iterabledata 1.0.5

Iterable data processing Python library

Iterable Data

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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, Brotli,

ZStandard

Why writing this lib?

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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

Requirements

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Python 3.8+

Installation

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pip install iterabledata or use this repository

Documentation

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In progress. Please see usage and examples.

Usage and examples

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Read compressed CSV file

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Read compressed csv.xz file

```{python}

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))

### Detect encoding and file delimiter



Detects encoding and delimiter of the selected CSV file and use it to open as iterable



```{python}



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))

Convert Parquet file to BSON compressed with LZMA using pipeline

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Uses pipeline class to iterate through parquet file and convert its

selected fields to JSON lines (NDJSON)

```{python}

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={})

### Convert gzipped JSON lines (NDJSON) file to BSON compressed with LZMA



Reads each row from JSON lines file using Gzip codec and writes BSON data using LZMA codec



```{python}



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)

More examples and tests

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See tests for example usage and tests