As such, we need to first load the JSON data as a dict as such: import json

However, this function takes a dict object as an argument. To load nested JSON as a DataFrame we need to take advantage of the json_normalize function. This takes the raw JSON data and loads it directly into a DataFrame. In the first step, we loaded our data directly via the read_json function in the Pandas library. To approach the first issue, we’ll have to modify the approach by which we loaded our data.

The id column should could be used to index our data (optional).

The location column contains nested JSON data that didn’t import properly.

This gets us pretty close but there are two noticeable issues, one being of grave importance: We’ll use this handy Python script for generating random personal information, producing the following JSON-format data, saved as a local file named people.json. Project Setupįor this project, we’ll create some sample data of random people with information. This method, found in the DataFrame class, is a powerful tool for converting data from CSV to JSON.

Among the many convenient methods and functions found in the Pandas library is the to_json method. Pandas is a powerful data science library whereby developers and data scientists can access, analyze, and manipulate data efficiently and conveniently. 6.1 : Expecting value: line 1 column 1 (char 0).

Convert json to csv python