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I have a function that divides the values of a numeric column into the specified number of bins/categories, based on the ranges they fall into. These categories are numbered from 0 to the limit specified. And the column values are replaced with their category number. I implemented the binning function which worked fine. I now want to display the ranges the column values get divided into. Following is what I tried:

What I have tried:

Binning function code:

def binning_fun_cut(df, binning_inputs):
    col_name = binning_inputs[0]
    num_of_bins = binning_inputs[1]
    
    range_table = pd.value_counts(df[col_name], bins=num_of_bins, sort=False)
    belief = range_table.to_dict()
    
    df[col_name] = pd.cut(df[col_name], bins=num_of_bins, labels=range(num_of_bins)).astype(str)
    
    result_dict = df.to_dict(orient='records')         
    for record in result_dict:                         
        for key, value in record.items():              
            if isinstance(value, float):               
                record[key] = str(value) 
    
    return {'Range of binned': belief, 'dataf': result_dict}   


Error:
ValueError: [TypeError("'pandas._libs.interval.Interval' object is not iterable"), TypeError('vars() argument must have __dict__ attribute')]


Expected output (for the variable 'belief'):

(1.004, 1.01]      7
(1.01, 1.015]    159
(1.015, 1.02]    106
(1.02, 1.025]     81


How do I get the expected output?
Posted
Updated 11-Mar-23 7:38am

1 solution

I don't do Python, but the error seems to be telling you that you're trying to iterate (for...) over something that is not a collection, but is a single object.
 
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Comments
Apoorva 2022 11-Mar-23 14:23pm    
I did debugging & the code works when I print the result. I'm trying to return the result as a dictionary using Fast API & that is when I get the specified error.

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