Stata dta in Python

This is a package for using Stata .dta files in Python. The main functionality of the package is in its Dta class and subclasses, which encapsulate the information from a .dta file, and provide methods for adding, replacing, or deleting this information.

You can create Dta objects from .dta files or from iterables of Python values. You can manipulate Dta objects in basic ways (add observations, replace data values, rename data variables etc.), and you can save Dta objects to .dta files.

This package has been tested on Python 3.1, 3.2, and 3.3. Some parts of this package do not work in Python 2. Support for Python 2 might be added at a later date.

Currently, this package supports .dta file formats 114, 115, and 117.

Requirements

Python 3.1 - 3.4

Installation

Download the package, either with:

git clone https://github.com/jrfiedler/stata-dta-in-python

or by downloading a zip archive (there's a button on the right side of this page) and unzipping.

Then, in the main folder, use:

python setup.py install

to install.

Changelog

0.2.0

• Added quick access to data variables, as in dta.varname_
• Added stata_math module provides functions that understand missing values and quick-access data variables
• New method quiet() silences warnings and other 'unexpected' output
• New method get(row, col) for getting a single data value

See examples "Quick access to data variables" and "Math with missing values" in EXAMPLES.rst.

Example usage

>>> from stata_dta import open_dta, display_diff

>>> dta1 = open_dta("C:/Program Files (x86)/Stata12/auto.dta")
(1978 Automobile Data)

>>> dta2 = open_dta("C:/Program Files (x86)/Stata13/auto.dta")
(1978 Automobile Data)

>>> display_diff(dta1, dta2)
    class types differ:
        Dta115 vs Dta117
    formats differ:
        114 vs 117
    time stamps differ:
        13 Apr 2011 17:45 vs 13 Apr 2013 17:45

>>> dta1.list("make rep weight disp", in_=range(6))
    +--------------------------------------------------+
    | make                   rep78    weight  displa~t |
    +--------------------------------------------------+
 0. | AMC Concord                3     2,930       121 |
 1. | AMC Pacer                  3     3,350       258 |
 2. | AMC Spirit                 .     2,640       121 |
 3. | Buick Century              3     3,250       196 |
 4. | Buick Electra              4     4,080       350 |
    +--------------------------------------------------+
 5. | Buick LeSabre              3     3,670       231 |
    +--------------------------------------------------+

>>> dta1[:6, ::3].list()
    +--------------------------------------------------+
    | make                   rep78    weight  displa~t |
    +--------------------------------------------------+
 0. | AMC Concord                3     2,930       121 |
 1. | AMC Pacer                  3     3,350       258 |
 2. | AMC Spirit                 .     2,640       121 |
 3. | Buick Century              3     3,250       196 |
 4. | Buick Electra              4     4,080       350 |
    +--------------------------------------------------+
 5. | Buick LeSabre              3     3,670       231 |
    +--------------------------------------------------+

>>> from stata_dta import Dta115, Dta117
>>> v = [[0, 0.1, "0.2", 0.3], [1, 1.1, "1.2"], [2], [3, 3.1, 3.2, 3.3]]
>>> for row in v:
...     print(row)
...
[0, 0.1, '0.2', 0.3]
[1, 1.1, '1.2']
[2]
[3, 3.1, 3.2, 3.3]

>>> dta3 = Dta117(v)
>>> dta2.describe()

  obs:             4
 vars:             4                          31 Dec 2013 17:11
 size:            80
----------------------------------------------------------------------
              storage   display    value
variable name   type    format     label      variable label
----------------------------------------------------------------------
var0            byte    %8.0g
var1            double  %10.0g
var2            str3    %9s
var3            double  %10.0g
----------------------------------------------------------------------
Sorted by:
     Note:  dataset has changed since last saved

>>> dta3.list()
    +---------------------------------------------+
    |     var0        var1       var2        var3 |
    +---------------------------------------------+
 0. |        0         0.1        0.2         0.3 |
 1. |        1         1.1        1.2           . |
 2. |        2           .                      . |
 3. |        3         3.1        3.2         3.3 |
    +---------------------------------------------+

>>> dta3.summ()

    Variable |       Obs        Mean    Std. Dev.       Min        Max
-------------+--------------------------------------------------------
        var0 |         4         1.5     1.29099          0          3
        var1 |         3     1.43333     1.52753        0.1        3.1
        var2 |         0
        var3 |         2         1.8     2.12132        0.3        3.3

>>> dta3.save("example.dta")

For more examples, see EXAMPLES.md.

Contributors

• James Fiedler
• Matthew Koslovsky

Contact

James Fiedler, [email protected]

License

Copyright (c) 2014, James Fiedler (MIT License)