The transform() function converts each row to a JSON-encoded string using the json.dumps() function. The extract() function reads the ‘example.csv’ file and yields each row as a dictionary. In this example, we define three functions: extract(), transform(), and load(). Graph.add_chain(extract, transform, load) Here is an example code snippet that demonstrates how to use Bonobo for CSV to JSON conversion: import bonobo

It provides a simple and intuitive interface for defining data pipelines, and it supports various data formats including CSV and JSON. Here are two examples: Bonoboīonobo is a Python-based ETL (Extract, Transform, Load) framework that can be used for data integration, data processing, and data conversion. There are several other tools and libraries available in Python for CSV to JSON conversion.

Other tools for CSV to JSON conversion in Python The orient=’records’ parameter tells Pandas to output each row as a separate JSON object. We then use the df.to_json() function to convert the DataFrame to a JSON-encoded string. In this example, we use the pd.read_csv() function to read the ‘example.csv’ file into a Pandas DataFrame. Here is an example code snippet that demonstrates how to use the to_json() function: import pandas as pd The to_json() function in Pandas can be used to convert CSV files to JSON format, and it offers several options for customizing the output format. Pandas is a powerful and popular Python library that provides data manipulation and analysis tools. Using to_json() function in Pandas library for CSV to JSON conversion

Csv to json python