While was designed with flexibility and extensibility in mind, there is also a less flexible but significantly faster Cython/C engine for reading and writing ASCII files. By default, read() and write() will attempt to use this engine when dealing with compatible formats. The following formats are currently compatible with the fast engine:

  • basic
  • commented_header
  • csv
  • no_header
  • rdb
  • tab

The fast engine can also be enabled through the format parameter by prefixing a compatible format with “fast” and then an underscore. In this case, read() will not fall back on an ordinary reader if fast reading fails. For example:

>>> from astropy.table import Table
>>> t ='file.csv', format='fast_csv')  
>>> t.write('output.csv', format='ascii.fast_csv')  

To disable the fast engine, specify fast_reader=False or fast_writer=False. For example:

>>> t ='file.csv', format='csv', fast_reader=False) 
>>> t.write('file.csv', format='csv', fast_writer=False) 


Guessing and Fast reading

By default read() will try to guess the format of in the input data by successively trying different formats until one succeeds ([reference the guessing section]). For the default 'ascii' format this means that a number of pure Python readers with no fast implementation will be tried before getting to the fast readers.

For optimum performance, turn off guessing entirely (guess=False) or narrow down the format options as much as possible by specifying the format (e.g. format='csv') and/or other options such as the delimiter.


Since the fast engine is not part of the ordinary infrastructure, fast readers raise an error when passed certain parameters which are not implemented in the fast reader infrastructure. In this case read() will fall back on the ordinary reader. These parameters are:

  • Negative header_start (except for commented-header format)
  • Negative data_start
  • data_start=None
  • comment string not of length 1
  • delimiter string not of length 1
  • quotechar string not of length 1
  • converters
  • Outputter
  • Inputter
  • data_Splitter
  • header_Splitter

Parallel and fast conversion options

In addition to True and False, the parameter fast_reader can also be a dict specifying any of three additional parameters, parallel, use_fast_converter and exponent_style. For example:

>>>'data.txt', format='basic', fast_reader={'parallel': True, 'use_fast_converter': True}) 

These options allow for even faster table reading when enabled, but both are disabled by default because they come with some caveats.

The parallel parameter can be used to enable multiprocessing via the multiprocessing module, and can either be set to a number (the number of processes to use) or True, in which case the number of processes will be multiprocessing.cpu_count(). Note that this can cause issues within the IPython Notebook and so enabling multiprocessing in this context is discouraged.

Setting use_fast_converter to be True enables a faster but slightly imprecise conversion method for floating-point values, as described below.

The exponent_style parameter allows to define a different character from the default 'e' for exponential formats in the input file. The special setting 'fortran' enables auto-detection of any valid exponent character under Fortran notation. For details see the section on Fortran-style exponents.


The fast engine supports the same functionality as the ordinary writing engine and is generally about 2 to 4 times faster than the ordinary engine. An IPython notebook testing the relative performance of the fast writer against the ordinary writing system and the data analysis library Pandas is available here. The speed advantage of the faster engine is greatest for integer data and least for floating-point data; the fast engine is around 3.6 times faster for a sample file including a mixture of floating-point, integer, and text data. Also note that stripping string values slows down the writing process, so specifying strip_whitespace=False can improve performance.

Fast converter

Input floating-point values should ideally be converted to the nearest possible floating-point approximation; that is, the conversion should be correct within half of the distance between the two closest representable values, or 0.5 ULP. The ordinary readers, as well as the default fast reader, are guaranteed to convert floating-point values within 0.5 ULP, but there is also a faster and less accurate conversion method accessible via use_fast_converter. If the input data has less than about 15 significant figures, or if accuracy is relatively unimportant, this converter might be the best option in performance-critical scenarios.

Here is an IPython notebook analyzing the error of the fast converter, both in decimal values and in ULP. For values with a reasonably small number of significant figures, the fast converter is guaranteed to produce an optimal conversion (within 0.5 ULP). Once the number of significant figures exceeds the precision of 64-bit floating-point values, the fast converter is no longer guaranteed to be within 0.5 ULP, but about 60% of values end up within 0.5 ULP and about 90% within 1.0 ULP. Another notebook analyzing the fast converter’s behavior with extreme values (such as subnormals and values out of the range of floats) is available here.

Speed gains

The fast ASCII engine was designed based on the general parsing strategy used in the Pandas data analysis library, so its performance is generally comparable (although slightly slower by default) to the Pandas read_csv method. Here is an IPython notebook comparing the performance of the ordinary reader, the fast reader, the fast reader with the fast converter enabled, numpy’s genfromtxt, and Pandas’ read_csv for different kinds of table data in a basic space-delimited file.

In summary, genfromtxt and the ordinary reader are very similar in terms of speed, while read_csv is slightly faster than the fast engine for integer and floating-point data; for pure floating-point data, enabling the fast converter yields a speedup of about 50%. Also note that Pandas uses the exact same method as the fast converter in AstroPy when converting floating-point data.

The difference in performance between the fast engine and Pandas for text data depends on the extent to which data values are repeated, as Pandas is almost twice as fast as the fast engine when every value is identical and the reverse is true when values are randomized. This is because the fast engine uses fixed-size numpy string arrays for text data, while Pandas uses variable-size object arrays and uses an underlying set to avoid copying repeated values.

Overall, the fast engine tends to be around 4 or 5 times faster than the ordinary ASCII engine. If the input data is very large (generally about 100,000 rows or greater), and particularly if the data doesn’t contain primarily integer data or repeated string values, specifying parallel as True can yield further performance gains. Although IPython doesn’t work well with multiprocessing, there is a script available for testing the performance of the fast engine in parallel, and a sample result may be viewed here. This profile uses the fast converter for both the serial and parallel AstroPy readers.

Another point worth noting is that the fast engine uses memory mapping if a filename is supplied as input. If you want to avoid this for whatever reason, supply an open file object instead. However, this will generally be less efficient from both a time and a memory perspective, as the entire file input will have to be read at once.