Pandas dataframe split into sessions

Question

This is an extension for my question .

Make it easier. Suppose I have a pandas framework as shown below.

df = pd.DataFrame([[1.1, 1.1, 2.5, 2.6, 2.5, 3.4,2.6,2.6,3.4], list('AAABBBBAB'), [1.1, 1.7, 2.5, 2.6, 3.3, 3.8,4.0,4.2,4.3]]).T df.columns = ['col1', 'col2','col3'] 

dataframe:

  col1 col2 col3 0 1.1 A 1.1 1 1.1 A 1.7 2 2.5 A 2.5 3 2.6 B 2.6 4 2.5 B 3.3 5 3.4 B 3.8 6 2.6 B 4 7 2.6 A 4.2 8 3.4 B 4.3 

I want to group this based on some conditions. The logic is based on the values ​​of col1 col2 and the cumulative difference col3:

• Go to col1 and find other occurrences of the same value.
• In my case, the first value of col1 is "1.1", and again their single value in line2.
• Then check the col2 value if they are similar, then get the cumulative difference in col 3.
• If the cumulative difference is greater than 0.5, then mark this as a new session.
• If the col1 values ​​are the same, but the col2 values ​​are different, then mark them as a new session

expected output:

  col1 col2 col3 session 0 1.1 A 1.1 0 1 1.1 A 1.7 1 2 2.5 A 2.5 2 3 2.6 B 2.6 4 4 2.5 B 3.3 3 5 3.4 B 3.8 7 6 2.6 B 4 5 7 2.6 A 4.2 6 8 3.4 B 4.3 7 

Answer 1

As in the excellent answer you linked to;) first create the session number:

 In [11]: g = df.groupby(['col1', 'col2']) In [12]: df['session_number'] = g['col3'].apply(lambda s: (s - s.shift(1) > 0.5).fillna(0).cumsum(skipna=False)) 

Then I think that you want to set the value_value of these columns, this may be enough for many use cases (although it might be worth doing sort ):

 In [13]: df1 = df.set_index(['col1', 'col2', 'session_number']) In [14]: df1 Out[14]: col3 col1 col2 session_number 1.1 A 0 1.1 1 1.7 2.5 A 0 2.5 2.6 B 0 2.6 2.5 B 0 3.3 3.4 B 0 3.8 2.6 B 1 4 A 0 4.2 3.4 B 0 4.3 

If you really want, you can take off the session number:

 In [15]: g1 = df.groupby(['col1', 'col2', 'session_number']) # I think there is a slightly neater way, but I forget.. In [16]: df1['session'] = g1.apply(lambda x: 1).cumsum() # could -1 here if it matters In [17]: df1 Out[17]: col3 session col1 col2 session_number 1.1 A 0 1.1 1 1 1.7 2 2.5 A 0 2.5 3 2.6 B 0 2.6 6 2.5 B 0 3.3 4 3.4 B 0 3.8 8 2.6 B 1 4 7 A 0 4.2 5 3.4 B 0 4.3 8 

If you want this in columns (as in your question) reset_index , and you could delete the session column:

 In [18]: df1.reset_index() Out[18]: col1 col2 session_number col3 session 0 1.1 A 0 1.1 1 1 1.1 A 1 1.7 2 2 2.5 A 0 2.5 3 3 2.6 B 0 2.6 6 4 2.5 B 0 3.3 4 5 3.4 B 0 3.8 8 6 2.6 B 1 4 7 7 2.6 A 0 4.2 5 8 3.4 B 0 4.3 8