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Tools for documentation-aware data reading, writing, and analysis

Project description

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metacsv - Tools for documentation-aware data reading, writing, and analysis

See the full documentation at ReadTheDocs

Overview

MetaCSV provides tools to read in CSV data with a yaml-compliant header directly into a pandas Series, DataFrame, or Panel or an xarray DataArray or Dataset.

Data specification

Data can be specified using a yaml-formatted header, with the YAML start-mark string (---) above and the YAML end-mark string (...) below the yaml block. Only one yaml block is allowed. If the doc-separation string is not the first (non-whitespace) line in the file, all of the file’s contents will be interpreted by the csv reader. The yaml data can have arbitrary complexity.

>>> import metacsv, numpy as np
>>> import StringIO as io # import io for python 3
>>> doc = io.StringIO('''
... ---
... author: A Person
... date:   2000-12-31
... variables:
...     pop:
...       name: Population
...       unit: millions
...     gdp:
...       name: Product
...       unit: 2005 $Bn
... ...
... region,year,pop,gdp
... USA,2010,309.3,13599.3
... USA,2011,311.7,13817.0
... CAN,2010,34.0,1240.0
... CAN,2011,34.3,1276.7
... ''')

Using MetaCSV-formatted files in python

Read MetaCSV-formatted data into python using pandas-like syntax:

>>> df = metacsv.read_csv(doc, index_col=[0,1])
>>> df   # doctest: +NORMALIZE_WHITESPACE
<metacsv.core.containers.DataFrame (4, 2)>
               pop      gdp
region year
USA    2010  309.3  13599.3
       2011  311.7  13817.0
CAN    2010   34.0   1240.0
       2011   34.3   1276.7
<BLANKLINE>
Variables
    gdp:
        name            Product
        unit            2005 $Bn
    pop:
        name            Population
        unit            millions
Attributes
    author:        A Person
    date:          2000-12-31

These properties can be transferred from one data container to another:

>>> np.random.seed(1)
>>> s = metacsv.Series(np.random.random(6))
>>> s
<metacsv.core.containers.Series (6L,)>
0    0.417022
1    0.720324
2    0.000114
3    0.302333
4    0.146756
5    0.092339
dtype: float64
>>> s.attrs = df.attrs
>>> s
<metacsv.core.containers.Series (6L,)>
0    0.417022
1    0.720324
2    0.000114
3    0.302333
4    0.146756
5    0.092339
dtype: float64
<BLANKLINE>
Attributes
    author:         A Person
    date:           2000-12-31

All MetaCSV attributes, including the attrs Attribute object, can be copied, assigned to new objects, and deleted. Since these attributes are largely unstable across normal pandas data processing, it is recommended that attributes be copied before data work is attempted and then reassigned before IO conversions.

Exporting MetaCSV data to other formats

CSV

A MetaCSV Series or DataFrame can be written as a yaml-prefixed CSV using the same to_csv syntax as it’s pandas counterpart:

>>> df.attrs['new attribute'] = 'changed in python!'
>>> df.to_csv('my_new_data.csv')

The resulting csv will include a yaml-formatted header with the original metadata updated to include attr[‘new attribute’].,

pandas

The coordinates and MetaCSV attributes can be easily stripped from a MetaCSV Container:

>>> df.to_pandas()    # doctest: +NORMALIZE_WHITESPACE
               pop      gdp
region year
USA    2010  309.3  13599.3
       2011  311.7  13817.0
CAN    2010   34.0   1240.0
       2011   34.3   1276.7

xarray/netCDF

xArray provides a pandas-like interface to operating on indexed ndarray data. It is modeled on the netCDF data storage format used frequently in climate science, but is useful for many applications with higher-order data.

>>> ds = df.to_xarray()
>>> ds
<xarray.Dataset>
Dimensions:  (region: 2, year: 2)
Coordinates:
  * region   (region) object 'USA' 'CAN'
  * year     (year) int64 2010 2011
Data variables:
    pop      (region, year) float64 309.3 311.7 34.0 34.3
    gdp      (region, year) float64 1.36e+04 1.382e+04 1.24e+03 1.277e+03
Attributes:
    author: A Person
    date: 2000-12-31
    new attribute: changed in python!
>>> ds.to_netcdf('my_netcdf_data.nc')

Pickling

Pickling works just like pandas.

>>> df.to_pickle('my_metacsv_pickle.pkl')
>>> metacsv.read_pickle('my_metacsv_pickle.pkl')
<metacsv.core.containers.DataFrame (4, 2)>
               pop      gdp
region year
USA    2010  309.3  13599.3
       2011  311.7  13817.0
CAN    2010   34.0   1240.0
       2011   34.3   1276.7

Variables
    gdp:       OrderedDict([('name', 'Product'), ('unit', '2005 $Bn')])
    pop:       OrderedDict([('name', 'Population'), ('unit', 'millions')])
Attributes
    author:    A Person
    date:      2000-12-31
    new attribute: changed in python!

Others

Currently, MetaCSV only supports conversion to CSV and to netCDF through the xarray module. However, feel free to suggest additional features and to contribute your own!

Conversion to other types on the fly

Special conversion utilities allow you to convert any metacsv, pandas, or xarray container or a CSV filepath into any other type in this group.

All of these conversion utilities are also methods on metacsv containers.

  • to_csv

to_csv allows you to write any container or csv file to a metacsv-formatted csv file. Keyword arguments attrs, coords, and variables will be attached to the data before it is written. Any conflicts in these attributes will be updated with the arguments to this function

>>> import pandas as pd, numpy as np, xarray as xr, metacsv
>>> df = pd.DataFrame(np.random.random((3,4)), columns=list('abcd'))
>>> df
          a         b         c         d
0  0.558083  0.665184  0.226173  0.339905
1  0.541712  0.835804  0.326078  0.179103
2  0.332869  0.435573  0.904612  0.823884

>>> metacsv.to_csv(df, 'mycsv.csv', attrs={'author': 'my name', 'date': '2015-12-31'})
>>>
>>> df2 = metacsv.read_csv('mycsv.csv', index_col=[0])
>>> df2
<metacsv.core.containers.DataFrame (3, 4)>
          a         b         c         d
0  0.558083  0.665184  0.226173  0.339905
1  0.541712  0.835804  0.326078  0.179103
2  0.332869  0.435573  0.904612  0.823884

Attributes
    author:    my name
    date:      2015-12-31
    new attribute: changed in python!

>>> metacsv.to_csv(df2, 'mycsv.csv', attrs={'author': 'new name'})
>>>
>>> metacsv.read_csv('mycsv.csv', index_col=[0])
<metacsv.core.containers.DataFrame (3, 4)>
          a         b         c         d
0  0.558083  0.665184  0.226173  0.339905
1  0.541712  0.835804  0.326078  0.179103
2  0.332869  0.435573  0.904612  0.823884

Attributes
    author:    new name
    date:      2015-12-31
    new attribute: changed in python!
  • to_header

to_header allows you to write the special attributes directly to a metacsv-formatted header file. The special attributes may be individually specified or taken from a metacsv container. The header_file argument to both read_csv and to_csv allow the creation of special header files which allow you to separate the metacsv-formatted header from the data if desired.

For example, say you have a table to read into pandas

>>> import metacsv, pandas as pd
>>> pd.DataFrame(
    [['x',1,2,3],['y',4,5,6],['z',7,8,9]], columns=['index','a','b','c']).to_csv('mycsv.csv', index=None)
>>> metacsv.read_csv('mycsv.csv')
<metacsv.core.containers.DataFrame (3, 4)>
  index  a  b  c
0     x  1  2  3
1     y  4  5  6
2     z  7  8  9

A separate header file can be created and used which can then be read in with the data:

>>> metacsv.to_header('mycsv.header', attrs={'author': 'me'}, coords='index')
>>> metacsv.read_csv('mycsv.csv', header_file='mycsv.header')
<metacsv.core.containers.DataFrame (3, 3)>
       a  b  c
index
x      1  2  3
y      4  5  6
z      7  8  9

Coordinates
  * index      (index) object x, y, z
Attributes
    author:         me
  • to_xarray

to_xarray returns any container or csv file as an xarray container. Table data (CSV files and DataFrames) will create xarray.Dataset objects, while Series objects will create xarray.DataArray objects. Keyword arguments attrs, coords, and variables will be attached to the data before it is written. Any conflicts in these attributes will be updated with the arguments to this function.

  • to_dataarray

to_dataarray returns any container or csv file as an xarray.DataArray. Table data (CSV files and DataFrames) will be stacked, with columns re-arranged as new xarray.Coordinates. Keyword arguments attrs, coords, and variables will be attached to the data before it is written. Any conflicts in these attributes will be updated with the arguments to this function.

  • to_dataset

to_dataarray returns any container or csv file as an xarray.DataArray. Table data (CSV files and DataFrames) will be stacked, with columns re-arranged as new xarray.Coordinates. Keyword arguments attrs, coords, and variables will be attached to the data before it is written. Any conflicts in these attributes will be updated with the arguments to this function.

  • to_pandas

to_pandas strips special attributes and returns an ordinary Series or DataFrame object.

  • to_netcdf

to_netcdf first converts a container or csv file to an xarray.Dataset using the to_dataset function, then writes the dataset to file with the xarray ds.to_netcdf method.

>>> metacsv.to_netcdf('mycsv.csv', 'mycsv.nc', header_file='mycsv.header')
>>> import xarray as xr
>>> xr.open_dataset('mycsv.nc')
<xarray.Dataset>
Dimensions:  (index: 3)
Coordinates:
  * index    (index) |S1 'x' 'y' 'z'
Data variables:
    a        (index) int64 1 4 7
    b        (index) int64 2 5 8
    c        (index) int64 3 6 9
Attributes:
    author: me

Special attributes

The coords and variables attributes are keywords and are not simply passed to the MetaCSV object’s attrs attribute.

Variables

Variables are attributes which apply to speicific columns or data variables. In MetaCSV containers, variables are displayed as a separate set of attributes. On conversion to xarray, these attributes are assigned to variable-specific attrs:

>>> ds = df.to_xarray()
>>> ds
<xarray.Dataset>
Dimensions:  (index: 4)
Coordinates:
  * index    (index) int64 0 1 2 3
Data variables:
    region   (index) object 'USA' 'USA' 'CAN' 'CAN'
    year     (index) int64 2010 2011 2010 2011
    pop      (index) float64 309.3 311.7 34.0 34.3
    gdp      (index) float64 1.36e+04 1.382e+04 1.24e+03 1.277e+03
Attributes:
    date: 2000-12-31
    author: A Person

>>> ds.pop
<xarray.DataArray 'pop' (index: 4)>
array([ 309.3,  311.7,   34. ,   34.3])
Coordinates:
  * index    (index) int64 0 1 2 3
Attributes:
    name: Population
    unit: millions

Note that at present, variables are not persistent across slicing operations.

parse_vars

Variables have a special argument to read_csv: parse_vars allows parsing of one-line variable definitions in the format var: description [unit]:

>>> doc = io.StringIO('''
---
author: A Person
date:   2000-12-31
variables:
    pop: Population [millions]
    gdp: Product [2005 $Bn]
...
region,year,pop,gdp
USA,2010,309.3,13599.3
USA,2011,311.7,13817.0
CAN,2010,34.0,1240.0
CAN,2011,34.3,1276.7
''')

>>> metacsv.read_csv(doc, index_col=0, parse_vars=True)
<metacsv.core.containers.DataFrame (4, 3)>
        year    pop      gdp
region
USA     2010  309.3  13599.3
USA     2011  311.7  13817.0
CAN     2010   34.0   1240.0
CAN     2011   34.3   1276.7

Variables
    gdp:       {u'description': 'Product', u'unit': '2005 $Bn'}
    pop:       {u'description': 'Population', u'unit': 'millions'}
Attributes
    date:      2000-12-31
    author:    A Person

Coordinates

The conceptual foundation of coordinates is taken from xarray, where data is treated as an ndarray rather than a table. If you plan to only work with the pandas-like features of metacsv, you do not really need coordinates.

That said, specifying the coords attribute in a csv results in automatic index handling:

>>> doc = io.StringIO('''
---
author: A Person
date:   2000-12-31
variables:
    pop:
      name: Population
      unit: millions
    gdp:
      name: Product
      unit: 2005 $Bn
coords:
    - region
    - year
...
region,year,pop,gdp
USA,2010,309.3,13599.3
USA,2011,311.7,13817.0
CAN,2010,34.0,1240.0
CAN,2011,34.3,1276.7
''')

>>> df = metacsv.read_csv(doc)
>>> df
<metacsv.core.containers.DataFrame (4, 2)>
               pop      gdp
region year
USA    2010  309.3  13599.3
       2011  311.7  13817.0
CAN    2010   34.0   1240.0
       2011   34.3   1276.7

Coordinates
  * region     (region) object CAN, USA
  * year       (year) int64 2010, 2011
Variables
    gdp:       OrderedDict([('name', 'Product'), ('unit', '2005 $Bn')])
    pop:       OrderedDict([('name', 'Population'), ('unit', 'millions')])
Attributes
    date:      2000-12-31
    author:    A Person

Coordinates become especially useful, however, when moving to xarray objects or netCDF files. The DataFrame above will have no trouble, as region and year are orthoganal:

>>> df.to_xarray()
<xarray.Dataset>
Dimensions:  (region: 2, year: 2)
Coordinates:
  * region   (region) object 'USA' 'CAN'
  * year     (year) int64 2010 2011
Data variables:
    pop      (region, year) float64 309.3 311.7 34.0 34.3
    gdp      (region, year) float64 1.36e+04 1.382e+04 1.24e+03 1.277e+03
Attributes:
    date: 2000-12-31
    author: A Person

This becomes more complicated when columns in the index are not independent and cannot be thought of as orthogonal. In this case, you can specify coords as a dict-like attribute either in the CSV header or as an argument to the conversion method:

doc = io.StringIO('''
---
coords:
    region:
    regname: 'region'
    continent: 'region'
    year:
...
region,regname,continent,year,pop,gdp
USA,United States,North America,2010,309.3,13599.3
USA,United States,North America,2011,311.7,13817.0
CAN,Canada,North America,2010,34.0,1240.0
CAN,Canada,North America,2011,34.3,1276.7
''')

>>> metacsv.to_xarray(doc)
<xarray.Dataset>
Dimensions:    (region: 2, year: 2)
Coordinates:
  * region     (region) object 'USA' 'CAN'
  * year       (year) int64 2010 2011
    regname    (region) object 'United States' 'Canada'
    continent  (region) object 'North America' 'North America'
Data variables:
    pop        (region, year) float64 309.3 311.7 34.0 34.3
    gdp        (region, year) float64 1.36e+04 1.382e+04 1.24e+03 1.277e+03

Note that the resulting Dataset is not indexed by the cartesian product of all four coordinates, but only by the base coordinates, indicated by the *. Without first setting the coords attribute this way, the resulting data would have NaN values corresponding to (USA, Canada) and (CAN, United States).

TODO

  • Allow automatic coersion of xarray.Dataset and xarray.DataArray objects to MetaCSV containers.
  • Extend metacsv functionality to Panel objects
  • Make coords and attrs persistent across slicing operations (try df['pop'].to_xarray() from above example and watch it fail…)
  • Improve hooks between pandas and metacsv:
    • update coord names on df.index.names assignment
    • update coords on stack/unstack
    • update coords on
  • Improve parser to automatically strip trailing commas and other excel relics
  • Enable read_csv(engine='C')… this currently does not work.
  • Handle attributes indexed by coord/variable names –> assign to coord/variable-specific attrs
  • Let’s start an issue tracker and get rid of this section!
  • Should we rethink “special attribute,” naming e.g. coords? Maybe these should have some special prefix like _coords when included in yaml headers to avoid confusion with other generic attributes…
  • Allow attribute assertions (e.g. version='>1.6.0') in read_csv call
  • Improve test coverage
  • Improve documentation & build readthedocs page

Feature Requests

  • Create syntax for multi-csv –> Panel or combining using filename regex
  • Eventually? allow for on-disk manipulation of many/large files with dask/xarray
  • Eventually? add xml, SQL, other structured syntax language conversions

Changelog

Here you can find the recent changes to MetaCSV..

version dev

released Ongoing

Updated CHANGES.

version 0.0.1

released 2016-05-04

First release on PyPi.

Project details


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