The TextAdapter module reads csv data and produces a NumPy array containing the parsed data. The following features are currently implemented:

  • The TextAdapter engine is written in C to ensure text is parsed as fast as data can be read from the source. Text is read and parsed in small chunks instead of reading entire data into memory at once, which enables very large files to be read and parsed without running out of memory.
  • Python slicing notation can be used to specify a subset of records to be read from the data source, as well as a subset of fields.
  • In additional to specifying a delimiter character, fields can be specified by fixed field widths as well as a regular expression. This enables a larger variety of csv-like and other types of text files to be parsed.
  • A gzipped file can be parsed without having to uncompress it first. Parsing speed is about the same as an uncompressed version of same file.
  • An index of record offsets in a file can be built to allow fast random access to records. This index can be saved to disk and loaded again later.
  • Converter functions can be specified for converting parsed text to proper dtype for storing in NumPy array. If Numba is installed, converter functions will be compiled to llvm bytecode on the fly for faster execution.
  • The TextAdapter engine has automatic type inference so the user does not have to specify dtypes of the output array. The user can still specify dtypes manually if desired.
  • Remote data stored in Amazon S3 can be read. An index can be built and stored with S3 data. Index can be read remotely, allowing for random access to S3 data.


The TextAdapter module contains the following factory methods for creating TextAdapter objects:

text_adapter (source, parser=’csv’, compression=None, comment=’#’,
quote=’”’, num_records=0, header=0, field_names=True, indexing=False, index_name=None, encoding=’utf-8’)
Create a text adapter for reading CSV, JSON, or fixed width
text files, or a text file defined by regular expressions.
source - filename, file object, StringIO object, BytesIO object, S3 key, http url, or python generator
parser - Type of parser for parsing text. Valid parser types are ‘csv’, ‘fixed width’, ‘regex’, and ‘json’.
encoding - type of character encoding (current ascii and utf8 are supported)
compression - type of data compression (currently only gzip is supported)
comment - character used to indicate comment line
quote - character used to quote fields
num_records - limits parsing to specified number of records; defaults to all records
header - number of lines in file header; these lines are skipped when parsing
footer - number of lines in file footer; these lines are skipped when parsing
indexing - create record index on the fly as characters are read
index_name - name of file to write index to
output - type of output object (numpy array or pandas dataframe)
If parser is set to ‘csv’, additional parameters include:
delimiter - Delimiter character used to define fields in data source. Default is ‘,’.
If parser is set to ‘fixed_width’, additional parameters include:
field_widths - List of field widths
If parser is set to ‘regex’, additional parameters include:
regex - Regular expression used to define records and fields in data source. See the regular expression example in the Advanced Usage section.
s3_text_adapter (access_key, secret_key, bucket_name, key_name, remote_s3_index=False)
parser=’csv’, compression=None, comment=’#’, quote=’”’, num_records=0, header=0, field_names=True, indexing=False, index_name=None, encoding=’utf-8’)
Create a text adapter for reading a text file from S3. Text file can be
CSV, JSON, fixed width, or defined by regular expressions

In addition to the arguments described for the text_adapter function above, the s3_text_adapter function also has the following parameters:

access_key - AWS access key
secret_key - AWS secret key
bucket_name - name of S3 bucket
key_name - name of key in S3 bucket
remote_s3_index - use remote S3 index (index name must be key name + ‘.idx’ extension)

The TextAdapter object returned by the text_adapter factory method contains the following methods:

set_converter (field, converter, use_numba=True)
Set converter function for field
field - field to apply converter function
converter - python function object
use_numba - If true, numba will be used to compile function. Otherwise the function will be executed as a normal Python function, resulting in slower performance.
set_missing_values (missing_values)
Set strings for each field that represents a missing value
missing_values - dict of field name or number, and list of missing value strings

Default missing values: ‘NA’, ‘NaN’, ‘inf’, ‘-inf’, ‘None’, ‘none’, ‘’

set_fill_values (fill_values, loose=False)
Set fill values for each field
fill_values - dict of field name or number, and fill value
loose - If value cannot be converted, and value does not match any of the missing values, replace with fill value anyway.

Default fill values for each data type: | int - 0 | float - numpy.nan | char - 0 | bool - False | object - numpy.nan | string - numpy.nan

create_index (index_name=None, density=1)
Create an index of record offsets in file
index_name - Name of file on disk used to store index. If None, index will be created in memory but not saved.
density - density of index. Value of 1 will index every record, value of 2 will index every other record, etc.
to_array ()
Parses entire data source and returns data as NumPy array object
to_dataframe ()
Parses entire data source and returns data as Pandas DataFrame object

The TextAdapter object contains the following properties:

size (readonly)
Number of records in data source. This value is only set if entire data source has been read or indexed, or number of recods was specified in text_adapter factory method when creating object.
field_count (readonly)
Number of fields in each record
Field names to use when creating output NumPy array. Field names can be set here before reading data or in text_adapter function with field_names parameter.
NumPy dtypes for each field, specified as a dict of fields and associated dtype. (Example: {0:’u4’, 1:’f8’, 2:’S10’})
Fields in data source to parse, specified as a list of field numbers or names (Examples: [0, 1, 2] or [‘f1’, ‘f3’, ‘f5’]). This filter stays in effect until it is reset to empty list, or is overridden with array slicing (Example: adapter[[0, 1, 3, 4]][:]).
See the NumPy data types documentation for more details:

The TextAdapter object supports array slicing:

Read all records: adapter[:]
Read first 100 records: adapter[0:100]
Read last record (only if data has been indexed or entire dataset has been read once before): adapter[-1]
Read first field in all records by specifying field number: adapter[0][:]
Read first field in all records by specifying field name: adapter[‘f0’][:]
Read first and third fields in all records: adapter[[0, 2]][:]

Basic Usage

Create TextAdapter object for data source:

>>> import iopro
>>> adapter = iopro.text_adapter('data.csv', parser='csv')

Parse text and store records in NumPy array using slicing notation:

>>> # read all records
>>> array = adapter[:]

>>> # read first ten records
>>> array = adapter[0:10]

>>> # read last record
>>> array = adapter[-1]

>>> # read every other record
>>> array = adapter[::2]

Advanced Usage

user defined converter function for field 0:

>>> import iopro
>>> import io

>>> data = '1, abc, 3.3\n2, xxx, 9.9'
>>> adapter = iopro.text_adapter(io.StringIO(data), parser='csv', field_names=False)

>>> # Override default converter for first field
>>> adapter.set_converter(0, lambda x: int(x)*2)
>>> adapter[:]
array([(2L, ' abc', 3.3), (4L, ' xxx', 9.9)],
          dtype=[('f0', '<u8'), ('f1', 'S4'), ('f2', '<f8')])

overriding default missing and fill values:

>>> import iopro
>>> import io

>>> data = '1,abc,inf\n2,NA,9.9'
>>> adapter = iopro.text_adapter(io.StringIO(data), parser='csv', field_names=False)

>>> # Define field dtypes (example: set field 1 to string object and field 2 to float)
>>> adapter.field_types = {1:'O', 2:'f4'}

>>> # Define list of strings for each field that represent missing values
>>> adapter.set_missing_values({1:['NA'], 2:['inf']})

>>> # Set fill value for missing values in each field
>>> adapter.set_fill_values({1:'xxx', 2:999.999})
>>> adapter[:]
array([(' abc', 999.9990234375), ('xxx', 9.899999618530273)],
          dtype=[('f0', 'O'), ('f1', '<f4')])

creating and saving tuple of index arrays for gzip file, and reloading indices:

>>> import iopro
>>> adapter = iopro.text_adapter('data.gz', parser='csv', compression='gzip')

>>> # Build index of records and save index to disk.
>>> adapter.create_index(index_name='index_file')

>>> # Create new adapter object and load index from disk.
>>> adapter = iopro.text_adapter('data.gz', parser='csv', compression='gzip', indexing=True, index_name='index_file')

>>> # Read last record
>>> adapter[-1]
array([(100, 101, 102)],dtype=[('f0', '<u4'), ('f1', '<u4'), ('f2', '<u4')])

Use regular expression for finer control of extracting data:

>>> import iopro
>>> import io

>>> # Define regular expression to extract dollar amount, percentage, and month.
>>> # Each set of parentheses defines a field.
>>> data = '$2.56, 50%, September 20 1978\n$1.23, 23%, April 5 1981'
>>> regex_string = '([0-9]\.[0-9][0-9]+)\,\s ([0-9]+)\%\,\s ([A-Za-z]+)'
>>> adapter = iopro.text_adapter(io.StringIO(data), parser='regex', regex_string=regex_string, field_names=False, infer_types=False)

>>> # set dtype of field to float
>>> adapter.field_types = {0:'f4', 1:'u4', 2:'S10'}
>>> adapter[:]
array([(2.56, 50L, 'September'), (1.23, 23L, 'April')],
    dtype=[('f0', '<f8'), ('f1', '<u8'), ('f2', 'S9')])