plot_time_series_snapshots.py

def plot_data(df, x_variable, y_variable, title):
    """
    Plot the x- and y- variables against each other, where the variables are columns in
    a pandas dataframe
    df: Pandas dataframe. 
    x_variable: String. Name of x-variable column
    y_variable: String. Name of y-variable column
    title: String. Desired title name
    """
    fig, ax = plt.subplots()
    ax.plot_date(df[x_variable], 
                 df[y_variable], marker='', linestyle='-', label=y_variable)
    fig.autofmt_xdate()
    plt.title(title)
    plt.show()
  

###Execute in main block

#Plot the data on a yearly basis, using 2019 as an example year
plot_data(
df=electricity_demand_df[(electricity_demand_df['Date_Time']>=pd.to_datetime('2019-01-01')) & 
                         (electricity_demand_df['Date_Time']<pd.to_datetime('2020-01-01'))], 
                                    x_variable='Date_Time', 
                                    y_variable='Electricity_Demand_MWh', 
                                    title='TX Electricity Demand: 2019')
#Plot the data on a monthly basis, using January 2017 as an example
plot_data(df=electricity_demand_df[(electricity_demand_df['Date_Time']>=pd.to_datetime('2017-01-01')) & 
                                   (electricity_demand_df['Date_Time']<pd.to_datetime('2017-02-01'))], 
                                    x_variable='Date_Time', 
                                    y_variable='Electricity_Demand_MWh', 
                                    title='TX Electricity Demand: December 2017')
#Plot the data on a weekly basis, using July 1-7, 2019 as an example
plot_data(df=electricity_demand_df[(electricity_demand_df['Date_Time']>=pd.to_datetime('2019-07-01')) & 
                                   (electricity_demand_df['Date_Time']<pd.to_datetime('2019-07-07'))], 
                                    x_variable='Date_Time', 
                                    y_variable='Electricity_Demand_MWh', 
                                    title='TX Electricity Demand: December 2017')