3 different formats of JSON files are taken and converted to CSV file by changing the value of. I then get the error: `sequence expected`įirst, your JSON has nested objects, so it normally cannot be directly converted to CSV. This video explains how to convert JSON file to CSV file using Pandas library in Python. I am using Django and the error I received is: `file' object has no attribute 'writerow'` Please check out the notebook for the source code and stay tuned if you are interested in the practical aspect of machine learning.I have a JSON file I want to convert to a CSV file. 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. Converting JSON file to CSV file using Pandas To convert our Json file, there is a function in Pandas called tocsv () that saves our file in CSV format. 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. To write a csv file to a new folder or nested folder you will first need to create it using either Pathlib or os: > from pathlib import Path > filepath Path('folder/subfolder/out.csv') > (parentsTrue, existokTrue) > df.

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