1，功能:将数据进行离散化
pandas.cut(x,bins,right=True,labels=None,retbins=False,precision=3,include_lowest=False)
参数说明:
x :进行划分的一维数组
bins : 1,整数---将x划分为多少个等间距的区间
In[1]:pd.cut(np.array([0.2,1.4,2.5,6.2,9.7,2.1]),3,retbins=True)
Out[1]: ([(0.19, 3.367], (0.19, 3.367], (0.19, 3.367], (3.367,6.533], (6.533,9.7], (0.19, 3.367]] Categories (3, interval[float64]): [(0.19,3.367] < (3.367, 6.533] < (6.533, 9.7]],array([ 0.1905 , 3.36666667, 6.53333333, 9.7 ]))
2,序列—将x划分在指定的序列中，若不在该序列中，则是NaN
In[2]:pd.cut(np.array([0.2,1.4,2.5,6.2,9.7,2.1]),[1,2,3],retbins=True)
Out[2]: ([NaN, (1, 2], (2, 3], NaN, NaN, (2, 3]] Categories(2, interval[int64]): [(1, 2] < (2, 3]], array([1, 2, 3]))
right :是否包含右端点
labels :是否用标记来代替返回的bins
In[3]:pd.cut([1,2,3,4],4,labels=['one','two','three','four'])
Out[3]: [one, two, three, four]Categories (4, object): [one
precision:精度
include_lowest:是否包含左端点
返回值:
如果retbins = False 则返回x中每个值对应的bin的列表，否者则返回x中每个值对应的bin的列表和对应的bins
