Define and count unique patterns in a pandas frame

You will find snippets with reproducible input and an example of the desired output at the end of the question.

A task:

I have a dataframe like this:

There are two columns in the data frame with patterns 1 and 0 like this:

Or that:

The number of columns will change as well as the length of the templates. However, the only numbers in the data frame will be 0 or 1.

I would like to identify these patterns, count each occurrence, and build a data file containing the results. To simplify it all, I would like to focus onand ignore zeros . The desired result in this particular case would be:

I would like the procedure to identify that, as an example, the pattern [1,1,1] occurs twice in column_A and not at all in column_B. Note that I have used template sums as indices in the dataframe.

Reproducible input:

import pandas as pd
df = pd.DataFrame({'column_A':[1,1,1,0,0,0,1,0,0,1,1,1],
                   'column_B':[1,1,1,1,1,0,0,0,1,1,0,0]})

colnames = list(df)
df[colnames] = df[colnames].apply(pd.to_numeric)
datelist = pd.date_range(pd.datetime.today().strftime('%Y-%m-%d'), periods=len(df)).tolist()
df['dates'] = datelist
df = df.set_index(['dates'])
print(df)

      

        
        
        
      

    

Desired output:

df2 = pd.DataFrame({'pattern':[5,3,2,1],
               'column_A':[0,2,0,1],
               'column_B':[1,0,1,0]})
df2 = df2.set_index(['pattern'])
print(df2)

      

        
        
        
      

    

My attempts:

I was working on a solution that includes nested for loops, where I calculate the running sums, which are reset every time the observation is zero. It also includes features like df.apply(lambda x: x.value_counts())

. But this is useless, to say the least, and is not yet 100% correct.

Thanks for any other suggestions!