How to bin into the degree in a logarithm scale in a Pandas dataframe?

Question

I want to bin as efficiently as possible numbers of a Pandas dataframe column into the degree in a logarithm scale. By logarithm scale, I mean the way to display numbers over a power(unit, degree) range:

Degree	Power(10, degree)	Power(2, degree)
-2	0.001	0.25
-1	0.1	0.5
0	1	1
1	10	2
2	100	4

Giving the following toy dataset:

df = pandas.DataFrame(data={'number' : [0.478, 897.12, 12.56, 8.89, 1578.45, 0.089, 0.007]})

I would like to easily calculate the column log_10_class:

number	log_10_class
0.478	0
897.12	3
12.56	2
8.89	1
1578.45	4
0.089	-1
0.007	-2

Answer 1

def bin_log_scale(
    numb_sr: pandas.core.series.Series,
    log_unit : float,
    min_pow_deg: int,
    max_pow_deg: int
):
    """ Bin a float into a degree of a logarithm scale
    Parameters:
        - numb_sr : Pandas serie of number
        - log_unit : logarithm scale unit
        - min_pow_deg : minimal power degree
        - max_pow_deg : maximal power degree
    Returns:
        - a Pandas serie identifying the degree of the logarithm scale
    """
    # Initialisation with the minimum class
    class_sr = pandas.Series(
        data = min_pow_deg,
        index = numb_sr.index
    )
    # Incrementation of the class each time numbers are higher its lower bound 
    for pow_deg in range(min_pow_deg, max_pow_deg):
        class_sr = class_sr + 1*(numb_sr > pow(log_unit, pow_deg))
        
    return class_sr

#  Create the new column with the log-10 scale class
df['log_10_class']=bin_log_scale(
    numb_sr = df['number'],
    log_unit = 10,
    min_pow_deg = -2,
    max_pow_deg = 4
)
df