Suppose we have a JSON file called my_file. read_json('C:/Users/Zach/Desktop/json_file.json', orient=' records')Įxample 2: Converting a JSON File with an “Index” Format We can load this JSON file into a pandas DataFrame by simply specifying the path to it along with orient=’ records‘ as follows: #load JSON file into pandas DataFrameĭf = pd. Suppose we have a JSON file called my_file.json in the following format: [ Repeat the above steps for both the nested files and then follow either example 1 or example 2 for conversion.
Step 3: Convert the flattened dataframe into CSV file. Step 2: Flatten the different column values using pandas methods. Example 1: Converting a JSON File with a “Records” Format Step 1: Load the nested json file with the help of json.load () method. The following examples show how to use this function for a variety of different JSON strings. Default is ‘index’ but you can specify ‘split’, ‘records’, ‘columns’, or ‘values’ instead. orient: the orientation of the JSON file.Fortunately this is easy to do using the pandas read_json() function, which uses the following syntax: Please check out the notebook for the source code and stay tuned if you are interested in the practical aspect of machine learning.Occasionally you may want to convert a JSON file into a pandas DataFrame. I recommend you to check out the documentation for the json_normalize() API and to know about other things you can do. I hope this article will help you to save time in flattening JSON data. Pandas json_normalize() function is a quick, convenient, and powerful way for flattening JSON into a DataFrame. The simplest way to do that is using the Python request modules: import requests URL = ' ' data = json.loads(requests.get(URL).text) # Flattening JSON data pd.json_normalize(data) Conclusion Often, you need to work with API’s response in JSON format.
JSON is a standard format for transferring data in REST APIs. After that, json_normalize() is called on the data to flatten it into a DataFrame. To work around it, you need help from a 3rd module, for example, the Python json module: import json # load data using Python JSON module with open('data/simple.json','r') as f: data = json.loads(f.read()) # Flattening JSON data pd.json_normalize(data)ĭata = json.loads(f.read()) loads data using Python json module. However, Pandas json_normalize() function only accepts a dict or a list of dicts. Often, the JSON data you will be working on is stored locally as a.
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