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launch an interactive ipython session with specified files opened and parsed

Project description

pyopen is a command-line tool for quickly inspecting files using python. It drops you into an interactive IPython session with the parsed files available as local variables.

Currently supports csv/tsv files (uses pandas and also supports gzip / bz2 compression), excel, json, yaml, hdf5, and pickle files.


pip install pyopen

To run the tests:



$ pyopen test/data/data1.csv

f1 test/data/data1.csv 4.9 kB pandas_csv

Python 2.7.10 (default, May 26 2015, 13:01:57)
Type "copyright", "credits" or "license" for more information.

IPython 4.0.0 -- An enhanced Interactive Python.
?         -> Introduction and overview of IPython's features.
%quickref -> Quick reference.
help      -> Python's own help system.
object?   -> Details about 'object', use 'object??' for extra details.
1 files loaded:

f1 test/data/data1.csv 4.9 kB pandas_csv
    pandas.DataFrame: 99 rows x 8 cols.
    Columns: patient genome contig interbase_start interbase_end ref alt validation

Variables defined:
    f1 : the parsed file
    loaded : object with attributes for each parsed file (abbreviated filenames, tab completes)
    loaded_filenames : dict from filename to parsed file
    loaded_absolute_filenames : dict from absolute filename to parsed file
    loaded_list : list of the parsed files

To print this message again later, type 'info()'. To quit type 'quit()'.

The variable ‘f1’ is a pandas DataFrame with the contents of the csv file:

In [1]: f1.head()
  patient  genome contig  interbase_start  interbase_end ref alt validation
0     PT1  GRCh37   chr1         10719783       10719784   T   G   untested
1     PT1  GRCh37   chr1         11137693       11137694   G   A   verified
2     PT1  GRCh37   chr1         11826130       11826131   A   T   verified
3     PT1  GRCh37   chr1         17664605       17664606   C   G   verified
4     PT1  GRCh37   chr1         26670492       26670493   G   T   verified

You can also pass URLs to pyopen:

$ pyopen
In [1]: f1.mean()

time         8.072857e+08
response     2.107552e+02
bytes        1.556397e+04
referer               NaN
useragent             NaN
dtype: float64
Out[1]: (30969, 9)

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