如何从pandas数据帧中的当前行中减去前一行并将其应用于每一行;没有使用循环？ [英] How do I subtract the previous row from the current row in a pandas dataframe and apply it to every row; without using a loop?

问题描述

我正在使用Python3.5，我正在使用pandas。我已经从yahoo finance加载了库存数据并将文件保存到csv。我的DataFrames从csv加载这些数据。这是我的DataFrame的十行csv文件的副本

I am using Python3.5 and I am working with pandas. I have loaded stock data from yahoo finance and have saved the files to csv. My DataFrames load this data from the csv. This is a copy of the ten rows of the csv file that is my DataFrame

  Date       Open       High      Low     Close    Volume   Adj Close  
1990-04-12  26.875000  26.875000  26.625  26.625      6100  250.576036
1990-04-16  26.500000  26.750000  26.375  26.750       500  251.752449
1990-04-17  26.750000  26.875000  26.750  26.875      2300  252.928863
1990-04-18  26.875000  26.875000  26.500  26.625      3500  250.576036
1990-04-19  26.500000  26.750000  26.500  26.750       700  251.752449
1990-04-20  26.750000  26.875000  26.750  26.875      2100  252.928863
1990-04-23  26.875000  26.875000  26.750  26.875       700  252.928863
1990-04-24  27.000000  27.000000  26.000  26.000      2400  244.693970
1990-04-25  25.250000  25.250000  24.875  25.125      9300  236.459076
1990-04-26  25.000000  25.250000  24.750  25.000      1200  235.282663

我知道我可以使用iloc，loc，ix但是我索引的这些值只会给出我的特定行和列，并且不会对每一行执行操作。
例如：open列中的第一行数据值为26.875，下面的行为26.50。价格下跌.375美分。我希望能够捕获前一天增加或减少的百分比，以完成此示例.375除以26.875 =从一天到下一天减少1.4％。我希望能够在每一行上运行此计算，因此我知道它从前一天增加或减少了多少。我尝试过的索引函数但它们是绝对的，我不想使用循环。有没有办法用ix，iloc，loc或其他函数做到这一点？

I know that I can use iloc, loc, ix but these values that I index will only give my specific rows and columns and will not perform the operation on every row. For example: Row one of the data in the open column has a value of 26.875 and the row below it has 26.50. The price dropped .375 cents. I want to be able to capture the % of Increase or Decrease from the previous day so to finish this example .375 divided by 26.875 = 1.4% decrease from one day to the next. I want to be able to run this calculation on every row so I know how much it has increased or decreased from the previous day. The index functions I have tried but they are absolute, and I don't want to use a loop. Is there a way I can do this with the ix, iloc, loc or another function?

推荐答案

你可以用 pct_change（）或/和 diff（）方法

演示：

In [138]: df.Close.pct_change() * 100
Out[138]:
0         NaN
1    0.469484
2    0.467290
3   -0.930233
4    0.469484
5    0.467290
6    0.000000
7   -3.255814
8   -3.365385
9   -0.497512
Name: Close, dtype: float64

In [139]: df.Close.diff()
Out[139]:
0      NaN
1    0.125
2    0.125
3   -0.250
4    0.125
5    0.125
6    0.000
7   -0.875
8   -0.875
9   -0.125
Name: Close, dtype: float64

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