Work in progress. Documentation in progress
Iterable data is a Python lib to read data files row by row and write data files. Iterable classes are similar to files or csv.DictReader or reading parquet files row by row.
This library was written to simplify data processing and conversion between formats.
Supported file types:
- BSON
- JSON
- NDJSON (JSON lines)
- XML
- XLS
- XLSX
- Parquet
- ORC
- Avro
- Pickle
Supported file compression: GZip, BZip2, LZMA (.xz), LZ4, ZIP
Python has many high-quality data processing tools and libraries, especially pandas and other data frames lib. The only issue with most of them is flat data. Data frames don't support complex data types, and you must flatten data each time.
pyiterable helps you read any data as a Python dictionary instead of flattening data. It makes it much easier to work with such data sources as JSON, NDJSON, or BSON files.
This code is used in several tools written by its author. It's command line tool undatum and data processing ETL engine datacrafter
Python 3.8+
pip install iterabledata or use this repository
In progress. Please see usage and examples.
Read compressed csv.xz file
from iterable.helpers.detect import open_iterable
source = open_iterable('data.csv.xz')
n = 0
for row in iterable:
n += 1
# Add data processing code here
if n % 1000 == 0: print('Processing %d' % (n))
Detects encoding and delimiter of the selected CSV file and use it to open as iterable
from iterable.helpers.detect import open_iterable
from iterable.helpers.utils import detect_encoding, detect_delimiter
delimiter = detect_delimiter('data.csv')
encoding = detect_encoding('data.csv')
source = open_iterable('data.csv', iterableargs={'encoding' : encoding['encoding'], 'delimiter' : delimiter)
n = 0
for row in iterable:
n += 1
# Add data processing code here
if n % 1000 == 0: print('Processing %d' % (n))
Uses pipeline class to iterate through parquet file and convert its selected fields to JSON lines (NDJSON)
from iterable.helpers.detect import open_iterable
from iterable.pipeline import pipeline
source = open_iterable('data/data.parquet')
destination = open_iterable('data/data.jsonl.xz', mode='w')
def extract_fields(record, state):
out = {}
record = dict(record)
print(record)
for k in ['name',]:
out[k] = record[k]
return out
def print_process(stats, state):
print(stats)
pipeline(source, destination=destination, process_func=extract_fields, trigger_on=2, trigger_func=print_process, final_func=print_process, start_state={})
Reads each row from JSON lines file using Gzip codec and writes BSON data using LZMA codec
from iterable.datatypes import JSONLinesIterable, BSONIterable
from iterable.codecs import GZIPCodec, LZMACodec
codecobj = GZIPCodec('data.jsonl.gz', mode='r', open_it=True)
iterable = JSONLinesIterable(codec=codecobj)
codecobj = LZMACodec('data.bson.xz', mode='wb', open_it=False)
write_iterable = BSONIterable(codec=codecobj, mode='w')
n = 0
for row in iterable:
n += 1
if n % 10000 == 0: print('Processing %d' % (n))
write_iterable.write(row)
See tests for example usage and tests
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