power_cols = ["Datetime", "PowerConsumption_Zone1", "PowerConsumption_Zone2", "PowerConsumption_Zone3"]

powerconsumption = pd.read_csv(
    "powerconsumption.csv",
    usecols=power_cols,
    parse_dates=["Datetime"]
).assign(total_consumption = lambda x: x["PowerConsumption_Zone1"] + x["PowerConsumption_Zone2"] + x["PowerConsumption_Zone3"],
         hour = lambda x: x["Datetime"].dt.hour,
         day_of_week = lambda x: x["Datetime"].dt.dayofweek
)

day_of_week = ["Monday", "Tuesday", "Wednesday", "Thursday", "Friday", "Saturday","Sunday"]

powerconsumption["day_name"] = pd.Categorical(powerconsumption["Datetime"].dt.day_name(), categories=day_of_week)

import seaborn as sns

(powerconsumption
 .set_index(["Datetime"])
 .resample("H")
 .mean()
 .loc["2017-01", ["PowerConsumption_Zone1", "PowerConsumption_Zone2", "PowerConsumption_Zone3"]]
 .plot
 .area(
     title="Hourly Eclectricity Consumption by Zone - Jan 2017",
     xlabel="Date",
     ylabel="Consumption (KwH)"
 )
)


sns.despine()

sns.heatmap(
    powerconsumption.pivot_table(
        index="day_name",
        columns="hour",
        values="total_consumption",
        aggfunc="mean"
    ),
    cbar=None,
    cmap="viridis"
).set(title="Power Consumption by Day of Week and Hour",
     ylabel="Day of Week",
     xlabel="Hour");