6      ┆ 7      ┆ 8      │
# └────────┴────────┴────────┴────────┴────────┴────────┘

Parameters:

  • step —

    Integer Number of rows in the unstacked frame.

  • how ("vertical", "horizontal") (defaults to: "vertical") —

    Direction of the unstack.

  • columns (Object) (defaults to: nil) —

    Column to include in the operation.

  • fill_values (Object) (defaults to: nil) —

    Fill values that don't fit the new size with this value.

Returns:



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# File 'lib/polars/data_frame.rb', line 4707

def unstack(step:, how: "vertical", columns: nil, fill_values: nil)
  if !columns.nil?
    df = select(columns)
  else
    df = self
  end

  height = df.height
  if how == "vertical"
    n_rows = step
    n_cols = (height / n_rows.to_f).ceil
  else
    n_cols = step
    n_rows = (height / n_cols.to_f).ceil
  end

  n_fill = n_cols * n_rows - height

  if n_fill > 0
    if !fill_values.is_a?(::Array)
      fill_values = [fill_values] * df.width
    end

    df = df.select(
      df.get_columns.zip(fill_values).map do |s, next_fill|
        s.extend_constant(next_fill, n_fill)
      end
    )
  end

  if how == "horizontal"
    df = (
      df.with_columns(
        (Polars.arange(0, n_cols * n_rows, eager: true) % n_cols).alias(
          "__sort_order"
        )
      )
      .sort("__sort_order")
      .drop("__sort_order")
    )
  end

  zfill_val = Math.log10(n_cols).floor + 1
  slices =
    df.get_columns.flat_map do |s|
      n_cols.times.map do |slice_nbr|
        s.slice(slice_nbr * n_rows, n_rows).alias("%s_%0#{zfill_val}d" % [s.name, slice_nbr])
      end
    end

  _from_rbdf(DataFrame.new(slices)._df)
end

#update(other, on: nil, how: "left", left_on: nil, right_on: nil, include_nulls: false, maintain_order: "left") ⇒ DataFrame

Note:

This functionality is considered unstable. It may be changed at any point without it being considered a breaking change.

Note:

This is syntactic sugar for a left/inner join that preserves the order of the left DataFrame by default, with an optional coalesce when include_nulls: false.

Update the values in this DataFrame with the values in other.

Examples:

Update df values with the non-null values in new_df, by row index:

df = Polars::DataFrame.new(
  {
    "A" => [1, 2, 3, 4],
    "B" => [400, 500, 600, 700]
  }
)
new_df = Polars::DataFrame.new(
  {
    "B" => [-66, nil, -99],
    "C" => [5, 3, 1]
  }
)
df.update(new_df)
# =>
# shape: (4, 2)
# ┌─────┬─────┐
# │ A   ┆ B   │
# │ --- ┆ --- │
# │ i64 ┆ i64 │
# ╞═════╪═════╡
# │ 1   ┆ -66 │
# │ 2   ┆ 500 │
# │ 3   ┆ -99 │
# │ 4   ┆ 700 │
# └─────┴─────┘

Update df values with the non-null values in new_df, by row index, but only keeping those rows that are common to both frames:

df.update(new_df, how: "inner")
# =>
# shape: (3, 2)
# ┌─────┬─────┐
# │ A   ┆ B   │
# │ --- ┆ --- │
# │ i64 ┆ i64 │
# ╞═════╪═════╡
# │ 1   ┆ -66 │
# │ 2   ┆ 500 │
# │ 3   ┆ -99 │
# └─────┴─────┘

Update df values with the non-null values in new_df, using a full outer join strategy that defines explicit join columns in each frame:

df.update(new_df, left_on: ["A"], right_on: ["C"], how: "full")
# =>
# shape: (5, 2)
# ┌─────┬─────┐
# │ A   ┆ B   │
# │ --- ┆ --- │
# │ i64 ┆ i64 │
# ╞═════╪═════╡
# │ 1   ┆ -99 │
# │ 2   ┆ 500 │
# │ 3   ┆ 600 │
# │ 4   ┆ 700 │
# │ 5   ┆ -66 │
# └─────┴─────┘

Update df values including null values in new_df, using a full outer join strategy that defines explicit join columns in each frame:

df.update(new_df, left_on: "A", right_on: "C", how: "full", include_nulls: true)
# =>
# shape: (5, 2)
# ┌─────┬──────┐
# │ A   ┆ B    │
# │ --- ┆ ---  │
# │ i64 ┆ i64  │
# ╞═════╪══════╡
# │ 1   ┆ -99  │
# │ 2   ┆ 500  │
# │ 3   ┆ null │
# │ 4   ┆ 700  │
# │ 5   ┆ -66  │
# └─────┴──────┘

Parameters:

  • other (DataFrame) —

    DataFrame that will be used to update the values

  • on (Object) (defaults to: nil) —

    Column names that will be joined on. If set to nil (default), the implicit row index of each frame is used as a join key.

  • how ('left', 'inner', 'full') (defaults to: "left") —
    • 'left' will keep all rows from the left table; rows may be duplicated if multiple rows in the right frame match the left row's key.
    • 'inner' keeps only those rows where the key exists in both frames.
    • 'full' will update existing rows where the key matches while also adding any new rows contained in the given frame.
  • left_on (Object) (defaults to: nil) —

    Join column(s) of the left DataFrame.

  • right_on (Object) (defaults to: nil) —

    Join column(s) of the right DataFrame.

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

    Overwrite values in the left frame with null values from the right frame. If set to false (default), null values in the right frame are ignored.

  • maintain_order ('none', 'left', 'right', 'left_right', 'right_left') (defaults to: "left") —

    Which order of rows from the inputs to preserve. See DataFrame.join for details. Unlike join this function preserves the left order by default.

Returns:



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# File 'lib/polars/data_frame.rb', line 6667

def update(
  other,
  on: nil,
  how: "left",
  left_on: nil,
  right_on: nil,
  include_nulls: false,
  maintain_order: "left"
)
  Utils.require_same_type(self, other)
  lazy
  .update(
    other.lazy,
    on: on,
    how: how,
    left_on: left_on,
    right_on: right_on,
    include_nulls: include_nulls,
    maintain_order: maintain_order
  )
  .collect(optimizations: QueryOptFlags._eager)
end

#upsample(time_column:, every:, group_by: nil, maintain_order: false) ⇒ DataFrame

Upsample a DataFrame at a regular frequency.

The every and offset arguments are created with the following string language:

  • 1ns (1 nanosecond)
  • 1us (1 microsecond)
  • 1ms (1 millisecond)
  • 1s (1 second)
  • 1m (1 minute)
  • 1h (1 hour)
  • 1d (1 day)
  • 1w (1 week)
  • 1mo (1 calendar month)
  • 1y (1 calendar year)
  • 1i (1 index count)

Or combine them: "3d12h4m25s" # 3 days, 12 hours, 4 minutes, and 25 seconds

Examples:

Upsample a DataFrame by a certain interval.

df = Polars::DataFrame.new(
  {
    "time" => [
      DateTime.new(2021, 2, 1),
      DateTime.new(2021, 4, 1),
      DateTime.new(2021, 5, 1),
      DateTime.new(2021, 6, 1)
    ],
    "groups" => ["A", "B", "A", "B"],
    "values" => [0, 1, 2, 3]
  }
).set_sorted("time")
df.upsample(
  time_column: "time", every: "1mo", group_by: "groups", maintain_order: true
).select(Polars.all.forward_fill)
# =>
# shape: (7, 3)
# ┌─────────────────────┬────────┬────────┐
# │ time                ┆ groups ┆ values │
# │ ---                 ┆ ---    ┆ ---    │
# │ datetime[ns]        ┆ str    ┆ i64    │
# ╞═════════════════════╪════════╪════════╡
# │ 2021-02-01 00:00:00 ┆ A      ┆ 0      │
# │ 2021-03-01 00:00:00 ┆ A      ┆ 0      │
# │ 2021-04-01 00:00:00 ┆ A      ┆ 0      │
# │ 2021-05-01 00:00:00 ┆ A      ┆ 2      │
# │ 2021-04-01 00:00:00 ┆ B      ┆ 1      │
# │ 2021-05-01 00:00:00 ┆ B      ┆ 1      │
# │ 2021-06-01 00:00:00 ┆ B      ┆ 3      │
# └─────────────────────┴────────┴────────┘

Parameters:

  • time_column (Object) —

    time column will be used to determine a date_range. Note that this column has to be sorted for the output to make sense.

  • every (String) —

    interval will start 'every' duration

  • group_by (Object) (defaults to: nil) —

    First group by these columns and then upsample for every group

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

    Keep the ordering predictable. This is slower.

Returns:



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# File 'lib/polars/data_frame.rb', line 3388

def upsample(
  time_column:,
  every:,
  group_by: nil,
  maintain_order: false
)
  if group_by.nil?
    group_by = []
  end
  if group_by.is_a?(::String)
    group_by = [group_by]
  end

  every = Utils.parse_as_duration_string(every)

  _from_rbdf(
    _df.upsample(group_by, time_column, every, maintain_order)
  )
end

#var(ddof: 1) ⇒ DataFrame

Aggregate the columns of this DataFrame to their variance value.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6, 7, 8],
    "ham" => ["a", "b", "c"]
  }
)
df.var
# =>
# shape: (1, 3)
# ┌─────┬─────┬──────┐
# │ foo ┆ bar ┆ ham  │
# │ --- ┆ --- ┆ ---  │
# │ f64 ┆ f64 ┆ str  │
# ╞═════╪═════╪══════╡
# │ 1.0 ┆ 1.0 ┆ null │
# └─────┴─────┴──────┘
df.var(ddof: 0)
# =>
# shape: (1, 3)
# ┌──────────┬──────────┬──────┐
# │ foo      ┆ bar      ┆ ham  │
# │ ---      ┆ ---      ┆ ---  │
# │ f64      ┆ f64      ┆ str  │
# ╞══════════╪══════════╪══════╡
# │ 0.666667 ┆ 0.666667 ┆ null │
# └──────────┴──────────┴──────┘

Parameters:

  • ddof (Integer) (defaults to: 1) —

    Degrees of freedom

Returns:



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# File 'lib/polars/data_frame.rb', line 5544

def var(ddof: 1)
  lazy.var(ddof: ddof).collect(optimizations: QueryOptFlags._eager)
end

#vstack(other, in_place: false) ⇒ DataFrame

Grow this DataFrame vertically by stacking a DataFrame to it.

Examples:

df1 = Polars::DataFrame.new(
  {
    "foo" => [1, 2],
    "bar" => [6, 7],
    "ham" => ["a", "b"]
  }
)
df2 = Polars::DataFrame.new(
  {
    "foo" => [3, 4],
    "bar" => [8, 9],
    "ham" => ["c", "d"]
  }
)
df1.vstack(df2)
# =>
# shape: (4, 3)
# ┌─────┬─────┬─────┐
# │ foo ┆ bar ┆ ham │
# │ --- ┆ --- ┆ --- │
# │ i64 ┆ i64 ┆ str │
# ╞═════╪═════╪═════╡
# │ 1   ┆ 6   ┆ a   │
# │ 2   ┆ 7   ┆ b   │
# │ 3   ┆ 8   ┆ c   │
# │ 4   ┆ 9   ┆ d   │
# └─────┴─────┴─────┘

Parameters:

  • other (DataFrame) —

    DataFrame to stack.

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

    Modify in place

Returns:



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# File 'lib/polars/data_frame.rb', line 3959

def vstack(other, in_place: false)
  if in_place
    _df.vstack_mut(other._df)
    self
  else
    _from_rbdf(_df.vstack(other._df))
  end
end

#width ⇒ Integer

Get the width of the DataFrame.

Examples:

df = Polars::DataFrame.new({"foo" => [1, 2, 3, 4, 5]})
df.width
# => 1

Returns:

  • (Integer) 


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# File 'lib/polars/data_frame.rb', line 207

def width
  _df.width
end

#with_columns(*exprs, **named_exprs) ⇒ DataFrame

Add columns to this DataFrame.

Added columns will replace existing columns with the same name.

Examples:

Pass an expression to add it as a new column.

df = Polars::DataFrame.new(
  {
    "a" => [1, 2, 3, 4],
    "b" => [0.5, 4, 10, 13],
    "c" => [true, true, false, true]
  }
)
df.with_columns((Polars.col("a") ** 2).alias("a^2"))
# =>
# shape: (4, 4)
# ┌─────┬──────┬───────┬─────┐
# │ a   ┆ b    ┆ c     ┆ a^2 │
# │ --- ┆ ---  ┆ ---   ┆ --- │
# │ i64 ┆ f64  ┆ bool  ┆ i64 │
# ╞═════╪══════╪═══════╪═════╡
# │ 1   ┆ 0.5  ┆ true  ┆ 1   │
# │ 2   ┆ 4.0  ┆ true  ┆ 4   │
# │ 3   ┆ 10.0 ┆ false ┆ 9   │
# │ 4   ┆ 13.0 ┆ true  ┆ 16  │
# └─────┴──────┴───────┴─────┘

Added columns will replace existing columns with the same name.

df.with_columns(Polars.col("a").cast(Polars::Float64))
# =>
# shape: (4, 3)
# ┌─────┬──────┬───────┐
# │ a   ┆ b    ┆ c     │
# │ --- ┆ ---  ┆ ---   │
# │ f64 ┆ f64  ┆ bool  │
# ╞═════╪══════╪═══════╡
# │ 1.0 ┆ 0.5  ┆ true  │
# │ 2.0 ┆ 4.0  ┆ true  │
# │ 3.0 ┆ 10.0 ┆ false │
# │ 4.0 ┆ 13.0 ┆ true  │
# └─────┴──────┴───────┘

Multiple columns can be added by passing a list of expressions.

df.with_columns(
  [
    (Polars.col("a") ** 2).alias("a^2"),
    (Polars.col("b") / 2).alias("b/2"),
    (Polars.col("c").not_).alias("not c"),
  ]
)
# =>
# shape: (4, 6)
# ┌─────┬──────┬───────┬─────┬──────┬───────┐
# │ a   ┆ b    ┆ c     ┆ a^2 ┆ b/2  ┆ not c │
# │ --- ┆ ---  ┆ ---   ┆ --- ┆ ---  ┆ ---   │
# │ i64 ┆ f64  ┆ bool  ┆ i64 ┆ f64  ┆ bool  │
# ╞═════╪══════╪═══════╪═════╪══════╪═══════╡
# │ 1   ┆ 0.5  ┆ true  ┆ 1   ┆ 0.25 ┆ false │
# │ 2   ┆ 4.0  ┆ true  ┆ 4   ┆ 2.0  ┆ false │
# │ 3   ┆ 10.0 ┆ false ┆ 9   ┆ 5.0  ┆ true  │
# │ 4   ┆ 13.0 ┆ true  ┆ 16  ┆ 6.5  ┆ false │
# └─────┴──────┴───────┴─────┴──────┴───────┘

Multiple columns also can be added using positional arguments instead of a list.

df.with_columns(
  (Polars.col("a") ** 2).alias("a^2"),
  (Polars.col("b") / 2).alias("b/2"),
  (Polars.col("c").not_).alias("not c"),
)
# =>
# shape: (4, 6)
# ┌─────┬──────┬───────┬─────┬──────┬───────┐
# │ a   ┆ b    ┆ c     ┆ a^2 ┆ b/2  ┆ not c │
# │ --- ┆ ---  ┆ ---   ┆ --- ┆ ---  ┆ ---   │
# │ i64 ┆ f64  ┆ bool  ┆ i64 ┆ f64  ┆ bool  │
# ╞═════╪══════╪═══════╪═════╪══════╪═══════╡
# │ 1   ┆ 0.5  ┆ true  ┆ 1   ┆ 0.25 ┆ false │
# │ 2   ┆ 4.0  ┆ true  ┆ 4   ┆ 2.0  ┆ false │
# │ 3   ┆ 10.0 ┆ false ┆ 9   ┆ 5.0  ┆ true  │
# │ 4   ┆ 13.0 ┆ true  ┆ 16  ┆ 6.5  ┆ false │
# └─────┴──────┴───────┴─────┴──────┴───────┘

Use keyword arguments to easily name your expression inputs.

df.with_columns(
  ab: Polars.col("a") * Polars.col("b"),
  not_c: Polars.col("c").not_
)
# =>
# shape: (4, 5)
# ┌─────┬──────┬───────┬──────┬───────┐
# │ a   ┆ b    ┆ c     ┆ ab   ┆ not_c │
# │ --- ┆ ---  ┆ ---   ┆ ---  ┆ ---   │
# │ i64 ┆ f64  ┆ bool  ┆ f64  ┆ bool  │
# ╞═════╪══════╪═══════╪══════╪═══════╡
# │ 1   ┆ 0.5  ┆ true  ┆ 0.5  ┆ false │
# │ 2   ┆ 4.0  ┆ true  ┆ 8.0  ┆ false │
# │ 3   ┆ 10.0 ┆ false ┆ 30.0 ┆ true  │
# │ 4   ┆ 13.0 ┆ true  ┆ 52.0 ┆ false │
# └─────┴──────┴───────┴──────┴───────┘

Parameters:

  • exprs (Array) —

    Column(s) to add, specified as positional arguments. Accepts expression input. Strings are parsed as column names, other non-expression inputs are parsed as literals.

  • named_exprs (Hash) —

    Additional columns to add, specified as keyword arguments. The columns will be renamed to the keyword used.

Returns:



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# File 'lib/polars/data_frame.rb', line 5195

def with_columns(*exprs, **named_exprs)
  lazy.with_columns(*exprs, **named_exprs).collect(optimizations: QueryOptFlags._eager)
end

#with_columns_seq(*exprs, **named_exprs) ⇒ DataFrame

Add columns to this DataFrame.

Added columns will replace existing columns with the same name.

This will run all expression sequentially instead of in parallel. Use this when the work per expression is cheap.

Parameters:

  • exprs (Array) —

    Column(s) to add, specified as positional arguments. Accepts expression input. Strings are parsed as column names, other non-expression inputs are parsed as literals.

  • named_exprs (Hash) —

    Additional columns to add, specified as keyword arguments. The columns will be renamed to the keyword used.

Returns:



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# File 'lib/polars/data_frame.rb', line 5215

def with_columns_seq(
  *exprs,
  **named_exprs
)
  lazy
  .with_columns_seq(*exprs, **named_exprs)
  .collect(optimizations: QueryOptFlags._eager)
end

#with_row_index(name: "index", offset: 0) ⇒ DataFrame

Add a column at index 0 that counts the rows.

Examples:

df = Polars::DataFrame.new(
  {
    "a" => [1, 3, 5],
    "b" => [2, 4, 6]
  }
)
df.with_row_index
# =>
# shape: (3, 3)
# ┌───────┬─────┬─────┐
# │ index ┆ a   ┆ b   │
# │ ---   ┆ --- ┆ --- │
# │ u32   ┆ i64 ┆ i64 │
# ╞═══════╪═════╪═════╡
# │ 0     ┆ 1   ┆ 2   │
# │ 1     ┆ 3   ┆ 4   │
# │ 2     ┆ 5   ┆ 6   │
# └───────┴─────┴─────┘

Parameters:

  • name (String) (defaults to: "index") —

    Name of the column to add.

  • offset (Integer) (defaults to: 0) —

    Start the row count at this offset.

Returns:



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# File 'lib/polars/data_frame.rb', line 2852

def with_row_index(name: "index", offset: 0)
  _from_rbdf(_df.with_row_index(name, offset))
end

#write_avro(file, compression = "uncompressed", name: "") ⇒ nil

Write to Apache Avro file.

Parameters:

  • file (String) —

    File path to which the file should be written.

  • compression ("uncompressed", "snappy", "deflate") (defaults to: "uncompressed") —

    Compression method. Defaults to "uncompressed".

  • name (String) (defaults to: "") —

    Schema name. Defaults to empty string.

Returns:

  • (nil) 


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# File 'lib/polars/data_frame.rb', line 1113

def write_avro(file, compression = "uncompressed", name: "")
  if compression.nil?
    compression = "uncompressed"
  end
  if Utils.pathlike?(file)
    file = Utils.normalize_filepath(file)
  end
  if name.nil?
    name = ""
  end

  _df.write_avro(file, compression, name)
end

#write_csv(file = nil, include_bom: false, compression: "uncompressed", compression_level: nil, check_extension: true, include_header: true, separator: ",", line_terminator: "\n", quote_char: '"', batch_size: 1024, datetime_format: nil, date_format: nil, time_format: nil, float_scientific: nil, float_precision: nil, decimal_comma: false, null_value: nil, quote_style: nil, storage_options: nil, credential_provider: "auto", retries: nil) ⇒ String?

Write to comma-separated values (CSV) file.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3, 4, 5],
    "bar" => [6, 7, 8, 9, 10],
    "ham" => ["a", "b", "c", "d", "e"]
  }
)
df.write_csv("file.csv")

Parameters:

  • file (String, nil) (defaults to: nil) —

    File path to which the result should be written. If set to nil (default), the output is returned as a string instead.

  • include_header (Boolean) (defaults to: true) —

    Whether to include header in the CSV output.

  • separator (String) (defaults to: ",") —

    Separate CSV fields with this symbol.

  • quote_char (String) (defaults to: '"') —

    Byte to use as quoting character.

  • batch_size (Integer) (defaults to: 1024) —

    Number of rows that will be processed per thread.

  • datetime_format (String, nil) (defaults to: nil) —

    A format string, with the specifiers defined by the chrono Rust crate. If no format specified, the default fractional-second precision is inferred from the maximum timeunit found in the frame's Datetime cols (if any).

  • date_format (String, nil) (defaults to: nil) —

    A format string, with the specifiers defined by the chrono Rust crate.

  • time_format (String, nil) (defaults to: nil) —

    A format string, with the specifiers defined by the chrono Rust crate.

  • float_precision (Integer, nil) (defaults to: nil) —

    Number of decimal places to write, applied to both Float32 and Float64 datatypes.

  • null_value (String, nil) (defaults to: nil) —

    A string representing null values (defaulting to the empty string).

Returns:



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# File 'lib/polars/data_frame.rb', line 1026

def write_csv(
  file = nil,
  include_bom: false,
  compression: "uncompressed",
  compression_level: nil,
  check_extension: true,
  include_header: true,
  separator: ",",
  line_terminator: "\n",
  quote_char: '"',
  batch_size: 1024,
  datetime_format: nil,
  date_format: nil,
  time_format: nil,
  float_scientific: nil,
  float_precision: nil,
  decimal_comma: false,
  null_value: nil,
  quote_style: nil,
  storage_options: nil,
  credential_provider: "auto",
  retries: nil
)
  Utils._check_arg_is_1byte("separator", separator, false)
  Utils._check_arg_is_1byte("quote_char", quote_char, true)
  if null_value == ""
    null_value = nil
  end

  should_return_buffer = false
  if file.nil?
    target = StringIO.new
    target.set_encoding(Encoding::BINARY)
    should_return_buffer = true
  else
    target = file
  end

  engine = "in-memory"

  lazy.sink_csv(
    target,
    include_bom: include_bom,
    include_header: include_header,
    separator: separator,
    line_terminator: line_terminator,
    quote_char: quote_char,
    batch_size: batch_size,
    datetime_format: datetime_format,
    date_format: date_format,
    time_format: time_format,
    float_scientific: float_scientific,
    float_precision: float_precision,
    decimal_comma: decimal_comma,
    null_value: null_value,
    quote_style: quote_style,
    storage_options: storage_options,
    credential_provider: credential_provider,
    retries: retries,
    optimizations: QueryOptFlags._eager,
    engine: engine
  )

  if should_return_buffer
    return target.string.force_encoding(Encoding::UTF_8)
  end

  nil
end

#write_database(table_name, connection = nil, if_table_exists: "fail") ⇒ Integer

Note:

This functionality is experimental. It may be changed at any point without it being considered a breaking change.

Write the data in a Polars DataFrame to a database.

Parameters:

  • table_name (String) —

    Schema-qualified name of the table to create or append to in the target SQL database.

  • connection (Object) (defaults to: nil) —

    An existing Active Record connection against the target database.

  • if_table_exists ('append', 'replace', 'fail') (defaults to: "fail") —

    The insert mode:

    • 'replace' will create a new database table, overwriting an existing one.
    • 'append' will append to an existing table.
    • 'fail' will fail if table already exists.

Returns:

  • (Integer) 


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# File 'lib/polars/data_frame.rb', line 1326

def write_database(table_name, connection = nil, if_table_exists: "fail")
  if !defined?(ActiveRecord)
    raise Error, "Active Record not available"
  elsif ActiveRecord::VERSION::MAJOR < 7
    raise Error, "Requires Active Record 7+"
  end

  valid_write_modes = ["append", "replace", "fail"]
  if !valid_write_modes.include?(if_table_exists)
    msg = "write_database `if_table_exists` must be one of #{valid_write_modes.inspect}, got #{if_table_exists.inspect}"
    raise ArgumentError, msg
  end

  with_connection(connection) do |connection|
    table_exists = connection.table_exists?(table_name)
    if table_exists && if_table_exists == "fail"
      raise ArgumentError, "Table already exists"
    end

    create_table = !table_exists || if_table_exists == "replace"
    maybe_transaction(connection, create_table) do
      if create_table
        mysql = connection.adapter_name.match?(/mysql|trilogy/i)
        force = if_table_exists == "replace"
        connection.create_table(table_name, id: false, force: force) do |t|
          schema.each do |c, dtype|
            options = {}
            column_type =
              case dtype
              when Binary
                :binary
              when Boolean
                :boolean
              when Date
                :date
              when Datetime
                :datetime
              when Decimal
                if mysql
                  options[:precision] = dtype.precision || 65
                  options[:scale] = dtype.scale || 30
                end
                :decimal
              when Float32
                options[:limit] = 24
                :float
              when Float64
                options[:limit] = 53
                :float
              when Int8
                options[:limit] = 1
                :integer
              when Int16
                options[:limit] = 2
                :integer
              when Int32
                options[:limit] = 4
                :integer
              when Int64
                options[:limit] = 8
                :integer
              when UInt8
                if mysql
                  options[:limit] = 1
                  options[:unsigned] = true
                else
                  options[:limit] = 2
                end
                :integer
              when UInt16
                if mysql
                  options[:limit] = 2
                  options[:unsigned] = true
                else
                  options[:limit] = 4
                end
                :integer
              when UInt32
                if mysql
                  options[:limit] = 4
                  options[:unsigned] = true
                else
                  options[:limit] = 8
                end
                :integer
              when UInt64
                if mysql
                  options[:limit] = 8
                  options[:unsigned] = true
                  :integer
                else
                  options[:precision] = 20
                  options[:scale] = 0
                  :decimal
                end
              when String
                :text
              when Time
                :time
              else
                raise ArgumentError, "column type not supported yet: #{dtype}"
              end
            t.column c, column_type, **options
          end
        end
      end

      quoted_table = connection.quote_table_name(table_name)
      quoted_columns = columns.map { |c| connection.quote_column_name(c) }
      rows = cast({Polars::UInt64 => Polars::String}).rows(named: false).map { |row| "(#{row.map { |v| connection.quote(v) }.join(", ")})" }
      connection.exec_update("INSERT INTO #{quoted_table} (#{quoted_columns.join(", ")}) VALUES #{rows.join(", ")}")
    end
  end
end

#write_delta(target, mode: "error", storage_options: nil, delta_write_options: nil, delta_merge_options: nil) ⇒ nil

Write DataFrame as delta table.

Parameters:

  • target (Object) —

    URI of a table or a DeltaTable object.

  • mode ("error", "append", "overwrite", "ignore", "merge") (defaults to: "error") —

    How to handle existing data.

  • storage_options (Hash) (defaults to: nil) —

    Extra options for the storage backends supported by deltalake-rb.

  • delta_write_options (Hash) (defaults to: nil) —

    Additional keyword arguments while writing a Delta lake Table.

  • delta_merge_options (Hash) (defaults to: nil) —

    Keyword arguments which are required to MERGE a Delta lake Table.

Returns:

  • (nil) 


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# File 'lib/polars/data_frame.rb', line 1489

def write_delta(
  target,
  mode: "error",
  storage_options: nil,
  delta_write_options: nil,
  delta_merge_options: nil
)
  Polars.send(:_check_if_delta_available)

  if Utils.pathlike?(target)
    target = Polars.send(:_resolve_delta_lake_uri, target.to_s, strict: false)
  end

  data = self

  if mode == "merge"
    if delta_merge_options.nil?
      msg = "You need to pass delta_merge_options with at least a given predicate for `MERGE` to work."
      raise ArgumentError, msg
    end
    if target.is_a?(::String)
      dt = DeltaLake::Table.new(target, storage_options: storage_options)
    else
      dt = target
    end

    predicate = delta_merge_options.delete(:predicate)
    dt.merge(data, predicate, **delta_merge_options)
  else
    delta_write_options ||= {}

    DeltaLake.write(
      target,
      data,
      mode: mode,
      storage_options: storage_options,
      **delta_write_options
    )
  end
end

#write_iceberg(target, mode:) ⇒ nil

Note:

This functionality is currently considered unstable. It may be changed at any point without it being considered a breaking change.

Write DataFrame to an Iceberg table.

Parameters:

  • target (Object) —

    Name of the table or the Table object representing an Iceberg table.

  • mode ('append', 'overwrite') —

    How to handle existing data.

    • If 'append', will add new data.
    • If 'overwrite', will replace table with new data.

Returns:

  • (nil) 


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# File 'lib/polars/data_frame.rb', line 1456

def write_iceberg(target, mode:)
  require "iceberg"

  table =
    if target.is_a?(Iceberg::Table)
      target
    else
      raise Todo
    end

  data = self

  if mode == "append"
    table.append(data)
  else
    raise Todo
  end
end

#write_ipc(file, compression: "uncompressed", compat_level: nil, record_batch_size: nil, storage_options: nil, credential_provider: "auto", retries: nil) ⇒ nil

Write to Arrow IPC binary stream or Feather file.

Parameters:

  • file (String) —

    File path to which the file should be written.

  • compression ("uncompressed", "lz4", "zstd") (defaults to: "uncompressed") —

    Compression method. Defaults to "uncompressed".

  • compat_level (Object) (defaults to: nil) —

    Use a specific compatibility level when exporting Polars' internal data structures.

  • storage_options (Hash) (defaults to: nil) —

    Options that indicate how to connect to a cloud provider.

    The cloud providers currently supported are AWS, GCP, and Azure. See supported keys here:

    • aws
    • gcp
    • azure
    • Hugging Face (hf://): Accepts an API key under the token parameter: {'token': '...'}, or by setting the HF_TOKEN environment variable.

    If storage_options is not provided, Polars will try to infer the information from environment variables.

  • credential_provider (Object) (defaults to: "auto") —

    Provide a function that can be called to provide cloud storage credentials. The function is expected to return a hash of credential keys along with an optional credential expiry time.

  • retries (Integer) (defaults to: nil) —

    Number of retries if accessing a cloud instance fails.

Returns:

  • (nil) 


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# File 'lib/polars/data_frame.rb', line 1157

def write_ipc(
  file,
  compression: "uncompressed",
  compat_level: nil,
  record_batch_size: nil,
  storage_options: nil,
  credential_provider: "auto",
  retries: nil
)
  return_bytes = file.nil?
  if file.nil?
    target = StringIO.new
    target.set_encoding(Encoding::BINARY)
  else
    target = file
  end

  lazy.sink_ipc(
    target,
    compression: compression,
    compat_level: compat_level,
    record_batch_size: record_batch_size,
    storage_options: storage_options,
    credential_provider: credential_provider,
    retries: retries,
    optimizations: QueryOptFlags._eager,
    engine: "streaming"
  )
  return_bytes ? target.string : nil
end

#write_ipc_stream(file, compression: "uncompressed", compat_level: nil) ⇒ Object

Write to Arrow IPC record batch stream.

See "Streaming format" in https://arrow.apache.org/docs/python/ipc.html.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3, 4, 5],
    "bar" => [6, 7, 8, 9, 10],
    "ham" => ["a", "b", "c", "d", "e"]
  }
)
df.write_ipc_stream("new_file.arrow")

Parameters:

  • file (Object) —

    Path or writable file-like object to which the IPC record batch data will be written. If set to nil, the output is returned as a BytesIO object.

  • compression ('uncompressed', 'lz4', 'zstd') (defaults to: "uncompressed") —

    Compression method. Defaults to "uncompressed".

  • compat_level (Object) (defaults to: nil) —

    Use a specific compatibility level when exporting Polars' internal data structures.

Returns:



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# File 'lib/polars/data_frame.rb', line 1212

def write_ipc_stream(
  file,
  compression: "uncompressed",
  compat_level: nil
)
  return_bytes = file.nil?
  if return_bytes
    file = StringIO.new
    file.set_encoding(Encoding::BINARY)
  elsif Utils.pathlike?(file)
    file = Utils.normalize_filepath(file)
  end

  if compat_level.nil?
    compat_level = true
  end

  if compression.nil?
    compression = "uncompressed"
  end

  _df.write_ipc_stream(file, compression, compat_level)
  return_bytes ? file.string : nil
end

#write_json(file = nil) ⇒ nil

Serialize to JSON representation.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6, 7, 8]
  }
)
df.write_json
# => "[{\"foo\":1,\"bar\":6},{\"foo\":2,\"bar\":7},{\"foo\":3,\"bar\":8}]"

Parameters:

  • file (String) (defaults to: nil) —

    File path to which the result should be written.

Returns:

  • (nil) 


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# File 'lib/polars/data_frame.rb', line 908

def write_json(file = nil)
  if Utils.pathlike?(file)
    file = Utils.normalize_filepath(file)
  end
  to_string_io = !file.nil? && file.is_a?(StringIO)
  if file.nil? || to_string_io
    buf = StringIO.new
    buf.set_encoding(Encoding::BINARY)
    _df.write_json(buf)
    json_bytes = buf.string

    json_str = json_bytes.force_encoding(Encoding::UTF_8)
    if to_string_io
      file.write(json_str)
    else
      return json_str
    end
  else
    _df.write_json(file)
  end
  nil
end

#write_ndjson(file = nil, compression: "uncompressed", compression_level: nil, check_extension: true) ⇒ nil

Serialize to newline delimited JSON representation.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6, 7, 8]
  }
)
df.write_ndjson
# => "{\"foo\":1,\"bar\":6}\n{\"foo\":2,\"bar\":7}\n{\"foo\":3,\"bar\":8}\n"

Parameters:

  • file (String) (defaults to: nil) —

    File path to which the result should be written.

Returns:

  • (nil) 


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# File 'lib/polars/data_frame.rb', line 947

def write_ndjson(
  file = nil,
  compression: "uncompressed",
  compression_level: nil,
  check_extension: true
)
  should_return_buffer = false
  if file.nil?
    target = StringIO.new
    target.set_encoding(Encoding::BINARY)
    should_return_buffer = true
  elsif Utils.pathlike?(file)
    target = Utils.normalize_filepath(file)
  else
    target = file
  end

  engine = "in-memory"

  lazy.sink_ndjson(
    target,
    compression: compression,
    compression_level: compression_level,
    check_extension: check_extension,
    optimizations: QueryOptFlags._eager,
    engine: engine
  )

  if should_return_buffer
    return target.string.force_encoding(Encoding::UTF_8)
  end

  nil
end

#write_parquet(file, compression: "zstd", compression_level: nil, statistics: true, row_group_size: nil, data_page_size: nil, partition_by: nil, partition_chunk_size_bytes: 4_294_967_296, storage_options: nil, credential_provider: "auto", retries: nil, metadata: nil, arrow_schema: nil, mkdir: false) ⇒ nil

Write to Apache Parquet file.

Parameters:

  • file (String, Pathname, StringIO) —

    File path to which the file should be written.

  • compression ("lz4", "uncompressed", "snappy", "gzip", "lzo", "brotli", "zstd") (defaults to: "zstd") —

    Choose "zstd" for good compression performance. Choose "lz4" for fast compression/decompression. Choose "snappy" for more backwards compatibility guarantees when you deal with older parquet readers.

  • compression_level (Integer, nil) (defaults to: nil) —

    The level of compression to use. Higher compression means smaller files on disk.

    • "gzip" : min-level: 0, max-level: 10.
    • "brotli" : min-level: 0, max-level: 11.
    • "zstd" : min-level: 1, max-level: 22.
  • statistics (Boolean) (defaults to: true) —

    Write statistics to the parquet headers. This requires extra compute.

  • row_group_size (Integer, nil) (defaults to: nil) —

    Size of the row groups in number of rows. Defaults to 512^2 rows.

  • data_page_size (Integer, nil) (defaults to: nil) —

    Size of the data page in bytes. Defaults to 1024^2 bytes.

Returns:

  • (nil) 


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# File 'lib/polars/data_frame.rb', line 1261

def write_parquet(
  file,
  compression: "zstd",
  compression_level: nil,
  statistics: true,
  row_group_size: nil,
  data_page_size: nil,
  partition_by: nil,
  partition_chunk_size_bytes: 4_294_967_296,
  storage_options: nil,
  credential_provider: "auto",
  retries: nil,
  metadata: nil,
  arrow_schema: nil,
  mkdir: false
)
  if compression.nil?
    compression = "uncompressed"
  end
  if Utils.pathlike?(file)
    file = Utils.normalize_filepath(file)
  end

  target = file
  engine = "streaming"
  if !partition_by.nil?
    raise Todo
  end

  lazy.sink_parquet(
    target,
    compression: compression,
    compression_level: compression_level,
    statistics: statistics,
    row_group_size: row_group_size,
    data_page_size: data_page_size,
    storage_options: storage_options,
    credential_provider: credential_provider,
    retries: retries,
    metadata: metadata,
    arrow_schema: arrow_schema,
    engine: engine,
    mkdir: mkdir,
    optimizations: QueryOptFlags._eager
  )
end