rowgenerators 0.9.23

Generate row data from a variety of file formats

Row Generators

Application Urls

Application Urls provide structure and operations on URLS where the file the URL refers to can’t, in general, simply be downloaded. For instance, you may want to refer to a CSV file inside a ZIP archive, or a worksheet in an Excel file. In conjunction with Row Generators, Application Urls are often used to refer to tabular data stored on data repositories. For instance:

• Stored on the web: http://examples.com/file.csv

• Inside a zip file on the web: http://example.com/archive.zip#file.csv

• A worksheet in an Excel file: http://example.com/excel.xls#worksheet

• A worksheet in an Excel file in a ZIP Archive: http://example.com/archive.zip#excel.xls;worksheet

• An API: socrata+http://chhs.data.ca.gov/api/views/tthg-z4mf

Install

$ pip install appurl

Documentation

See the documentation at http://row-generators.readthedocs.io/

Development Notes

Running tests

Run python setup.py tests to run normal development tests. You can also run tox, which will try to run the tests with python 3.4, 3.5 and 3.6, ignoring non-existent interpreters.

Development Testing with Docker

Testing during development for other versions of Python is a bit of a pain, since you have to install the alternate version, and Tox will run all of the tests, not just the one you want.

One way to deal with this is to install Docker locally, then run the docker test container on the source directory. This is done automatically from the Makefile in appurl/tests

$ cd ./docker
$ make build # to create the container image
$ make shell # to run bash the container

You now have a docker container where the /code directory is the appurl source dir.

Now, run tox to build the tox virtual environments, then enter the specific version you want to run tests for and activate the virtual environment.

# tox
# cd .tox/py34
# source bin/activate # Activate the python 3.4 virtual env
# cd ../../
# python setup.py test # Cause test deps to get installed
#
# python -munittest appurl.test.test_basic.BasicTests.test_url_classes  # Run one test

Row Data Pipeline

The Rowpipe library manages row-oriented data transformers. Clients can create a RowProcessor() that has schema, composed of tables and columns, where each column cna have a “transform” that describes how to alter the data in the column.

from rowpipe.table import Table
from rowpipe.processor import RowProcessor

def doubleit(v):
    return int(v) * 2

env = {
    'doubleit': doubleit
}

t = Table('foobar')
t.add_column('id', datatype='int')
t.add_column('other_id', datatype='int', transform='^row.a')
t.add_column('i1', datatype='int', transform='^row.a;doubleit')
t.add_column('f1', datatype='float', transform='^row.b;doubleit')
t.add_column('i2', datatype='int', transform='^row.a')
t.add_column('f2', datatype='float', transform='^row.b')

In this table definition, other_id and i2 columns are initialized to the valu of the a column in the input row, The i1 column is initialized to the input row a column, then the doubleit function is called on the value. In the last step, all of the values are cast to the types specified in the datatype column.

The RowProcessor is then run using this table definition, and an input generator:

class Source(object):

    headers = 'a b'.split()

    def __iter__(self):
        for i in range(N):
            yield i, 2*i

rp = RowProcessor(Source(), t, env=env)

Then, rp is a generator that returns RowProxy objects, which can be indexed as integers or by clolumn number:

for row in rp:
    v1 = row['f1']
    v2 = row[3]

The RowProcessor creates Python code files and executes them.

Transforms can have several steps, seperated by ‘;’. The first, prefixes with a ‘^’, initializes the value for the rest of the transforms. A transform that is prefixes with a ‘!’ is executed on exceptions. Transform functions can have a variable signature; the tranform processor matches argument names. Valid argument names are:

• row. A rowProxy object for the input row. Allows access to any input row value

• row_n. Row number.

• scratch. A dict for temporary storage

• errors. A defaultdict(set) for storing error reports for columns. Keys are column names

• accumulator. A dict for accumulating value, such as sums.

• pipe. Unused

• bundle. Unused

• source. Reference to the input generator that is generating rows

• v . The input row value

• header_s. The header for the column in the input row.

• i_s. The index of the column in the input row

• header_d. The header for the column in the output row.

• i_d. The index of the column in the output row

… and there is a whole lot more. This documentation is woefully incomplete …

Notes

This repo still contains old code for Row Pipelines, which are in the pipeline.py file. These components can be combined to performd defined operations on rows, such as skipping rows based on a predicate, altering the number of rows, returning on ly the head or tail, etc. The code is not currently used ot tested.