API: cut interval formatting

[fancychildren]

it would be nice to have number in front of all labels. put number like 00, 01, 02 in front of labels so that it would order appropriately.

Lib\site-packages\pandas\tools\tile.py

def _format_levels(bins, prec, right=True,
                   include_lowest=False):
    fmt = lambda v: _format_label(v, precision=prec)
    cnter=0
    if right:
        levels = []
        for a, b in zip(bins, bins[1:]):
            fa, fb = fmt(a), fmt(b)

            if a != b and fa == fb:
                raise ValueError('precision too low')

            formatted = '%02d: (%s, %s]' % (cnter, fa, fb)
            cnter=cnter +1
            levels.append(formatted)

        if include_lowest:
            levels[0] = '[' + levels[0][1:]
    else:
        levels = ['[%s, %s)' % (fmt(a), fmt(b))
                  for a, b in zip(bins, bins[1:])]

    return levels

[jreback]

this is going to be turned into a Categorical, so ordering will happen automatically. interested to do for 0.15.1?

[jreback]

@fancychildren can you post a full example (that doesn't order correctly)

[fancychildren]

here is the example. after i create the cuts and apply summary to the dataframe, the currLoanSize bucket is ordered as if it's a string, instead of a number of the lower boundary.

after i tweak the code, it appears like this

not sure if i am not using it correctly. but i would be nice to be able to order by the value of lower boundary, while keeping the label.

[jreback]

can you do a programatic example, e.g. df = DataFrame....... eventually we'll turn this into a test

[fancychildren]

import StringIO
from pandas import *
import numpy as np


data = StringIO.StringIO('''upb coupon
0.00    3.00
25000.00    3.00
50000.00    3.00
75000.00    3.00
100000.00   3.00
125000.00   3.00
150000.00   3.00
175000.00   3.00
200000.00   3.00
225000.00   3.00
250000.00   3.00
275000.00   3.00
300000.00   3.00
325000.00   3.00
350000.00   3.00
375000.00   3.00
400000.00   3.00
425000.00   3.00
450000.00   3.00
475000.00   3.00
500000.00   3.00
525000.00   3.00
550000.00   3.00
575000.00   3.00
600000.00   3.00
625000.00   3.00
650000.00   3.00
675000.00   3.00
700000.00   3.00
725000.00   3.00
750000.00   3.00
775000.00   3.00
800000.00   3.00
825000.00   3.00
850000.00   3.00
875000.00   3.00
900000.00   3.00
925000.00   3.00
950000.00   3.00
975000.00   3.00
1000000.00  3.00
''')

df = read_csv(data, sep='\t')

df['currLoanSize'] = cut(df['upb'], bins=[0,50000,100000,200000,400000,9999999])
df['count_pct'] = 1.0/len(df['coupon'])

def f_summary(group):
    return Series({'counts': len(group['coupon']),
                   'count%': np.sum(group['count_pct']),
                   },
                  index = ['counts', 'count%']
                  )

print df.groupby('currLoanSize').apply(f_summary)