pandas - Converting float into range in Python -


i doing data analysis pandas , struggling find nice, clean way of summing range of numbers. have data frame column of floats, not interested in exact number, rough range. want run pivot , count how many values in range. therefore ideally want create new column in data frame, converts column of floats range. df[number] = 3.5, df[range] = 0-10

the ranges should 0-10, 10-20, ... >100

this may sound arbitrary, i've been struggling find answer on this. many thanks

pandas has cut function this

in [18]: s = pd.series(np.random.uniform(0, 110, 100))  in [19]: s out[19]: 0     57.614427 1     30.576853 2     95.578943 3     53.010340 4     63.947381         ... 95    42.252644 96    14.814418 97    81.271527 98     5.732966 99    90.932890   in [12]: s = pd.series(np.random.uniform(0, 110, 100))  in [13]: s out[13]: 0      2.652461 1     46.536276 2      6.455352 3      6.075963 4     40.013378         ... 95    39.775493 96    99.688307 97    41.064469 98    91.401904 99    60.580600 dtype: float64  in [14]: cuts = np.arange(0, 101, 10)  in [15]: pd.cut(s, cuts) out[15]: 0       (0, 10] 1      (40, 50] 2       (0, 10] 3       (0, 10] 4      (40, 50]         ... 95     (30, 40] 96    (90, 100] 97     (40, 50] 98    (90, 100] 99     (60, 70] dtype: category categories (10, object): [(0, 10] < (10, 20] < (20, 30] < (30, 40] ... (60, 70] < (70, 80] < (80, 90] <                           (90, 100]]

see docs controlling happens endpoints.

note in 0.18 (coming out soonish) result intervalindex instead of categorical, make things nicer.

to counts per interval, use value_counts method

in [17]: pd.cut(s, cuts).value_counts() out[17]: (30, 40]     15 (40, 50]     13 (50, 60]     12 (60, 70]     10 (0, 10]      10 (90, 100]     8 (70, 80]      8 (80, 90]      7 (10, 20]      6 (20, 30]      3 dtype: int64

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