Pandas - Slice between two indexes - pandas

Question

I need to process data with tensorflow for classification. Therefore I need to create DataFrames for each unit which was processed in my machine. The machine continously writes process data and also writes when a unit enters and leaves the machine.
A value in 'uid_in' means the unit with the logged number entered the machine, 'uid_out' means the unit left the machine.
I need to create a DataFrame like this for each unit processes by the machine.
[...]
time uhz1 uhz2 lhz1 lh2 uid_in uid_out
5 08:05:00 201 200 101 100 1.0 NaN #Unit1 enters the machine
6 08:06:00 201 200 99 101 2.0 NaN
[...]
14 08:14:00 199 199 99 101 10.0 NaN
15 08:15:00 201 201 100 100 11.0 1.0 #Unit1 leaves the machine
[...]
How can I create the Dataframe df.loc[enter:leave] for each unit automatically?
When I try to pass a DataFrame.index it does not work in df.loc
start = df[df.uid_in.isin([123])]
end = df[df.uid_out.isin([123])]
unit1_df = df.loc[start:end]

Answer 1

Your code almost worked out!
Original DataFrame:
time uhz1 uhz2 lhz1 lh2 uid_in uid_out
0 08:00:00 201 199 100 100 NaN NaN
1 08:01:00 199 199 100 99 NaN NaN
[...]
5 08:05:00 201 200 101 100 1.0 NaN
[...]
55 08:55:00 241 241 140 140 NaN 41.0
[...]
58 08:58:00 244 244 143 143 NaN NaN
59 08:59:00 245 245 144 144 NaN NaN
New code:
start = df[df.uid_in.eq(1.0)].index[0]
end = df[df.uid_out.eq(1.0)].index[0]
unit1_df = df.loc[start:end]
print(unit1_df)
Output
time uhz1 uhz2 lhz1 lh2 uid_in uid_out
5 08:05:00 201 200 101 100 1.0 NaN
6 08:06:00 201 200 99 101 2.0 NaN
[...]
14 08:14:00 199 199 99 101 10.0 NaN
15 08:15:00 201 201 100 100 11.0 1.0

Answer 2

I think you were pretty close. I modified your statements and picked out the start and end indices of start and end, as Ian indicated.
""" time uhz1 uhz2 lhz1 lh2 uid_in uid_out
5 08:05:00 201 200 101 100 1.0 NaN
6 08:06:00 201 200 99 101 2.0 NaN
14 08:14:00 199 199 99 101 10.0 NaN
15 08:15:00 201 201 100 100 11.0 1.0
"""
import pandas as pd
df = pd.read_clipboard()
start = df.uid_in.eq(1.0).index[0]
end = df.uid_out.eq(1.0).index[0]
unit1_df = df.loc[start:end]
unit1_df
Output:
time uhz1 uhz2 lhz1 lh2 uid_in uid_out
5 08:05:00 201 200 101 100 1.0 NaN
6 08:06:00 201 200 99 101 2.0 NaN
14 08:14:00 199 199 99 101 10.0 NaN
15 08:15:00 201 201 100 100 11.0 1.0
One-liner:
unit1_df = df.loc[df.uid_in.eq(1.0).index[0]:df.uid_out.eq(1.0).index[0]]