Module: Polars::IO

Included in:
Polars
Defined in:
lib/polars/io.rb

Instance Method Summary collapse

Instance Method Details

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

Read into a DataFrame from Apache Avro format.

Parameters:

  • file (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.rb', line 465

def read_avro(file, columns: nil, n_rows: nil)
  if file.is_a?(String) || (defined?(Pathname) && file.is_a?(Pathname))
    file = Utils.format_path(file)
  end

  DataFrame._read_avro(file, n_rows: n_rows, columns: columns)
end

#read_csv(file, 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: 100, 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") ⇒ 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:

  • file (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: 100) —

    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.

Returns:



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# File 'lib/polars/io.rb', line 91

def read_csv(
  file,
  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: 100,
  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"
)
  _check_arg_is_1byte("sep", sep, false)
  _check_arg_is_1byte("comment_char", comment_char, false)
  _check_arg_is_1byte("quote_char", quote_char, true)
  _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(file) do |data|
    df = DataFrame._read_csv(
      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
    )
  end

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

#read_csv_batched(file, 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: 100, 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") ⇒ 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:

  • file (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: 100) —

    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.

Returns:

  • (BatchedCsvReader) 


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# File 'lib/polars/io.rb', line 689

def read_csv_batched(
  file,
  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: 100,
  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"
)
  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(
    file,
    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,
    new_columns: new_columns
  )
end

#read_ipc(file, 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:

  • file (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.rb', line 497

def read_ipc(
  file,
  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(file, **storage_options) do |data|
    DataFrame._read_ipc(
      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(file) ⇒ Hash

Get a schema of the IPC file without reading data.

Parameters:

  • file (Object) —

    Path to a file or a file-like object.

Returns:

  • (Hash) 


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# File 'lib/polars/io.rb', line 763

def read_ipc_schema(file)
  if file.is_a?(String) || (defined?(Pathname) && file.is_a?(Pathname))
    file = Utils.format_path(file)
  end

  _ipc_schema(file)
end

#read_json(file) ⇒ DataFrame

Read into a DataFrame from a JSON file.

Parameters:

  • file (Object) —

    Path to a file or a file-like object.

Returns:



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# File 'lib/polars/io.rb', line 579

def read_json(file)
  DataFrame._read_json(file)
end

#read_ndjson(file) ⇒ DataFrame

Read into a DataFrame from a newline delimited JSON file.

Parameters:

  • file (Object) —

    Path to a file or a file-like object.

Returns:



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# File 'lib/polars/io.rb', line 589

def read_ndjson(file)
  DataFrame._read_ndjson(file)
end

#read_parquet(file, columns: nil, n_rows: nil, storage_options: nil, parallel: "auto", row_count_name: nil, row_count_offset: 0, low_memory: 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 parquet-reader. A rechunk is an expensive operation.

Read into a DataFrame from a parquet file.

Parameters:

  • file (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 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.

Returns:



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# File 'lib/polars/io.rb', line 550

def read_parquet(
  file,
  columns: nil,
  n_rows: nil,
  storage_options: nil,
  parallel: "auto",
  row_count_name: nil,
  row_count_offset: 0,
  low_memory: false
)
  _prepare_file_arg(file) do |data|
    DataFrame._read_parquet(
      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
    )
  end
end

#read_parquet_schema(file) ⇒ Hash

Get a schema of the Parquet file without reading data.

Parameters:

  • file (Object) —

    Path to a file or a file-like object.

Returns:

  • (Hash) 


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# File 'lib/polars/io.rb', line 777

def read_parquet_schema(file)
  if file.is_a?(String) || (defined?(Pathname) && file.is_a?(Pathname))
    file = Utils.format_path(file)
  end

  _parquet_schema(file)
end

#scan_csv(file, has_header: true, sep: ",", comment_char: nil, quote_char: '"', skip_rows: 0, dtypes: nil, null_values: nil, ignore_errors: false, cache: true, with_column_names: nil, infer_schema_length: 100, 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") ⇒ 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:

  • file (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: 100) —

    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.

Returns:



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# File 'lib/polars/io.rb', line 244

def scan_csv(
  file,
  has_header: true,
  sep: ",",
  comment_char: nil,
  quote_char: '"',
  skip_rows: 0,
  dtypes: nil,
  null_values: nil,
  ignore_errors: false,
  cache: true,
  with_column_names: nil,
  infer_schema_length: 100,
  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"
)
  _check_arg_is_1byte("sep", sep, false)
  _check_arg_is_1byte("comment_char", comment_char, false)
  _check_arg_is_1byte("quote_char", quote_char, true)

  if file.is_a?(String) || (defined?(Pathname) && file.is_a?(Pathname))
    file = Utils.format_path(file)
  end

  LazyFrame._scan_csv(
    file,
    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,
  )
end

#scan_ipc(file, 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:

  • file (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.rb', line 326

def scan_ipc(
  file,
  n_rows: nil,
  cache: true,
  rechunk: true,
  row_count_name: nil,
  row_count_offset: 0,
  storage_options: nil,
  memory_map: true
)
  LazyFrame._scan_ipc(
    file,
    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(file, infer_schema_length: 100, 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:

  • file (String) —

    Path to a file.

  • infer_schema_length (Integer) (defaults to: 100) —

    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.rb', line 428

def scan_ndjson(
  file,
  infer_schema_length: 100,
  batch_size: 1024,
  n_rows: nil,
  low_memory: false,
  rechunk: true,
  row_count_name: nil,
  row_count_offset: 0
)
  if file.is_a?(String) || (defined?(Pathname) && file.is_a?(Pathname))
    file = Utils.format_path(file)
  end

  LazyFrame._scan_ndjson(
    file,
    infer_schema_length: infer_schema_length,
    batch_size: batch_size,
    n_rows: n_rows,
    low_memory: low_memory,
    rechunk: rechunk,
    row_count_name: row_count_name,
    row_count_offset: row_count_offset,
  )
end

#scan_parquet(file, n_rows: nil, cache: true, parallel: "auto", 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:

  • file (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.rb', line 376

def scan_parquet(
  file,
  n_rows: nil,
  cache: true,
  parallel: "auto",
  rechunk: true,
  row_count_name: nil,
  row_count_offset: 0,
  storage_options: nil,
  low_memory: false
)
  if file.is_a?(String) || (defined?(Pathname) && file.is_a?(Pathname))
    file = Utils.format_path(file)
  end

  LazyFrame._scan_parquet(
    file,
    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
  )
end