factors = np.random.randn(30)
In [11]:
pd.cut(factors, 5)
Out[11]:
[(-0.411, 0.575], (-0.411, 0.575], (-0.411, 0.575], (-0.411, 0.575], (0.575, 1.561], ..., (-0.411, 0.575], (-1.397, -0.411], (0.575, 1.561], (-2.388, -1.397], (-0.411, 0.575]]
Length: 30
Categories (5, object): [(-2.388, -1.397] < (-1.397, -0.411] < (-0.411, 0.575] < (0.575, 1.561] < (1.561, 2.547]]
In [14]:
pd.qcut(factors, 5)
Out[14]:
[(-0.348, 0.0899], (-0.348, 0.0899], (0.0899, 1.19], (0.0899, 1.19], (0.0899, 1.19], ..., (0.0899, 1.19], (-1.137, -0.348], (1.19, 2.547], [-2.383, -1.137], (-0.348, 0.0899]]
Length: 30
Categories (5, object): [[-2.383, -1.137] < (-1.137, -0.348] < (-0.348, 0.0899] < (0.0899, 1.19] < (1.19, 2.547]]`
cut是等距，qcut是等频
qcut方法，参考链接：http://pandas.pydata.org/pandas-docs/stable/generated/pandas.qcut.html
1).参数：pandas.qcut(x, q, labels=None, retbins=False, precision=3, duplicates='raise')
>>>x 要进行分组的数据，数据类型为一维数组，或Series对象
>>>q 组数，即要将数据分成几组，后边举例说明
>>>labels 可以理解为组标签，这里注意标签个数要和组数相等
>>>retbins 默认为False,当为False时，返回值是Categorical类型（具有value_counts()方法），为True是返回值是元组
pandas.cut:
pandas.cut(x, bins, right=True, labels=None, retbins=False, precision=3, include_lowest=False)
参数：
- x，类array对象，且必须为一维，待切割的原形式
- bins, 整数、序列尺度、或间隔索引。如果bins是一个整数，它定义了x宽度范围内的等宽面元数量，但是在这种情况下，x的范围在每个边上被延长1%，以保证包括x的最小值或最大值。如果bin是序列，它定义了允许非均匀bin宽度的bin边缘。在这种情况下没有x的范围的扩展。
- right,布尔值。是否是左开右闭区间
- labels,用作结果箱的标签。必须与结果箱相同长度。如果FALSE，只返回整数指标面元。
- retbins,布尔值。是否返回面元组
- precision，整数。返回面元的小数点几位
- include_lowest，布尔值。第一个区间的左端点是否包含
返回值：
-
ages = [20, 22, 25, 27, 21, 23, 37, 31, 61, 45, 41, 32]
-
-
bins = [18, 25, 35, 60, 100]
-
cats = pd.cut(ages, bins)
-
若labels为False则返回整数填充的Categorical或数组或Series
若retbins为True还返回用浮点数填充的N维数组
demo:
>>> pd.cut(np.array([.2, 1.4, 2.5, 6.2, 9.7, 2.1]), 3, retbins=True)
...
([(0.19, 3.367], (0.19, 3.367], (0.19, 3.367], (3.367, 6.533], ...
Categories (3, interval[float64]): [(0.19, 3.367] < (3.367, 6.533] ...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
>>> pd.cut(np.array([.2, 1.4, 2.5, 6.2, 9.7, 2.1]),
... 3, labels=["good", "medium", "bad"])
...
[good, good, good, medium, bad, good]
Categories (3, object): [good < medium < bad]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
>>> pd.cut(np.ones(5), 4, labels=False)
array([1, 1, 1, 1, 1])pandas.qcut
pandas.qcut(x, q, labels=None, retbins=False, precision=3, duplicates=’raise’)
参数：
1.x
2.q,整数或分位数组成的数组。
3.labels,
4.retbins
5.precisoon
6.duplicates
结果中超过边界的值将会变成NA
demo:
>>> pd.qcut(range(5), 4)
...
[(-0.001, 1.0], (-0.001, 1.0], (1.0, 2.0], (2.0, 3.0], (3.0, 4.0]]
Categories (4, interval[float64]): [(-0.001, 1.0] < (1.0, 2.0] ...
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
>>> pd.qcut(range(5), 3, labels=["good", "medium", "bad"])
...
[good, good, medium, bad, bad]
Categories (3, object): [good < medium < bad]
~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
pd.qcut(range(5), 4, labels=False)
array([0, 0, 1, 2, 3])