atoti.Session.read_csv()#

Session.read_csv(path, /, *, keys=frozenset({}), table_name=None, separator=',', encoding='utf-8', process_quotes=True, partitioning=None, types={}, columns={}, array_separator=None, date_patterns={}, default_values={}, client_side_encryption=None, **kwargs)#

Read a CSV file into a table.

Parameters:

  • path (Path | str) –

    The path to the CSV file to load.

    .gz, .tar.gz and .zip files containing compressed CSV(s) are also supported.

    The path can also be a glob pattern (e.g. path/to/directory/**.*.csv).

  • keys (Set[str] | Sequence[str]) –

    The columns that will become keys of the table.

    If a Set is given, the keys will be ordered as the table columns.

  • table_name (str | None) – The name of the table to create. Required when path is a glob pattern. Otherwise, defaults to the capitalized final component of the path argument.

  • separator (str | None) –

    The character separating the values of each line.

    If None, the separator will be inferred in a preliminary partial read.

  • encoding (str) – The encoding to use to read the CSV.

  • process_quotes (bool | None) –

    Whether double quotes should be processed to follow the official CSV specification:

    • True:

      Each field may or may not be enclosed in double quotes (however some programs, such as Microsoft Excel, do not use double quotes at all). If fields are not enclosed with double quotes, then double quotes may not appear inside the fields.

      • A double quote appearing inside a field must be escaped by preceding it with another double quote.

      • Fields containing line breaks, double quotes, and commas should be enclosed in double-quotes.

    • False: all double-quotes within a field will be treated as any regular character, following Excel’s behavior. In this mode, it is expected that fields are not enclosed in double quotes. It is also not possible to have a line break inside a field.

    • None: the behavior will be inferred in a preliminary partial read.

  • partitioning (str | None) –

    The description of how the data will be split across partitions of the table.

    Default rules:

    • Only non-joined tables are automatically partitioned.

    • Tables are automatically partitioned by hashing their key columns. If there are no key columns, all the dictionarized columns are hashed.

    • Joined tables can only use a sub-partitioning of the table referencing them.

    • Automatic partitioning is done modulo the number of available cores.

    Example

    hash4(country) splits the data across 4 partitions based on the country column’s hash value.

  • types (Mapping[str, DataType]) – Types for some or all columns of the table. Types for non specified columns will be inferred from the first 1,000 lines.

  • columns (Mapping[str, str] | Sequence[str]) –

    The collection used to name, rename, or filter the CSV file columns.

    • If an empty collection is passed, the CSV file must have a header. The CSV column names will be used as the Table column names.

    • If a non empty Mapping with str keys is passed, the CSV file must have a header and the mapping keys must be column names of the CSV file. Columns of the CSV file absent from the mapping keys will not be loaded. The mapping values will be used as the Table column names. The other parameters of this method accepting column names expect to be passed values of this mapping, not keys.

    • If a non empty Sequence is passed, the CSV file must not have a header and the sequence must have as many elements as there are columns in the CSV file. The sequence elements will be used as the Table column names.

    >>> import csv
>>> from pathlib import Path
>>> from tempfile import mkdtemp
>>> directory = mkdtemp()
>>> file_path = Path(directory) / "largest-cities.csv"
>>> with open(file_path, "w") as csv_file:
...     writer = csv.writer(csv_file)
...     writer.writerows(
...         [
...             ("city", "area", "country", "population"),
...             ("Tokyo", "Kantō", "Japan", 14_094_034),
...             ("Johannesburg", "Gauteng", "South Africa", 4_803_262),
...             (
...                 "Barcelona",
...                 "Community of Madrid",
...                 "Madrid",
...                 3_223_334,
...             ),
...         ]
...     )

    Dropping the population column and renaming and reordering the remaining ones:

    >>> table = session.read_csv(
...     file_path,
...     columns={"country": "Country", "area": "Region", "city": "City"},
...     keys={"Country"},
... )
>>> table.head().sort_index()
                           Region          City
Country
Japan                       Kantō         Tokyo
Madrid        Community of Madrid     Barcelona
South Africa              Gauteng  Johannesburg

    Loading a headerless CSV file:

    >>> file_path = Path(directory) / "largest-cities-headerless.csv"
>>> with open(file_path, "w") as csv_file:
...     writer = csv.writer(csv_file)
...     writer.writerows(
...         [
...             ("Tokyo", "Kantō", "Japan", 14_094_034),
...             ("Johannesburg", "Gauteng", "South Africa", 4_803_262),
...             (
...                 "Madrid",
...                 "Community of Madrid",
...                 "Spain",
...                 3_223_334,
...             ),
...         ]
...     )
>>> table = session.read_csv(
...     file_path,
...     keys={"Country"},
...     columns=["City", "Area", "Country", "Population"],
... )
>>> table.head().sort_index()
                      City                 Area  Population
Country
Japan                Tokyo                Kantō    14094034
South Africa  Johannesburg              Gauteng     4803262
Spain               Madrid  Community of Madrid     3223334

  • array_separator (str | None) –

    The character separating array elements.

    If not None, any field containing this separator will be parsed as an array.

  • date_patterns (Mapping[str, str]) – A column name to date pattern mapping that can be used when the built-in date parsers fail to recognize the formatted dates in the passed files.

  • default_values (Mapping[str, ConstantValue | None]) – Mapping from column name to column default_value.

  • client_side_encryption (ClientSideEncryptionConfig | None) – The client side encryption configuration to use when loading data.

Return type:

Table