Extracting significand and exponent for base-10 representation from decimal formatted string

Question

I am looking for an efficient Python implementation of a function that takes a decimal formatted string, e.g.

2.05000
200
0.012

and returns a tuple of two integers representing the significand and exponent of the input in base-10 floating point format, e.g.

(205,-2)
(2,2)
(12,-3)

List comprehension would be a nice bonus.

I have a gut feeling that there exists an efficient (and possibly Pythonic) way of doing this but it eludes me...

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Solution applied to pandas

import pandas as pd
import numpy as np
ser1 = pd.Series(['2.05000', '- 2.05000', '00 205', '-205', '-0', '-0.0', '0.00205', '0', np.nan])

ser1 = ser1.str.replace(' ', '')
parts = ser1.str.split('.').apply(pd.Series)

# remove all white spaces
# strip leading zeros (even those after a minus sign)
parts.ix[:,0] = '-'*parts.ix[:,0].str.startswith('-') + parts.ix[:,0].str.lstrip('-').str.lstrip('0')

parts.ix[:,1] = parts.ix[:,1].fillna('')        # fill non-existamt decimal places
exponents = -parts.ix[:,1].str.len()
parts.ix[:,0] += parts.ix[:,1]                  # append decimal places to digit before decimal point

parts.ix[:,1] = parts.ix[:,0].str.rstrip('0')   # strip following zeros

exponents += parts.ix[:,0].str.len() - parts.ix[:,1].str.len()

parts.ix[:,1][(parts.ix[:,1] == '') | (parts.ix[:,1] == '-')] = '0'
significands = parts.ix[:,1].astype(float)

df2 = pd.DataFrame({'exponent': exponents, 'significand': significands})
df2

Input:

0      2.05000
1    - 2.05000
2       00 205
3         -205
4           -0
5         -0.0
6      0.00205
7            0
8          NaN
dtype: object

Output:

   exponent  significand
0        -2          205
1        -2         -205
2         0          205
3         0         -205
4         0            0
5         0            0
6        -5          205
7         0            0
8       NaN          NaN

[9 rows x 2 columns]

Answer 1

Here's a straight-forward string processing solution.

def sig_exp(num_str):
    parts = num_str.split('.', 2)
    decimal = parts[1] if len(parts) > 1 else ''
    exp = -len(decimal)
    digits = parts[0].lstrip('0') + decimal
    trimmed = digits.rstrip('0')
    exp += len(digits) - len(trimmed)
    sig = int(trimmed) if trimmed else 0
    return sig, exp

>>> for x in ['2.05000', '200', '0.012', '0.0']:
    print sig_exp(x)

(205, -2)
(2, 2)
(12, -3)
(0, 0)

I'll leave the handling of negative numbers as an exercise for the reader.