items = [
    datetime.datetime(2018, 3, 19, 16, 51, 48),
    datetime.datetime(2018, 3, 19, 17, 25, 19),
    datetime.datetime(2018, 3, 20, 6, 33, 35),
    datetime.datetime(2018, 3, 19, 23, 21, 35),
    datetime.datetime(2018, 3, 19, 15, 8, 41),
    datetime.datetime(2018, 3, 19, 21, 44, 16),
    datetime.datetime(2018, 3, 19, 18, 21, 28),
    datetime.datetime(2018, 3, 20, 7, 20, 22),
    datetime.datetime(2018, 3, 20, 11, 15, 43)
]

import pandas as pd

def _generate_timeseries(items, start_ts, end_ts):
    # add start/end times to the data
    items.insert(0, start_ts)
    items.append(end_ts)

    # value each datetime as one occurrence
    data = [1 for x in range(len(items))]
    timeseries = pd.Series(data, index=items)
    hourly_data = timeseries.resample('H').sum()

    timeline = hourly_data.tolist()
    return [{'mentions': x} for x in timeline[1:-1]]

timeline =[
    {'mentions': 4}, {'mentions': 2}, {'mentions': 1}, {'mentions': 0}, {'mentions': 3}, {'mentions': 2}, {'mentions': 2}, {'mentions': 1}, {'mentions': 1}, {'mentions': 0}, {'mentions': 1}, {'mentions': 0}, {'mentions': 14}, {'mentions': 1}, {'mentions': 4}, {'mentions': 2}, {'mentions': 3}, {'mentions': 2}, {'mentions': 1}, {'mentions': 2}, {'mentions': 6}, {'mentions': 2}, {'mentions': 2}
]