using two separate sets of time series data to correlate against other datasets

I have two sets of time series data that I want to correlate against my heart rate.

Currently, the two sets look a bit like this,

I've removed user_id as a column for obvious reasons :P

Scheduled,

Actual,

I want to take my heart rate data

These are the functions I used for test data,

def get_stats(group):
    diff = group.astype(int).abs().diff()

    return {'min': group.min(), 'max': group.max(),\
            'mean': group.mean(),'median': group.median(),\
           'var': group.var(),'std': group.std()}


def timeFilter(df):
    df_Meds = df.reset_index()

    start_date = '03-01-2020'
    end_date = '12-30-2021'
    mask = (df_Meds['created'] > start_date) & (df_Meds['created'] <= end_date)

    df = df_Meds.loc[mask]
    return df

Passing in just one substance with one set of times,like this

scheduled_substance_user=scheduled_substance_df[scheduled_substance_df.user_id =="xxx"]
dexamphet_scheduled = scheduled_substance_user[scheduled_substance_user.substance =="Dexamphetamine"]
# dexamphet_scheduled.iloc[0]

dexamphet_scheduled.scheduledtimes.iloc[0]

returning this,

[8.0, 14.0, 18.0]

I get something like this,

test_df = SubstanceTimingHeartRate(Biometrics,dexamphet_scheduled.scheduledtimes.iloc[0])

test_df[test_df.medication >0]

but now I want to label is

1. What the substance is,
2. Use that substances duration
3. If it was an actual, or scheduled timestamp.

Appreciate any help with this - python is super complex to me.

Very confused about how best to approach this with these and appreciate any help.

Update to code so far,

scheduled_substance_user=scheduled_substance_df[scheduled_substance_df.user_id =="xxxx"]

n=0
sentiment_scheduled=pd.DataFrame()
for x in scheduled_substance_user.substance.unique():
    drug_scheduled = scheduled_substance_user[scheduled_substance_user.substance ==x]
    # print("get here?")
    print("\tdrug",x,drug_scheduled.substance.unique())
    if n<1:

        n=n+1

        # print("get here?")

        sentiment_scheduled=SubstanceTimingSentiment(Emotions,drug_scheduled.scheduledtimes.iloc[0],drug_scheduled.duration.iloc[0],x,drug_scheduled.index[0],drug_scheduled.enddate.iloc[0])
        print("\t Unique substances",sentiment_scheduled.substance.unique())
       

    else:
    # print("n>=1?")
        # drug_scheduled = scheduled_substance_user[scheduled_substance_user.substance ==x]


        sentiment_scheduled.append(SubstanceTimingSentiment(Emotions,drug_scheduled.scheduledtimes.iloc[0],drug_scheduled.duration.iloc[0],x,drug_scheduled.index[0],drug_scheduled.enddate.iloc[0]), ignore_index=True)
        print("\t Unique substances in dataframe",sentiment_scheduled.substance.unique())

and the function,

def SubstanceTimingSentiment(emo_d,substance_timing,duration,substance_name,startdate,enddate):
    print("call this function?")
    start_date=pd.to_datetime(startdate)
    end_date=pd.to_datetime(enddate)
    # convert times to datetime
    med_times = pd.to_datetime(substance_timing)
    # df.created = pd.to_datetime(df.created)
    df = emo_d.reset_index()

    # drop rows that are empty except for column 0 (i.e., except for df.created)
    # df.dropna(subset=df.columns[1:], inplace=True)

    # convert times to datetime i.e., ['09:00:00', '12:00:00', '15:00:00']
    df.created = pd.to_datetime(df.created)
    df = df[(df.created > start_date) & (df.created < end_date)]
    print("\tstart and end times:",startdate,enddate)

    taken = pd.to_datetime(substance_timing)

    # generate time arrays
    duration = int(duration) # hours
    active = np.array([(taken + pd.Timedelta(f'{h}H')).time for h in range(duration)]).ravel()
    after = (taken + pd.Timedelta(f'{duration}H')).time

    # define boolean masks by label
    conditions = {
        1: df.created.dt.floor('H').dt.time.isin(active),
        2: df.created.dt.floor('H').dt.time.isin(after),
    }

    # create medication column with np.select()
    df['medication'] = np.select(conditions.values(), conditions.keys(), default=0)
    df['substance'] = substance_name
    df = df[['created', 
        'sentiment', 'magnitude','substance','medication']].set_index('created')
    return df

Currently when it is looping through, it isn't appending the new dataframe from the function, and just keeping the first substance in place :/ like this (log)

drug Dexamphetamine ['Dexamphetamine']
call this function?
    start and end times: 2021-10-29 2021-11-08
     Unique substances ['Dexamphetamine']
    drug Modafinil ['Modafinil']
call this function?
    start and end times: 1996-12-02 2021-11-08
     Unique substances in dataframe ['Dexamphetamine']
    drug Nicotine ['Nicotine']
call this function?
    start and end times: 2022-02-01 2022-02-27
     Unique substances in dataframe ['Dexamphetamine']
    drug L-Theanine ['L-Theanine']
call this function?
    start and end times: 1990-12-26 2022-01-19
     Unique substances in dataframe ['Dexamphetamine']
    drug Ritalin ['Ritalin']
call this function?
    start and end times: 1996-12-02 2022-01-31
     Unique substances in dataframe ['Dexamphetamine']
    drug Vysanth ['Vysanth']
call this function?
    start and end times: 1996-12-02 2021-11-08
     Unique substances in dataframe ['Dexamphetamine']

from using two separate sets of time series data to correlate against other datasets