Displaying Decimal Numbers in JSON Data with Pandas

Since you are using the json_load method from pandas to parse a JSON feed, there may be a limitation when dealing with decimal points. The problem occurs because some APIs return data with commas as thousands separators, while others use periods (.) instead.

Here is an article on how to display all decimals coming from a JSON feed when using pandas to transform data:

Problem Statement

When you use json_load to parse a JSON feed that contains decimal values, such as stock prices or financial data, you may encounter problems displaying these numbers correctly. Specifically, the code struggles with cases where the API returns data with commas (,) as thousands separators.

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

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

import pandas as pd



Replace ' with your actual API endpoint URL

url = '


def fetch_json_data(url, parameters):

    """

    Extract the JSON data from the specified URL and return it to a pandas DataFrame.

    

    Parameters:

    url (str): API endpoint URL.

    params (dict): dictionary of request parameters to filter responses.

    

    Return:

    pd.DataFrame: DataFrame containing the retrieved JSON data.

    """

    
Set API request parameters

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

    

    
Extract the JSON data using the pandas built-in function read_json()

    data = pd.read_json(url, parameters=parameters)

    

    returned data


def main():

    url = '

    params = {'symbol': 'BTCUSDT', 'interval': '1m', 'limit': 1000} 
Adjust these parameters according to your needs

    data = fetch_json_data(url, parameters)

    

    print (data)


if __name__ == '__main__':

    main ()

How ​​it works:

The fetch_json_data() function takes the API endpoint URL and request parameters as input. First, we convert the query parameters to a dictionary with only non-numeric keys (timestamp, open, high, low, close) that are useful for filtering responses.

We then use pd.read_json() to retrieve 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 your specific needs.

• Be aware of API speed limits to avoid excessive requests.

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

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

transforming transforming investor behavior