CODEWITHSUNDEEP
pythonnumpypandasastropy
Dalek
Dalek

Reputation: 4318

reading an ascii file with headers given in the first rows into a pandas dataframe

I have a huge set of catalogues which have different columns and the different header names for each column, where the description for each header name is given as comments at the beginning of my ascii files in a row. What is the best way to read them into a pandas.DataFrame while it can set the name of the column as well without being needed to define it from the beginning. The following is an example of my catalogues:

#   1 MAG_AUTO            Kron-like elliptical aperture magnitude         [mag]
#   2 rh                  half light radius (analyse)                     [pixel]
#   3 MU_MAX              Peak surface brightness above background        [mag * arcsec**(-2)]
#   4 FWHM_IMAGE          FWHM assuming a gaussian core                   [pixel]
#   5 CLASS_STAR          S/G classifier output                          
18.7462 4.81509 20.1348 6.67273 0.0286538
18.2440 7.17988 20.6454 21.6235 0.0286293
18.3102 3.11273 19.0960 8.26081 0.0430532
21.1751 2.92533 21.9931 5.52080 0.0290418
19.3998 1.86182 19.3166 3.42346 0.986598
20.0801 3.52828 21.3484 6.76799 0.0303842
21.9427 2.08458 22.0577 5.59344 0.981466
20.7726 1.86017 20.8130 3.69570 0.996121
23.0836 2.23427 23.3689 4.49985 0.706207
23.2443 1.62021 23.1089 3.54191 0.973419
20.6343 3.99555 21.9426 6.94700 0.0286164
23.4012 2.00408 23.3412 4.35926 0.946349
23.8427 1.54819 23.8241 3.83407 0.897079
20.3344 2.69910 20.9401 4.38988 0.0355277
21.7506 2.43451 22.2115 4.62045 0.0786921

Upvotes: 4

Views: 10182

Answers (2)

Tom Aldcroft
Tom Aldcroft

Reputation: 2542

This is a file in Sextractor format. The astropy.io.ascii reader understands this format natively so this is a snap to read:

>>> from astropy.io import ascii
>>> dat = ascii.read('table.dat')
>>> dat
<Table masked=False length=3>
MAG_AUTO    rh       MU_MAX    FWHM_IMAGE CLASS_STAR
  mag            mag / arcsec2    pix               
float64  float64    float64     float64    float64  
-------- ------- ------------- ---------- ----------
 18.7462 4.81509       20.1348    6.67273  0.0286538
  18.244 7.17988       20.6454    21.6235  0.0286293
 18.3102 3.11273        19.096    8.26081  0.0430532
...

Note that using the astropy ASCII reader you get a table that also retains the unit meta data.

If you still want to convert this to a pandas dataframe that's easy as well with DataFrame(dat.as_array()). Version 1.1 of astropy (and the current master) will have methods to_pandas and from_pandas that make this conversion more robust (see http://astropy.readthedocs.org/en/latest/table/pandas.html).

Upvotes: 10

Paul H
Paul H

Reputation: 68186

Ok, assuming all of your header info is encoded in the exact same way, here's how I would do this:

import re

import pandas

COMMENT_CHAR = '#'
columns = []
with open('test.dat', 'r') as td:
    for line in td:

        # find the commented lines
        if line[0] == COMMENT_CHAR:
            info = re.split(' +', line)
            columns.append(info[2])

        # when we seethe first line that doesn't start with 
        # COMMENT_CHAR, we pass the remaining lines of the 
        # file to pandas.read_table and break our loop
        else:
            _dfs = [
                pandas.DataFrame([line.split(' ')], columns=columns, dtype=float),
                pandas.read_table(td, sep='\s', header=None, names=columns)
            ]
            df = pandas.concat(_dfs, ignore_index=True)

To break down the initial parsing a bit, re.split(' +', line) turns this:

# 1 MAG_AUTO Kron-like elliptical aperture magnitude [mag]

into

['#', '1', 'MAG_AUTO', 'Kron-like', 'elliptical', 'aperture', 'magnitude', '[mag]']

So we take the column name as the 3 element (index = 2).

All this produces a dataframe that looks like this:

print(df.head())
   MAG_AUTO       rh   MU_MAX  FWHM_IMAGE  CLASS_STAR
0   18.7462  4.81509  20.1348     6.67273    0.0286538
1   18.2440  7.17988  20.6454    21.62350    0.028629
2   18.3102  3.11273  19.0960     8.26081    0.043053
3   21.1751  2.92533  21.9931     5.52080    0.029042
4   19.3998  1.86182  19.3166     3.42346    0.986598

And df.info() gives us:

<class 'pandas.core.frame.DataFrame'>
Int64Index: 15 entries, 0 to 14
Data columns (total 5 columns):
MAG_AUTO      15 non-null float64
rh            15 non-null float64
MU_MAX        15 non-null float64
FWHM_IMAGE    15 non-null float64
CLASS_STAR    15 non-null float64
dtypes: float64(5)
memory usage: 720.0 bytes

Upvotes: 2

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