remove redundant signals in pandas - pandas

Question

I want to build correspondance between col1 and col2 with certain rule.
Label1 is like an on switch, and label2 is like an off switch. Once label1 is on, further operation on label1 will not re-open the switch until it is switched off by label2. Then label1 can switch on again.
For example, I have a following table:
index label1 label2 note
1 F T label2 is invalid because not switch on yet
2 T F label1 switch on
3 F F
4 T F useless action because it's on already
5 F T switch off
6 F F
7 T F switch on
8 F F
9 F T switch off
10 F F
11 F T invalid off operation, not on
The correct output is something like:
label1ix label2ix
2 5
7 9
What I tries is :
df['label2ix'] = df.loc[df.label2==T, index] # find the label2==True index
df['label2ix'].bfill(inplace=True) # backfill the column
mask = (df['label1'] == T) # label1==True, then get the index and label2ix
newdf = pd.Dataframe(df.loc[mask, ['index', 'label2ix']])
This is not correct because I have got is:
label1ix label2ix note
2 5 correct
4 5 wrong operation
7 9 correct
I am not sure how to filter out row 4.
I have got another idea,
df['label2ix'] = df.loc[df.label2==T, index] # find the label2==True index
df['label2ix'].bfill(inplace=True) # backfill the column
groups = df.groupby('label2ix')
firstlabel1 = groups['label1'].first()
But for this solution, I don't know how to get the first label1=T in each group.
And I am not sure if there is any more efficient way to do that? Grouping is usually slow.

Answer 1

Not tested yet, but here are few things you can try:
Option 1: For the first approach, you can filter out the 4 by:
newdf.groupby('label2ix').min()
but this approach might not work with more general data.
Option 2: This might work better in general:
# copy all on and off switches to a common column
# 0 - off, 1 - on
df['state'] = np.select([df.label1=='T', df.label2=='T'], [1,0], default=np.nan)
# ffill will fill the na with the state before it
# until changed by a new switch
df['state'] = df['state'].ffill().fillna(0)
# mark the changes of states
df['change'] = df['state'].diff()
At this point, df will be:
index label1 label2 state change
0 1 F T 0.0 NaN
1 2 T F 1.0 1.0
2 3 F F 1.0 0.0
3 4 T F 1.0 0.0
4 5 F T 0.0 -1.0
5 6 F F 0.0 0.0
6 7 T F 1.0 1.0
7 8 F F 1.0 0.0
8 9 F T 0.0 -1.0
9 10 F F 0.0 0.0
10 11 F T 0.0 0.0
which should be easy to track all the state changes:
switch_ons = df.loc[df['change'].eq(1), 'index']
switch_offs = df.loc[df['change'].eq(-1), 'index']
# return df
new_df = pd.DataFrame({'label1ix':switch_ons.values,
'label2ix':switch_offs.values})
and output:
label1ix label2ix
0 2 5
1 7 9