xcsv 0.4.0

Package for working with extended CSV (XCSV) files

xcsv

xcsv is a package for reading and writing extended CSV files.

Extended CSV format

• Extended header section of parseable atttributes, introduced by '#'.
• Header row of variable and units for each column.
• Data rows.

Example

Extended header section

• No leading/trailing whitespace.
• Each line introduced by a comment ('#') character.
• Each line contains a single header item.
• Key/value separator ': '.
• Multi-line values naturally continued over to the next lines following the line introducing the key.
• Continuation lines that contain the delimiter character in the value must be escaped by a leading delimiter.
• Preferably use a common vocabulary for attribute name, such as CF conventions.
• Preferably include recommended attributes from Attribute Convention for Data Discovery (ACDD).
• Preferably use units from Unified Code for Units of Measure and/or Udunits.
• Units in parentheses.
• Certain special keys are used to further process the data, for example the missing_value key.

# id: 1
# title: The title
# summary: This dataset...
# The second summary paragraph.
# : The third summary paragraph.  Escaped because it contains the delimiter in a URL https://dummy.domain
# authors: A B, C D
# latitude: -73.86 (degree_north)
# longitude: -65.46 (degree_east)
# elevation: 1897 (m a.s.l.)
# [a]: 2012 not a complete year

Header row

• No leading/trailing whitespace.
• Preferably use a common vocabulary for variable name, such as CF conventions.
• Units in parentheses.
• Optional notes in square brackets, that reference an item in the extended header section.

time (year) [a],depth (m)

Data row

• No leading/trailing whitespace.

2012,0.575

Automated post-processing of the data

Depending on the presence of special keys in the extended header section, these will be used to automatically post-process the data. To turn off this automatic behaviour, either remove or rename these keys, or set parse_metadata=False when reading in the data.

• missing_value: This is used to define those values in the data that are to be considered as missing values. This is typically a value that is outside the domain of the data such as -999.99, or can be a symbolic value such as NA. All such values appearing in the data will be masked, appearing as an NA value to pandas (i.e. pd.isna(value) returns True). Note that pandas itself will automatically do this for certain values regardless of this key, such as for the strings NaN or NA, or the constant None.

Install

The package can be installed from PyPI:

$ pip install xcsv

Using the package

The package has a general XCSV class, that has a metadata attribute that holds the parsed contents of the extended file header section and the parsed column headers from the data table, and a data attribute that holds the data table (including the column headers as-is).

The metadata attribute is a dict, with the following general structure:

{'header': {}, 'column_headers': {}}

and the data attribute is a pandas.DataFrame, and so has all the features of the pandas package.

The package also has a Reader class for reading an extended CSV file into an XCSV object, and similarly a Writer class for writing an XCSV object to a file in the extended CSV format. In addition there is a File class that provides a convenient context manager for reading and writing these files.

Examples

Simple read and print

Read in a file and print the contents to stdout. This shows how the contents of the extended CSV file are stored in the XCSV object. Note how multi-line values, such as summary here, are stored in a list. Given the following script called, say, simple_read.py:

import argparse

import xcsv

parser = argparse.ArgumentParser()
parser.add_argument('filename', help='filename.csv')
args = parser.parse_args()

with xcsv.File(args.filename) as f:
    content = f.read()
    print(content.metadata)
    print(content.data)

Running it would produce:

$ python3 simple_read.py example.csv
{'header': {'id': '1', 'title': 'The title', 'summary': ['This dataset...', 'The second summary paragraph.', 'The third summary paragraph.  Escaped because it contains the delimiter in a URL https://dummy.domain'], 'authors': 'A B, C D', 'latitude': {'value': '-73.86', 'units': 'degree_north'}, 'longitude': {'value': '-65.46', 'units': 'degree_east'}, 'elevation': {'value': '1897', 'units': 'm a.s.l.'}, '[a]': '2012 not a complete year'}, 'column_headers': {'time (year) [a]': {'name': 'time', 'units': 'year', 'notes': 'a'}, 'depth (m)': {'name': 'depth', 'units': 'm', 'notes': None}}}
   time (year) [a]  depth (m)
0             2012      0.575
1             2011      1.125
2             2010      2.225

Simple read and print with missing values

If the above example header section included the following:

# missing_value: -999.99

and the data section looked like:

time (year) [a],depth (m)
2012,0.575
2011,1.125
2010,2.225
2009,-999
2008,999
2007,-999.99
2006,999.99
2005,NA
2004,NaN

Running it would produce:

$ python3 simple_read.py missing_example.csv
{'header': {'id': '1', 'title': 'The title', 'summary': ['This dataset...', 'The second summary paragraph.', 'The third summary paragraph.  Escaped because it contains the delimiter in a URL https://dummy.domain'], 'authors': 'A B, C D', 'latitude': {'value': '-73.86', 'units': 'degree_north'}, 'longitude': {'value': '-65.46', 'units': 'degree_east'}, 'elevation': {'value': '1897', 'units': 'm a.s.l.'}, 'missing_value': '-999.99', '[a]': '2012 not a complete year'}, 'column_headers': {'time (year) [a]': {'name': 'time', 'units': 'year', 'notes': 'a'}, 'depth (m)': {'name': 'depth', 'units': 'm', 'notes': None}}}
   time (year) [a]  depth (m)
0             2012      0.575
1             2011      1.125
2             2010      2.225
3             2009   -999.000
4             2008    999.000
5             2007        NaN
6             2006    999.990
7             2005        NaN
8             2004        NaN

Note that the -999.99 value has been automatically masked as a missing value (shown as NaN in the printed pandas DataFrame), as well as the NA and NaN strings in the original data, which pandas automatically masks itself, irrespective of the missing_value header item.

Simple read and plot

Read a file and plot the data:

import argparse

import matplotlib.pyplot as plt

import xcsv

parser = argparse.ArgumentParser()
parser.add_argument('filename', help='filename.csv')
args = parser.parse_args()

with xcsv.File(args.filename) as f:
    content = f.read()
    content.data.plot(x='depth (m)', y='time (year) [a]')
    plt.show()

Simple read and write

Read a file in, manipulate the data in some way, and write this modified XCSV object out to a new file:

import argparse

import xcsv

parser = argparse.ArgumentParser()
parser.add_argument('in_filename', help='in_filename.csv')
parser.add_argument('out_filename', help='out_filename.csv')
args = parser.parse_args()

with xcsv.File(args.in_filename) as f:
    content = f.read()

# Manipulate the data...

with xcsv.File(args.out_filename, mode='w') as f:
    f.write(xcsv=content)