Find a specific template in a pandas data frame

Question

I would like to find a specific template in the dataframe pandas column and return the corresponding index values ​​for a subset of the data.

Here's a sample data frame with a possible template:

A fragment for creating a data frame:

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)

Dataframe:

            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

Here an interesting picture takes place in Column Athe dates from 2017-07-10to 2017-07-12and what I would like to get:

Desired conclusion:

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

If the same template happens several times, I would like a subset of the DataFrame in the same way, and also count how many times the template happens, but I hope it is more straight forward if I get the first step, figured it out.

Thanks for any suggestions!

Answer 1

:

, , rolling. ,

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

:

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

:

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

Answer 2

:

[df.index[i - len(pattern)] # Get the datetime index 
 for i in range(len(pattern), len(df)) # For each 3 consequent elements 
 if all(df['ColA'][i-len(pattern):i] == pattern)] # If the pattern matched 

# [Timestamp('2017-07-10 00:00:00')]

Answer 3

- , . .

, :

indexes=df[(df.ColA==pattern[0])&(df["ColA"].shift(-1)==pattern[1])&(df["ColA"].shift(-2)==pattern[2])].index

, , .

Answer 4

for col in df:
    index = df[col][(df[col] == pattern[0]) & (df[col].shift(-1) == pattern[1]) & (df[col].shift(-2) == pattern[2])].index
    if not index.empty: print(index)