Pandas的qcut()与cut()

>>> factors = np.random.randn(9)
[ 2.12046097  0.24486218  1.64494175 -0.27307614 -2.11238291 2.15422205 -0.46832859  0.16444572  1.52536248]

pd.qcut()

qcut是根据这些值的频率来选择箱子的均匀间隔，即每个箱子中含有的数的数量是相同的

传入q参数

>>> pd.qcut(factors, 3) #返回每个数对应的分组
[(1.525, 2.154], (-0.158, 1.525], (1.525, 2.154], (-2.113, -0.158], (-2.113, -0.158], (1.525, 2.154], (-2.113, -0.158], (-0.158, 1.525], (-0.158, 1.525]]
Categories (3, interval[float64]): [(-2.113, -0.158] < (-0.158, 1.525] < (1.525, 2.154]]

>>> pd.qcut(factors, 3).value_counts() #计算每个分组中含有的数的数量
(-2.113, -0.158]    3
(-0.158, 1.525]     3
(1.525, 2.154]      3

传入lable参数

>>> pd.qcut(factors, 3,labels=["a","b","c"]) #返回每个数对应的分组，但分组名称由label指示
[c, b, c, a, a, c, a, b, b]
Categories (3, object): [a < b < c]

>>> pd.qcut(factors, 3,labels=False) #返回每个数对应的分组，但仅显示分组下标
[2 1 2 0 0 2 0 1 1]

传入retbins参数

>>> pd.qcut(factors, 3,retbins=True)# 返回每个数对应的分组，且额外返回bins，即每个边界值
[(1.525, 2.154], (-0.158, 1.525], (1.525, 2.154], (-2.113, -0.158], (-2.113, -0.158], (1.525, 2.154], (-2.113, -0.158], (-0.158, 1.525], (-0.158, 1.525]]
Categories (3, interval[float64]): [(-2.113, -0.158] < (-0.158, 1.525] < (1.525, 2.154],array([-2.113,  -0.158 ,  1.525,  2.154]))

参数	说明
x	ndarray或Series
q	integer，指示划分的组数
labels	array或bool，默认为None。当传入数组时，分组的名称由label指示；当传入Flase时，仅显示分组下标
retbins	bool，是否返回bins，默认为False。当传入True时，额外返回bins，即每个边界值。
precision	int，精度，默认为3

---

pd.cut()

cut将根据值本身来选择箱子均匀间隔，即每个箱子的间距都是相同的

传入bins参数

>>> pd.cut(factors, 3) #返回每个数对应的分组
[(0.732, 2.154], (-0.69, 0.732], (0.732, 2.154], (-0.69, 0.732], (-2.117, -0.69], (0.732, 2.154], (-0.69, 0.732], (-0.69, 0.732], (0.732, 2.154]]
Categories (3, interval[float64]): [(-2.117, -0.69] < (-0.69, 0.732] < (0.732, 2.154]]

>>> pd.cut(factors, bins=[-3,-2,-1,0,1,2,3])
[(2, 3], (0, 1], (1, 2], (-1, 0], (-3, -2], (2, 3], (-1, 0], (0, 1], (1, 2]]
Categories (6, interval[int64]): [(-3, -2] < (-2, -1] < (-1, 0] < (0, 1] (1, 2] < (2, 3]]

>>> pd.cut(factors, 3).value_counts() #计算每个分组中含有的数的数量
Categories (3, interval[float64]): [(-2.117, -0.69] < (-0.69, 0.732] < (0.732, 2.154]]
(-2.117, -0.69]    1
(-0.69, 0.732]     4
(0.732, 2.154]     4

传入lable参数

>>> pd.cut(factors, 3,labels=["a","b","c"]) #返回每个数对应的分组，但分组名称由label指示
[c, b, c, b, a, c, b, b, c]
Categories (3, object): [a < b < c]

>>> pd.cut(factors, 3,labels=False) #返回每个数对应的分组，但仅显示分组下标
[2 1 2 1 0 2 1 1 2]

传入retbins参数

>>> pd.cut(factors, 3,retbins=True)# 返回每个数对应的分组，且额外返回bins，即每个边界值
([(0.732, 2.154], (-0.69, 0.732], (0.732, 2.154], (-0.69, 0.732], (-2.117, -0.69], (0.732, 2.154], (-0.69, 0.732], (-0.69, 0.732], (0.732, 2.154]]
Categories (3, interval[float64]): [(-2.117, -0.69] < (-0.69, 0.732] < (0.732, 2.154]], array([-2.11664951, -0.69018126,  0.7320204 ,  2.15422205]))

参数	说明
x	array，仅能使用一维数组
bins	integer或sequence of scalars，指示划分的组数或指定组距
labels	array或bool，默认为None。当传入数组时，分组的名称由label指示；当传入Flase时，仅显示分组下标
retbins	bool，是否返回bins，默认为False。当传入True时，额外返回bins，即每个边界值。
precision	int，精度，默认为3

 

转载地址：https://blog.youkuaiyun.com/starter_____/article/details/79327997#t1