![]() ![]() load data using Python JSON module with open ('multiplelevels.json','r') as f: data json.loads (f. You can download the example JSON from here. Please check out the notebook for the source code and stay tuned if you are interested in the practical aspect of machine learning. Pandas jsonnormalize () This API is mainly designed to convert semi-structured JSON data into a flat table or DataFrame. Example: Suppose the JSON file looks like this: We want to convert the above JSON to CSV file with key as headers. I recommend you to check out the documentation for read_json() and json_normalize() APIs, and to know about other things you can do. Converting JSON to CSV For simple JSON data consisting of key and value pairs, keys will be headers for the CSV file and values the descriptive data. ![]() 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. 3 different formats of JSON files are taken and converted to CSV file by changing the value of. What about JSON with a nested list? Let’s see how to convert the following JSON into a DataFrame: This video explains how to convert JSON file to CSV file using Pandas library in Python. 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 ![]()
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