| Name | feco3 JSON |
| Version |
0.5.0
JSON |
| download |
| home_page | |
| Summary | A Rust-backed Python library for parsing .fec files. |
| upload_time | 2023-08-18 06:43:05 |
| maintainer | |
| docs_url | None |
| author | |
| requires_python | >=3.7 |
| license | |
| keywords |
fec
fec-file
rust
pyo3
arrow
pyarrow
|
| VCS |
 |
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
|
| coveralls test coverage |
No coveralls.
|
# FECo3
Python bindings for a `.fec` file parser in rust.
Install with `pip install feco3`
- [API docs](https://nickcrews.github.io/feco3/)
- [Repository](https://github.com/NickCrews/feco3)
## Example
```python
import pyarrow as pa
import feco3
# ruff: noqa: E501
# You can supply a URL or a path to a file.
src = "https://docquery.fec.gov/dcdev/posted/1002596.fec"
# src = "path/to/file.fec"
# src = pathlib.Path("path/to/file.fec")
# This doesn't actually read or parse any data yet
fec = feco3.FecFile(src)
print(fec)
# FecFile(src='https://docquery.fec.gov/dcdev/posted/1002596.fec')
# Only when we access something do we actually start parsing.
# Still, we only parse as far as we need to, so this is quite fast.
# This is useful, for example, if you only need the header or cover,
# or if you only want to look at the itemizations in certain forms.
print(fec.header)
print(fec.cover)
# Header(fec_version='8.1', software_name='NetFile', software_version='199199', report_id=None, report_number='0')
# Cover(form_type='F3N', filer_committee_id='C00479188')
# Iterate through the itemizations in the file in batches of pyarrow RecordBatches.
# By iterating, this keeps us from having to load the entire file into memory.
# By using pyarrow, we can avoid copying the underlying data from Rust to Python.
# It integrates well with the rest of the Python data ecosystem, for example
# it's easy to convert to a pandas DataFrames.
batcher = feco3.PyarrowBatcher(fec, max_batch_size=1024 * 1024)
for batch in batcher:
# The record code for this kind of itemizations, eg. 'SA11AI'
assert isinstance(batch.code, str)
# A pyarrow RecordBatch of the itemizations
assert isinstance(batch.records, pa.RecordBatch)
df = batch.records.to_pandas()
print(batch.code)
print(df.head(3))
# SA15
# filer_committee_id_number transaction_id back_reference_tran_id_number back_reference_sched_name ... conduit_zip_code memo_code memo_text_description reference_code
# 0 C00479188 INCA994 ...
# 1 C00479188 INCA992 ...
# 2 C00479188 INCA993 ...
# [3 rows x 44 columns]
# TEXT
# filer_committee_id_number transaction_id_number back_reference_tran_id_number back_reference_sched_form_name text
# 0 C00479188 TPAYC760 PAYC760 SC/10 PERSONAL FUNDS
# SC/10
# filer_committee_id_number transaction_id_number receipt_line_number entity_type ... lender_candidate_state lender_candidate_district memo_code memo_text_description
# 0 C00479188 PAYC760 13B CAN ...
# [1 rows x 37 columns] ...
```
Raw data
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