>>>tank_numbers = [1, 3]
>>>primary_key = 'Time'
>>>data_column = 'Volume'
>>>
>>>parse_csv('csv_file.csv', tank_numbers, primary_key, data_column)

[
  {'Time': '2017-10-19T18:52:41.118408', 'UNLEADED': '4406', 'SUPER': '8317'}
  {'Time': '2017-10-19T18:53:13.894066', 'UNLEADED': '4406', 'SUPER': '8317'}
]

# Remove unneeded tanks from data
d2 = [row for row in d1 if int(row['Tank']) in tanks]

# Remove unneeded keys from rows
keys = [primary_key, data_key, 'Product']
d3 = [{key:row[key] for key in keys} for row in d2]

# Create new row template
tank_names = _get_tank_names(tanks=tanks, data=d1)
row_template = {key:None for key in (tank_names + [primary_key])}

# New rows from row template
d4 = []
for row in d3:
    # update new row with available keys
    new_row = {key:row.get(key) for key in row_template}

    # update new row with matching values
    for key in new_row:
        if key in row.values():
            new_row[key] = row[data_key]

    # remove keys with None value
    new_row = {k:v for k,v in new_row.items() if v is not None}
    d4.append(new_row)

# Merge all rows based on Time key
merged = {}
for row in d4:
    if row[primary_key] in merged:
        merged[row[primary_key]].update(row)
    else:
        merged[row[primary_key]] = row

return [value for value in merged.values()]