Class: Polars::DataFrame

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

Overview

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

Instance Method Summary collapse

Constructor Details

#initialize(data = nil, schema: nil, columns: nil, schema_overrides: nil, orient: nil, infer_schema_length: 100, nan_to_null: false) ⇒ DataFrame

Create a new DataFrame.

Parameters:

  • data (Hash, Array, Series, nil) (defaults to: nil) —

    Two-dimensional data in various forms. Hash must contain Arrays. Array may contain Series.

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

    Column labels to use for resulting DataFrame. If specified, overrides any labels already present in the data. Must match data dimensions.

  • orient ("col", "row", nil) (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.



21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
 
# File 'lib/polars/data_frame.rb', line 21

def initialize(data = nil, schema: nil, columns: nil, schema_overrides: nil, orient: nil, infer_schema_length: 100, nan_to_null: false)
  schema ||= columns

  if defined?(ActiveRecord) && (data.is_a?(ActiveRecord::Relation) || data.is_a?(ActiveRecord::Result))
    raise ArgumentError, "Use read_database instead"
  end

  if data.nil?
    self._df = self.class.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 = self.class.hash_to_rbdf(data, schema: schema, schema_overrides: schema_overrides, nan_to_null: nan_to_null)
  elsif data.is_a?(::Array)
    self._df = self.class.sequence_to_rbdf(data, schema: schema, schema_overrides: schema_overrides, orient: orient, infer_schema_length: infer_schema_length)
  elsif data.is_a?(Series)
    self._df = self.class.series_to_rbdf(data, schema: schema, schema_overrides: schema_overrides)
  else
    raise ArgumentError, "DataFrame constructor called with unsupported type; got #{data.class.name}"
  end
end

Instance Method Details

#!=(other) ⇒ DataFrame

Not equal.

Returns:



192
193
194
 
# File 'lib/polars/data_frame.rb', line 192

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

#%(other) ⇒ DataFrame

Returns the modulo.

Returns:



275
276
277
278
279
280
281
282
 
# File 'lib/polars/data_frame.rb', line 275

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:



227
228
229
230
231
232
233
234
 
# File 'lib/polars/data_frame.rb', line 227

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:



251
252
253
254
255
256
257
258
 
# File 'lib/polars/data_frame.rb', line 251

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:



263
264
265
266
267
268
269
270
 
# File 'lib/polars/data_frame.rb', line 263

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:



239
240
241
242
243
244
245
246
 
# File 'lib/polars/data_frame.rb', line 239

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:



206
207
208
 
# File 'lib/polars/data_frame.rb', line 206

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

#<=(other) ⇒ DataFrame

Less than or equal.

Returns:



220
221
222
 
# File 'lib/polars/data_frame.rb', line 220

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

#==(other) ⇒ DataFrame

Equal.

Returns:



185
186
187
 
# File 'lib/polars/data_frame.rb', line 185

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

#>(other) ⇒ DataFrame

Greater than.

Returns:



199
200
201
 
# File 'lib/polars/data_frame.rb', line 199

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

#>=(other) ⇒ DataFrame

Greater than or equal.

Returns:



213
214
215
 
# File 'lib/polars/data_frame.rb', line 213

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

#[](*args) ⇒ Object

Returns subset of the DataFrame.

Returns:

Raises:

  • (ArgumentError) 


316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
 
# File 'lib/polars/data_frame.rb', line 316

def [](*args)
  if args.size == 2
    row_selection, col_selection = args

    # df[.., unknown]
    if row_selection.is_a?(Range)

      # multiple slices
      # df[.., ..]
      if col_selection.is_a?(Range)
        raise Todo
      end
    end

    # df[2, ..] (select row as df)
    if row_selection.is_a?(Integer)
      if col_selection.is_a?(::Array)
        df = self[0.., col_selection]
        return df.slice(row_selection, 1)
      end
      # df[2, "a"]
      if col_selection.is_a?(::String) || col_selection.is_a?(Symbol)
        return self[col_selection][row_selection]
      end
    end

    # column selection can be "a" and ["a", "b"]
    if col_selection.is_a?(::String) || col_selection.is_a?(Symbol)
      col_selection = [col_selection]
    end

    # df[.., 1]
    if col_selection.is_a?(Integer)
      series = to_series(col_selection)
      return series[row_selection]
    end

    if col_selection.is_a?(::Array)
      # df[.., [1, 2]]
      if Utils.is_int_sequence(col_selection)
        series_list = col_selection.map { |i| to_series(i) }
        df = self.class.new(series_list)
        return df[row_selection]
      end
    end

    df = self[col_selection]
    return df[row_selection]
  elsif args.size == 1
    item = args[0]

    # select single column
    # df["foo"]
    if item.is_a?(::String) || item.is_a?(Symbol)
      return Utils.wrap_s(_df.get_column(item.to_s))
    end

    # df[idx]
    if item.is_a?(Integer)
      return slice(_pos_idx(item, 0), 1)
    end

    # df[..]
    if item.is_a?(Range)
      return Slice.new(self).apply(item)
    end

    if item.is_a?(::Array) && item.all? { |v| Utils.strlike?(v) }
      # select multiple columns
      # df[["foo", "bar"]]
      return _from_rbdf(_df.select(item.map(&:to_s)))
    end

    if Utils.is_int_sequence(item)
      item = Series.new("", item)
    end

    if item.is_a?(Series)
      dtype = item.dtype
      if dtype == String
        return _from_rbdf(_df.select(item))
      elsif dtype == UInt32
        return _from_rbdf(_df.take_with_series(item._s))
      elsif [UInt8, UInt16, UInt64, Int8, Int16, Int32, Int64].include?(dtype)
        return _from_rbdf(
          _df.take_with_series(_pos_idxs(item, 0)._s)
        )
      end
    end
  end

  # Ruby-specific
  if item.is_a?(Expr) || item.is_a?(Series)
    return filter(item)
  end

  raise ArgumentError, "Cannot get item of type: #{item.class.name}"
end

#[]=(*key, value) ⇒ Object

Set item.

Returns:



418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
 
# File 'lib/polars/data_frame.rb', line 418

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

  if Utils.strlike?(key)
    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_column(value.alias(key.to_s))._df
  elsif key.is_a?(::Array)
    row_selection, col_selection = key

    if Utils.strlike?(col_selection)
      s = self[col_selection]
    elsif col_selection.is_a?(Integer)
      raise Todo
    else
      raise ArgumentError, "column selection not understood: #{col_selection}"
    end

    s[row_selection] = value

    if col_selection.is_a?(Integer)
      replace_column(col_selection, s)
    elsif Utils.strlike?(col_selection)
      replace(col_selection, s)
    end
  else
    raise Todo
  end
end

#clear(n = 0) ⇒ DataFrame Also known as: cleared

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:



2760
2761
2762
2763
2764
2765
2766
2767
2768
2769
2770
 
# File 'lib/polars/data_frame.rb', line 2760

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

#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:



102
103
104
 
# File 'lib/polars/data_frame.rb', line 102

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:



135
136
137
 
# File 'lib/polars/data_frame.rb', line 135

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

#delete(name) ⇒ Series

Drop in place if exists.

Parameters:

  • name (Object) —

    Column to drop.

Returns:



2720
2721
2722
 
# File 'lib/polars/data_frame.rb', line 2720

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

#describe ⇒ DataFrame

Summary statistics for a DataFrame.

Examples:

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]
  }
)
df.describe
# =>
# shape: (7, 6)
# ┌────────────┬──────────┬──────────┬──────────┬──────┬──────┐
# │ describe   ┆ a        ┆ b        ┆ c        ┆ d    ┆ e    │
# │ ---        ┆ ---      ┆ ---      ┆ ---      ┆ ---  ┆ ---  │
# │ str        ┆ f64      ┆ f64      ┆ f64      ┆ str  ┆ str  │
# ╞════════════╪══════════╪══════════╪══════════╪══════╪══════╡
# │ count      ┆ 3.0      ┆ 3.0      ┆ 3.0      ┆ 3    ┆ 3    │
# │ null_count ┆ 0.0      ┆ 1.0      ┆ 0.0      ┆ 1    ┆ 1    │
# │ mean       ┆ 2.266667 ┆ 4.5      ┆ 0.666667 ┆ null ┆ null │
# │ std        ┆ 1.101514 ┆ 0.707107 ┆ 0.57735  ┆ null ┆ null │
# │ min        ┆ 1.0      ┆ 4.0      ┆ 0.0      ┆ b    ┆ eur  │
# │ max        ┆ 3.0      ┆ 5.0      ┆ 1.0      ┆ c    ┆ usd  │
# │ median     ┆ 2.8      ┆ 4.5      ┆ 1.0      ┆ null ┆ null │
# └────────────┴──────────┴──────────┴──────────┴──────┴──────┘

Returns:



1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
1207
1208
1209
1210
1211
1212
1213
1214
1215
1216
1217
1218
1219
1220
1221
1222
1223
1224
1225
1226
1227
1228
1229
1230
1231
1232
1233
 
# File 'lib/polars/data_frame.rb', line 1195

def describe
  describe_cast = lambda do |stat|
    columns = []
    self.columns.each_with_index do |s, i|
      if self[s].is_numeric || self[s].is_boolean
        columns << stat[0.., i].cast(:f64)
      else
        # for dates, strings, etc, we cast to string so that all
        # statistics can be shown
        columns << stat[0.., i].cast(:str)
      end
    end
    self.class.new(columns)
  end

  summary = _from_rbdf(
    Polars.concat(
      [
        describe_cast.(
          self.class.new(columns.to_h { |c| [c, [height]] })
        ),
        describe_cast.(null_count),
        describe_cast.(mean),
        describe_cast.(std),
        describe_cast.(min),
        describe_cast.(max),
        describe_cast.(median)
      ]
    )._df
  )
  summary.insert_column(
    0,
    Polars::Series.new(
      "describe",
      ["count", "null_count", "mean", "std", "min", "max", "median"],
    )
  )
  summary
end

#drop(*columns) ⇒ 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.

Returns:



2682
2683
2684
 
# File 'lib/polars/data_frame.rb', line 2682

def drop(*columns)
  lazy.drop(*columns).collect(_eager: true)
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:



2710
2711
2712
 
# File 'lib/polars/data_frame.rb', line 2710

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

#drop_nulls(subset: nil) ⇒ DataFrame

Return a new DataFrame where the null values are dropped.

Examples:

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

Parameters:

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

    Subset of column(s) on which drop_nulls will be applied.

Returns:



1576
1577
1578
 
# File 'lib/polars/data_frame.rb', line 1576

def drop_nulls(subset: nil)
  lazy.drop_nulls(subset: subset).collect(_eager: true)
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:



153
154
155
 
# File 'lib/polars/data_frame.rb', line 153

def dtypes
  _df.dtypes
end

#each(&block) ⇒ Object

Returns an enumerator.

Returns:



309
310
311
 
# File 'lib/polars/data_frame.rb', line 309

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:



4521
4522
4523
 
# File 'lib/polars/data_frame.rb', line 4521

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 Also known as: frame_equal

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:



1388
1389
1390
 
# File 'lib/polars/data_frame.rb', line 1388

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)
  },
  columns: {"x" => :u32, "y" => :f64, "z" => :str}
)
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) 


942
943
944
945
 
# File 'lib/polars/data_frame.rb', line 942

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

#explode(columns) ⇒ 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.

Returns:



2958
2959
2960
 
# File 'lib/polars/data_frame.rb', line 2958

def explode(columns)
  lazy.explode(columns).collect(no_optimization: true)
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:



2622
2623
2624
2625
 
# File 'lib/polars/data_frame.rb', line 2622

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

#fill_nan(fill_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:

  • fill_value (Object) —

    Value to fill NaN with.

Returns:



2923
2924
2925
 
# File 'lib/polars/data_frame.rb', line 2923

def fill_nan(fill_value)
  lazy.fill_nan(fill_value).collect(no_optimization: true)
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:



2883
2884
2885
2886
2887
2888
2889
2890
 
# File 'lib/polars/data_frame.rb', line 2883

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(no_optimization: true)
      ._df
  )
end

#filter(predicate) ⇒ 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:

  • predicate (Expr) —

    Expression that evaluates to a boolean Series.

Returns:



1161
1162
1163
 
# File 'lib/polars/data_frame.rb', line 1161

def filter(predicate)
  lazy.filter(predicate).collect
end

#flags ⇒ Hash

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

Returns:

  • (Hash) 


160
161
162
 
# File 'lib/polars/data_frame.rb', line 160

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

#fold(&operation) ⇒ 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", 2],
    "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:



4330
4331
4332
4333
4334
4335
4336
4337
 
# File 'lib/polars/data_frame.rb', line 4330

def fold(&operation)
  acc = to_series(0)

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

#gather_every(n, offset = 0) ⇒ DataFrame Also known as: take_every

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:



4558
4559
4560
 
# File 'lib/polars/data_frame.rb', line 4558

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

#get_column(name) ⇒ Series

Get a single column as Series 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
# ]

Parameters:

  • name (String) —

    Name of the column to retrieve.

Returns:



2800
2801
2802
 
# File 'lib/polars/data_frame.rb', line 2800

def get_column(name)
  self[name]
end

#get_column_index(name) ⇒ Series Also known as: find_idx_by_name

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:



1248
1249
1250
 
# File 'lib/polars/data_frame.rb', line 1248

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

#get_columns ⇒ Array

Get the DataFrame as a Array of Series.

Returns:



2778
2779
2780
 
# File 'lib/polars/data_frame.rb', line 2778

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

#group_by(by, maintain_order: false) ⇒ GroupBy Also known as: groupby, group

Start a group by operation.

Examples:

df = Polars::DataFrame.new(
  {
    "a" => ["a", "b", "a", "b", "b", "c"],
    "b" => [1, 2, 3, 4, 5, 6],
    "c" => [6, 5, 4, 3, 2, 1]
  }
)
df.group_by("a").agg(Polars.col("b").sum).sort("a")
# =>
# shape: (3, 2)
# ┌─────┬─────┐
# │ a   ┆ b   │
# │ --- ┆ --- │
# │ str ┆ i64 │
# ╞═════╪═════╡
# │ a   ┆ 4   │
# │ b   ┆ 11  │
# │ c   ┆ 6   │
# └─────┴─────┘

Parameters:

  • by (Object) —

    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.

Returns:



1684
1685
1686
1687
1688
1689
1690
1691
1692
1693
 
# File 'lib/polars/data_frame.rb', line 1684

def group_by(by, maintain_order: false)
  if !Utils.bool?(maintain_order)
    raise TypeError, "invalid input for group_by arg `maintain_order`: #{maintain_order}."
  end
  GroupBy.new(
    self,
    by,
    maintain_order: maintain_order
  )
end

#group_by_dynamic(index_column, every:, period: nil, offset: nil, truncate: true, include_boundaries: false, closed: "left", by: nil, start_by: "window") ⇒ DataFrame Also known as: groupby_dynamic

Group based on a time value (or index value of type :i32, :i64).

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",
  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 :i32, :i64. Note that :i32 gets temporarily cast to :i64, so if performance matters use an :i64 column.

  • every —

    Interval of the window.

  • period (defaults to: nil) —

    Length of the window, if None it is equal to 'every'.

  • offset (defaults to: nil) —

    Offset of the window if None and period is None it will be equal to negative every.

  • truncate (defaults to: true) —

    Truncate the time value to the window lower bound.

  • 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.

  • by (defaults to: nil) —

    Also group by this column/these columns

Returns:



2024
2025
2026
2027
2028
2029
2030
2031
2032
2033
2034
2035
2036
2037
2038
2039
2040
2041
2042
2043
2044
2045
2046
2047
 
# File 'lib/polars/data_frame.rb', line 2024

def group_by_dynamic(
  index_column,
  every:,
  period: nil,
  offset: nil,
  truncate: true,
  include_boundaries: false,
  closed: "left",
  by: nil,
  start_by: "window"
)
  DynamicGroupBy.new(
    self,
    index_column,
    every,
    period,
    offset,
    truncate,
    include_boundaries,
    closed,
    by,
    start_by
  )
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 :u64.

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:



4595
4596
4597
4598
4599
4600
4601
 
# File 'lib/polars/data_frame.rb', line 4595

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:



1515
1516
1517
 
# File 'lib/polars/data_frame.rb', line 1515

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) 


69
70
71
 
# File 'lib/polars/data_frame.rb', line 69

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:



2524
2525
2526
2527
2528
2529
2530
2531
2532
2533
2534
 
# File 'lib/polars/data_frame.rb', line 2524

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:



302
303
304
 
# File 'lib/polars/data_frame.rb', line 302

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

#insert_column(index, series) ⇒ DataFrame Also known as: insert_at_idx

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.

  • series (Series) —

    Series to insert.

Returns:



1114
1115
1116
1117
1118
1119
1120
 
# File 'lib/polars/data_frame.rb', line 1114

def insert_column(index, series)
  if index < 0
    index = columns.length + index
  end
  _df.insert_column(index, series._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:



4628
4629
4630
 
# File 'lib/polars/data_frame.rb', line 4628

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:



3435
3436
3437
 
# File 'lib/polars/data_frame.rb', line 3435

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:



4642
4643
4644
 
# File 'lib/polars/data_frame.rb', line 4642

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:



3460
3461
3462
 
# File 'lib/polars/data_frame.rb', line 3460

def is_unique
  Utils.wrap_s(_df.is_unique)
end

#item ⇒ Object

Return the dataframe as a scalar.

Equivalent to df[0,0], with a check that the shape is (1,1).

Examples:

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

Returns:



466
467
468
469
470
471
 
# File 'lib/polars/data_frame.rb', line 466

def item
  if shape != [1, 1]
    raise ArgumentError, "Can only call .item if the dataframe is of shape (1,1), dataframe is of shape #{shape}"
  end
  self[0, 0]
end

#iter_rows(named: false, buffer_size: 500, &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: 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:



4474
4475
4476
4477
4478
4479
4480
4481
4482
4483
4484
4485
4486
4487
4488
4489
4490
4491
4492
4493
4494
4495
4496
4497
4498
4499
4500
4501
4502
4503
4504
4505
 
# File 'lib/polars/data_frame.rb', line 4474

def iter_rows(named: false, buffer_size: 500, &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

#join(other, left_on: nil, right_on: nil, on: nil, how: "inner", suffix: "_right", join_nulls: false) ⇒ 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.

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

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

Returns:



2364
2365
2366
2367
2368
2369
2370
2371
2372
2373
2374
2375
2376
 
# File 'lib/polars/data_frame.rb', line 2364

def join(other, left_on: nil, right_on: nil, on: nil, how: "inner", suffix: "_right", join_nulls: false)
  lazy
    .join(
      other.lazy,
      left_on: left_on,
      right_on: right_on,
      on: on,
      how: how,
      suffix: suffix,
      join_nulls: join_nulls
    )
    .collect(no_optimization: true)
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) ⇒ 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 None.

  • by (Object) (defaults to: nil) —

    join on these columns before doing asof join

  • 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

  • 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.

Returns:



2234
2235
2236
2237
2238
2239
2240
2241
2242
2243
2244
2245
2246
2247
2248
2249
2250
2251
2252
2253
2254
2255
2256
2257
2258
2259
2260
2261
2262
2263
2264
 
# File 'lib/polars/data_frame.rb', line 2234

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
)
  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
    )
    .collect(no_optimization: true)
end

#lazy ⇒ LazyFrame

Start a lazy query from this point.

Returns:



3467
3468
3469
 
# File 'lib/polars/data_frame.rb', line 3467

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:



1484
1485
1486
 
# File 'lib/polars/data_frame.rb', line 1484

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

#map_rows(return_dtype: nil, inference_size: 256, &f) ⇒ Object Also known as: apply

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:



2438
2439
2440
2441
2442
2443
2444
2445
 
# File 'lib/polars/data_frame.rb', line 2438

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

#max(axis: 0) ⇒ 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:



3727
3728
3729
3730
3731
3732
3733
3734
3735
 
# File 'lib/polars/data_frame.rb', line 3727

def max(axis: 0)
  if axis == 0
    lazy.max.collect(_eager: true)
  elsif axis == 1
    Utils.wrap_s(_df.max_horizontal)
  else
    raise ArgumentError, "Axis should be 0 or 1."
  end
end

#mean(axis: 0, null_strategy: "ignore") ⇒ 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 │
# └─────┴─────┴──────┘

Parameters:

  • axis (Integer) (defaults to: 0) —

    Either 0 or 1.

  • null_strategy ("ignore", "propagate") (defaults to: "ignore") —

    This argument is only used if axis == 1.

Returns:



3845
3846
3847
3848
3849
3850
3851
3852
3853
3854
 
# File 'lib/polars/data_frame.rb', line 3845

def mean(axis: 0, null_strategy: "ignore")
  case axis
  when 0
    lazy.mean.collect(_eager: true)
  when 1
    Utils.wrap_s(_df.mean_horizontal(null_strategy))
  else
    raise ArgumentError, "Axis should be 0 or 1."
  end
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:



3960
3961
3962
 
# File 'lib/polars/data_frame.rb', line 3960

def median
  lazy.median.collect(_eager: true)
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:



4757
4758
4759
 
# File 'lib/polars/data_frame.rb', line 4757

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

#min(axis: 0) ⇒ 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:



3759
3760
3761
3762
3763
3764
3765
3766
3767
 
# File 'lib/polars/data_frame.rb', line 3759

def min(axis: 0)
  if axis == 0
    lazy.min.collect(_eager: true)
  elsif axis == 1
    Utils.wrap_s(_df.min_horizontal)
  else
    raise ArgumentError, "Axis should be 0 or 1."
  end
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:



3695
3696
3697
3698
3699
3700
3701
3702
3703
 
# File 'lib/polars/data_frame.rb', line 3695

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:



4133
4134
4135
4136
4137
4138
4139
4140
4141
4142
4143
4144
4145
4146
4147
4148
4149
 
# File 'lib/polars/data_frame.rb', line 4133

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:



4183
4184
4185
 
# File 'lib/polars/data_frame.rb', line 4183

def null_count
  _from_rbdf(_df.null_count)
end

#partition_by(groups, 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:

  • groups (Object) —

    Groups to partition by.

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

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

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

    If true, return the partitions in a dictionary keyed by the distinct group values instead of a list.

Returns:



3308
3309
3310
3311
3312
3313
3314
3315
3316
3317
3318
3319
3320
3321
3322
3323
3324
3325
3326
3327
3328
3329
3330
3331
3332
 
# File 'lib/polars/data_frame.rb', line 3308

def partition_by(groups, maintain_order: true, include_key: true, as_dict: false)
  if groups.is_a?(::String)
    groups = [groups]
  elsif !groups.is_a?(::Array)
    groups = Array(groups)
  end

  if as_dict
    out = {}
    if groups.length == 1
      _df.partition_by(groups, maintain_order, include_key).each do |df|
        df = _from_rbdf(df)
        out[df[groups][0, 0]] = df
      end
    else
      _df.partition_by(groups, maintain_order, include_key).each do |df|
        df = _from_rbdf(df)
        out[df[groups].row(0)] = df
      end
    end
    out
  else
    _df.partition_by(groups, maintain_order, include_key).map { |df| _from_rbdf(df) }
  end
end

#pipe(func, *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_column(Polars.col(col_name).cast(:i64))
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:

  • func (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:



1616
1617
1618
 
# File 'lib/polars/data_frame.rb', line 1616

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

#pivot(on, 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:

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

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

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

    One or multiple keys to group by

  • on (Object) —

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

  • 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.

Returns:



2999
3000
3001
3002
3003
3004
3005
3006
3007
3008
3009
3010
3011
3012
3013
3014
3015
3016
3017
3018
3019
3020
3021
3022
3023
3024
3025
3026
3027
3028
3029
3030
3031
3032
3033
3034
3035
3036
3037
3038
3039
3040
3041
3042
3043
3044
3045
3046
3047
3048
3049
3050
3051
3052
3053
3054
3055
 
# File 'lib/polars/data_frame.rb', line 2999

def pivot(
  on,
  index: nil,
  values: nil,
  aggregate_function: nil,
  maintain_order: true,
  sort_columns: false,
  separator: "_"
)
  index = Utils._expand_selectors(self, index)
  on = Utils._expand_selectors(self, on)
  if !values.nil?
    values = Utils._expand_selectors(self, values)
  end

  if aggregate_function.is_a?(::String)
    case aggregate_function
    when "first"
      aggregate_expr = F.element.first._rbexpr
    when "sum"
      aggregate_expr = F.element.sum._rbexpr
    when "max"
      aggregate_expr = F.element.max._rbexpr
    when "min"
      aggregate_expr = F.element.min._rbexpr
    when "mean"
      aggregate_expr = F.element.mean._rbexpr
    when "median"
      aggregate_expr = F.element.median._rbexpr
    when "last"
      aggregate_expr = F.element.last._rbexpr
    when "len"
      aggregate_expr = F.len._rbexpr
    when "count"
      warn "`aggregate_function: \"count\"` input for `pivot` is deprecated. Use `aggregate_function: \"len\"` instead."
      aggregate_expr = F.len._rbexpr
    else
      raise ArgumentError, "Argument aggregate fn: '#{aggregate_fn}' was not expected."
    end
  elsif aggregate_function.nil?
    aggregate_expr = nil
  else
    aggregate_expr = aggregate_function._rbexpr
  end

  _from_rbdf(
    _df.pivot_expr(
      on,
      index,
      values,
      maintain_order,
      sort_columns,
      aggregate_expr,
      separator
    )
  )
end

#plot(x = nil, y = nil, type: nil, group: nil, stacked: nil) ⇒ Vega::LiteChart Originally defined in module Plot

Plot data.

Returns:

  • (Vega::LiteChart)

Raises:

  • (ArgumentError)

#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:



3986
3987
3988
 
# File 'lib/polars/data_frame.rb', line 3986

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:



4017
4018
4019
 
# File 'lib/polars/data_frame.rb', line 4017

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

#rechunk ⇒ DataFrame

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

Returns:



4157
4158
4159
 
# File 'lib/polars/data_frame.rb', line 4157

def rechunk
  _from_rbdf(_df.rechunk)
end

#rename(mapping) ⇒ 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.

Returns:



1063
1064
1065
 
# File 'lib/polars/data_frame.rb', line 1063

def rename(mapping)
  lazy.rename(mapping).collect(no_optimization: true)
end

#replace(column, new_col) ⇒ DataFrame

Replace a column by a new Series.

Examples:

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

Parameters:

  • column (String) —

    Column to replace.

  • new_col (Series) —

    New column to insert.

Returns:



1417
1418
1419
1420
 
# File 'lib/polars/data_frame.rb', line 1417

def replace(column, new_col)
  _df.replace(column.to_s, new_col._s)
  self
end

#replace_column(index, series) ⇒ DataFrame Also known as: replace_at_idx

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.

  • series (Series) —

    Series that will replace the column.

Returns:



1283
1284
1285
1286
1287
1288
1289
 
# File 'lib/polars/data_frame.rb', line 1283

def replace_column(index, series)
  if index < 0
    index = columns.length + index
  end
  _df.replace_column(index, series._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:



1032
1033
1034
 
# File 'lib/polars/data_frame.rb', line 1032

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

#rolling(index_column:, period:, offset: nil, closed: "right", by: nil) ⇒ RollingGroupBy Also known as: groupby_rolling, group_by_rolling

Create rolling groups based on a time column.

Also works for index values of type :i32 or :i64.

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_column(
  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 group by on indices, dtype needs to be one of :i32, :i64. Note that :i32 gets temporarily cast to :i64, so if performance matters use an :i64 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.

  • by (Object) (defaults to: nil) —

    Also group by this column/these columns.

Returns:



1781
1782
1783
1784
1785
1786
1787
1788
1789
 
# File 'lib/polars/data_frame.rb', line 1781

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



4378
4379
4380
4381
4382
4383
4384
4385
4386
4387
4388
4389
4390
4391
4392
4393
4394
4395
4396
4397
4398
4399
4400
4401
4402
4403
4404
4405
4406
4407
4408
4409
4410
4411
4412
 
# File 'lib/polars/data_frame.rb', line 4378

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:



4435
4436
4437
4438
4439
4440
4441
4442
4443
4444
 
# File 'lib/polars/data_frame.rb', line 4435

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

#sample(n: nil, frac: 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 │
# ╞═════╪═════╪═════╡
# │ 3   ┆ 8   ┆ c   │
# │ 2   ┆ 7   ┆ b   │
# └─────┴─────┴─────┘

Parameters:

  • n (Integer) (defaults to: nil) —

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

  • frac (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:



4223
4224
4225
4226
4227
4228
4229
4230
4231
4232
4233
4234
4235
4236
4237
4238
4239
4240
4241
4242
4243
4244
4245
4246
4247
4248
4249
 
# File 'lib/polars/data_frame.rb', line 4223

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

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

    return _from_rbdf(
      _df.sample_frac(frac._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
# => {"foo"=>Polars::Int64, "bar"=>Polars::Float64, "ham"=>Polars::String}

Returns:

  • (Hash) 


178
179
180
 
# File 'lib/polars/data_frame.rb', line 178

def schema
  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:



3559
3560
3561
 
# File 'lib/polars/data_frame.rb', line 3559

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

#set_sorted(column, descending: false) ⇒ DataFrame

Indicate that one or multiple columns are sorted.

Parameters:

  • column (Object) —

    Columns that are sorted

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

    Whether the columns are sorted in descending order.

Returns:



4769
4770
4771
4772
4773
4774
4775
4776
 
# File 'lib/polars/data_frame.rb', line 4769

def set_sorted(
  column,
  descending: false
)
  lazy
    .set_sorted(column, descending: descending)
    .collect(no_optimization: true)
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:



57
58
59
 
# File 'lib/polars/data_frame.rb', line 57

def shape
  _df.shape
end

#shift(n, 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) —

    Number of places to shift (may be negative).

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

    Fill the resulting null values with this value.

Returns:



3377
3378
3379
 
# File 'lib/polars/data_frame.rb', line 3377

def shift(n, fill_value: nil)
  lazy.shift(n, fill_value: fill_value).collect(_eager: true)
end

#shift_and_fill(periods, fill_value) ⇒ DataFrame

Shift the values by a given period and fill the resulting null values.

Examples:

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

Parameters:

  • periods (Integer) —

    Number of places to shift (may be negative).

  • fill_value (Object) —

    fill nil values with this value.

Returns:



3410
3411
3412
 
# File 'lib/polars/data_frame.rb', line 3410

def shift_and_fill(periods, fill_value)
  shift(periods, fill_value: fill_value)
end

#shrink_to_fit(in_place: false) ⇒ DataFrame

Shrink DataFrame memory usage.

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

Returns:



4530
4531
4532
4533
4534
4535
4536
4537
4538
4539
 
# File 'lib/polars/data_frame.rb', line 4530

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:



1451
1452
1453
1454
1455
1456
 
# File 'lib/polars/data_frame.rb', line 1451

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

#sort(by, reverse: false, nulls_last: false) ⇒ DataFrame

Sort the DataFrame by column.

Examples:

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

    By which column to sort.

  • reverse (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:



1340
1341
1342
1343
1344
 
# File 'lib/polars/data_frame.rb', line 1340

def sort(by, reverse: false, nulls_last: false)
  lazy
    .sort(by, reverse: reverse, nulls_last: nulls_last)
    .collect(no_optimization: true)
end

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

Sort the DataFrame by column in-place.

Parameters:

  • by (String) —

    By which column to sort.

  • reverse (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:



1356
1357
1358
 
# File 'lib/polars/data_frame.rb', line 1356

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



3893
3894
3895
 
# File 'lib/polars/data_frame.rb', line 3893

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

#sum(axis: 0, null_strategy: "ignore") ⇒ 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 │
# └─────┴─────┴──────┘
df.sum(axis: 1)
# =>
# shape: (3,)
# Series: 'foo' [str]
# [
#         "16a"
#         "27b"
#         "38c"
# ]

Parameters:

  • axis (Integer) (defaults to: 0) —

    Either 0 or 1.

  • null_strategy ("ignore", "propagate") (defaults to: "ignore") —

    This argument is only used if axis == 1.

Returns:



3807
3808
3809
3810
3811
3812
3813
3814
3815
3816
 
# File 'lib/polars/data_frame.rb', line 3807

def sum(axis: 0, null_strategy: "ignore")
  case axis
  when 0
    lazy.sum.collect(_eager: true)
  when 1
    Utils.wrap_s(_df.sum_horizontal(null_strategy))
  else
    raise ArgumentError, "Axis should be 0 or 1."
  end
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:



1546
1547
1548
 
# File 'lib/polars/data_frame.rb', line 1546

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

#to_a ⇒ Array

Returns an array representing the DataFrame

Returns:



295
296
297
 
# File 'lib/polars/data_frame.rb', line 295

def to_a
  rows(named: true)
end

#to_csv(**options) ⇒ String

Write to comma-separated values (CSV) string.

Returns:



757
758
759
 
# File 'lib/polars/data_frame.rb', line 757

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

#to_dummies(columns: nil, separator: "_", drop_first: 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 (defaults to: nil) —

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

Returns:



4048
4049
4050
4051
4052
4053
 
# File 'lib/polars/data_frame.rb', line 4048

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

#to_h(as_series: true) ⇒ Hash

Convert DataFrame to a hash mapping column name to values.

Returns:

  • (Hash) 


478
479
480
481
482
483
484
 
# File 'lib/polars/data_frame.rb', line 478

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 dictionary.

Note that this is slow.

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:



497
498
499
500
501
502
503
504
 
# File 'lib/polars/data_frame.rb', line 497

def to_hashes
  rbdf = _df
  names = columns

  height.times.map do |i|
    names.zip(rbdf.row_tuple(i)).to_h
  end
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) 


518
519
520
521
522
523
524
525
 
# File 'lib/polars/data_frame.rb', line 518

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:



287
288
289
 
# File 'lib/polars/data_frame.rb', line 287

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:



553
554
555
556
557
558
 
# File 'lib/polars/data_frame.rb', line 553

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

    Name for the struct Series

Returns:



4672
4673
4674
 
# File 'lib/polars/data_frame.rb', line 4672

def to_struct(name)
  Utils.wrap_s(_df.to_struct(name))
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:



1004
1005
1006
1007
 
# File 'lib/polars/data_frame.rb', line 1004

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: true, subset: nil, keep: "first") ⇒ 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
# =>
# 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: true) —

    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: "first") —

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

Returns:



4093
4094
4095
4096
4097
4098
4099
4100
 
# File 'lib/polars/data_frame.rb', line 4093

def unique(maintain_order: true, subset: nil, keep: "first")
  self._from_rbdf(
    lazy
      .unique(maintain_order: maintain_order, subset: subset, keep: keep)
      .collect(no_optimization: true)
      ._df
  )
end

#unnest(names) ⇒ 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:

  • names (Object) —

    Names of the struct columns that will be decomposed by its fields

Returns:



4708
4709
4710
4711
4712
4713
 
# File 'lib/polars/data_frame.rb', line 4708

def unnest(names)
  if names.is_a?(::String)
    names = [names]
  end
  _from_rbdf(_df.unnest(names))
end

#unpivot(on, index: nil, variable_name: nil, value_name: nil) ⇒ DataFrame Also known as: melt

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::Selectors.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) —

    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:



3101
3102
3103
3104
3105
3106
 
# File 'lib/polars/data_frame.rb', line 3101

def unpivot(on, 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:



3180
3181
3182
3183
3184
3185
3186
3187
3188
3189
3190
3191
3192
3193
3194
3195
3196
3197
3198
3199
3200
3201
3202
3203
3204
3205
3206
3207
3208
3209
3210
3211
3212
3213
3214
3215
3216
3217
3218
3219
3220
3221
3222
3223
3224
3225
3226
3227
3228
3229
3230
3231
 
# File 'lib/polars/data_frame.rb', line 3180

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_column(
        (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

#upsample(time_column:, every:, 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", 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

  • 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:



2113
2114
2115
2116
2117
2118
2119
2120
2121
2122
2123
2124
2125
2126
2127
2128
2129
2130
2131
 
# File 'lib/polars/data_frame.rb', line 2113

def upsample(
  time_column:,
  every:,
  by: nil,
  maintain_order: false
)
  if by.nil?
    by = []
  end
  if by.is_a?(::String)
    by = [by]
  end

  every = Utils.parse_as_duration_string(every)

  _from_rbdf(
    _df.upsample(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:



3934
3935
3936
 
# File 'lib/polars/data_frame.rb', line 3934

def var(ddof: 1)
  lazy.var(ddof: ddof).collect(_eager: true)
end

#vstack(df, 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:

  • df (DataFrame) —

    DataFrame to stack.

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

    Modify in place

Returns:



2573
2574
2575
2576
2577
2578
2579
2580
 
# File 'lib/polars/data_frame.rb', line 2573

def vstack(df, in_place: false)
  if in_place
    _df.vstack_mut(df._df)
    self
  else
    _from_rbdf(_df.vstack(df._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) 


84
85
86
 
# File 'lib/polars/data_frame.rb', line 84

def width
  _df.width
end

#with_column(column) ⇒ DataFrame

Return a new DataFrame with the column added or replaced.

Examples:

Added

df = Polars::DataFrame.new(
  {
    "a" => [1, 3, 5],
    "b" => [2, 4, 6]
  }
)
df.with_column((Polars.col("b") ** 2).alias("b_squared"))
# =>
# shape: (3, 3)
# ┌─────┬─────┬───────────┐
# │ a   ┆ b   ┆ b_squared │
# │ --- ┆ --- ┆ ---       │
# │ i64 ┆ i64 ┆ i64       │
# ╞═════╪═════╪═══════════╡
# │ 1   ┆ 2   ┆ 4         │
# │ 3   ┆ 4   ┆ 16        │
# │ 5   ┆ 6   ┆ 36        │
# └─────┴─────┴───────────┘

Replaced

df.with_column(Polars.col("a") ** 2)
# =>
# shape: (3, 2)
# ┌─────┬─────┐
# │ a   ┆ b   │
# │ --- ┆ --- │
# │ i64 ┆ i64 │
# ╞═════╪═════╡
# │ 1   ┆ 2   │
# │ 9   ┆ 4   │
# │ 25  ┆ 6   │
# └─────┴─────┘

Parameters:

  • column (Object) —

    Series, where the name of the Series refers to the column in the DataFrame.

Returns:



2488
2489
2490
2491
2492
 
# File 'lib/polars/data_frame.rb', line 2488

def with_column(column)
  lazy
    .with_column(column)
    .collect(no_optimization: true, string_cache: false)
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:



3671
3672
3673
 
# File 'lib/polars/data_frame.rb', line 3671

def with_columns(*exprs, **named_exprs)
  lazy.with_columns(*exprs, **named_exprs).collect(_eager: true)
end

#with_row_index(name: "index", offset: 0) ⇒ DataFrame Also known as: with_row_count

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:



1648
1649
1650
 
# File 'lib/polars/data_frame.rb', line 1648

def with_row_index(name: "index", offset: 0)
  _from_rbdf(_df.with_row_index(name, offset))
end

#write_avro(file, compression = "uncompressed") ⇒ 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".

Returns:

  • (nil) 


769
770
771
772
773
774
775
776
777
778
 
# File 'lib/polars/data_frame.rb', line 769

def write_avro(file, compression = "uncompressed")
  if compression.nil?
    compression = "uncompressed"
  end
  if Utils.pathlike?(file)
    file = Utils.normalize_filepath(file)
  end

  _df.write_avro(file, compression)
end

#write_csv(file = nil, has_header: true, include_header: nil, sep: ",", quote: '"', batch_size: 1024, datetime_format: nil, date_format: nil, time_format: nil, float_precision: nil, null_value: 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.

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

    Whether to include header in the CSV output.

  • sep (String) (defaults to: ",") —

    Separate CSV fields with this symbol.

  • quote (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 :f32 and :f64 datatypes.

  • null_value (String, nil) (defaults to: nil) —

    A string representing null values (defaulting to the empty string).

Returns:



694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
 
# File 'lib/polars/data_frame.rb', line 694

def write_csv(
  file = nil,
  has_header: true,
  include_header: nil,
  sep: ",",
  quote: '"',
  batch_size: 1024,
  datetime_format: nil,
  date_format: nil,
  time_format: nil,
  float_precision: nil,
  null_value: nil
)
  include_header = has_header if include_header.nil?

  if sep.length > 1
    raise ArgumentError, "only single byte separator is allowed"
  elsif quote.length > 1
    raise ArgumentError, "only single byte quote char is allowed"
  elsif null_value == ""
    null_value = nil
  end

  if file.nil?
    buffer = StringIO.new
    buffer.set_encoding(Encoding::BINARY)
    _df.write_csv(
      buffer,
      include_header,
      sep.ord,
      quote.ord,
      batch_size,
      datetime_format,
      date_format,
      time_format,
      float_precision,
      null_value
    )
    return buffer.string.force_encoding(Encoding::UTF_8)
  end

  if Utils.pathlike?(file)
    file = Utils.normalize_filepath(file)
  end

  _df.write_csv(
    file,
    include_header,
    sep.ord,
    quote.ord,
    batch_size,
    datetime_format,
    date_format,
    time_format,
    float_precision,
    null_value,
  )
  nil
end

#write_ipc(file, compression: "uncompressed") ⇒ 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".

Returns:

  • (nil) 


788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
 
# File 'lib/polars/data_frame.rb', line 788

def write_ipc(file, compression: "uncompressed")
  return_bytes = file.nil?
  if return_bytes
    file = StringIO.new
    file.set_encoding(Encoding::BINARY)
  end
  if Utils.pathlike?(file)
    file = Utils.normalize_filepath(file)
  end

  if compression.nil?
    compression = "uncompressed"
  end

  _df.write_ipc(file, compression)
  return_bytes ? file.string : nil
end

#write_ipc_stream(file, compression: "uncompressed") ⇒ 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 None, the output is returned as a BytesIO object.

  • compression ('uncompressed', 'lz4', 'zstd') (defaults to: "uncompressed") —

    Compression method. Defaults to "uncompressed".

Returns:



827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
 
# File 'lib/polars/data_frame.rb', line 827

def write_ipc_stream(
  file,
  compression: "uncompressed"
)
  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 compression.nil?
    compression = "uncompressed"
  end

  _df.write_ipc_stream(file, compression)
  return_bytes ? file.string : nil
end

#write_json(file = nil, pretty: false, row_oriented: false) ⇒ nil

Serialize to JSON representation.

Examples:

df = Polars::DataFrame.new(
  {
    "foo" => [1, 2, 3],
    "bar" => [6, 7, 8]
  }
)
df.write_json
# => "{\"columns\":[{\"name\":\"foo\",\"datatype\":\"Int64\",\"bit_settings\":\"\",\"values\":[1,2,3]},{\"name\":\"bar\",\"datatype\":\"Int64\",\"bit_settings\":\"\",\"values\":[6,7,8]}]}"
df.write_json(row_oriented: true)
# => "[{\"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.

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

    Pretty serialize json.

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

    Write to row oriented json. This is slower, but more common.

Returns:

  • (nil) 


584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
 
# File 'lib/polars/data_frame.rb', line 584

def write_json(
  file = nil,
  pretty: false,
  row_oriented: false
)
  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, pretty, row_oriented)
    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, pretty, row_oriented)
  end
  nil
end

#write_ndjson(file = nil) ⇒ 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) 


627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
 
# File 'lib/polars/data_frame.rb', line 627

def write_ndjson(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_ndjson(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_ndjson(file)
  end
  nil
end

#write_parquet(file, compression: "zstd", compression_level: nil, statistics: false, row_group_size: nil, data_page_size: nil) ⇒ 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: false) —

    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) 


871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
 
# File 'lib/polars/data_frame.rb', line 871

def write_parquet(
  file,
  compression: "zstd",
  compression_level: nil,
  statistics: false,
  row_group_size: nil,
  data_page_size: nil
)
  if compression.nil?
    compression = "uncompressed"
  end
  if Utils.pathlike?(file)
    file = Utils.normalize_filepath(file)
  end

  if statistics == true
    statistics = {
      min: true,
      max: true,
      distinct_count: false,
      null_count: true
    }
  elsif statistics == false
    statistics = {}
  elsif statistics == "full"
    statistics = {
      min: true,
      max: true,
      distinct_count: true,
      null_count: true
    }
  end

  _df.write_parquet(
    file, compression, compression_level, statistics, row_group_size, data_page_size
  )
end