根据列的值连续性，按行分组/分组熊猫DataFrame

Question

如果价格跨越移动平均数（由“趋势”列中的更改给出），我需要一些帮助来创建不同的行组。我将通过实例来解释它。以下是我拥有的数据：

  close  avg  diff trend 
date           
2017-02-22 13.78 13.578652 0.201348  1 
2017-02-23 13.80 13.580854 0.219146  1 
2017-02-24 13.67 13.581741 0.088259  1 
2017-03-01 13.65 13.582421 0.067579  1 
2017-03-02 13.67 13.583292 0.086708  1 
2017-03-03 13.60 13.583458 0.016542  1 
2017-03-06 13.40 13.581633 -0.181633 -1 
2017-03-07 13.48 13.580621 -0.100621 -1 
2017-03-08 13.25 13.577332 -0.327332 -1 
2017-03-09 12.95 13.571090 -0.621090 -1 
2017-03-10 13.40 13.569387 -0.169387 -1 
2017-03-13 13.35 13.567204 -0.217204 -1 
2017-03-14 13.19 13.563451 -0.373451 -1 
2017-03-15 13.85 13.566302 0.283698  1 
2017-03-16 13.91 13.569722 0.340278  1 
2017-03-17 13.40 13.568033 -0.168033 -1 
2017-03-21 13.19 13.565079 -0.375079 -1 
2017-03-22 12.95 13.558959 -0.608959 -1 
2017-03-23 12.82 13.551606 -0.731606 -1 
2017-03-24 12.93 13.545421 -0.615421 -1 
2017-03-27 12.90 13.538999 -0.638999 -1 
2017-03-28 13.20 13.535626 -0.335626 -1 
2017-03-29 13.22 13.532485 -0.312485 -1 
2017-03-30 13.20 13.529177 -0.329177 -1 
2017-03-31 13.28 13.526698 -0.246698 -1 
2017-04-03 13.27 13.524143 -0.254143 -1 
2017-04-04 13.20 13.520918 -0.320918 -1 
2017-04-05 13.30 13.518720 -0.218720 -1 
2017-04-06 13.73 13.520822 0.209178  1 
2017-04-07 13.61 13.521710 0.088290  1 
2017-04-10 13.78 13.524280 0.255720  1 
2017-04-11 13.66 13.525630 0.134370  1 

我想不同的群体当趋势列等于1，和不同群体的趋势等于-1。就像这样：

  close  avg  diff trend 
date           
------- GROUP 1 for trend == 1: -------- 
2017-02-22 13.78 13.578652 0.201348  1 
2017-02-23 13.80 13.580854 0.219146  1 
2017-02-24 13.67 13.581741 0.088259  1 
2017-03-01 13.65 13.582421 0.067579  1 
2017-03-02 13.67 13.583292 0.086708  1 
2017-03-03 13.60 13.583458 0.016542  1 
------- GROUP 2 for trend == 1: -------- 
2017-03-15 13.85 13.566302 0.283698  1 
2017-03-16 13.91 13.569722 0.340278  1 
------- GROUP 3 for trend == 1: -------- 
2017-04-06 13.73 13.520822 0.209178  1 
2017-04-07 13.61 13.521710 0.088290  1 
2017-04-10 13.78 13.524280 0.255720  1 
2017-04-11 13.66 13.525630 0.134370  1 

与同为潮流== -1：

  close  avg  diff trend 
date           
------- GROUP 1 for trend == -1: -------- 
2017-03-06 13.40 13.581633 -0.181633 -1 
2017-03-07 13.48 13.580621 -0.100621 -1 
2017-03-08 13.25 13.577332 -0.327332 -1 
2017-03-09 12.95 13.571090 -0.621090 -1 
2017-03-10 13.40 13.569387 -0.169387 -1 
2017-03-13 13.35 13.567204 -0.217204 -1 
2017-03-14 13.19 13.563451 -0.373451 -1 
------- GROUP 2 for trend == -1: -------- 
2017-03-17 13.40 13.568033 -0.168033 -1 
2017-03-21 13.19 13.565079 -0.375079 -1 
2017-03-22 12.95 13.558959 -0.608959 -1 
2017-03-23 12.82 13.551606 -0.731606 -1 
2017-03-24 12.93 13.545421 -0.615421 -1 
2017-03-27 12.90 13.538999 -0.638999 -1 
2017-03-28 13.20 13.535626 -0.335626 -1 
2017-03-29 13.22 13.532485 -0.312485 -1 
2017-03-30 13.20 13.529177 -0.329177 -1 
2017-03-31 13.28 13.526698 -0.246698 -1 
2017-04-03 13.27 13.524143 -0.254143 -1 
2017-04-04 13.20 13.520918 -0.320918 -1 
2017-04-05 13.30 13.518720 -0.218720 -1 

关于如何实现这一目标的任何提示将受到欢迎。如果有程序解决方案（不使用循环），我会很高兴。

Answer 1

使用shift()和cumsum()和事实的bool等于1，你可以建立一组检查的连续性：

代码：

df.groupby((df.trend != df.trend.shift()).cumsum()) 

测试代码：

df = pd.read_fwf(StringIO(u""" 
    date   close  avg  diff trend 
    2017-03-01 13.65 13.582421 0.067579  1 
    2017-03-02 13.67 13.583292 0.086708  1 
    2017-03-03 13.60 13.583458 0.016542  1 
    2017-03-06 13.40 13.581633 -0.181633 -1 
    2017-03-07 13.48 13.580621 -0.100621 -1 
    2017-03-08 13.25 13.577332 -0.327332 -1 
    2017-03-09 12.95 13.571090 -0.621090  1 
    2017-03-10 13.40 13.569387 -0.169387  1 
    2017-03-13 13.35 13.567204 -0.217204 -1 
    2017-03-14 13.19 13.563451 -0.373451 -1 
    2017-03-15 13.85 13.566302 0.283698  1 
    2017-03-16 13.91 13.569722 0.340278  1 
    2017-03-17 13.40 13.568033 -0.168033  1"""), 
       header=1).set_index(['date']) 

for group in df.groupby((df.trend != df.trend.shift()).cumsum()): 
    print(group) 

结果：

(1,    close  avg  diff trend 
date           
2017-03-01 13.65 13.582421 0.067579  1 
2017-03-02 13.67 13.583292 0.086708  1 
2017-03-03 13.60 13.583458 0.016542  1) 
(2,    close  avg  diff trend 
date           
2017-03-06 13.40 13.581633 -0.181633  -1 
2017-03-07 13.48 13.580621 -0.100621  -1 
2017-03-08 13.25 13.577332 -0.327332  -1) 
(3,    close  avg  diff trend 
date           
2017-03-09 12.95 13.571090 -0.621090  1 
2017-03-10 13.40 13.569387 -0.169387  1) 
(4,    close  avg  diff trend 
date           
2017-03-13 13.35 13.567204 -0.217204  -1 
2017-03-14 13.19 13.563451 -0.373451  -1) 
(5,    close  avg  diff trend 
date           
2017-03-15 13.85 13.566302 0.283698  1 
2017-03-16 13.91 13.569722 0.340278  1 
2017-03-17 13.40 13.568033 -0.168033  1)