pandas :如何在数据框列中查找特定模式? [英] Pandas: How to find a particular pattern in a dataframe column?

问题描述

我想在pandas数据框列中找到特定的模式，并返回相应的索引值以对数据框进行子集化.

I'd like to find a particular pattern in a pandas dataframe column, and return the corresponding index values in order to subset the dataframe.

这是一个带有可能模式的示例数据框:

Here's a sample dataframe with a possible pattern:

片段以生成数据框:

import pandas as pd
import numpy as np

Observations = 10
Columns = 2
np.random.seed(123)
df = pd.DataFrame(np.random.randint(90,110,size=(Observations, Columns)),
                  columns = ['ColA','ColB'])
datelist = pd.date_range(pd.datetime(2017, 7, 7).strftime('%Y-%m-%d'),
                         periods=Observations).tolist()
df['Dates'] = datelist
df = df.set_index(['Dates'])

pattern = [100,90,105]
print(df)

数据框:

            ColA  ColB
Dates                 
2017-07-07   103    92
2017-07-08    92    96
2017-07-09   107   109
2017-07-10   100    91
2017-07-11    90   107
2017-07-12   105    99
2017-07-13    90   104
2017-07-14    90   105
2017-07-15   109   104
2017-07-16    94    90

在这里，感兴趣的模式出现在日期2017-07-10至2017-07-12的Column A中，这就是我想得出的结论:

Here, the pattern of interest occurs in Column A on the dates 2017-07-10 to 2017-07-12, and that's what I'd like to end up with:

所需的输出:

2017-07-10   100    91
2017-07-11    90   107
2017-07-12   105    99

如果同一模式发生多次，我想以相同的方式对数据帧进行子集化，并计算该模式出现的次数，但是只要我整理出第一步，我希望这会更直接.

If the same pattern occurs several times, I would like to subset the dataframe the same way, and also count how many times the pattern occurs, but I hope that's more straight forward as long as I get the first step sorted out.

谢谢您的任何建议！

推荐答案

以下是解决方案:

使用

Check if the pattern was found in any of the columns using rolling. This will give you the last index of the group matching the pattern

matched = df.rolling(len(pattern)).apply(lambda x: all(np.equal(x, pattern)))
matched = matched.sum(axis = 1).astype(bool)   #Sum to perform boolean OR

matched
Out[129]: 
Dates
2017-07-07    False
2017-07-08    False
2017-07-09    False
2017-07-10    False
2017-07-11    False
2017-07-12     True
2017-07-13    False
2017-07-14    False
2017-07-15    False
2017-07-16    False
dtype: bool

对于每个匹配项，添加完整模式的索引:

For each match, add the indexes of the complete pattern:

idx_matched = np.where(matched)[0]
subset = [range(match-len(pattern)+1, match+1) for match in idx_matched]

获取所有模式:

result = pd.concat([df.iloc[subs,:] for subs in subset], axis = 0)

result
Out[128]: 
            ColA  ColB
Dates                 
2017-07-10   100    91
2017-07-11    90   107
2017-07-12   105    99

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