Pandas how to use pd.cut()

Question

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]?

Answer 1

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 

Answer 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) 

Answer 3

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)

Answer 4

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