Here is the snippet:
test = pd.DataFrame({'days': [0,31,45]}) test['range'] = pd.cut(test.days, [0,30,60]) Output:
days range 0 0 NaN 1 31 (30, 60] 2 45 (30, 60] I am surprised that 0 is not in (0, 30], what should I do to categorize 0 as (0, 30]?
5 Answers
test['range'] = pd.cut(test.days, [0,30,60], include_lowest=True) print (test) days range 0 0 (-0.001, 30.0] 1 31 (30.0, 60.0] 2 45 (30.0, 60.0] See difference:
test = pd.DataFrame({'days': [0,20,30,31,45,60]}) test['range1'] = pd.cut(test.days, [0,30,60], include_lowest=True) #30 value is in [30, 60) group test['range2'] = pd.cut(test.days, [0,30,60], right=False) #30 value is in (0, 30] group test['range3'] = pd.cut(test.days, [0,30,60]) print (test) days range1 range2 range3 0 0 (-0.001, 30.0] [0, 30) NaN 1 20 (-0.001, 30.0] [0, 30) (0, 30] 2 30 (-0.001, 30.0] [30, 60) (0, 30] 3 31 (30.0, 60.0] [30, 60) (30, 60] 4 45 (30.0, 60.0] [30, 60) (30, 60] 5 60 (30.0, 60.0] NaN (30, 60] Or use numpy.searchsorted, but values of days has to be sorted:
arr = np.array([0,30,60]) test['range1'] = arr.searchsorted(test.days) test['range2'] = arr.searchsorted(test.days, side='right') - 1 print (test) days range1 range2 0 0 0 0 1 20 1 0 2 30 1 1 3 31 2 1 4 45 2 1 5 60 2 2 pd.cut documentation
Include parameter right=False
test = pd.DataFrame({'days': [0,31,45]}) test['range'] = pd.cut(test.days, [0,30,60], right=False) test days range 0 0 [0, 30) 1 31 [30, 60) 2 45 [30, 60) You can use labels to pd.cut() as well. The following example contains the grade of students in the range from 0-10. We're adding a new column called 'grade_cat' to categorize the grades.
bins represent the intervals: 0-4 is one interval, 5-6 is one interval, and so on The corresponding labels are "poor", "normal", etc
bins = [0, 4, 6, 10] labels = ["poor","normal","excellent"] student['grade_cat'] = pd.cut(student['grade'], bins=bins, labels=labels) A sample of how the .cut works
s=pd.Series([168,180,174,190,170,185,179,181,175,169,182,177,180,171]) pd.cut(s,3) #To add labels to bins pd.cut(s,3,labels=["Small","Medium","Large"]) This can be used directly on a range
1@jezrael has explained almost all the use-cases of pd.cut()
One use-case that i would like to add is the following
pd.cut(np.array([1,2,3,4,5,6]),3)
the number of bins are decided by the second parameter, thus we have following output
[(0.995,2.667],(0.995,2.667],(2.667,4.333],(2.667,4.333], (4.333,6.0], (4.333,6.0]] Categories (3, interval[float64]): [(0.995,2.667] < (2.667,4.333] < (4.333,6.0]] Similarly if we use the number of bin parameter(second parameter) as 2 following will be the output
[(0.995, 3.5], (0.995, 3.5], (0.995, 3.5], (3.5, 6.0], (3.5, 6.0], (3.5, 6.0]] Categories (2, interval[float64]): [(0.995, 3.5] < (3.5, 6.0]] 
