Reading and Writing CSV files¶

Arrow supports reading and writing columnar data from/to 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, date32, time32[s], timestamp[s], timestamp[ns], string or binary data
opportunistic dictionary encoding of string and binary columns (disabled by default)
detecting various spellings of null values such as NaN or #N/A
writing CSV files with options to configure the exact output format

Usage¶

CSV reading and writing functionality is available through the pyarrow.csv module. In many cases, you will simply call the 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 write CSV files, just call write_csv() with a pyarrow.RecordBatch or pyarrow.Table and a path or file-like object:

>>> import pyarrow as pa
>>> import pyarrow.csv as csv
>>> csv.write_csv(table, "tips.csv")
>>> with pa.CompressedOutputStream("tips.csv.gz", "gzip") as out:
...     csv.write_csv(table, out)

Note

The writer does not yet support all Arrow types.

Customized parsing¶

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

Customized conversion¶

To alter how CSV data is converted to Arrow types and data, you should create a ConvertOptions instance and pass it to read_csv():

import pyarrow as pa
import pyarrow.csv as csv

table = csv.read_csv('tips.csv.gz', convert_options=pa.csv.ConvertOptions(
    column_types={
        'total_bill': pa.decimal128(precision=10, scale=2),
        'tip': pa.decimal128(precision=10, scale=2),
    }
))

Incremental reading¶

For memory-constrained environments, it is also possible to read a CSV file one batch at a time, using open_csv().

There are a few caveats:

For now, the incremental reader is always single-threaded (regardless of ReadOptions.use_threads)
Type inference is done on the first block and types are frozen afterwards; to make sure the right data types are inferred, either set ReadOptions.block_size to a large enough value, or use ConvertOptions.column_types to set the desired data types explicitly.

Character encoding¶

By default, CSV files are expected to be encoded in UTF8. Non-UTF8 data is accepted for binary columns. The encoding can be changed using the ReadOptions class.

Customized writing¶

To alter the default write settings in case of writing CSV files with different conventions, you can create a WriteOptions instance and pass it to write_csv():

>>> import pyarrow as pa
>>> import pyarrow.csv as csv
>>> # Omit the header row (include_header=True is the default)
>>> options = csv.WriteOptions(include_header=False)
>>> csv.write_csv(table, "data.csv", options)

Incremental writing¶

To write CSV files one batch at a time, create a CSVWriter. This requires the output (a path or file-like object), the schema of the data to be written, and optionally write options as described above:

>>> import pyarrow as pa
>>> import pyarrow.csv as csv
>>> with csv.CSVWriter("data.csv", table.schema) as writer:
>>>     writer.write_table(table)

Performance¶

Due to the structure of CSV files, one cannot expect the same levels of performance as when reading dedicated binary formats like Parquet. Nevertheless, Arrow strives to reduce the overhead of reading CSV files. A reasonable expectation is at least 100 MB/s per core on a performant desktop or laptop computer (measured in source CSV bytes, not target Arrow data bytes).

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

Note

The number of concurrent threads is automatically inferred by Arrow. You can inspect and change it using the cpu_count() and set_cpu_count() functions, respectively.

Timestamps

Feather File Format