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for _ in range(num_payloads_per_forecast):
for lat in lats:
values = binary_reader.get(value_data_type, num_lon)
dict_data[lat] = values
df = pd.DataFrame.from_items(dict_data.items(), orient="index", columns=lons)
df = df.replace(NA_VALUES, float('NaN'))
df = df.sort_index(ascending=False)
df.index.name = 'lat'
df.columns.name = 'lon'
if parameter_grid is not None and parameter_grid.endswith(":sql"):
df = df.apply(parse_date_num, axis='index')
else:
df = df.round(rounding.get_num_decimal_places(parameter_grid))
if num_forecasts == 1:
return df
else:
dates_dict[dt.datetime.utcfromtimestamp(forecast_date_ux)] = df.copy()
return dates_dict