在 Pandas dataframe 中，如何根据满足不同条件的起始行和结束行过滤一组行？

Question

In a Pandas dataframe, how to filter a set of rows based on a start row and end row both satisfying different conditions?

if one of my string columns contain a particular substring, that row is a start row. Then, if there is another row where my string column contains another substring, that row is an end row. I need a way to just filter all rows between these two.

I tried to find the start_row using,

start_row = df_page['StringCol'].str.contains('SubStrForStartRow')

This gives me a boolean series that has 'True' for my start row. But, not sure how to further achieve what I described above.

For example, Consider a dataframe as follows

data = [['UnwantedRow', ''],['TransactionStart', ''],['Date1', 200],['Date2', 300],['TransactionEnd', ''],['UnwantedRow','']]
df = pandas.DataFrame(data, columns=['Transaction', 'Value'])

Using 'Start' and 'Stop' substrings, I want to be able to filter out all rows between the 'TransactionStart' row and the 'TransactionEnd' row. That is, the two rows which contain ['Date1', 200] and ['Date2', 300] alone.

Answer 1

Return the index number of the start and end rows with .index[0] and filter for those rows with iloc . The upperbound of iloc is exclusive, which is why I use end_row+1 :

data = [['UnwantedRow', ''],['TransactionStart', ''],['Date1', 200],['Date2', 300],['TransactionEnd', ''],['UnwantedRow','']]
df = pd.DataFrame(data, columns=['Transaction', 'Value'])
start_row = df[df['Transaction'].str.contains('TransactionStart')].index[0]
end_row =  df[df['Transaction'].str.contains('TransactionEnd')].index[0]
df = df.iloc[start_row:end_row+1]
df
Out[1]: 
        Transaction Value
1  TransactionStart      
2             Date1   200
3             Date2   300
4    TransactionEnd