11 把数据进行cut操作
现有数据ages如下
ages = [20, 22, 25, 27, 21, 23, 37, 31, 61, 45, 41, 32, 101]
想按照[(18, 25] < (25, 35] < (35, 60] < (60, 100]]把该数据进行Categories 操作
解决办法：
bins = [18, 25, 35, 60, 100]
cats = pd.cut(ages, bins)
cats
输出
[(18, 25], (18, 25], (18, 25], (25, 35], (18, 25], ..., (60.0, 100.0], (35.0, 60.0], (35.0, 60.0], (25.0, 35.0], NaN]
Length: 13
Categories (4, interval[int64]): [(18, 25] < (25, 35] < (35, 60] < (60, 100]]
此时的cats是非常特殊的一类数据在pd中称为是Categories
Categories可以方便的通过pd.Series()转换为Series
ser_cats = pd.Series(cats)
ser_cats
输出：
0 (18.0, 25.0]
1 (18.0, 25.0]
2 (18.0, 25.0]
3 (25.0, 35.0]
4 (18.0, 25.0]
5 (18.0, 25.0]
6 (35.0, 60.0]
7 (25.0, 35.0]
8 (60.0, 100.0]
9 (35.0, 60.0]
10 (35.0, 60.0]
11 (25.0, 35.0]
12 NaN
dtype: category
Categories (4, interval[int64]): [(18, 25] < (25, 35] < (35, 60] < (60, 100]]
当然这个分类方式看起来不太符合人类的习惯。你可以通过设定labels让输出更加符合人类的习惯
group_names = ['Youth', 'YoungAdult', 'MiddleAged', 'Senior']
cats = pd.cut(ages, bins, labels=group_names)
ser_cats = pd.Series(cats)
ser_cats
输出
0 Youth
1 Youth
2 Youth
3 YoungAdult
4 Youth
5 Youth
6 MiddleAged
7 YoungAdult
8 Senior
9 MiddleAged
10 MiddleAged
11 YoungAdult
12 NaN
dtype: category
Categories (4, object): [Youth < YoungAdult < MiddleAged < Senior]
12 把数据进行qcut操作
现有数据ages如下：
ages = [20, 22, 25, 27, 21, 23, 37, 31, 61, 45, 41, 32, 101]
要把数据按照[(19.999, 23.0] < (23.0, 31.0] < (31.0, 41.0] < (41.0, 101.0]]分成4类
也就是把数据的按照百分位[0, .25, .5, .75, 1.]进行分类
解决方法如下：
ages = [20, 22, 25, 27, 21, 23, 37, 31, 61, 45, 41, 32, 101]
cats = pd.qcut(ages, 4)
#按[0, .25, .5, .75, 1.]分类
cats
输出
[(19.999, 23.0], (19.999, 23.0], (23.0, 31.0], (23.0, 31.0], (19.999, 23.0], ..., (41.0, 101.0], (41.0, 101.0], (31.0, 41.0], (31.0, 41.0], (41.0, 101.0]]
Length: 13
Categories (4, interval[float64]): [(19.999, 23.0] < (23.0, 31.0] < (31.0, 41.0] < (41.0, 101.0]]
