Module: Polars::IO

Included in:
Polars
Defined in:
lib/polars/io/csv.rb,
lib/polars/io/ipc.rb,
lib/polars/io/avro.rb,
lib/polars/io/json.rb,
lib/polars/io/ndjson.rb,
lib/polars/io/parquet.rb,
lib/polars/io/database.rb

Instance Method Summary collapse

Instance Method Details

#read_avro(source, columns: nil, n_rows: nil) ⇒ DataFrame

Read into a DataFrame from Apache Avro format.

Parameters:

  • source (Object) —

    Path to a file or a file-like object.

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

    Columns to select. Accepts a list of column indices (starting at zero) or a list of column names.

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

    Stop reading from Apache Avro file after reading n_rows.

Returns:



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# File 'lib/polars/io/avro.rb', line 14

def read_avro(source, columns: nil, n_rows: nil)
  if Utils.pathlike?(source)
    source = Utils.normalize_filepath(source)
  end
  projection, column_names = Utils.handle_projection_columns(columns)

  rbdf = RbDataFrame.read_avro(source, column_names, projection, n_rows)
  Utils.wrap_df(rbdf)
end

#read_csv(source, has_header: true, columns: nil, new_columns: nil, sep: ",", comment_char: nil, quote_char: '"', skip_rows: 0, dtypes: nil, null_values: nil, ignore_errors: false, parse_dates: false, n_threads: nil, infer_schema_length: N_INFER_DEFAULT, batch_size: 8192, n_rows: nil, encoding: "utf8", low_memory: false, rechunk: true, storage_options: nil, skip_rows_after_header: 0, row_count_name: nil, row_count_offset: 0, sample_size: 1024, eol_char: "\n", truncate_ragged_lines: false) ⇒ DataFrame

Note:

This operation defaults to a rechunk operation at the end, meaning that all data will be stored continuously in memory. Set rechunk: false if you are benchmarking the csv-reader. A rechunk is an expensive operation.

Read a CSV file into a DataFrame.

Parameters:

  • source (Object) —

    Path to a file or a file-like object.

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

    Indicate if the first row of dataset is a header or not. If set to false, column names will be autogenerated in the following format: column_x, with x being an enumeration over every column in the dataset starting at 1.

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

    Columns to select. Accepts a list of column indices (starting at zero) or a list of column names.

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

    Rename columns right after parsing the CSV file. If the given list is shorter than the width of the DataFrame the remaining columns will have their original name.

  • sep (String) (defaults to: ",") —

    Single byte character to use as delimiter in the file.

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

    Single byte character that indicates the start of a comment line, for instance #.

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

    Single byte character used for csv quoting. Set to nil to turn off special handling and escaping of quotes.

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

    Start reading after skip_rows lines.

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

    Overwrite dtypes during inference.

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

    Values to interpret as null values. You can provide a:

    • String: All values equal to this string will be null.
    • Array: All values equal to any string in this array will be null.
    • Hash: A hash that maps column name to a null value string.
  • ignore_errors (Boolean) (defaults to: false) —

    Try to keep reading lines if some lines yield errors. First try infer_schema_length: 0 to read all columns as :str to check which values might cause an issue.

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

    Try to automatically parse dates. If this does not succeed, the column remains of data type :str.

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

    Number of threads to use in csv parsing. Defaults to the number of physical cpu's of your system.

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

    Maximum number of lines to read to infer schema. If set to 0, all columns will be read as :utf8. If set to nil, a full table scan will be done (slow).

  • batch_size (Integer) (defaults to: 8192) —

    Number of lines to read into the buffer at once. Modify this to change performance.

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

    Stop reading from CSV file after reading n_rows. During multi-threaded parsing, an upper bound of n_rows rows cannot be guaranteed.

  • encoding ("utf8", "utf8-lossy") (defaults to: "utf8") —

    Lossy means that invalid utf8 values are replaced with � characters. When using other encodings than utf8 or utf8-lossy, the input is first decoded im memory with Ruby.

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

    Reduce memory usage at expense of performance.

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

    Make sure that all columns are contiguous in memory by aggregating the chunks into a single array.

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

    Extra options that make sense for a particular storage connection.

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

    Skip this number of rows when the header is parsed.

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

    If not nil, this will insert a row count column with the given name into the DataFrame.

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

    Offset to start the row_count column (only used if the name is set).

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

    Set the sample size. This is used to sample statistics to estimate the allocation needed.

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

    Single byte end of line character.

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

    Truncate lines that are longer than the schema.

Returns:



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# File 'lib/polars/io/csv.rb', line 93

def read_csv(
  source,
  has_header: true,
  columns: nil,
  new_columns: nil,
  sep: ",",
  comment_char: nil,
  quote_char: '"',
  skip_rows: 0,
  dtypes: nil,
  null_values: nil,
  ignore_errors: false,
  parse_dates: false,
  n_threads: nil,
  infer_schema_length: N_INFER_DEFAULT,
  batch_size: 8192,
  n_rows: nil,
  encoding: "utf8",
  low_memory: false,
  rechunk: true,
  storage_options: nil,
  skip_rows_after_header: 0,
  row_count_name: nil,
  row_count_offset: 0,
  sample_size: 1024,
  eol_char: "\n",
  truncate_ragged_lines: false
)
  Utils._check_arg_is_1byte("sep", sep, false)
  Utils._check_arg_is_1byte("comment_char", comment_char, false)
  Utils._check_arg_is_1byte("quote_char", quote_char, true)
  Utils._check_arg_is_1byte("eol_char", eol_char, false)

  projection, columns = Utils.handle_projection_columns(columns)

  storage_options ||= {}

  if columns && !has_header
    columns.each do |column|
      if !column.start_with?("column_")
        raise ArgumentError, "Specified column names do not start with \"column_\", but autogenerated header names were requested."
      end
    end
  end

  if projection || new_columns
    raise Todo
  end

  df = nil
  _prepare_file_arg(source) do |data|
    df = _read_csv_impl(
      data,
      has_header: has_header,
      columns: columns || projection,
      sep: sep,
      comment_char: comment_char,
      quote_char: quote_char,
      skip_rows: skip_rows,
      dtypes: dtypes,
      null_values: null_values,
      ignore_errors: ignore_errors,
      parse_dates: parse_dates,
      n_threads: n_threads,
      infer_schema_length: infer_schema_length,
      batch_size: batch_size,
      n_rows: n_rows,
      encoding: encoding == "utf8-lossy" ? encoding : "utf8",
      low_memory: low_memory,
      rechunk: rechunk,
      skip_rows_after_header: skip_rows_after_header,
      row_count_name: row_count_name,
      row_count_offset: row_count_offset,
      sample_size: sample_size,
      eol_char: eol_char,
      truncate_ragged_lines: truncate_ragged_lines
    )
  end

  if new_columns
    Utils._update_columns(df, new_columns)
  else
    df
  end
end

#read_csv_batched(source, has_header: true, columns: nil, new_columns: nil, sep: ",", comment_char: nil, quote_char: '"', skip_rows: 0, dtypes: nil, null_values: nil, missing_utf8_is_empty_string: false, ignore_errors: false, parse_dates: false, n_threads: nil, infer_schema_length: N_INFER_DEFAULT, batch_size: 50_000, n_rows: nil, encoding: "utf8", low_memory: false, rechunk: true, skip_rows_after_header: 0, row_count_name: nil, row_count_offset: 0, sample_size: 1024, eol_char: "\n", raise_if_empty: true, truncate_ragged_lines: false, decimal_comma: false) ⇒ BatchedCsvReader

Read a CSV file in batches.

Upon creation of the BatchedCsvReader, polars will gather statistics and determine the file chunks. After that work will only be done if next_batches is called.

Examples:

reader = Polars.read_csv_batched(
  "./tpch/tables_scale_100/lineitem.tbl", sep: "|", parse_dates: true
)
reader.next_batches(5)

Parameters:

  • source (Object) —

    Path to a file or a file-like object.

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

    Indicate if the first row of dataset is a header or not. If set to False, column names will be autogenerated in the following format: column_x, with x being an enumeration over every column in the dataset starting at 1.

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

    Columns to select. Accepts a list of column indices (starting at zero) or a list of column names.

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

    Rename columns right after parsing the CSV file. If the given list is shorter than the width of the DataFrame the remaining columns will have their original name.

  • sep (String) (defaults to: ",") —

    Single byte character to use as delimiter in the file.

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

    Single byte character that indicates the start of a comment line, for instance #.

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

    Single byte character used for csv quoting, default = ". Set to nil to turn off special handling and escaping of quotes.

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

    Start reading after skip_rows lines.

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

    Overwrite dtypes during inference.

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

    Values to interpret as null values. You can provide a:

    • String: All values equal to this string will be null.
    • Array: All values equal to any string in this array will be null.
    • Hash: A hash that maps column name to a null value string.
  • ignore_errors (Boolean) (defaults to: false) —

    Try to keep reading lines if some lines yield errors. First try infer_schema_length: 0 to read all columns as :str to check which values might cause an issue.

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

    Try to automatically parse dates. If this does not succeed, the column remains of data type :str.

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

    Number of threads to use in csv parsing. Defaults to the number of physical cpu's of your system.

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

    Maximum number of lines to read to infer schema. If set to 0, all columns will be read as :str. If set to nil, a full table scan will be done (slow).

  • batch_size (Integer) (defaults to: 50_000) —

    Number of lines to read into the buffer at once. Modify this to change performance.

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

    Stop reading from CSV file after reading n_rows. During multi-threaded parsing, an upper bound of n_rows rows cannot be guaranteed.

  • encoding ("utf8", "utf8-lossy") (defaults to: "utf8") —

    Lossy means that invalid utf8 values are replaced with � characters. When using other encodings than utf8 or utf8-lossy, the input is first decoded im memory with Ruby. Defaults to utf8.

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

    Reduce memory usage at expense of performance.

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

    Make sure that all columns are contiguous in memory by aggregating the chunks into a single array.

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

    Skip this number of rows when the header is parsed.

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

    If not nil, this will insert a row count column with the given name into the DataFrame.

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

    Offset to start the row_count column (only used if the name is set).

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

    Set the sample size. This is used to sample statistics to estimate the allocation needed.

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

    Single byte end of line character.

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

    Truncate lines that are longer than the schema.

Returns:

  • (BatchedCsvReader) 


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# File 'lib/polars/io/csv.rb', line 410

def read_csv_batched(
  source,
  has_header: true,
  columns: nil,
  new_columns: nil,
  sep: ",",
  comment_char: nil,
  quote_char: '"',
  skip_rows: 0,
  dtypes: nil,
  null_values: nil,
  missing_utf8_is_empty_string: false,
  ignore_errors: false,
  parse_dates: false,
  n_threads: nil,
  infer_schema_length: N_INFER_DEFAULT,
  batch_size: 50_000,
  n_rows: nil,
  encoding: "utf8",
  low_memory: false,
  rechunk: true,
  skip_rows_after_header: 0,
  row_count_name: nil,
  row_count_offset: 0,
  sample_size: 1024,
  eol_char: "\n",
  raise_if_empty: true,
  truncate_ragged_lines: false,
  decimal_comma: false
)
  projection, columns = Utils.handle_projection_columns(columns)

  if columns && !has_header
    columns.each do |column|
      if !column.start_with?("column_")
        raise ArgumentError, "Specified column names do not start with \"column_\", but autogenerated header names were requested."
      end
    end
  end

  if projection || new_columns
    raise Todo
  end

  BatchedCsvReader.new(
    source,
    has_header: has_header,
    columns: columns || projection,
    sep: sep,
    comment_char: comment_char,
    quote_char: quote_char,
    skip_rows: skip_rows,
    dtypes: dtypes,
    null_values: null_values,
    missing_utf8_is_empty_string: missing_utf8_is_empty_string,
    ignore_errors: ignore_errors,
    parse_dates: parse_dates,
    n_threads: n_threads,
    infer_schema_length: infer_schema_length,
    batch_size: batch_size,
    n_rows: n_rows,
    encoding: encoding == "utf8-lossy" ? encoding : "utf8",
    low_memory: low_memory,
    rechunk: rechunk,
    skip_rows_after_header: skip_rows_after_header,
    row_count_name: row_count_name,
    row_count_offset: row_count_offset,
    sample_size: sample_size,
    eol_char: eol_char,
    new_columns: new_columns,
    raise_if_empty: raise_if_empty,
    truncate_ragged_lines: truncate_ragged_lines,
    decimal_comma: decimal_comma
  )
end

#read_database(query, schema_overrides: nil) ⇒ DataFrame Also known as: read_sql

Read a SQL query into a DataFrame.

Parameters:

  • query (Object) —

    ActiveRecord::Relation or ActiveRecord::Result.

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

    A hash mapping column names to dtypes, used to override the schema inferred from the query.

Returns:



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# File 'lib/polars/io/database.rb', line 12

def read_database(query, schema_overrides: nil)
  if !defined?(ActiveRecord)
    raise Error, "Active Record not available"
  end

  result =
    if query.is_a?(ActiveRecord::Result)
      query
    elsif query.is_a?(ActiveRecord::Relation)
      query.connection.select_all(query.to_sql)
    elsif query.is_a?(::String)
      ActiveRecord::Base.connection.select_all(query)
    else
      raise ArgumentError, "Expected ActiveRecord::Relation, ActiveRecord::Result, or String"
    end

  data = {}
  schema_overrides = (schema_overrides || {}).transform_keys(&:to_s)

  result.columns.each_with_index do |k, i|
    column_type = result.column_types[i]

    data[k] =
      if column_type
        result.rows.map { |r| column_type.deserialize(r[i]) }
      else
        result.rows.map { |r| r[i] }
      end

    polars_type =
      case column_type&.type
      when :binary
        Binary
      when :boolean
        Boolean
      when :date
        Date
      when :datetime, :timestamp
        Datetime
      when :decimal
        Decimal
      when :float
        Float64
      when :integer
        Int64
      when :string, :text
        String
      when :time
        Time
      # TODO fix issue with null
      # when :json, :jsonb
      #   Struct
      end

    schema_overrides[k] ||= polars_type if polars_type
  end

  DataFrame.new(data, schema_overrides: schema_overrides)
end

#read_ipc(source, columns: nil, n_rows: nil, memory_map: true, storage_options: nil, row_count_name: nil, row_count_offset: 0, rechunk: true) ⇒ DataFrame

Read into a DataFrame from Arrow IPC (Feather v2) file.

Parameters:

  • source (Object) —

    Path to a file or a file-like object.

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

    Columns to select. Accepts a list of column indices (starting at zero) or a list of column names.

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

    Stop reading from IPC file after reading n_rows.

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

    Try to memory map the file. This can greatly improve performance on repeated queries as the OS may cache pages. Only uncompressed IPC files can be memory mapped.

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

    Extra options that make sense for a particular storage connection.

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

    If not nil, this will insert a row count column with give name into the DataFrame.

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

    Offset to start the row_count column (only use if the name is set).

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

    Make sure that all data is contiguous.

Returns:



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# File 'lib/polars/io/ipc.rb', line 27

def read_ipc(
  source,
  columns: nil,
  n_rows: nil,
  memory_map: true,
  storage_options: nil,
  row_count_name: nil,
  row_count_offset: 0,
  rechunk: true
)
  storage_options ||= {}
  _prepare_file_arg(source, **storage_options) do |data|
    _read_ipc_impl(
      data,
      columns: columns,
      n_rows: n_rows,
      row_count_name: row_count_name,
      row_count_offset: row_count_offset,
      rechunk: rechunk,
      memory_map: memory_map
    )
  end
end

#read_ipc_schema(source) ⇒ Hash

Get a schema of the IPC file without reading data.

Parameters:

  • source (Object) —

    Path to a file or a file-like object.

Returns:

  • (Hash) 


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# File 'lib/polars/io/ipc.rb', line 164

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

  Plr.ipc_schema(source)
end

#read_ipc_stream(source, columns: nil, n_rows: nil, storage_options: nil, row_index_name: nil, row_index_offset: 0, rechunk: true) ⇒ DataFrame

Read into a DataFrame from Arrow IPC record batch stream.

See "Streaming format" on https://arrow.apache.org/docs/python/ipc.html.

Parameters:

  • source (Object) —

    Path to a file or a file-like object.

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

    Columns to select. Accepts a list of column indices (starting at zero) or a list of column names.

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

    Stop reading from IPC stream after reading n_rows.

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

    Extra options that make sense for a particular storage connection.

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

    Insert a row index column with the given name into the DataFrame as the first column. If set to nil (default), no row index column is created.

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

    Start the row index at this offset. Cannot be negative. Only used if row_index_name is set.

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

    Make sure that all data is contiguous.

Returns:



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# File 'lib/polars/io/ipc.rb', line 108

def read_ipc_stream(
  source,
  columns: nil,
  n_rows: nil,
  storage_options: nil,
  row_index_name: nil,
  row_index_offset: 0,
  rechunk: true
)
  storage_options ||= {}
  _prepare_file_arg(source, **storage_options) do |data|
    _read_ipc_stream_impl(
      data,
      columns: columns,
      n_rows: n_rows,
      row_index_name: row_index_name,
      row_index_offset: row_index_offset,
      rechunk: rechunk
    )
  end
end

#read_json(source, schema: nil, schema_overrides: nil, infer_schema_length: N_INFER_DEFAULT) ⇒ DataFrame

Read into a DataFrame from a JSON file.

Parameters:

  • source (Object) —

    Path to a file or a file-like object.

Returns:



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# File 'lib/polars/io/json.rb', line 9

def read_json(
  source,
  schema: nil,
  schema_overrides: nil,
  infer_schema_length: N_INFER_DEFAULT
)
  if Utils.pathlike?(source)
    source = Utils.normalize_filepath(source)
  end

  rbdf =
    RbDataFrame.read_json(
      source,
      infer_schema_length,
      schema,
      schema_overrides
    )
  Utils.wrap_df(rbdf)
end

#read_ndjson(source, schema: nil, schema_overrides: nil, ignore_errors: false) ⇒ DataFrame

Read into a DataFrame from a newline delimited JSON file.

Parameters:

  • source (Object) —

    Path to a file or a file-like object.

Returns:



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# File 'lib/polars/io/ndjson.rb', line 9

def read_ndjson(
  source,
  schema: nil,
  schema_overrides: nil,
  ignore_errors: false
)
  if Utils.pathlike?(source)
    source = Utils.normalize_filepath(source)
  end

  rbdf =
    RbDataFrame.read_ndjson(
      source,
      ignore_errors,
      schema,
      schema_overrides
    )
  Utils.wrap_df(rbdf)
end

#read_parquet(source, columns: nil, n_rows: nil, storage_options: nil, parallel: "auto", row_count_name: nil, row_count_offset: 0, low_memory: false, use_statistics: true, rechunk: true) ⇒ DataFrame

Note:

This operation defaults to a rechunk operation at the end, meaning that all data will be stored continuously in memory. Set rechunk: false if you are benchmarking the parquet-reader. A rechunk is an expensive operation.

Read into a DataFrame from a parquet file.

Parameters:

  • source (String, Pathname, StringIO) —

    Path to a file or a file-like object.

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

    Columns to select. Accepts a list of column indices (starting at zero) or a list of column names.

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

    Stop reading from parquet file after reading n_rows.

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

    Extra options that make sense for a particular storage connection.

  • parallel ("auto", "columns", "row_groups", "none") (defaults to: "auto") —

    This determines the direction of parallelism. 'auto' will try to determine the optimal direction.

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

    If not nil, this will insert a row count column with give name into the DataFrame.

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

    Offset to start the row_count column (only use if the name is set).

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

    Reduce memory pressure at the expense of performance.

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

    Use statistics in the parquet to determine if pages can be skipped from reading.

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

    Make sure that all columns are contiguous in memory by aggregating the chunks into a single array.

Returns:



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# File 'lib/polars/io/parquet.rb', line 38

def read_parquet(
  source,
  columns: nil,
  n_rows: nil,
  storage_options: nil,
  parallel: "auto",
  row_count_name: nil,
  row_count_offset: 0,
  low_memory: false,
  use_statistics: true,
  rechunk: true
)
  _prepare_file_arg(source) do |data|
    _read_parquet_impl(
      data,
      columns: columns,
      n_rows: n_rows,
      parallel: parallel,
      row_count_name: row_count_name,
      row_count_offset: row_count_offset,
      low_memory: low_memory,
      use_statistics: use_statistics,
      rechunk: rechunk
    )
  end
end

#read_parquet_schema(source) ⇒ Hash

Get a schema of the Parquet file without reading data.

Parameters:

  • source (Object) —

    Path to a file or a file-like object.

Returns:

  • (Hash) 


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# File 'lib/polars/io/parquet.rb', line 127

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

  Plr.parquet_schema(source)
end

#scan_csv(source, has_header: true, sep: ",", comment_char: nil, quote_char: '"', skip_rows: 0, dtypes: nil, null_values: nil, missing_utf8_is_empty_string: false, ignore_errors: false, cache: true, with_column_names: nil, infer_schema_length: N_INFER_DEFAULT, n_rows: nil, encoding: "utf8", low_memory: false, rechunk: true, skip_rows_after_header: 0, row_count_name: nil, row_count_offset: 0, parse_dates: false, eol_char: "\n", raise_if_empty: true, truncate_ragged_lines: false, decimal_comma: false, glob: true) ⇒ LazyFrame

Lazily read from a CSV file or multiple files via glob patterns.

This allows the query optimizer to push down predicates and projections to the scan level, thereby potentially reducing memory overhead.

Parameters:

  • source (Object) —

    Path to a file.

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

    Indicate if the first row of dataset is a header or not. If set to false, column names will be autogenerated in the following format: column_x, with x being an enumeration over every column in the dataset starting at 1.

  • sep (String) (defaults to: ",") —

    Single byte character to use as delimiter in the file.

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

    Single byte character that indicates the start of a comment line, for instance #.

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

    Single byte character used for csv quoting. Set to None to turn off special handling and escaping of quotes.

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

    Start reading after skip_rows lines. The header will be parsed at this offset.

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

    Overwrite dtypes during inference.

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

    Values to interpret as null values. You can provide a:

    • String: All values equal to this string will be null.
    • Array: All values equal to any string in this array will be null.
    • Hash: A hash that maps column name to a null value string.
  • ignore_errors (Boolean) (defaults to: false) —

    Try to keep reading lines if some lines yield errors. First try infer_schema_length: 0 to read all columns as :str to check which values might cause an issue.

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

    Cache the result after reading.

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

    Apply a function over the column names. This can be used to update a schema just in time, thus before scanning.

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

    Maximum number of lines to read to infer schema. If set to 0, all columns will be read as :str. If set to nil, a full table scan will be done (slow).

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

    Stop reading from CSV file after reading n_rows.

  • encoding ("utf8", "utf8-lossy") (defaults to: "utf8") —

    Lossy means that invalid utf8 values are replaced with � characters.

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

    Reduce memory usage in expense of performance.

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

    Reallocate to contiguous memory when all chunks/ files are parsed.

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

    Skip this number of rows when the header is parsed.

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

    If not nil, this will insert a row count column with the given name into the DataFrame.

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

    Offset to start the row_count column (only used if the name is set).

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

    Try to automatically parse dates. If this does not succeed, the column remains of data type :str.

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

    Single byte end of line character.

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

    Truncate lines that are longer than the schema.

Returns:



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# File 'lib/polars/io/csv.rb', line 557

def scan_csv(
  source,
  has_header: true,
  sep: ",",
  comment_char: nil,
  quote_char: '"',
  skip_rows: 0,
  dtypes: nil,
  null_values: nil,
  missing_utf8_is_empty_string: false,
  ignore_errors: false,
  cache: true,
  with_column_names: nil,
  infer_schema_length: N_INFER_DEFAULT,
  n_rows: nil,
  encoding: "utf8",
  low_memory: false,
  rechunk: true,
  skip_rows_after_header: 0,
  row_count_name: nil,
  row_count_offset: 0,
  parse_dates: false,
  eol_char: "\n",
  raise_if_empty: true,
  truncate_ragged_lines: false,
  decimal_comma: false,
  glob: true
)
  Utils._check_arg_is_1byte("sep", sep, false)
  Utils._check_arg_is_1byte("comment_char", comment_char, false)
  Utils._check_arg_is_1byte("quote_char", quote_char, true)

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

  _scan_csv_impl(
    source,
    has_header: has_header,
    sep: sep,
    comment_char: comment_char,
    quote_char: quote_char,
    skip_rows: skip_rows,
    dtypes: dtypes,
    null_values: null_values,
    ignore_errors: ignore_errors,
    cache: cache,
    with_column_names: with_column_names,
    infer_schema_length: infer_schema_length,
    n_rows: n_rows,
    low_memory: low_memory,
    rechunk: rechunk,
    skip_rows_after_header: skip_rows_after_header,
    encoding: encoding,
    row_count_name: row_count_name,
    row_count_offset: row_count_offset,
    parse_dates: parse_dates,
    eol_char: eol_char,
    truncate_ragged_lines: truncate_ragged_lines
  )
end

#scan_ipc(source, n_rows: nil, cache: true, rechunk: true, row_count_name: nil, row_count_offset: 0, storage_options: nil, memory_map: true) ⇒ LazyFrame

Lazily read from an Arrow IPC (Feather v2) file or multiple files via glob patterns.

This allows the query optimizer to push down predicates and projections to the scan level, thereby potentially reducing memory overhead.

Parameters:

  • source (String) —

    Path to a IPC file.

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

    Stop reading from IPC file after reading n_rows.

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

    Cache the result after reading.

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

    Reallocate to contiguous memory when all chunks/ files are parsed.

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

    If not nil, this will insert a row count column with give name into the DataFrame.

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

    Offset to start the row_count column (only use if the name is set).

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

    Extra options that make sense for a particular storage connection.

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

    Try to memory map the file. This can greatly improve performance on repeated queries as the OS may cache pages. Only uncompressed IPC files can be memory mapped.

Returns:



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# File 'lib/polars/io/ipc.rb', line 198

def scan_ipc(
  source,
  n_rows: nil,
  cache: true,
  rechunk: true,
  row_count_name: nil,
  row_count_offset: 0,
  storage_options: nil,
  memory_map: true
)
  _scan_ipc_impl(
    source,
    n_rows: n_rows,
    cache: cache,
    rechunk: rechunk,
    row_count_name: row_count_name,
    row_count_offset: row_count_offset,
    storage_options: storage_options,
    memory_map: memory_map
  )
end

#scan_ndjson(source, infer_schema_length: N_INFER_DEFAULT, batch_size: 1024, n_rows: nil, low_memory: false, rechunk: true, row_count_name: nil, row_count_offset: 0) ⇒ LazyFrame

Lazily read from a newline delimited JSON file.

This allows the query optimizer to push down predicates and projections to the scan level, thereby potentially reducing memory overhead.

Parameters:

  • source (String) —

    Path to a file.

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

    Infer the schema length from the first infer_schema_length rows.

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

    Number of rows to read in each batch.

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

    Stop reading from JSON file after reading n_rows.

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

    Reduce memory pressure at the expense of performance.

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

    Reallocate to contiguous memory when all chunks/ files are parsed.

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

    If not nil, this will insert a row count column with give name into the DataFrame.

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

    Offset to start the row_count column (only use if the name is set).

Returns:



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# File 'lib/polars/io/ndjson.rb', line 53

def scan_ndjson(
  source,
  infer_schema_length: N_INFER_DEFAULT,
  batch_size: 1024,
  n_rows: nil,
  low_memory: false,
  rechunk: true,
  row_count_name: nil,
  row_count_offset: 0
)
  if Utils.pathlike?(source)
    source = Utils.normalize_filepath(source)
  end

  rblf =
    RbLazyFrame.new_from_ndjson(
      source,
      infer_schema_length,
      batch_size,
      n_rows,
      low_memory,
      rechunk,
      Utils.parse_row_index_args(row_count_name, row_count_offset)
    )
  Utils.wrap_ldf(rblf)
end

#scan_parquet(source, n_rows: nil, cache: true, parallel: "auto", glob: true, rechunk: true, row_count_name: nil, row_count_offset: 0, storage_options: nil, low_memory: false) ⇒ LazyFrame

Lazily read from a parquet file or multiple files via glob patterns.

This allows the query optimizer to push down predicates and projections to the scan level, thereby potentially reducing memory overhead.

Parameters:

  • source (String) —

    Path to a file.

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

    Stop reading from parquet file after reading n_rows.

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

    Cache the result after reading.

  • parallel ("auto", "columns", "row_groups", "none") (defaults to: "auto") —

    This determines the direction of parallelism. 'auto' will try to determine the optimal direction.

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

    In case of reading multiple files via a glob pattern rechunk the final DataFrame into contiguous memory chunks.

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

    If not nil, this will insert a row count column with give name into the DataFrame.

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

    Offset to start the row_count column (only use if the name is set).

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

    Extra options that make sense for a particular storage connection.

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

    Reduce memory pressure at the expense of performance.

Returns:



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# File 'lib/polars/io/parquet.rb', line 163

def scan_parquet(
  source,
  n_rows: nil,
  cache: true,
  parallel: "auto",
  glob: true,
  rechunk: true,
  row_count_name: nil,
  row_count_offset: 0,
  storage_options: nil,
  low_memory: false
)
  if Utils.pathlike?(source)
    source = Utils.normalize_filepath(source)
  end

  _scan_parquet_impl(
    source,
    n_rows: n_rows,
    cache: cache,
    parallel: parallel,
    rechunk: rechunk,
    row_count_name: row_count_name,
    row_count_offset: row_count_offset,
    storage_options: storage_options,
    low_memory: low_memory,
    glob: glob
  )
end