Class: Polars::LazyFrame

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

Overview

Representation of a Lazy computation graph/query against a DataFrame.

Class Method Summary collapse

Instance Method Summary collapse

Constructor Details

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

Create a new LazyFrame.



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# File 'lib/polars/lazy_frame.rb', line 8

def initialize(data = nil, schema: nil, schema_overrides: nil, orient: nil, infer_schema_length: 100, nan_to_null: false)
  self._ldf = (
    DataFrame.new(
      data,
      schema: schema,
      schema_overrides: schema_overrides,
      orient: orient,
      infer_schema_length: infer_schema_length,
      nan_to_null: nan_to_null
    )
    .lazy
    ._ldf
  )
end

Class Method Details

.read_json(file) ⇒ LazyFrame

Read a logical plan from a JSON file to construct a LazyFrame.

Parameters:

  • file (String) —

    Path to a file or a file-like object.

Returns:



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# File 'lib/polars/lazy_frame.rb', line 39

def self.read_json(file)
  if Utils.pathlike?(file)
    file = Utils.normalize_filepath(file)
  end

  Utils.wrap_ldf(RbLazyFrame.read_json(file))
end

Instance Method Details

#cache ⇒ LazyFrame

Cache the result once the execution of the physical plan hits this node.

Returns:



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

def cache
  _from_rbldf(_ldf.cache)
end

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

Create an empty copy of the current LazyFrame.

The copy has an identical schema but no data.

Examples:

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

Returns:



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# File 'lib/polars/lazy_frame.rb', line 891

def clear(n = 0)
  DataFrame.new(columns: schema).clear(n).lazy
end

#collect(type_coercion: true, predicate_pushdown: true, projection_pushdown: true, simplify_expression: true, string_cache: false, no_optimization: false, slice_pushdown: true, common_subplan_elimination: true, comm_subexpr_elim: true, allow_streaming: false, _eager: false) ⇒ DataFrame

Collect into a DataFrame.

Note: use #fetch if you want to run your query on the first n rows only. This can be a huge time saver in debugging queries.

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]
  }
).lazy
df.group_by("a", maintain_order: true).agg(Polars.all.sum).collect
# =>
# shape: (3, 3)
# ┌─────┬─────┬─────┐
# │ a   ┆ b   ┆ c   │
# │ --- ┆ --- ┆ --- │
# │ str ┆ i64 ┆ i64 │
# ╞═════╪═════╪═════╡
# │ a   ┆ 4   ┆ 10  │
# │ b   ┆ 11  ┆ 10  │
# │ c   ┆ 6   ┆ 1   │
# └─────┴─────┴─────┘

Parameters:

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

    Do type coercion optimization.

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

    Do predicate pushdown optimization.

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

    Do projection pushdown optimization.

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

    Run simplify expressions optimization.

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

    This argument is deprecated. Please set the string cache globally. The argument will be ignored

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

    Turn off (certain) optimizations.

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

    Slice pushdown optimization.

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

    Will try to cache branching subplans that occur on self-joins or unions.

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

    Run parts of the query in a streaming fashion (this is in an alpha state)

Returns:



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# File 'lib/polars/lazy_frame.rb', line 333

def collect(
  type_coercion: true,
  predicate_pushdown: true,
  projection_pushdown: true,
  simplify_expression: true,
  string_cache: false,
  no_optimization: false,
  slice_pushdown: true,
  common_subplan_elimination: true,
  comm_subexpr_elim: true,
  allow_streaming: false,
  _eager: false
)
  if no_optimization
    predicate_pushdown = false
    projection_pushdown = false
    slice_pushdown = false
    common_subplan_elimination = false
    comm_subexpr_elim = false
  end

  if allow_streaming
    common_subplan_elimination = false
  end

  ldf = _ldf.optimization_toggle(
    type_coercion,
    predicate_pushdown,
    projection_pushdown,
    simplify_expression,
    slice_pushdown,
    common_subplan_elimination,
    comm_subexpr_elim,
    allow_streaming,
    _eager
  )
  Utils.wrap_df(ldf.collect)
end

#columns ⇒ Array

Get or set column names.

Examples:

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

Returns:



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# File 'lib/polars/lazy_frame.rb', line 65

def columns
  _ldf.collect_schema.keys
end

#describe_optimized_plan(type_coercion: true, predicate_pushdown: true, projection_pushdown: true, simplify_expression: true, slice_pushdown: true, common_subplan_elimination: true, comm_subexpr_elim: true, allow_streaming: false) ⇒ String

Create a string representation of the optimized query plan.

Returns:



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# File 'lib/polars/lazy_frame.rb', line 199

def describe_optimized_plan(
  type_coercion: true,
  predicate_pushdown: true,
  projection_pushdown: true,
  simplify_expression: true,
  slice_pushdown: true,
  common_subplan_elimination: true,
  comm_subexpr_elim: true,
  allow_streaming: false
)
  ldf = _ldf.optimization_toggle(
    type_coercion,
    predicate_pushdown,
    projection_pushdown,
    simplify_expression,
    slice_pushdown,
    common_subplan_elimination,
    comm_subexpr_elim,
    allow_streaming,
    false
  )

  ldf.describe_optimized_plan
end

#describe_plan ⇒ String

Create a string representation of the unoptimized query plan.

Returns:



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

def describe_plan
  _ldf.describe_plan
end

#drop(*columns) ⇒ LazyFrame

Remove one or multiple columns from a DataFrame.

Parameters:

  • columns (Object) —
    • Name of the column that should be removed.
    • List of column names.

Returns:



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# File 'lib/polars/lazy_frame.rb', line 1882

def drop(*columns)
  drop_cols = Utils._expand_selectors(self, *columns)
  _from_rbldf(_ldf.drop(drop_cols))
end

#drop_nulls(subset: nil) ⇒ LazyFrame

Drop rows with null values from this LazyFrame.

Examples:

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



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# File 'lib/polars/lazy_frame.rb', line 2467

def drop_nulls(subset: nil)
  if !subset.nil? && !subset.is_a?(::Array)
    subset = [subset]
  end
  _from_rbldf(_ldf.drop_nulls(subset))
end

#dtypes ⇒ Array

Get dtypes of columns in LazyFrame.

Examples:

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

Returns:



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# File 'lib/polars/lazy_frame.rb', line 83

def dtypes
  _ldf.collect_schema.values
end

#explode(columns) ⇒ LazyFrame

Explode lists to long format.

Examples:

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

Returns:



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# File 'lib/polars/lazy_frame.rb', line 2415

def explode(columns)
  columns = Utils.parse_into_list_of_expressions(columns)
  _from_rbldf(_ldf.explode(columns))
end

#fetch(n_rows = 500, type_coercion: true, predicate_pushdown: true, projection_pushdown: true, simplify_expression: true, string_cache: false, no_optimization: false, slice_pushdown: true, common_subplan_elimination: true, comm_subexpr_elim: true, allow_streaming: false) ⇒ DataFrame

Collect a small number of rows for debugging purposes.

Fetch is like a #collect operation, but it overwrites the number of rows read by every scan operation. This is a utility that helps debug a query on a smaller number of rows.

Note that the fetch does not guarantee the final number of rows in the DataFrame. Filter, join operations and a lower number of rows available in the scanned file influence the final number of rows.

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]
  }
).lazy
df.group_by("a", maintain_order: true).agg(Polars.all.sum).fetch(2)
# =>
# shape: (2, 3)
# ┌─────┬─────┬─────┐
# │ a   ┆ b   ┆ c   │
# │ --- ┆ --- ┆ --- │
# │ str ┆ i64 ┆ i64 │
# ╞═════╪═════╪═════╡
# │ a   ┆ 1   ┆ 6   │
# │ b   ┆ 2   ┆ 5   │
# └─────┴─────┴─────┘

Parameters:

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

    Collect n_rows from the data sources.

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

    Run type coercion optimization.

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

    Run predicate pushdown optimization.

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

    Run projection pushdown optimization.

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

    Run simplify expressions optimization.

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

    This argument is deprecated. Please set the string cache globally. The argument will be ignored

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

    Turn off optimizations.

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

    Slice pushdown optimization

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

    Will try to cache branching subplans that occur on self-joins or unions.

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

    Run parts of the query in a streaming fashion (this is in an alpha state)

Returns:



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# File 'lib/polars/lazy_frame.rb', line 790

def fetch(
  n_rows = 500,
  type_coercion: true,
  predicate_pushdown: true,
  projection_pushdown: true,
  simplify_expression: true,
  string_cache: false,
  no_optimization: false,
  slice_pushdown: true,
  common_subplan_elimination: true,
  comm_subexpr_elim: true,
  allow_streaming: false
)
  if no_optimization
    predicate_pushdown = false
    projection_pushdown = false
    slice_pushdown = false
    common_subplan_elimination = false
  end

  ldf = _ldf.optimization_toggle(
    type_coercion,
    predicate_pushdown,
    projection_pushdown,
    simplify_expression,
    slice_pushdown,
    common_subplan_elimination,
    comm_subexpr_elim,
    allow_streaming,
    false
  )
  Utils.wrap_df(ldf.fetch(n_rows))
end

#fill_nan(fill_value) ⇒ LazyFrame

Note:

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

Fill floating point NaN values.

Examples:

df = Polars::DataFrame.new(
  {
    "a" => [1.5, 2, Float::NAN, 4],
    "b" => [0.5, 4, Float::NAN, 13],
  }
).lazy
df.fill_nan(99).collect
# =>
# 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 the NaN values with.

Returns:



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# File 'lib/polars/lazy_frame.rb', line 2190

def fill_nan(fill_value)
  if !fill_value.is_a?(Expr)
    fill_value = F.lit(fill_value)
  end
  _from_rbldf(_ldf.fill_nan(fill_value._rbexpr))
end

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

Fill null values using the specified value or strategy.

Returns:



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# File 'lib/polars/lazy_frame.rb', line 2155

def fill_null(value = nil, strategy: nil, limit: nil, matches_supertype: nil)
  select(Polars.all.fill_null(value, strategy: strategy, limit: limit))
end

#filter(predicate) ⇒ LazyFrame

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

Examples:

Filter on one condition:

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

Filter on multiple conditions:

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

Parameters:

  • predicate (Object) —

    Expression that evaluates to a boolean Series.

Returns:



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# File 'lib/polars/lazy_frame.rb', line 934

def filter(predicate)
  _from_rbldf(
    _ldf.filter(
      Utils.parse_into_expression(predicate, str_as_lit: false)
    )
  )
end

#first ⇒ LazyFrame

Get the first row of the DataFrame.

Returns:



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# File 'lib/polars/lazy_frame.rb', line 2090

def first
  slice(0, 1)
end

#group_by(*by, maintain_order: false, **named_by) ⇒ LazyGroupBy 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]
  }
).lazy
df.group_by("a", maintain_order: true).agg(Polars.col("b").sum).collect
# =>
# shape: (3, 2)
# ┌─────┬─────┐
# │ a   ┆ b   │
# │ --- ┆ --- │
# │ str ┆ i64 │
# ╞═════╪═════╡
# │ a   ┆ 4   │
# │ b   ┆ 11  │
# │ c   ┆ 6   │
# └─────┴─────┘

Parameters:

  • by (Array) —

    Column(s) to group by.

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

    Make sure that the order of the groups remain consistent. This is more expensive than a default group by.

  • named_by (Hash) —

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

Returns:



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# File 'lib/polars/lazy_frame.rb', line 1071

def group_by(*by, maintain_order: false, **named_by)
  exprs = Utils.parse_into_list_of_expressions(*by, **named_by)
  lgb = _ldf.group_by(exprs, maintain_order)
  LazyGroupBy.new(lgb)
end

#group_by_dynamic(index_column, every:, period: nil, offset: nil, truncate: nil, include_boundaries: false, closed: "left", label: "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 (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 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 (Object) —

    Interval of the window.

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

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

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

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

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

    Truncate the time value to the window lower bound.

  • include_boundaries (Boolean) (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 (Object) (defaults to: nil) —

    Also group by this column/these columns

Returns:



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# File 'lib/polars/lazy_frame.rb', line 1418

def group_by_dynamic(
  index_column,
  every:,
  period: nil,
  offset: nil,
  truncate: nil,
  include_boundaries: false,
  closed: "left",
  label: "left",
  by: nil,
  start_by: "window"
)
  if !truncate.nil?
    label = truncate ? "left" : "datapoint"
  end

  index_column = Utils.parse_into_expression(index_column, str_as_lit: false)
  if offset.nil?
    offset = period.nil? ? "-#{every}" : "0ns"
  end

  if period.nil?
    period = every
  end

  period = Utils.parse_as_duration_string(period)
  offset = Utils.parse_as_duration_string(offset)
  every = Utils.parse_as_duration_string(every)

  rbexprs_by = by.nil? ? [] : Utils.parse_into_list_of_expressions(by)
  lgb = _ldf.group_by_dynamic(
    index_column,
    every,
    period,
    offset,
    label,
    include_boundaries,
    closed,
    rbexprs_by,
    start_by
  )
  LazyGroupBy.new(lgb)
end

#head(n = 5) ⇒ LazyFrame

Note:

Consider using the #fetch operation if you only want to test your query. The #fetch operation will load the first n rows at the scan level, whereas the #head/#limit are applied at the end.

Get the first n rows.

Parameters:

  • n (Integer) (defaults to: 5) —

    Number of rows to return.

Returns:



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# File 'lib/polars/lazy_frame.rb', line 2066

def head(n = 5)
  slice(0, n)
end

#include?(key) ⇒ Boolean

Check if LazyFrame includes key.

Returns:



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# File 'lib/polars/lazy_frame.rb', line 120

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

#interpolate ⇒ LazyFrame

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]
  }
).lazy
df.interpolate.collect
# =>
# shape: (4, 3)
# ┌──────┬──────┬──────────┐
# │ foo  ┆ bar  ┆ baz      │
# │ ---  ┆ ---  ┆ ---      │
# │ f64  ┆ f64  ┆ f64      │
# ╞══════╪══════╪══════════╡
# │ 1.0  ┆ 6.0  ┆ 1.0      │
# │ 5.0  ┆ 7.0  ┆ 3.666667 │
# │ 9.0  ┆ 9.0  ┆ 6.333333 │
# │ 10.0 ┆ null ┆ 9.0      │
# └──────┴──────┴──────────┘

Returns:



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# File 'lib/polars/lazy_frame.rb', line 2566

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

#join(other, left_on: nil, right_on: nil, on: nil, how: "inner", suffix: "_right", join_nulls: false, allow_parallel: true, force_parallel: false) ⇒ LazyFrame

Add a join operation to the Logical Plan.

Examples:

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

Parameters:

  • other (LazyFrame) —

    Lazy DataFrame to join with.

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

    Join column of the left DataFrame.

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

    Join column of the right DataFrame.

  • on (defaults to: nil) —

    Object Join column of both DataFrames. If set, left_on and right_on should be None.

  • 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.

  • 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:



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# File 'lib/polars/lazy_frame.rb', line 1702

def join(
  other,
  left_on: nil,
  right_on: nil,
  on: nil,
  how: "inner",
  suffix: "_right",
  join_nulls: false,
  allow_parallel: true,
  force_parallel: false
)
  if !other.is_a?(LazyFrame)
    raise ArgumentError, "Expected a `LazyFrame` as join table, got #{other.class.name}"
  end

  if how == "outer"
    how = "full"
  elsif how == "cross"
    return _from_rbldf(
      _ldf.join(
        other._ldf, [], [], allow_parallel, join_nulls, force_parallel, how, suffix
      )
    )
  end

  if !on.nil?
    rbexprs = Utils.parse_into_list_of_expressions(on)
    rbexprs_left = rbexprs
    rbexprs_right = rbexprs
  elsif !left_on.nil? && !right_on.nil?
    rbexprs_left = Utils.parse_into_list_of_expressions(left_on)
    rbexprs_right = Utils.parse_into_list_of_expressions(right_on)
  else
    raise ArgumentError, "must specify `on` OR `left_on` and `right_on`"
  end

  _from_rbldf(
    self._ldf.join(
      other._ldf,
      rbexprs_left,
      rbexprs_right,
      allow_parallel,
      force_parallel,
      join_nulls,
      how,
      suffix,
    )
  )
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) ⇒ LazyFrame

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 join_asof 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".

Parameters:

  • other (LazyFrame) —

    Lazy 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:



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# File 'lib/polars/lazy_frame.rb', line 1525

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
)
  if !other.is_a?(LazyFrame)
    raise ArgumentError, "Expected a `LazyFrame` as join table, got #{other.class.name}"
  end

  if on.is_a?(::String)
    left_on = on
    right_on = on
  end

  if left_on.nil? || right_on.nil?
    raise ArgumentError, "You should pass the column to join on as an argument."
  end

  if by_left.is_a?(::String) || by_left.is_a?(Expr)
    by_left_ = [by_left]
  else
    by_left_ = by_left
  end

  if by_right.is_a?(::String) || by_right.is_a?(Expr)
    by_right_ = [by_right]
  else
    by_right_ = by_right
  end

  if by.is_a?(::String)
    by_left_ = [by]
    by_right_ = [by]
  elsif by.is_a?(::Array)
    by_left_ = by
    by_right_ = by
  end

  tolerance_str = nil
  tolerance_num = nil
  if tolerance.is_a?(::String)
    tolerance_str = tolerance
  else
    tolerance_num = tolerance
  end

  _from_rbldf(
    _ldf.join_asof(
      other._ldf,
      Polars.col(left_on)._rbexpr,
      Polars.col(right_on)._rbexpr,
      by_left_,
      by_right_,
      allow_parallel,
      force_parallel,
      suffix,
      strategy,
      tolerance_num,
      tolerance_str
    )
  )
end

#last ⇒ LazyFrame

Get the last row of the DataFrame.

Returns:



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# File 'lib/polars/lazy_frame.rb', line 2083

def last
  tail(1)
end

#lazy ⇒ LazyFrame

Return lazy representation, i.e. itself.

Useful for writing code that expects either a DataFrame or LazyFrame.

Examples:

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

Returns:



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# File 'lib/polars/lazy_frame.rb', line 840

def lazy
  self
end

#limit(n = 5) ⇒ LazyFrame

Note:

Consider using the #fetch operation if you only want to test your query. The #fetch operation will load the first n rows at the scan level, whereas the #head/#limit are applied at the end.

Get the first n rows.

Alias for #head.

Parameters:

  • n (Integer) (defaults to: 5) —

    Number of rows to return.

Returns:



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# File 'lib/polars/lazy_frame.rb', line 2051

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

#max ⇒ LazyFrame

Aggregate the columns in the DataFrame to their maximum value.

Examples:

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

Returns:



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# File 'lib/polars/lazy_frame.rb', line 2277

def max
  _from_rbldf(_ldf.max)
end

#mean ⇒ LazyFrame

Aggregate the columns in the DataFrame to their mean value.

Examples:

df = Polars::DataFrame.new({"a" => [1, 2, 3, 4], "b" => [1, 2, 1, 1]}).lazy
df.mean.collect
# =>
# shape: (1, 2)
# ┌─────┬──────┐
# │ a   ┆ b    │
# │ --- ┆ ---  │
# │ f64 ┆ f64  │
# ╞═════╪══════╡
# │ 2.5 ┆ 1.25 │
# └─────┴──────┘

Returns:



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# File 'lib/polars/lazy_frame.rb', line 2337

def mean
  _from_rbldf(_ldf.mean)
end

#median ⇒ LazyFrame

Aggregate the columns in the DataFrame to their median value.

Examples:

df = Polars::DataFrame.new({"a" => [1, 2, 3, 4], "b" => [1, 2, 1, 1]}).lazy
df.median.collect
# =>
# shape: (1, 2)
# ┌─────┬─────┐
# │ a   ┆ b   │
# │ --- ┆ --- │
# │ f64 ┆ f64 │
# ╞═════╪═════╡
# │ 2.5 ┆ 1.0 │
# └─────┴─────┘

Returns:



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# File 'lib/polars/lazy_frame.rb', line 2357

def median
  _from_rbldf(_ldf.median)
end

#merge_sorted(other, key) ⇒ LazyFrame

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 LazyFrames must be equal.

Examples:

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

Parameters:

  • other (DataFrame) —

    Other DataFrame that must be merged

  • key (String) —

    Key that is sorted.

Returns:



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# File 'lib/polars/lazy_frame.rb', line 2666

def merge_sorted(other, key)
  _from_rbldf(_ldf.merge_sorted(other._ldf, key))
end

#min ⇒ LazyFrame

Aggregate the columns in the DataFrame to their minimum value.

Examples:

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

Returns:



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# File 'lib/polars/lazy_frame.rb', line 2297

def min
  _from_rbldf(_ldf.min)
end

#pipe(func, *args, **kwargs, &block) ⇒ LazyFrame

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"]}).lazy
df.pipe(cast_str_to_int, col_name: "b").collect
# =>
# 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:



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# File 'lib/polars/lazy_frame.rb', line 185

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

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

Aggregate the columns in the DataFrame to their quantile value.

Examples:

df = Polars::DataFrame.new({"a" => [1, 2, 3, 4], "b" => [1, 2, 1, 1]}).lazy
df.quantile(0.7).collect
# =>
# shape: (1, 2)
# ┌─────┬─────┐
# │ a   ┆ b   │
# │ --- ┆ --- │
# │ f64 ┆ f64 │
# ╞═════╪═════╡
# │ 3.0 ┆ 1.0 │
# └─────┴─────┘

Parameters:

  • quantile (Float) —

    Quantile between 0.0 and 1.0.

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

    Interpolation method.

Returns:



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# File 'lib/polars/lazy_frame.rb', line 2382

def quantile(quantile, interpolation: "nearest")
  quantile = Utils.parse_into_expression(quantile, str_as_lit: false)
  _from_rbldf(_ldf.quantile(quantile, interpolation))
end

#rename(mapping) ⇒ LazyFrame

Rename column names.

Parameters:

  • mapping (Hash) —

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

Returns:



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# File 'lib/polars/lazy_frame.rb', line 1893

def rename(mapping)
  existing = mapping.keys
  _new = mapping.values
  _from_rbldf(_ldf.rename(existing, _new))
end

#reverse ⇒ LazyFrame

Reverse the DataFrame.

Returns:



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# File 'lib/polars/lazy_frame.rb', line 1902

def reverse
  _from_rbldf(_ldf.reverse)
end

#rolling(index_column:, period:, offset: nil, closed: "right", by: nil) ⇒ LazyFrame Also known as: group_by_rolling, groupby_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::LazyFrame.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")
  ]
).collect
# =>
# 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:



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# File 'lib/polars/lazy_frame.rb', line 1163

def rolling(
  index_column:,
  period:,
  offset: nil,
  closed: "right",
  by: nil
)
  index_column = Utils.parse_into_expression(index_column)
  if offset.nil?
    offset = Utils.negate_duration_string(Utils.parse_as_duration_string(period))
  end

  rbexprs_by = (
    !by.nil? ? Utils.parse_into_list_of_expressions(by) : []
  )
  period = Utils.parse_as_duration_string(period)
  offset = Utils.parse_as_duration_string(offset)

  lgb = _ldf.rolling(index_column, period, offset, closed, rbexprs_by)
  LazyGroupBy.new(lgb)
end

#schema ⇒ Hash

Get the schema.

Examples:

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

Returns:

  • (Hash) 


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# File 'lib/polars/lazy_frame.rb', line 101

def schema
  _ldf.collect_schema
end

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

Select columns from this DataFrame.

Examples:

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

Parameters:

  • exprs (Array) —

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

  • named_exprs (Hash) —

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

Returns:



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# File 'lib/polars/lazy_frame.rb', line 1030

def select(*exprs, **named_exprs)
  structify = ENV.fetch("POLARS_AUTO_STRUCTIFY", "0") != "0"

  rbexprs = Utils.parse_into_list_of_expressions(
    *exprs, **named_exprs, __structify: structify
  )
  _from_rbldf(_ldf.select(rbexprs))
end

#set_sorted(column, descending: false) ⇒ LazyFrame

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:



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# File 'lib/polars/lazy_frame.rb', line 2678

def set_sorted(
  column,
  descending: false
)
  if !Utils.strlike?(column)
    msg = "expected a 'str' for argument 'column' in 'set_sorted'"
    raise TypeError, msg
  end
  with_columns(F.col(column).set_sorted(descending: descending))
end

#shift(n, fill_value: nil) ⇒ LazyFrame

Shift the values by a given period.

Examples:

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



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# File 'lib/polars/lazy_frame.rb', line 1948

def shift(n, fill_value: nil)
  if !fill_value.nil?
    fill_value = Utils.parse_into_expression(fill_value, str_as_lit: true)
  end
  n = Utils.parse_into_expression(n)
  _from_rbldf(_ldf.shift(n, fill_value))
end

#shift_and_fill(periods, fill_value) ⇒ LazyFrame

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

Examples:

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

Parameters:

  • periods (Integer) —

    Number of places to shift (may be negative).

  • fill_value (Object) —

    Fill nil values with the result of this expression.

Returns:



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# File 'lib/polars/lazy_frame.rb', line 1998

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

#sink_csv(path, include_bom: false, include_header: true, separator: ",", line_terminator: "\n", quote_char: '"', batch_size: 1024, datetime_format: nil, date_format: nil, time_format: nil, float_scientific: nil, float_precision: nil, null_value: nil, quote_style: nil, maintain_order: true, type_coercion: true, predicate_pushdown: true, projection_pushdown: true, simplify_expression: true, slice_pushdown: true, no_optimization: false) ⇒ DataFrame

Evaluate the query in streaming mode and write to a CSV file.

This allows streaming results that are larger than RAM to be written to disk.

Examples:

lf = Polars.scan_csv("/path/to/my_larger_than_ram_file.csv")
lf.sink_csv("out.csv")

Parameters:

  • path (String) —

    File path to which the file should be written.

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

    Whether to include UTF-8 BOM in the CSV output.

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

    Whether to include header in the CSV output.

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

    Separate CSV fields with this symbol.

  • line_terminator (String) (defaults to: "\n") —

    String used to end each row.

  • quote_char (String) (defaults to: '"') —

    Byte to use as quoting character.

  • batch_size (Integer) (defaults to: 1024) —

    Number of rows that will be processed per thread.

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

    A format string, with the specifiers defined by the chrono <https://docs.rs/chrono/latest/chrono/format/strftime/index.html>_ 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) (defaults to: nil) —

    A format string, with the specifiers defined by the chrono <https://docs.rs/chrono/latest/chrono/format/strftime/index.html>_ Rust crate.

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

    A format string, with the specifiers defined by the chrono <https://docs.rs/chrono/latest/chrono/format/strftime/index.html>_ Rust crate.

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

    Number of decimal places to write, applied to both Float32 and Float64 datatypes.

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

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

  • quote_style ("necessary", "always", "non_numeric", "never") (defaults to: nil) —

    Determines the quoting strategy used.

    • necessary (default): This puts quotes around fields only when necessary. They are necessary when fields contain a quote, delimiter or record terminator. Quotes are also necessary when writing an empty record (which is indistinguishable from a record with one empty field). This is the default.
    • always: This puts quotes around every field. Always.
    • never: This never puts quotes around fields, even if that results in invalid CSV data (e.g.: by not quoting strings containing the separator).
    • non_numeric: This puts quotes around all fields that are non-numeric. Namely, when writing a field that does not parse as a valid float or integer, then quotes will be used even if they aren`t strictly necessary.
  • maintain_order (Boolean) (defaults to: true) —

    Maintain the order in which data is processed. Setting this to false will be slightly faster.

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

    Do type coercion optimization.

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

    Do predicate pushdown optimization.

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

    Do projection pushdown optimization.

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

    Run simplify expressions optimization.

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

    Slice pushdown optimization.

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

    Turn off (certain) optimizations.

Returns:



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# File 'lib/polars/lazy_frame.rb', line 606

def sink_csv(
  path,
  include_bom: false,
  include_header: true,
  separator: ",",
  line_terminator: "\n",
  quote_char: '"',
  batch_size: 1024,
  datetime_format: nil,
  date_format: nil,
  time_format: nil,
  float_scientific: nil,
  float_precision: nil,
  null_value: nil,
  quote_style: nil,
  maintain_order: true,
  type_coercion: true,
  predicate_pushdown: true,
  projection_pushdown: true,
  simplify_expression: true,
  slice_pushdown: true,
  no_optimization: false
)
  Utils._check_arg_is_1byte("separator", separator, false)
  Utils._check_arg_is_1byte("quote_char", quote_char, false)

  lf = _set_sink_optimizations(
    type_coercion: type_coercion,
    predicate_pushdown: predicate_pushdown,
    projection_pushdown: projection_pushdown,
    simplify_expression: simplify_expression,
    slice_pushdown: slice_pushdown,
    no_optimization: no_optimization
  )

  lf.sink_csv(
    path,
    include_bom,
    include_header,
    separator.ord,
    line_terminator,
    quote_char.ord,
    batch_size,
    datetime_format,
    date_format,
    time_format,
    float_scientific,
    float_precision,
    null_value,
    quote_style,
    maintain_order
  )
end

#sink_ipc(path, compression: "zstd", maintain_order: true, type_coercion: true, predicate_pushdown: true, projection_pushdown: true, simplify_expression: true, slice_pushdown: true, no_optimization: false) ⇒ DataFrame

Evaluate the query in streaming mode and write to an IPC file.

This allows streaming results that are larger than RAM to be written to disk.

Examples:

lf = Polars.scan_csv("/path/to/my_larger_than_ram_file.csv")
lf.sink_ipc("out.arrow")

Parameters:

  • path (String) —

    File path to which the file should be written.

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

    Choose "zstd" for good compression performance. Choose "lz4" for fast compression/decompression.

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

    Maintain the order in which data is processed. Setting this to false will be slightly faster.

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

    Do type coercion optimization.

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

    Do predicate pushdown optimization.

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

    Do projection pushdown optimization.

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

    Run simplify expressions optimization.

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

    Slice pushdown optimization.

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

    Turn off (certain) optimizations.

Returns:



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# File 'lib/polars/lazy_frame.rb', line 504

def sink_ipc(
  path,
  compression: "zstd",
  maintain_order: true,
  type_coercion: true,
  predicate_pushdown: true,
  projection_pushdown: true,
  simplify_expression: true,
  slice_pushdown: true,
  no_optimization: false
)
  lf = _set_sink_optimizations(
    type_coercion: type_coercion,
    predicate_pushdown: predicate_pushdown,
    projection_pushdown: projection_pushdown,
    simplify_expression: simplify_expression,
    slice_pushdown: slice_pushdown,
    no_optimization: no_optimization
  )

  lf.sink_ipc(
    path,
    compression,
    maintain_order
  )
end

#sink_ndjson(path, maintain_order: true, type_coercion: true, predicate_pushdown: true, projection_pushdown: true, simplify_expression: true, slice_pushdown: true, no_optimization: false) ⇒ DataFrame

Evaluate the query in streaming mode and write to an NDJSON file.

This allows streaming results that are larger than RAM to be written to disk.

Examples:

lf = Polars.scan_csv("/path/to/my_larger_than_ram_file.csv")
lf.sink_ndjson("out.ndjson")

Parameters:

  • path (String) —

    File path to which the file should be written.

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

    Maintain the order in which data is processed. Setting this to false will be slightly faster.

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

    Do type coercion optimization.

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

    Do predicate pushdown optimization.

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

    Do projection pushdown optimization.

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

    Run simplify expressions optimization.

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

    Slice pushdown optimization.

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

    Turn off (certain) optimizations.

Returns:



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# File 'lib/polars/lazy_frame.rb', line 687

def sink_ndjson(
  path,
  maintain_order: true,
  type_coercion: true,
  predicate_pushdown: true,
  projection_pushdown: true,
  simplify_expression: true,
  slice_pushdown: true,
  no_optimization: false
)
  lf = _set_sink_optimizations(
    type_coercion: type_coercion,
    predicate_pushdown: predicate_pushdown,
    projection_pushdown: projection_pushdown,
    simplify_expression: simplify_expression,
    slice_pushdown: slice_pushdown,
    no_optimization: no_optimization
  )

  lf.sink_json(path, maintain_order)
end

#sink_parquet(path, compression: "zstd", compression_level: nil, statistics: true, row_group_size: nil, data_pagesize_limit: nil, maintain_order: true, type_coercion: true, predicate_pushdown: true, projection_pushdown: true, simplify_expression: true, no_optimization: false, slice_pushdown: true) ⇒ DataFrame

Persists a LazyFrame at the provided path.

This allows streaming results that are larger than RAM to be written to disk.

Examples:

lf = Polars.scan_csv("/path/to/my_larger_than_ram_file.csv")
lf.sink_parquet("out.parquet")

Parameters:

  • path (String) —

    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) (defaults to: nil) —

    The level of compression to use. Higher compression means smaller files on disk.

    • "gzip" : min-level: 0, max-level: 10.
    • "brotli" : min-level: 0, max-level: 11.
    • "zstd" : min-level: 1, max-level: 22.
  • statistics (Boolean) (defaults to: true) —

    Write statistics to the parquet headers. This requires extra compute.

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

    Size of the row groups in number of rows. If nil (default), the chunks of the DataFrame are used. Writing in smaller chunks may reduce memory pressure and improve writing speeds.

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

    Size limit of individual data pages. If not set defaults to 1024 * 1024 bytes

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

    Maintain the order in which data is processed. Setting this to false will be slightly faster.

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

    Do type coercion optimization.

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

    Do predicate pushdown optimization.

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

    Do projection pushdown optimization.

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

    Run simplify expressions optimization.

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

    Turn off (certain) optimizations.

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

    Slice pushdown optimization.

Returns:



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# File 'lib/polars/lazy_frame.rb', line 421

def sink_parquet(
  path,
  compression: "zstd",
  compression_level: nil,
  statistics: true,
  row_group_size: nil,
  data_pagesize_limit: nil,
  maintain_order: true,
  type_coercion: true,
  predicate_pushdown: true,
  projection_pushdown: true,
  simplify_expression: true,
  no_optimization: false,
  slice_pushdown: true
)
  lf = _set_sink_optimizations(
    type_coercion: type_coercion,
    predicate_pushdown: predicate_pushdown,
    projection_pushdown: projection_pushdown,
    simplify_expression: simplify_expression,
    slice_pushdown: slice_pushdown,
    no_optimization: no_optimization
  )

  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

  lf.sink_parquet(
    path,
    compression,
    compression_level,
    statistics,
    row_group_size,
    data_pagesize_limit,
    maintain_order
  )
end

#slice(offset, length = nil) ⇒ LazyFrame

Get a slice of this DataFrame.

Examples:

df = Polars::DataFrame.new(
  {
    "a" => ["x", "y", "z"],
    "b" => [1, 3, 5],
    "c" => [2, 4, 6]
  }
).lazy
df.slice(1, 2).collect
# =>
# shape: (2, 3)
# ┌─────┬─────┬─────┐
# │ a   ┆ b   ┆ c   │
# │ --- ┆ --- ┆ --- │
# │ str ┆ i64 ┆ i64 │
# ╞═════╪═════╪═════╡
# │ y   ┆ 3   ┆ 4   │
# │ z   ┆ 5   ┆ 6   │
# └─────┴─────┴─────┘

Parameters:

  • offset (Integer) —

    Start index. Negative indexing is supported.

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

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

Returns:



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# File 'lib/polars/lazy_frame.rb', line 2031

def slice(offset, length = nil)
  if length && length < 0
    raise ArgumentError, "Negative slice lengths (#{length}) are invalid for LazyFrame"
  end
  _from_rbldf(_ldf.slice(offset, length))
end

#sort(by, *more_by, reverse: false, nulls_last: false, maintain_order: false, multithreaded: true) ⇒ LazyFrame

Sort the DataFrame.

Sorting can be done by:

  • A single column name
  • An expression
  • Multiple expressions

Examples:

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

Parameters:

  • by (Object) —

    Column (expressions) to sort by.

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

    Sort in descending order.

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

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

Returns:



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# File 'lib/polars/lazy_frame.rb', line 264

def sort(by, *more_by, reverse: false, nulls_last: false, maintain_order: false, multithreaded: true)
  if by.is_a?(::String) && more_by.empty?
    return _from_rbldf(
      _ldf.sort(
        by, reverse, nulls_last, maintain_order, multithreaded
      )
    )
  end

  by = Utils.parse_into_list_of_expressions(by, *more_by)
  reverse = Utils.extend_bool(reverse, by.length, "reverse", "by")
  nulls_last = Utils.extend_bool(nulls_last, by.length, "nulls_last", "by")
  _from_rbldf(
    _ldf.sort_by_exprs(
      by, reverse, nulls_last, maintain_order, multithreaded
    )
  )
end

#std(ddof: 1) ⇒ LazyFrame

Aggregate the columns in the DataFrame to their standard deviation value.

Examples:

df = Polars::DataFrame.new({"a" => [1, 2, 3, 4], "b" => [1, 2, 1, 1]}).lazy
df.std.collect
# =>
# shape: (1, 2)
# ┌──────────┬─────┐
# │ a        ┆ b   │
# │ ---      ┆ --- │
# │ f64      ┆ f64 │
# ╞══════════╪═════╡
# │ 1.290994 ┆ 0.5 │
# └──────────┴─────┘
df.std(ddof: 0).collect
# =>
# shape: (1, 2)
# ┌──────────┬──────────┐
# │ a        ┆ b        │
# │ ---      ┆ ---      │
# │ f64      ┆ f64      │
# ╞══════════╪══════════╡
# │ 1.118034 ┆ 0.433013 │
# └──────────┴──────────┘

Returns:



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# File 'lib/polars/lazy_frame.rb', line 2225

def std(ddof: 1)
  _from_rbldf(_ldf.std(ddof))
end

#sum ⇒ LazyFrame

Aggregate the columns in the DataFrame to their sum value.

Examples:

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

Returns:



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# File 'lib/polars/lazy_frame.rb', line 2317

def sum
  _from_rbldf(_ldf.sum)
end

#tail(n = 5) ⇒ LazyFrame

Get the last n rows.

Parameters:

  • n (Integer) (defaults to: 5) —

    Number of rows.

Returns:



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# File 'lib/polars/lazy_frame.rb', line 2076

def tail(n = 5)
  _from_rbldf(_ldf.tail(n))
end

#take_every(n) ⇒ LazyFrame

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

Examples:

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

Returns:



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# File 'lib/polars/lazy_frame.rb', line 2148

def take_every(n)
  select(F.col("*").take_every(n))
end

#to_s ⇒ String

Returns a string representing the LazyFrame.

Returns:



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# File 'lib/polars/lazy_frame.rb', line 132

def to_s
  <<~EOS
    naive plan: (run LazyFrame#describe_optimized_plan to see the optimized plan)

    #{describe_plan}
  EOS
end

#unique(maintain_order: true, subset: nil, keep: "first") ⇒ LazyFrame

Drop duplicate rows from this DataFrame.

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

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.

Returns:



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# File 'lib/polars/lazy_frame.rb', line 2434

def unique(maintain_order: true, subset: nil, keep: "first")
  if !subset.nil? && !subset.is_a?(::Array)
    subset = [subset]
  end
  _from_rbldf(_ldf.unique(maintain_order, subset, keep))
end

#unnest(names) ⇒ LazyFrame

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"]
    }
  )
  .lazy
  .select(
    ["before", Polars.struct(Polars.col("^t_.$")).alias("t_struct"), "after"]
  )
)
df.fetch
# =>
# shape: (2, 3)
# ┌────────┬─────────────────────┬───────┐
# │ before ┆ t_struct            ┆ after │
# │ ---    ┆ ---                 ┆ ---   │
# │ str    ┆ struct[4]           ┆ str   │
# ╞════════╪═════════════════════╪═══════╡
# │ foo    ┆ {1,"a",true,[1, 2]} ┆ baz   │
# │ bar    ┆ {2,"b",null,[3]}    ┆ womp  │
# └────────┴─────────────────────┴───────┘
df.unnest("t_struct").fetch
# =>
# 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:



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# File 'lib/polars/lazy_frame.rb', line 2621

def unnest(names)
  if names.is_a?(::String)
    names = [names]
  end
  _from_rbldf(_ldf.unnest(names))
end

#unpivot(on, index: nil, variable_name: nil, value_name: nil, streamable: true) ⇒ LazyFrame 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:

lf = Polars::LazyFrame.new(
  {
    "a" => ["x", "y", "z"],
    "b" => [1, 3, 5],
    "c" => [2, 4, 6]
  }
)
lf.unpivot(Polars::Selectors.numeric, index: "a").collect
# =>
# 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 (String) (defaults to: nil) —

    Name to give to the variable column. Defaults to "variable"

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

    Name to give to the value column. Defaults to "value"

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

    Allow this node to run in the streaming engine. If this runs in streaming, the output of the unpivot operation will not have a stable ordering.

Returns:



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# File 'lib/polars/lazy_frame.rb', line 2522

def unpivot(
  on,
  index: nil,
  variable_name: nil,
  value_name: nil,
  streamable: true
)
  on = on.nil? ? [] : Utils._expand_selectors(self, on)
  index = index.nil? ? [] : Utils._expand_selectors(self, index)

  _from_rbldf(
    _ldf.unpivot(on, index, value_name, variable_name, streamable)
  )
end

#var(ddof: 1) ⇒ LazyFrame

Aggregate the columns in the DataFrame to their variance value.

Examples:

df = Polars::DataFrame.new({"a" => [1, 2, 3, 4], "b" => [1, 2, 1, 1]}).lazy
df.var.collect
# =>
# shape: (1, 2)
# ┌──────────┬──────┐
# │ a        ┆ b    │
# │ ---      ┆ ---  │
# │ f64      ┆ f64  │
# ╞══════════╪══════╡
# │ 1.666667 ┆ 0.25 │
# └──────────┴──────┘
df.var(ddof: 0).collect
# =>
# shape: (1, 2)
# ┌──────┬────────┐
# │ a    ┆ b      │
# │ ---  ┆ ---    │
# │ f64  ┆ f64    │
# ╞══════╪════════╡
# │ 1.25 ┆ 0.1875 │
# └──────┴────────┘

Returns:



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# File 'lib/polars/lazy_frame.rb', line 2257

def var(ddof: 1)
  _from_rbldf(_ldf.var(ddof))
end

#width ⇒ Integer

Get the width of the LazyFrame.

Examples:

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

Returns:

  • (Integer) 


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# File 'lib/polars/lazy_frame.rb', line 113

def width
  _ldf.collect_schema.length
end

#with_column(column) ⇒ LazyFrame

Add or overwrite column in a DataFrame.

Examples:

df = Polars::DataFrame.new(
  {
    "a" => [1, 3, 5],
    "b" => [2, 4, 6]
  }
).lazy
df.with_column((Polars.col("b") ** 2).alias("b_squared")).collect
# =>
# shape: (3, 3)
# ┌─────┬─────┬───────────┐
# │ a   ┆ b   ┆ b_squared │
# │ --- ┆ --- ┆ ---       │
# │ i64 ┆ i64 ┆ i64       │
# ╞═════╪═════╪═══════════╡
# │ 1   ┆ 2   ┆ 4         │
# │ 3   ┆ 4   ┆ 16        │
# │ 5   ┆ 6   ┆ 36        │
# └─────┴─────┴───────────┘
df.with_column(Polars.col("a") ** 2).collect
# =>
# shape: (3, 2)
# ┌─────┬─────┐
# │ a   ┆ b   │
# │ --- ┆ --- │
# │ i64 ┆ i64 │
# ╞═════╪═════╡
# │ 1   ┆ 2   │
# │ 9   ┆ 4   │
# │ 25  ┆ 6   │
# └─────┴─────┘

Parameters:

  • column (Object) —

    Expression that evaluates to column or a Series to use.

Returns:



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# File 'lib/polars/lazy_frame.rb', line 1871

def with_column(column)
  with_columns([column])
end

#with_columns(*exprs, **named_exprs) ⇒ LazyFrame

Add or overwrite multiple columns in a DataFrame.

Examples:

ldf = Polars::DataFrame.new(
  {
    "a" => [1, 2, 3, 4],
    "b" => [0.5, 4, 10, 13],
    "c" => [true, true, false, true]
  }
).lazy
ldf.with_columns(
  [
    (Polars.col("a") ** 2).alias("a^2"),
    (Polars.col("b") / 2).alias("b/2"),
    (Polars.col("c").is_not).alias("not c")
  ]
).collect
# =>
# 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 │
# └─────┴──────┴───────┴─────┴──────┴───────┘

Parameters:

  • exprs (Object) —

    List of Expressions that evaluate to columns.

Returns:



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# File 'lib/polars/lazy_frame.rb', line 1786

def with_columns(*exprs, **named_exprs)
  structify = ENV.fetch("POLARS_AUTO_STRUCTIFY", "0") != "0"

  rbexprs = Utils.parse_into_list_of_expressions(*exprs, **named_exprs, __structify: structify)

  _from_rbldf(_ldf.with_columns(rbexprs))
end

#with_context(other) ⇒ LazyFrame

Add an external context to the computation graph.

This allows expressions to also access columns from DataFrames that are not part of this one.

Examples:

df_a = Polars::DataFrame.new({"a" => [1, 2, 3], "b" => ["a", "c", nil]}).lazy
df_other = Polars::DataFrame.new({"c" => ["foo", "ham"]})
(
  df_a.with_context(df_other.lazy).select(
    [Polars.col("b") + Polars.col("c").first]
  )
).collect
# =>
# shape: (3, 1)
# ┌──────┐
# │ b    │
# │ ---  │
# │ str  │
# ╞══════╡
# │ afoo │
# │ cfoo │
# │ null │
# └──────┘

Parameters:

  • other (Object) —

    Lazy DataFrame to join with.

Returns:



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# File 'lib/polars/lazy_frame.rb', line 1823

def with_context(other)
  if !other.is_a?(::Array)
    other = [other]
  end

  _from_rbldf(_ldf.with_context(other.map(&:_ldf)))
end

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

Note:

This can have a negative effect on query performance. This may, for instance, block predicate pushdown optimization.

Add a column at index 0 that counts the rows.

Examples:

df = Polars::DataFrame.new(
  {
    "a" => [1, 3, 5],
    "b" => [2, 4, 6]
  }
).lazy
df.with_row_index.collect
# =>
# shape: (3, 3)
# ┌───────┬─────┬─────┐
# │ index ┆ a   ┆ b   │
# │ ---   ┆ --- ┆ --- │
# │ u32   ┆ i64 ┆ i64 │
# ╞═══════╪═════╪═════╡
# │ 0     ┆ 1   ┆ 2   │
# │ 1     ┆ 3   ┆ 4   │
# │ 2     ┆ 5   ┆ 6   │
# └───────┴─────┴─────┘

Parameters:

  • name (String) (defaults to: "index") —

    Name of the column to add.

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

    Start the row count at this offset.

Returns:



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# File 'lib/polars/lazy_frame.rb', line 2126

def with_row_index(name: "index", offset: 0)
  _from_rbldf(_ldf.with_row_index(name, offset))
end

#write_json(file) ⇒ nil

Write the logical plan of this LazyFrame to a file or string in JSON format.

Parameters:

  • file (String) —

    File path to which the result should be written.

Returns:

  • (nil) 


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# File 'lib/polars/lazy_frame.rb', line 146

def write_json(file)
  if Utils.pathlike?(file)
    file = Utils.normalize_filepath(file)
  end
  _ldf.write_json(file)
  nil
end