Function to calculate timespan for a certain event - python-3.x

Question

I have a pandas dataframe which looks like this
timestamp phase
2019-07-01 07:10:00 a
2019-07-01 07:11:00 a
2019-07-01 07:12:00 b
2019-07-01 07:13:00 b
2019-07-01 07:17:00 a
2019-07-01 07:19:00 a
2019-07-01 07:20:00 c
I am working on a function that creates a dataframe with an duration for every phase, till it hits the next phase.
I already have a solution but I have no clue how to write this in an user-defined-function, as I am new to python.
This is my "static" solution:
df['prev_phase'] = df["phase"].shift(1)
df['next_phase'] = df["phase"].shift(-1)
dfshift = df[df.next_phase != df.prev_phase]
dfshift["delta"] = (dfshift["timestamp"]-dfshift["timestamp"].shift()).fillna(0)
dfshift["helpcolumn"] = dfshift["phase"].shift(1)
dfshift2 = dfshift[dfshift.helpcolumn == dfshift["phase"]]
dfshift3 = dfshift2[["timestamp","phase","delta"]]
dfshift3["deltaminutes"] = dfshift3['delta'] / np.timedelta64(60, 's')
This gives me this as output (example):
timestamp phase delta deltam
2019-05-01 06:44:00 a 0 days 04:51:00 291.0
2019-05-01 07:25:00 b 0 days 00:40:00 40.0
2019-05-01 21:58:00 a 0 days 14:32:00 872.0
2019-05-01 22:07:00 c 0 days 00:08:00 8.0
I just need this in a function.
Thanks in advance
Edit for #Tom
timestamp phase
2019-05-05 08:58:00 a
2019-05-05 08:59:00 a
2019-05-05 09:00:00 b
2019-05-05 09:01:00 b
2019-05-05 09:02:00 b
2019-05-05 09:03:00 b
...
...
2019-05-05 09:38:00 b
2019-05-05 09:39:00 c
2019-05-05 09:40:00 c
2019-05-05 09:41:00 c
Those are the two colums + Index

Answer 1

df = pd.DataFrame({"timestamp": ["2019-07-01 07:10:00",
"2019-07-01 07:11:00",
"2019-07-01 07:12:00",
"2019-07-01 07:13:00",
"2019-07-01 07:17:00",
"2019-07-01 07:19:00",
"2019-07-01 07:20:00"],
"phase": ["a", "a", "b", "b", "a" ,"a", "c"]})
df["timestamp"] = pd.to_datetime(df["timestamp"])
# Create a 'phase_id' column to track when phase changes
df['phase_id'] = df['phase'].ne(df['phase'].shift()) + df.index
# Groupby new 'phase_id' variable and get time range for each phase
df_tdiff = df.groupby("phase_id").diff().reset_index()
df_tdiff.columns = ['phase_id', 'timediff']
# Merge this to old dataframe
df_new = pd.merge(df, df_tdiff, on=["phase_id"], how="left")
This then gives:
df_new
timestamp phase phase_id timediff
0 2019-07-01 07:10:00 a 1 00:01:00
1 2019-07-01 07:11:00 a 1 00:01:00
2 2019-07-01 07:12:00 b 3 00:01:00
3 2019-07-01 07:13:00 b 3 00:01:00
4 2019-07-01 07:17:00 a 5 00:02:00
5 2019-07-01 07:19:00 a 5 00:02:00
6 2019-07-01 07:20:00 c 7 NaT
Finally:
df_new = df_new.groupby("phase_id").first().reset_index(drop=True)
df_new
timestamp phase timediff
0 2019-07-01 07:10:00 a 00:01:00
1 2019-07-01 07:12:00 b 00:01:00
2 2019-07-01 07:17:00 a 00:02:00
3 2019-07-01 07:20:00 c NaT
Of course, if you need that all as a function (as originally requested), then:
def get_phase_timediff(df):
# Create a 'phase_id' column to track when phase changes
df['phase_id'] = df['phase'].ne(df['phase'].shift()) + df.index
# Groupby new 'phase_id' variable and get time range for each phase
df_tdiff = df.groupby("phase_id").diff().reset_index()
df_tdiff.columns = ['phase_id', 'timediff']
# Merge this to old dataframe
df_new = pd.merge(df, df_tdiff, on=["phase_id"], how="left")
# Groupby 'phase_id' again for final output
df_new = df_new.groupby("phase_id").first().reset_index(drop=True)
return(df_new)