This is achieved as follows: # set indexįirst_name last_name age location.City location.Stateĥ Pat Anderson 60 Miami Florida Step 3: Export to CSV FileĪt this point, our data is ready to be exported to a CSV file. This data will produce an extra column if exported directly to CSV (the index column.) There are several ways in which one could approach handling this, but a convenient means is the setindex method - it will create a visual representation of the change in the DataFrame as well. Here we see the keys of the location field automatically being converted into dot-delimited column names. Id first_name last_name age location.City location.StateĢ 3 Alice Jacobs 18 Los Angeles California # load JSON data and parse into Dictionary object # Load via context manager and read_json() method 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).

Converting json to csv python