Displaying Decimals in JSON Data with Pandas

Since you are using pandas’ “json_load” method to parse a JSON stream, there may be a limitation when working with decimal points. The problem arises because some APIs return data with commas as thousands separators, while others use periods (.) instead.

Here is an article explaining how to display all decimal places from a JSON stream when using pandas to convert the data:

Problem Statement

When you use “json_load” to parse a JSON stream that contains decimal values, such as stock quotes or financial data, you may encounter issues displaying these numbers correctly. Specifically, the code has trouble handling cases where the API returns data with commas (,) as thousands separators.

Solution: Use pd.read_json() with parse_dates=True

To solve this problem, we can take advantage of the pandas.read_json() function with the parse_dates=True parameter. This approach allows us to parse JSON files into a pandas dataframe without losing the precision of the data.

import pandas as pd

Replace ' with the actual API endpoint URL
url = '
define fetch_json_data(url, parameters):
"""
Fetch the JSON data from the specified URL and return it in a pandas DataFrame.
Parameters:
url (str): The API endpoint URL.
params (dict): A dictionary of query parameters to filter the response.
Returns:
pd.DataFrame: A DataFrame containing the fetched JSON data.
"""

Define API query parameters
params = {k: v for k, v in params.items() if k is not in ['timestamp', 'open', 'high', 'low', 'close']}

Fetch JSON data using Pandas' built-in read_json() function
data = pd.read_json(url, parameters=params)
return data
definition main():
url = '
params = {'symbol': 'BTCUSDT', 'interval': '1m', 'limit': 1000} 
Adjust these parameters as needed
data = fetch_json_data(url, parameters)
print(data)
if __name__ == '__main__':
main()

How ​​it works :

The fetch_json_data() function takes an API endpoint URL and query parameters as input. We first convert the query parameters into a dictionary containing only non-numeric keys (timestamp, open, high, low, close), which are useful for filtering the response.

We then use pd.read_json() to get the JSON data from the specified URL using these filtered parameters. The resulting DataFrame is then printed to the console.

Tips and Variations:

• You can customize the API request parameters to suit your specific needs.

• Please be aware of the API rate limits to avoid excessive requests.

• If you are working with large datasets, consider splitting the JSON data into smaller chunks using chunksize=10000.

• To display decimal values ​​as floats instead of integers, simply replace int() with float() in the pandas DataFrame.