.. currentmodule:: pyarrow.csv

Arrow supports reading columnar data from CSV files. The features currently offered are the following:

• multi-threaded or single-threaded reading
• automatic decompression of input files (based on the filename extension, such as my_data.csv.gz)
• fetching column names from the first row in the CSV file
• column-wise type inference and conversion to one of null, int64, float64, timestamp[s], string or binary data
• detecting various spellings of null values such as NaN or #N/A

CSV reading functionality is available through the :mod:`pyarrow.csv` module. In many cases, you will simply call the :func:`read_csv` function with the file path you want to read from:

>>> from pyarrow import csv
>>> fn = 'tips.csv.gz'
>>> table = csv.read_csv(fn)
>>> table
pyarrow.Table
total_bill: double
tip: double
sex: string
smoker: string
day: string
time: string
size: int64
>>> len(table)
244
>>> df = table.to_pandas()
>>> df.head()
   total_bill   tip     sex smoker  day    time  size
0       16.99  1.01  Female     No  Sun  Dinner     2
1       10.34  1.66    Male     No  Sun  Dinner     3
2       21.01  3.50    Male     No  Sun  Dinner     3
3       23.68  3.31    Male     No  Sun  Dinner     2
4       24.59  3.61  Female     No  Sun  Dinner     4

To alter the default parsing settings in case of reading CSV files with an unusual structure, you should create a :class:`ParseOptions` instance and pass it to :func:`read_csv`.

To alter how CSV data is converted to Arrow types and data, you should create a :class:`ConvertOptions` instance and pass it to :func:`read_csv`.

Due to the structure of CSV files, one cannot expect the same levels of performance as when reading dedicated binary formats like :ref:`Parquet <Parquet>`. Nevertheless, Arrow strives to reduce the overhead of reading CSV files.

Performance options can be controlled through the :class:`ReadOptions` class. Multi-threaded reading is the default for highest performance, distributing the workload efficiently over all available cores.