Convert Avro document to Json in Python using fastavro

100%

Convert Avro document to Json in Python using fastavro

✓ Published0🌍 Public

Last edited Aug 22, 2021

Created on Aug 22, 2021

This example converts Avro files into a single JSON document using Python’s `fastavro` library. It reads all `.avro` files from hourly subdirectories under a date-partitioned S3 path, iterates through records with `fastavro.reader`, and accumulates them into a list. The script then writes the collected data to `out.json` with `json.dump`, using sorted keys and indentation for readability. The code demonstrates batch processing of Avro data from multiple files into a structured, human-readable JSON output.

AI-generated description

Similar vizzes

Loading thumbnail…

Get multiple files from S3 at a time into a single Pandas DataFrame with AWS SDK for Pandas (awswrangler)

This example demonstrates how to load multiple JSON files from an Amazon S3 bucket directly into a single Pandas DataFrame using the AWS SDK for Pandas (awswrangler). It shows a concise workflow where a prefix path—structured as `s3://<bucket>/yyyy/mm/dd/`—targets all daily files at once, bypassing the need for manual iteration. The code calls `wr.s3.read_json()` with `lines=True` to parse newline-delimited JSON records from every file under that prefix, then applies `reset_index(drop=True)` to produce a clean, sequentially indexed DataFrame. The snippet highlights awswrangler’s ability to abstract S3 file discovery and batch reads into one efficient operation.

VVioletVivirand

60% match

Loading thumbnail…

Convert raw NBA JSON to CSV.

This example converts raw NBA JSON game logs into a single CSV file, first showing the transformation of nested statistics into flat, queryable rows. It reads JSON files for each season and measure type, extracting headers and parameters to build team-level records with prefixed column names like `pace_*` or `def_rating_*`. The script uses the CoffeeScript language and the Node.js `fs` and `csv` libraries, with the `csv().to.path` API to write the output. It also merges team abbreviations from a separate CSV, keying each row by team ID and season.

CCaged

51% match

Loading thumbnail…

batch processing in Python for Abbyy fine reader

This example demonstrates a Python batch-processing script for the Abbyy FineReader OCR cloud service, enabling automated document recognition across entire folders. The code defines an `AbbyyOnlineSdk` class that handles image uploads via HTTP POST requests, polls task status until completion, and downloads recognized text results. Two scripts are included: `SimpleFolderProcess.py` processes all files in an input folder and saves `.txt` results to an output folder, while `process-for-folders.py` extends this with command-line arguments for specifying source/target files, language, and output format (txt, pdf, rtf, docx, or xml), along with environment-variable support for credentials and proxy settings. The visualization of interest is the batch workflow: a loop iterates over source files, submits each for OCR, polls the cloud service at intervals, and writes the recognized text to corresponding output files, demonstrating asynchronous cloud-based processing with status tracking and result downloading.

Aadelaide01

50% match

Loading thumbnail…

JSON concatenate, dump to CSV

This example merges nine years of JSON-format Trump tweets into a single CSV file, consolidating chronological tweet data into one tabular output. It uses Python’s `json` library to load each yearly file and the `csv` module’s `csv.writer` to write rows. The script first writes a header row from the keys of the first tweet, then appends each tweet’s values for every file in sequence. The resulting `trumptweets.csv` preserves all original fields without transformation, enabling further analysis or visualization of the combined dataset.

Aabehmiel

49% match

Loading thumbnail…

Use ronnywang/pdf-table-extractor CLI to extract CSV files

This example demonstrates how to extract tabular data from PDF files into JSON using the ronnywang/pdf-table-extractor command-line tool. It shows a shell-based workflow that clones the pdf-table-extractor repository alongside Mozilla’s pdf.js library, builds pdf.js with gulp, and then runs the `parse-cmd.js` script to convert a specified PDF input into structured JSON output. The code relies on Node.js, npm, gulp, and the pdf.js rendering engine to parse table cells, making the extracted data available for further processing or visualization.

VVioletVivirand

48% match

Loading thumbnail…

ndjson-to-file

This example explores the challenge of writing large JSON datasets to disk. It demonstrates a failing workaround that serializes data in chunks to multiple files, and two broken attempts at using streaming libraries. The code uses `JSON.stringify` with `fs.writeFileSync` for the chunked approach, and attempts to use the `ndjson` and `JSONStream` libraries with `fs.createWriteStream` and piping for streaming output. The example highlights the difficulty of handling big data blobs that crash `JSON.stringify` and the struggle to implement a proper streaming solution.

11wheel

48% match

Loading thumbnail…

Gist f28ea71aa261d47cf5277019bc65887e

This example shows how to update a MongoDB database with neuroimaging metrics derived from FreeSurfer, using a JSON file as the data source. The notebook loads brain volume measurements and associated demographic metadata, then prepares them for database insertion. It uses pandas for data manipulation, nipype's `load_json` and `save_json` for file handling, and pymongo to interact with the MongoDB collection. The visualization highlights the structure of FreeSurfer-derived brain metrics alongside study-specific metadata fields.

Aakeshavan

48% match

Loading thumbnail…

CSV to JSON

This example demonstrates how to convert flat CSV data into a nested JSON structure using D3’s nesting and mapping functions. It shows the raw CSV text alongside the resulting JSON, grouping records by name and transforming year–amount pairs into arrays for two time-series variables. The code loads `data.csv` via `d3.csv`, coerces numeric columns, then applies `d3.nest()` and `.map()` to reshape the data. Output is displayed in HTML paragraphs using D3 selections.

SSpaceActuary

47% match