fecfile 0.6.0

a python parser for the .fec file format

This is a library for converting campaign finance filings stored in the .fec format into native python objects. It maps the comma/ASCII 28 delimited fields to canonical names based on the version the filing uses and then converts the values that are dates and numbers into the appropriate int, float, or datetime objects.

This library is in relatively early testing. I've used it on a couple of projects, but I wouldn't trust it to work on all filings. That said, if you do try using it, I'd love to hear about it!

Why?

The FEC makes a ton of data available via the "export" links on the main site and the developer API. For cases where those data sources are sufficient, they are almost certainly the easiest/best way to go. A few cases where one might need to be digging into raw filings are:

• Getting information from individual itemizations including addresses

• Getting data as soon as it has been filed, instead of waiting for it to be coded. (The FEC generally codes all filings received by 7pm eastern by 7am the next day. However, that means that a filing received at 11:59pm on Monday wouldn't be available until 7am on Wednesday, for example.)

• Getting more data than the rate-limit on the developer API would allow

• Maintaining ones own database with all relevant campaign finance data, perhaps synced with another data source

Raw filings can be found by either downloading the bulk data zip files or from http requests like this. This library includes helper methods for both.

Installation

To get started, install from pypi by running the following command in your preferred terminal:

pip install fecfile

Usage

For the vast majority of filings, the easiest way to use this library will be to load filings all at once by using the from_http(file_number), from_file(file_path), or loads(input) methods.

These methods will return a Python dictionary, with keys for header, filing, itemizations, and text. The itemizations dictionary contains lists of itemizations grouped by type (Schedule A, Schedule B, etc.).

Examples:

import fecfile



filing1 = fecfile.from_file('1229017.fec')

print('${:,.2f}'.format(filing1['filing']['col_a_total_receipts']))



filing2 = fecfile.from_http(1146148)

print(filing2['filing']['committee_name'])



filing3 = fecfile.from_http(1146148)

all_contributions = filing3['itemizations']['Schedule B']

mid_size_contributions = [item for item in all_contributions if 500 <= item[contribution_amount] < 1000]

print(len(mid_size_contributions))



with open('1229017.fec') as file:

    parsed = fecfile.loads(file.read())

    num_disbursements = len(parsed['itemizations']['Schedule B'])

    print(num_disbursements)



url = 'http://docquery.fec.gov/dcdev/posted/1229017.fec'

r = requests.get(url, headers={'User-Agent': 'Mozilla/5.0'})

parsed = fecfile.loads(r.text)

fecfile.print_example(parsed)

Note: the docquery.fec.gov urls cause problems with the requests library when a user-agent is not supplied. There may be a cleaner fix to that though.

Advanced Usage

For some large filings, loading the entire filing into memory like the above examples do would not be a good idea. For those cases, the fecfile library provides the iter_file and iter_http methods. Both are generator functions that yield FecItem objects, which consist of data and data_type attributes. The data_type attribute can be one of "header", "summary", "itemization", "text", or "F99_text". The data attribute is a dictionary for all data types except for "F99_text", for which it is a string.

import fecfile

import imaginary_database



# sometimes we only care about summary data, but want to be able to handle all filings, without

# knowing anything about them before we attempt to parse.

no_itemizations = {'filter_itemizations': []}

for i in range(1300000, 1320000):

    for item in fecfile.iter_http(i, options=no_itemizations):

        if item.data_type == 'summary':

            imaginary_database.add_to_db(item.data)



# Sometimes we only care about one type of itemization, but from a very large filing.

# In this example, we add up all the contributions from Delaware in ActBlue's 2018

# post-general filing

only_contributions = {'filter_itemizations': ['SA']}

de_total = 0

for item in fecfile.iter_http(1300352, options=only_contributions):

    if item.data_type == 'itemization':

        if item.data['contributor_state'] == 'DE':

            de_total += item.data['contribution_amount']

print(de_total)



# Sometimes we want to maintain a database where different types of itemizations live in their own

# tables and have foreign key relationships to a summary record.

file_path = '/path/to/99840.fec'

filing = None

for item in fecfile.iter_file(file_path):

    if item.data_type == 'summary':

        filing = imaginary_database.add_filing(file_number=99840, **item.data)

    if item.data_type == 'itemization':

        if item.data['form_type'].startswith('SA'):

            imaginary_database.add_contribution(filing=filing, **item.data)

        if item.data['form_type'].startswith('SB'):

            imaginary_database.add_disbursement(filing=filing, **item.data)

        if item.data['form_type'].startswith('SC'):

            imaginary_database.add_loan(filing=filing, **item.data)

You can also choose to use the parse_header and parse_line methods if you are implementing a different method of

iterating over a filing's content. Before version 0.6, the below example was the only way to use fecfile to parse

filings without loading the entire filing into memory. This approach should no longer be necessary, but is kept to

show how example usage for those methods.

import fecfile



version = None



with open('1263179.fec') as file:

    for line in file:

        if version is None:

            header, version = fecfile.parse_header(line)

        else:

            parsed = fecfile.parse_line(line, version)

            save_to_db(parsed)

API Reference

loads

loads(input, options={})

Deserialize input (a str instance

containing an FEC document) to a Python object.

Optionally, pass an array of strings to options['filter_itemizations'].

If included, loads will only parse lines that start with any of the

strings in that array. For example, passing

{'filter_itemizations': ['SC', 'SD']} to options, will only include

Schedule C and Schedule D itemizations. Also, passing

{'filter_itemizations': []} to options will result in only the header

and the filing being parsed and returned.

parse_header

parse_header(hdr)

Deserialize a str or a list of str instances containing

header information for an FEC document. Returns an Python object, the

version str used in the document, and the number of lines used

by the header.

The third return value of number of lines used by the header is only

useful for versions 1 and 2 of the FEC file format, when the header

was a multiline string beginning and ending with /*. This allows

us to pass in the entire contents of the file as a list of lines and

know where to start parsing the non-header lines.

parse_line

parse_line(line, version, line_num=None)

Deserialize a line (a str instance

containing a line from an FEC document) to a Python object.

version is a str instance for the version of the FEC file format

to be used, and is required.

line_num is optional and is used for debugging. If an error or

warning is encountered, whatever is passed in to line_num will be

included in the error/warning message.

from_http

from_http(file_number, options={})

Utility method for getting a parsed Python representation of an FEC

filing when you don't already have it on your computer. This method takes

either a str or int as a file_number and requests it from

the docquery.fec.gov server, then parses the response.

See above for how documentation on how to use the optional

options argument.

from_file

from_file(file_path, options={})

Utility method for getting a parsed Python representation of an FEC

filing when you have the .fec file on your computer. This method takes

a str of the path to the file, and returns the parsed Python object.

See above for how documentation on how to use the optional

options argument.

iter_http

iter_http(file_number, options={})

Makes an http request for the given file_number and iterates over the response, yielding FecItem instances, which consist of data and data_type attributes. The data_type attribute can be one of "header", "summary", "itemization", "text", or "F99_text". The data attribute is a dictionary for all data types except for "F99_text", for which it is a string. This method avoids loading the entire filing into memory, as the from_http method does.

See above for how documentation on how to use the optional

options argument.

iter_file

iter_file(file_path, options={})

Opens a file at the given file_path and iterates over its contents, yielding FecItem instances, which consist of data and data_type attributes. The data_type attribute can be one of "header", "summary", "itemization", "text", or "F99_text". The data attribute is a dictionary for all data types except for "F99_text", for which it is a string. This method avoids loading the entire filing into memory, as the from_file method does.

See above for how documentation on how to use the optional

options argument.

print_example

print_example(parsed)

Utility method for debugging - prints out a representative subset of

the Python object returned by one of the deserialization methods. For

filings with itemizations, it only prints the first of each type of

itemization included in the object.

Developing locally

Assuming you already have Python3 and the ability to create virtual environments installed, first clone this repository from github and cd into it:

git clone https://github.com/esonderegger/fecfile.git

cd fecfile

Then create a virtual environment for this project (I use the following commands, but there are several ways to get the desired result):

python3 -m venv ~/.virtualenvs/fecfile

source ~/.virtualenvs/fecfile/bin/activate

Next, install the dependencies:

python setup.py

Finally, make some changes, and run:

python tests.py

Thanks

This project would be impossible without the work done by the kind folks at The New York Times Newsdev team. In particular, this project relies heavily on fech although it actually uses a transformation of this fork.

Many thanks to Jacob Fenton for writing the caching logic and for providing valuable feedback about the overall design of this library.

Contributing

I would love some help with this, particularly with the mapping from strings to int, float, and datetime types. Please create an issue or make a pull request. Or reach out privately via email - that works too.

To do:

Almost too much to list:

• Handle files from before v6 when they were comma-delimited

• create a dumps method for writing .fec files for round-trip tests

• add more types to the types.json file

• elegantly handle errors

Changes

0.6.0 (April 10, 2019)

• add iter_http and iter_file functions, using a shared iter_lines function

• refactor loads to use the new generator functions for performance

0.5.3 (February 12, 2019)

• handle trailing whitespace in form_type field

• fix regex for F1M

• fix F5 for P3.2 and P3.3

0.5.2 (January 19, 2019)

• add Form 1 mappings for version 2

• add Schedule F mappings for version 2

• update mappings regexes for F6 and F65

• add H4 mappings for version 2

• add H3 mappings for version 2

0.5.1 (January 18, 2019)

• update mappings versions for F3Z

• add mappings and types for schedule I

• allow filtering of itemizations for comma-delimited filings

0.5.0 (January 17, 2019)

• add ability to filter which types of itemizations to parse

• cache mappings and types for much faster parsing

0.4.11 (January 12, 2019)

• handle files encoded in Windows 1252

• fix for duplicate col_b_transfers_from_affiliated mapping

• allow for form F4T

0.4.10 (November 7, 2018)

• strip whitespace when parsing values as float or date

• fix mis-mapped field in paper F3 filings

0.4.9 (November 6, 2018)

• add mappings for paper versions of F24

0.4.8 (November 6, 2018)

• fix v5/3 mapping for F3P to include v1 and v2

• make F5 mappings more explicit and include v6.1

• add types for F3S

0.4.7 (November 2, 2018)

• add types for F4, SF, and SL

• fix issue causing incorrect mapping for F9

0.4.6 (October 29, 2018)

• add mappings for paper versions of F4

• add mappings for paper versions of F56

• add mappings for paper versions of F91

• add mappings for paper versions of F92

• add mappings for paper versions of F93

• add mappings for paper versions of F94

• add mappings for paper versions of F99

• add mappings for paper versions of H5 and H6

• add mappings for paper versions of Schedule L

• add mapping for version P3.4 for F3Z1 and F3Z2

0.4.5 (October 27, 2018)

• add mappings for paper versions of F3Z

0.4.4 (October 17, 2018)

• fixed out of order mappings for paper versions of F3

0.4.3 (October 10, 2018)

• add mappings for paper versions of F76

• add mappings for paper versions of F9

• add mappings for paper versions of F2

• add mappings for paper versions of F7

0.4.2 (October 9, 2018)

• add mappings for paper versions of F57

• add mappings for paper versions of F5

• add mappings for paper versions of F3L

• add mappings for paper versions of F3S

0.4.1 (October 4, 2018)

• add mappings for versions P1 and P2 of Schedule B

• add mappings for versions P1 and P2 of Schedule A

• add mappings for versions P1 and P2 of F3

• add F99_text field to returned object for form 99 filings

• add hdr mappings for paper versions 1 and 2

• do not split on commas when we know the form is using ascii 28

• add mappings for paper versions of F65

• add mappings for paper versions of schedule C1

• add mappings for paper versions of schedule C2

• add mappings for paper versions of schedule E

0.4.0 (October 2, 2018)

• Updated documentation

• add paper versions for schedule F

0.3.9 (October 1, 2018)

• add paper versions for H1, H2, H3, and H4

0.3.8 (September 28, 2018)

• add paper versions for the F1S

0.3.7 (September 27, 2018)

• add paper versions of F1M

• add paper versions for F3X

• add F3P paper filing mappings

0.3.6 (September 27, 2018)

• add F6 paper mappings and fix missing commas

0.3.5 (September 26)

• add all paper versions of form F1

0.3.4 (September 18, 2018)

• expose parse_header and parse_line to consumers of this library

0.3.3 (September 18, 2018)

• add version 8.3 to mappings

0.3.2 (August 29, 2018)

• versions 1 and 2 of schedule H1 and H2

0.3.1 (August 29, 2018)

• added more mappings

• add a method to determine which mappings are missing

0.3.0 (August 27, 2018)

• Rework warnings and errors for cases where mappings are missing

• add mappings

0.2.3 (August 24, 2018)

• fix for filings that use both quotes and the field separator

0.2.2 (August 23, 2018)

• add support for F13, F132, and F133

0.2.1 (August 21, 2018)

• Fix regression that broke paper filings

0.2.0 (August 2, 2018)

• Add parsing for versions 1 and 2 of the .fec format

0.1.9 (July 18, 2018)

• add parsing for senate paper filings

0.1.8 (June 26, 2018)

• interest rate should never have been a float field

0.1.7 (June 26, 2018)

• handle n/a in number fields

0.1.6 (June 25, 2018)

• more types

• update documentation

• handle percent signs in interest rates

0.1.5 (June 21, 2018)

• Initial published version