I am running a basic script that loops over a nested dictionary, grabs data from each record, and appends it to a Pandas DataFrame. The data looks something like this:

```py
data = {"SomeCity": {"Date1": {record1, record2, record3, ...}, "Date2": {}, ...}, ...}

```

In total it has a few million records. The script itself looks like this:

```py
city = ["SomeCity"]
df = DataFrame({}, columns=['Date', 'HouseID', 'Price'])
for city in cities:
    for dateRun in data[city]:
        for record in data[city][dateRun]:
            recSeries = Series([record['Timestamp'], 
                                record['Id'], 
                                record['Price']],
                                index = ['Date', 'HouseID', 'Price'])
            FredDF = FredDF.append(recSeries, ignore_index=True)

```

This runs painfully slow, however. Before I look for a way to parallelize it, I just want to make sure I'm not missing something obvious that would make this perform faster as it is, as I'm still quite new to Pandas.