===============================================================================0
导入模块
>>> from TidyDistribution import TidySample
>>> import numpy as np
>>> import pandas as pd
===============================================================================1
ndarray对象测试
===============================================================================2
不指定随机数种子
>>> arr = np.arange(10)
>>> res = TidySample(arr)
>>> print(res)
===============================================================================3
指定随机数种子
>>> arr = np.arange(10)
>>> res = TidySample(arr, random_state=10)
>>> print(res)
===============================================================================4
指定抽样大小
>>> arr = np.arange(10)
>>> res = TidySample(arr, size=4)
>>> print(res)
===============================================================================5
有放回抽样
>>> arr = np.arange(10)
>>> res = TidySample(arr, size=10, isreplace=1)
>>> print(res)
===============================================================================6
指定抽样比例
>>> arr = np.arange(10)
>>> res = TidySample(arr, frac=0.4, isreplace=1)
>>> print(res)
===============================================================================7
指定抽样概率
>>> arr = np.arange(10)
>>> res = TidySample(arr, frac=0.4, weight_p=[0,0,0,0,0,0,0.1,0.1,0.2,0.6])
>>> print(res)
===============================================================================8
列表对象测试
===============================================================================9
不指定随机数种子
>>> lst = [i for i in range(10)]
>>> res = TidySample(lst)
>>> print(res)
===============================================================================10
指定随机数种子
>>> lst = [i for i in range(10)]
>>> res = TidySample(lst, random_state=10)
>>> print(res)
===============================================================================11
指定抽样大小
>>> lst = [i for i in range(10)]
>>> res = TidySample(lst, size=4)
>>> print(res)
===============================================================================12
有放回抽样
>>> lst = [i for i in range(10)]
>>> res = TidySample(lst, size=10, isreplace=1)
>>> print(res)
===============================================================================13
指定抽样比例
>>> lst = [i for i in range(10)]
>>> res = TidySample(lst, frac=0.4, isreplace=1)
>>> print(res)
===============================================================================14
指定抽样概率
>>> lst = [i for i in range(10)]
>>> res = TidySample(lst, frac=0.4, weight_p=[0,0,0,0,0,0,0.1,0.1,0.2,0.6])
>>> print(res)
===============================================================================15
DataFrame对象测试
===============================================================================16
不指定随机数种子
>>> df = pd.DataFrame({"x": [i for i in range(10)], "y": [i**2 for i in range(10)]})
>>> res = TidySample(df)
>>> print(res)
===============================================================================17
指定随机数种子
>>> df = pd.DataFrame({"x": [i for i in range(10)], "y": [i**2 for i in range(10)]})
>>> res = TidySample(df, random_state=10)
>>> print(res)
===============================================================================18
指定抽样大小
>>> df = pd.DataFrame({"x": [i for i in range(10)], "y": [i**2 for i in range(10)]})
>>> res = TidySample(df, size=4)
>>> print(res)
===============================================================================19
有放回抽样
>>> df = pd.DataFrame({"x": [i for i in range(10)], "y": [i**2 for i in range(10)]})
>>> res = TidySample(df, size=10, isreplace=1)
>>> print(res)
===============================================================================20
指定抽样比例
>>> df = pd.DataFrame({"x": [i for i in range(10)], "y": [i**2 for i in range(10)]})
>>> res = TidySample(df, frac=0.4, isreplace=1)
>>> print(res)
===============================================================================21
指定抽样概率
>>> df = pd.DataFrame({"x": [i for i in range(10)], "y": [i**2 for i in range(10)]})
>>> res = TidySample(df, frac=0.4, weight_p=[0,0,0,0,0,0,0.1,0.1,0.2,0.6])
>>> print(res)
===============================================================================22
Series对象测试
===============================================================================23
不指定随机数种子
>>> s = pd.Series([i%3 for i in range(10, 20)])
>>> res = TidySample(s)
>>> print(res)
===============================================================================24
指定随机数种子
>>> s = pd.Series([i%3 for i in range(10, 20)])
>>> res = TidySample(s, random_state=10)
>>> print(res)
===============================================================================25
指定抽样大小
>>> s = pd.Series([i%3 for i in range(10, 20)])
>>> res = TidySample(s, size=4)
>>> print(res)
===============================================================================26
有放回抽样
>>> s = pd.Series([i%3 for i in range(10, 20)])
>>> res = TidySample(s, size=10, isreplace=1)
>>> print(res)
===============================================================================27
指定抽样比例
>>> s = pd.Series([i%3 for i in range(10, 20)])
>>> res = TidySample(s, frac=0.4, isreplace=1)
>>> print(res)
===============================================================================28
指定抽样概率
>>> s = pd.Series([i%3 for i in range(10, 20)])
>>> res = TidySample(s, frac=0.4, weight_p=[0,0,0,0,0,0,0.1,0.1,0.2,0.6])
>>> print(res)
===============================================================================29
