diff --git a/Z-Assignment 11- Deadline Dec19th_DataDrivenBuildingExplorationStep/assignment11_Guermandi.py b/Z-Assignment 11- Deadline Dec19th_DataDrivenBuildingExplorationStep/assignment11_Guermandi.py
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+# -*- coding: utf-8 -*-
+import numpy as np
+import pandas as pd
+import matplotlib.pyplot as plt
+
+DataFolderPath = "/Users/federico/Desktop/roba/Data"
+ConsumptionFileName = "consumption_5545.csv"
+ConsumptionFilePath = DataFolderPath+"/"+ConsumptionFileName 
+
+DF_consumption = pd.read_csv(ConsumptionFilePath,sep = ",",index_col=0) 
+previousIndex= DF_consumption.index
+NewparsedIndex = pd.to_datetime(previousIndex) #converts to datetime
+DF_consumption.index= NewparsedIndex #update the index, now that is datetime
+DF_consumption.head(24)
+DF_JunefirstTillthird = DF_consumption["2014-06-01 00:00:00":"2014-06-04 00:00:00"] #creates the DataFrame we are going to use
+DF_JunefirstTillthird.head(5)
+DF_JunefirstTillthird.describe() #Data about the value of the previous DataFrame
+# In[48]:
+
+
+
+# Now let's import some weather data!
+weatherSourceFileName = "Austin_weather_2014.csv"
+weatherSourceFilePath = DataFolderPath+"/"+weatherSourceFileName 
+DF_weatherSource = pd.read_csv(weatherSourceFilePath,sep = ";",index_col=0)
+DF_weatherSource.index
+
+previousIndex_weatherSource= DF_weatherSource.index
+NewparsedIndex_weatherSource = pd.to_datetime(previousIndex_weatherSource)
+DF_weatherSource.index= NewparsedIndex_weatherSource
+
+#  we usually do this
+series_temperature = DF_weatherSource['temperature'] #series that contains temperature
+
+# Nut now I would prefer to have it as a dataframe with just one column, we will then see why !!
+DF_temperature = DF_weatherSource[['temperature']]
+
+# let's do the same for irradiation!!!
+IrradianceSourceFileName = "irradiance_2014_gen.csv"
+IrradianceSourceFilePath =  DataFolderPath+"/"+IrradianceSourceFileName 
+DF_irradianceSource = pd.read_csv(IrradianceSourceFilePath, sep = ";",index_col= 1) #like in line 11
+DF_irradianceSource.head(5)
+
+previousIndex_irradianceSource= DF_irradianceSource.index
+NewparsedIndex_irradianceSource = pd.to_datetime(previousIndex_irradianceSource)
+DF_irradianceSource.index= NewparsedIndex_irradianceSource #Update the index 
+
+# IF I want take just the column "gen " as a dataframe with a single column !
+DF_irradiance = DF_irradianceSource[["gen"]] # to take it as a DF
+DF_irradiance[DF_irradianceSource["gen"] < 0] = 0
+
+DF_joined = DF_consumption.join([DF_temperature,DF_irradiance])
+DF_joined.head(24)
+
+# what to do with Nans 
+DF_joined.dropna() #it will remove all Nans !!!!! 
+
+DF_tmepirr = DF_joined["2014-06-01 00:00:00":"2014-06-04 00:00:00"]
+
+
+from mpl_toolkits.axes_grid1 import host_subplot
+import mpl_toolkits.axisartist as AA
+import matplotlib.pyplot as plt
+#DF_JunefirstTillthird
+#DF_tmepirr
+index= DF_JunefirstTillthird.index
+X = pd.to_datetime(index) #converts to datetime
+ #update the index, now that is datetime
+
+host = host_subplot(111, axes_class=AA.Axes)
+plt.subplots_adjust(right=0.75)
+
+par1 = host.twinx()
+par2 = host.twinx()
+
+offset = 60
+new_fixed_axis = par2.get_grid_helper().new_fixed_axis
+par2.axis["right"] = new_fixed_axis(loc="right", axes=par2,
+                                        offset=(offset, 0))
+
+par2.axis["right"].toggle(all=True)
+
+
+host.set_xlabel("Dates")
+host.set_ylabel("AC [Watts]")
+par1.set_ylabel("Temperature [C]")
+par2.set_ylabel("Irradiance")
+
+p1, = host.plot(X, DF_tmepirr["air conditioner_5545"] , label="AC [Watts]")
+p2, = par1.plot(X, (DF_tmepirr["temperature"]-32)/1.8, label="Temperature")
+p3, = par2.plot(X, DF_tmepirr["gen"], label="Irradiance")
+
+
+host.legend()
+
+host.axis["left"].label.set_color(p1.get_color())
+par1.axis["right"].label.set_color(p2.get_color())
+par2.axis["right"].label.set_color(p3.get_color())
+
+plt.draw()
+plt.show()
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