/
timeseries_result.py
48 lines (33 loc) · 1.54 KB
/
timeseries_result.py
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from typing import List
import pandas as pd
from vortexasdk.api.search_result import Result
from vortexasdk.api.timeseries_item import TimeSeriesItem
from vortexasdk.logger import get_logger
from vortexasdk.result_conversions import create_dataframe, create_list
logger = get_logger(__name__)
class TimeSeriesResult(Result):
"""Container class that holds the result obtained from calling a time series endpoint."""
def to_list(self) -> List[TimeSeriesItem]:
"""Represents time series as a list."""
# noinspection PyTypeChecker
return create_list(super().to_list(), TimeSeriesItem)
def to_df(self, columns=None) -> pd.DataFrame:
"""Represents the timeseries as a dataframe.
Returns a `pd.DataFrame`, of time series items with columns:
key: The time series key
value: The value of the time series for a given key
count: The number of records contributing to this time series record.
# Example:
If we're aggregating Crude exports in tonnes by day, then the `key` column holds the date,
the `value` column holds the Crude exports on that day, and the `count` column holds
the number of cargo movements contributing towards this day's tonnage.
"""
df = create_dataframe(
columns=columns,
default_columns=DEFAULT_COLUMNS,
data=super().to_list(),
logger_description="TimeSeries",
)
df["key"] = pd.to_datetime(df["key"])
return df
DEFAULT_COLUMNS = ["key", "value", "count"]