pyarrow.csv.read_csv#

pyarrow.csv.read_csv(input_file,read_options=None,parse_options=None,convert_options=None,MemoryPoolmemory_pool=None)#

Read a Table from a stream of CSV data.

Parameters:

input_filestr, path or file-like object

The location of CSV data. If a string or path, and if it endswith a recognized compressed file extension (e.g. “.gz” or “.bz2”),the data is automatically decompressed when reading.

read_optionspyarrow.csv.ReadOptions, optional

Options for the CSV reader (see pyarrow.csv.ReadOptions constructorfor defaults)

parse_optionspyarrow.csv.ParseOptions, optional

Options for the CSV parser(see pyarrow.csv.ParseOptions constructor for defaults)

convert_optionspyarrow.csv.ConvertOptions, optional

Options for converting CSV data(see pyarrow.csv.ConvertOptions constructor for defaults)

memory_poolMemoryPool, optional

Pool to allocate Table memory from

Returns:

pyarrow.Table

Contents of the CSV file as a in-memory table.

Examples

Defining an example file from bytes object:

>>>importio>>>s=(..."animals,n_legs,entry\n"..."Flamingo,2,2022-03-01\n"..."Horse,4,2022-03-02\n"..."Brittle stars,5,2022-03-03\n"..."Centipede,100,2022-03-04"...)>>>print(s)animals,n_legs,entryFlamingo,2,2022-03-01Horse,4,2022-03-02Brittle stars,5,2022-03-03Centipede,100,2022-03-04>>>source=io.BytesIO(s.encode())

Reading from the file

>>>frompyarrowimportcsv>>>csv.read_csv(source)pyarrow.Tableanimals: stringn_legs: int64entry: date32[day]----animals: [["Flamingo","Horse","Brittle stars","Centipede"]]n_legs: [[2,4,5,100]]entry: [[2022-03-01,2022-03-02,2022-03-03,2022-03-04]]