<jupyter_start><jupyter_text>Features<jupyter_code>dataset_seattle.describe()
dataset_seattle.head(10)
dataset_seattle.columns<jupyter_output><empty_output><jupyter_text>Seattle<jupyter_code>dataset_seattle['date'] = pd.to_datetime(dataset_seattle['Date'], format="%m/%d")
dataset_seattle_1 = dataset_seattle.groupby('date')['Heating:Electricity [kW](Hourly)'].sum().reset_index()
dataset_seattle_1 = dataset_seattle.set_index('date')

y = dataset_seattle_1['Electricity:Facility [kW](Hourly)'].resample('MS').mean()
y2 = dataset_seattle_1['Cooling:Electricity [kW](Hourly)'].resample('MS').mean()
y3 = dataset_seattle_1['Heating:Electricity [kW](Hourly)'].resample('MS').mean()

y4 = dataset_seattle_1['Fans:Electricity [kW](Hourly)'].resample('MS').mean()
y5 = dataset_seattle_1['InteriorEquipment:Electricity [kW](Hourly)'].resample('MS').mean()
y6 = dataset_seattle_1['Gas:Facility [kW](Hourly)'].resample('MS').mean()
y7 = dataset_seattle_1['InteriorEquipment:Gas [kW](Hourly)'].resample('MS').mean()
y8 = dataset_seattle_1['Water Heater:WaterSystems:Gas [kW](Hourly)'].resample('MS').mean()

x = ['jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct', 'nov', 'dec']
plt.plot(x, y2, x, y3, x, y4, x, y5, x, y7, x, y8)
plt.legend([
            'Cooling:Electricity [kW](Hourly)', 
            'Heating:Electricity [kW](Hourly)',
            'Fans:Electricity [kW](Hourly)',
            'InteriorEquipment:Electricity [kW](Hourly)',
            'InteriorEquipment:Gas [kW](Hourly)',
            'Water Heater:WaterSystems:Gas [kW](Hourly)'
           ], loc='upper left')
plt.show()
dataset_seattle['date'] = pd.to_datetime(dataset_seattle['Date'], format="%m/%d")
dataset_seattle_1 = dataset_seattle.groupby('date')['Heating:Electricity [kW](Hourly)'].sum().reset_index()
dataset_seattle_1 = dataset_seattle.set_index('date')

y = dataset_seattle_1['Electricity:Facility [kW](Hourly)'].resample('MS').mean()
y6 = dataset_seattle_1['Gas:Facility [kW](Hourly)'].resample('MS').mean()

x = ['jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct', 'nov', 'dec']
plt.plot(x, y, x, y6)
plt.legend(['Electricity:Facility [kW](Hourly)',
            'Gas:Facility [kW](Hourly)'
           ], loc='upper left')
plt.show()
dataset_seattle['date'] = pd.to_datetime(dataset_seattle['Date'], format="%m/%d")
dataset_seattle_1 = dataset_seattle.set_index('date')
heatmap1_data = pd.pivot_table(dataset_seattle, values='Cooling:Electricity [kW](Hourly)', 
                     index=['month'], 
                     columns='day_of_week')
plt.figure(figsize=(20, 8))
sns.heatmap(heatmap1_data, cmap="Reds")
heatmap1_data = pd.pivot_table(dataset_seattle, values='Electricity:Facility [kW](Hourly)', 
                     index=['month'], 
                     columns='day_of_week')
plt.figure(figsize=(20, 8))
sns.heatmap(heatmap1_data, cmap="Reds")
heatmap1_data = pd.pivot_table(dataset_seattle, values='Gas:Facility [kW](Hourly)', 
                     index=['month'], 
                     columns='day_of_week')
plt.figure(figsize=(20, 8))
sns.heatmap(heatmap1_data, cmap="Reds")<jupyter_output><empty_output><jupyter_text>Fairbanks<jupyter_code>dataset_fairbanks['date'] = pd.to_datetime(dataset_fairbanks['Date'], format="%m/%d")
dataset_fairbanks_1 = dataset_fairbanks.groupby('date')['Heating:Electricity [kW](Hourly)'].sum().reset_index()
dataset_fairbanks_1 = dataset_fairbanks.set_index('date')

y = dataset_fairbanks_1['Electricity:Facility [kW](Hourly)'].resample('MS').mean()
y2 = dataset_fairbanks_1['Cooling:Electricity [kW](Hourly)'].resample('MS').mean()
y3 = dataset_fairbanks_1['Heating:Electricity [kW](Hourly)'].resample('MS').mean()

y4 = dataset_fairbanks_1['Fans:Electricity [kW](Hourly)'].resample('MS').mean()
y5 = dataset_fairbanks_1['InteriorEquipment:Electricity [kW](Hourly)'].resample('MS').mean()
y6 = dataset_fairbanks_1['Gas:Facility [kW](Hourly)'].resample('MS').mean()
y7 = dataset_fairbanks_1['InteriorEquipment:Gas [kW](Hourly)'].resample('MS').mean()
y8 = dataset_fairbanks_1['Water Heater:WaterSystems:Gas [kW](Hourly)'].resample('MS').mean()

x = ['jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct', 'nov', 'dec']
plt.plot(x, y2, x, y3, x, y4, x, y5, x, y7, x, y8)
plt.legend([
            'Cooling:Electricity [kW](Hourly)', 
            'Heating:Electricity [kW](Hourly)',
            'Fans:Electricity [kW](Hourly)',
            'InteriorEquipment:Electricity [kW](Hourly)',
            'InteriorEquipment:Gas [kW](Hourly)',
            'Water Heater:WaterSystems:Gas [kW](Hourly)'
           ], loc='upper left')
plt.show()
dataset_fairbanks['date'] = pd.to_datetime(dataset_fairbanks['Date'], format="%m/%d")
dataset_fairbanks_1 = dataset_fairbanks.groupby('date')['Heating:Electricity [kW](Hourly)'].sum().reset_index()
dataset_fairbanks_1 = dataset_fairbanks.set_index('date')

y = dataset_fairbanks_1['Electricity:Facility [kW](Hourly)'].resample('MS').mean()
y6 = dataset_fairbanks_1['Gas:Facility [kW](Hourly)'].resample('MS').mean()

x = ['jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct', 'nov', 'dec']
plt.plot(x, y, x, y6)
plt.legend(['Electricity:Facility [kW](Hourly)',
            'Gas:Facility [kW](Hourly)' 
           ], loc='upper left')
plt.show()
heatmap1_data = pd.pivot_table(dataset_fairbanks, values='Cooling:Electricity [kW](Hourly)', 
                     index=['month'], 
                     columns='day_of_week')
plt.figure(figsize=(20, 8))
sns.heatmap(heatmap1_data, cmap="Reds")
heatmap1_data = pd.pivot_table(dataset_fairbanks, values='Electricity:Facility [kW](Hourly)', 
                     index=['month'], 
                     columns='day_of_week')
plt.figure(figsize=(20, 8))
sns.heatmap(heatmap1_data, cmap="Reds")
heatmap1_data = pd.pivot_table(dataset_fairbanks, values='Heating:Electricity [kW](Hourly)', 
                     index=['month'], 
                     columns='day_of_week')
plt.figure(figsize=(20, 8))
sns.heatmap(heatmap1_data, cmap="Reds")<jupyter_output><empty_output><jupyter_text>Los Angeles<jupyter_code>dataset_los_angeles['date'] = pd.to_datetime(dataset_los_angeles['Date'], format="%m/%d")
dataset_los_angeles_1 = dataset_los_angeles.groupby('date')['Heating:Electricity [kW](Hourly)'].sum().reset_index()
dataset_los_angeles_1 = dataset_los_angeles.set_index('date')

y = dataset_los_angeles_1['Electricity:Facility [kW](Hourly)'].resample('MS').mean()
y2 = dataset_los_angeles_1['Cooling:Electricity [kW](Hourly)'].resample('MS').mean()
y3 = dataset_los_angeles_1['Heating:Electricity [kW](Hourly)'].resample('MS').mean()

y4 = dataset_los_angeles_1['Fans:Electricity [kW](Hourly)'].resample('MS').mean()
y5 = dataset_los_angeles_1['InteriorEquipment:Electricity [kW](Hourly)'].resample('MS').mean()
y6 = dataset_los_angeles_1['Gas:Facility [kW](Hourly)'].resample('MS').mean()
y7 = dataset_los_angeles_1['InteriorEquipment:Gas [kW](Hourly)'].resample('MS').mean()
y8 = dataset_los_angeles_1['Water Heater:WaterSystems:Gas [kW](Hourly)'].resample('MS').mean()

x = ['jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct', 'nov', 'dec']
plt.plot(x, y2, x, y3, x, y4, x, y5, x, y7, x, y8)
plt.legend([
            'Cooling:Electricity [kW](Hourly)', 
            'Heating:Electricity [kW](Hourly)',
            'Fans:Electricity [kW](Hourly)',
            'InteriorEquipment:Electricity [kW](Hourly)',
            'InteriorEquipment:Gas [kW](Hourly)',
            'Water Heater:WaterSystems:Gas [kW](Hourly)'
           ], loc='upper left')
plt.show()
dataset_los_angeles['date'] = pd.to_datetime(dataset_los_angeles['Date'], format="%m/%d")
dataset_los_angeles_1 = dataset_los_angeles.groupby('date')['Heating:Electricity [kW](Hourly)'].sum().reset_index()
dataset_los_angeles_1 = dataset_los_angeles.set_index('date')

y = dataset_los_angeles_1['Electricity:Facility [kW](Hourly)'].resample('MS').mean()
y6 = dataset_los_angeles_1['Gas:Facility [kW](Hourly)'].resample('MS').mean()

x = ['jan', 'feb', 'mar', 'apr', 'may', 'jun', 'jul', 'aug', 'sep', 'oct', 'nov', 'dec']
plt.plot(x, y, x, y6)
plt.legend(['Electricity:Facility [kW](Hourly)',
            'Gas:Facility [kW](Hourly)'
           ], loc='upper left')
plt.show()
heatmap1_data = pd.pivot_table(dataset_los_angeles, values='Heating:Electricity [kW](Hourly)', 
                     index=['month'], 
                     columns='day_of_week')
plt.figure(figsize=(20, 8))
sns.heatmap(heatmap1_data, cmap="Reds")
heatmap1_data = pd.pivot_table(dataset_los_angeles, values='Cooling:Electricity [kW](Hourly)', 
                     index=['month'], 
                     columns='day_of_week')
plt.figure(figsize=(20, 8))
sns.heatmap(heatmap1_data, cmap="Reds")
heatmap1_data = pd.pivot_table(dataset_los_angeles, values='Electricity:Facility [kW](Hourly)', 
                     index=['month'], 
                     columns='day_of_week')
plt.figure(figsize=(20, 8))
sns.heatmap(heatmap1_data, cmap="Reds")<jupyter_output><empty_output>
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