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.

Please check out the notebook for the source code and stay tuned if you are interested in the practical aspect of machine learning.(image by author) 7. I recommend you to check out the documentation for read_json() and json_normalize() APIs, and to know about other things you can do. I hope this article will help you to save time in converting JSON data into a DataFrame. When dealing with nested JSON, we can use the Pandas built-in json_normalize() function.

Pandas read_json() function is a quick and convenient way for converting simple flattened JSON into a Pandas DataFrame. notation to access property from a deeply nested object. Glom is a Python library that allows us to use. from glom import glom df = pd.read_json('data/nested_deep.json') df.apply( lambda row: glom(row, 'grade.math')) 0 60 1 89 2 79 Name: students, dtype: int64

How can we do that more effectively? The answer is using read_json with glom. What about JSON with a nested list? Let’s see how to convert the following JSON into a DataFrame:

Pandas read_json() works great for flattened JSON like we have in the previous example. Same as reading from a local file, it returns a DataFrame, and columns that are numerical are cast to numeric types by default. Image by author > df.info() RangeIndex: 3 entries, 0 to 2 Data columns (total 5 columns): # Column Non-Null Count Dtype - 0 id 3 non-null object 1 name 3 non-null object 2 math 3 non-null int64 3 physics 3 non-null int64 4 chemistry 3 non-null int64 dtypes: int64(3), object(2) memory usage: 248.0+ bytes

Pandas json to csv