Merge two rows in the same Dataframe if their index is the same?

For anyone interested – I ended up writing my own function to:

1. go through dataframe
2. taking note of rows that need merging by taking note of the indexes
3. aggregate or average the values across all rows
4. delete all but one row of each set that needed merging replacing its values with the aggregations or averages (depending on what I needed)

code:

def groupDataOnTimeBlock(data, timeBlock_type, timeBlock_factor):
    '''
    Filter Dataframe to merge lines which are within the same time block.
    i.e. being part of the same x number of seconds, weeks, months... 

    data:
        Dataframe to filter.

    timeBlock_type:
        Time period with which to group data rows. This can be data per:
            SECONDS, DAYS, MILLISECONDS

    timeBlock_factor:
        Number of timeBlock types to group on.
    ''' 

    pd.options.mode.chained_assignment = None  # default='warn'

    tBt = timeBlock_type.upper()
    tBf = timeBlock_factor

    if tBt == 'SEC' or tBt == 'SECOND' or tBt == 'SECONDS':
        roundType = 'SECONDS'
    elif tBt == 'MINS' or tBt == 'MINUTES' or tBt == 'MIN':
        roundType = 'MINUTES'
    elif tBt == 'MILLI' or tBt == 'MILLISECONDS':
        roundType = 'MILLISECONDS'
    elif tBt == 'WEEK' or tBt == 'WEEKS':
        roundType = 'WEEKS'
    else:
        raise ValueError ('Invalid time block type entered')

    numElements = len(data.columns)
    anchorValue = timeStampReformat(data.iloc[1,len(data.columns)-7], roundType, tBf)
    delIndex = []
    mergeCount = 0
    av_agg_arr = np.zeros([1,numElements], dtype=float)

    #Cycling through dataframe to get averages and note which rows to delete
    for i, row in data.iterrows(): #i is the index value, from 0
        backDate = timeStampReformat(row['Timestamp'], roundType, tBf)
        data.loc[i,'Timestamp'] = backDate #can be done better. Not all rows need updating.

        if (backDate > anchorValue): #if data should be grouped
            delIndex.pop() #remove last index as this is the final row to use
            delIndex.append(i) #add current row so that it isnt missed.
            print('collate')
            if mergeCount != 0:
                av_agg_arr = av_agg_arr/mergeCount
                for idx in range(1,numElements-1):
                    if isinstance(row.values[idx],float):
                        data.iloc[i-1, idx] = av_agg_arr[0, idx] #configure previous (index i -1) row. This is the last of the prior datetime group

            anchorValue = backDate
            mergeCount = 0

            # Re-initialising aggregates and passing in current row values.
            av_agg_arr = av_agg_arr - av_agg_arr 
            for idx in range(1,numElements-1):
                if isinstance(row.values[idx],float):
                    if not pd.isnull(row.values[idx]):
                        av_agg_arr[0,idx] += row.values[idx]
        else: #else if data is still part of same datetime group
            for idx in range(1,numElements-1):
                if isinstance(row.values[idx],float):
                    if not pd.isnull(row.values[idx]):
                        av_agg_arr[0,idx] += row.values[idx]
            mergeCount += 1
            delIndex.append(i) #picking out index value of row

    data.drop(data.index[delIndex], inplace=True) #delete all flagged rows
    data.reset_index()

    pd.options.mode.chained_assignment = 'warn'  # default='warn'
    return data