from typing import Optional
import pandas as pd
from dataset_hub._core.data_bundle import DataBundle
from dataset_hub._core.get_data import get_data as _get_data
task_type = "timeseries"
[docs]def get_household_power(verbose: Optional[bool] = None) -> pd.DataFrame:
"""
Load and return the Individual Household Electric Power Consumption dataset.
Measurements of **electric power consumption in a single household** with a one-minute \
sampling rate over a period of almost 4 years (December 2006 – November 2010). \
Each record contains several electrical and sub-metering measurements.
This dataset is designed for minute-level analysis of total household energy \
consumption, capturing overall usage patterns of a single home.
Original dataset: This dataset is available on the UCI Machine Learning \
Repository:
`Individual Household Electric Power Consumption <https://archive.ics.uci.edu/dataset/235/individual+household+electric+power+consumption>`_
Columns:
- ``Date`` (str): Date of measurement in format dd/mm/yyyy
- ``Time`` (str): Time of measurement in format hh:mm:ss
- ``Global_active_power`` (float): Household global minute-averaged active \
power (kilowatt)
- ``Global_reactive_power`` (float): Household global minute-averaged reactive \
power (kilowatt)
- ``Voltage`` (float): Minute-averaged voltage (volt)
- ``Sub_metering_1`` (float): Energy sub-metering No. 1 \
(kitchen: dishwasher, oven, microwave; watt-hour of active energy)
- ``Sub_metering_2`` (float): Energy sub-metering No. 2 \
(laundry room: washing machine, tumble-drier, refrigerator, light; watt-hour)
- ``Sub_metering_3`` (float): Energy sub-metering No. 3 \
(electric water-heater, air-conditioner; watt-hour)
- ``Global_intensity`` 🚩 (float): Household global minute-averaged current \
intensity (ampere)
Notes:
- Missing values are present in approximately 1.25% of the rows.
- Active energy not covered by sub-meterings 1–3 can be calculated as: \
``(Global_active_power*1000/60 - Sub_metering_1 - Sub_metering_2 - Sub_metering_3)`` \
in watt-hour.
Args:
verbose (bool, optional):
If True, the function prints a link to the dataset documentation \
in the log output after loading. (e.g., on this page)
Default is None, which uses the global :ref:`settings`.
Returns:
pandas.DataFrame: The household power consumption dataset with all features.
Quick Start:
.. code-block:: python
from dataset_hub.timeseries import get_household_power
df = get_household_power()
""" # noqa
dataset: DataBundle[pd.DataFrame] = _get_data(
dataset_name="household_power", task_type=task_type, verbose=verbose
)
return dataset["data"]