Updating read me

utils.py

@@ -41,7 +41,6 @@ def aggregate_by_year(df, date_column, figsize=(15, 8)):
 
     return df_yearly
 
-
 def predicted_temperature(humidity, beta_0, beta_1):
     """
     Return a predicted temperature based on the humidity.

workshop.py

@@ -24,12 +24,8 @@
 print(len(weather_data))
 print(weather_data.head(3))
 
-# Drop categorical columns
-weather_data = weather_data.drop(
-    columns=['Summary', 'Precip Type', 'Loud Cover', 'Daily Summary'])
-
-
-# TODO: Print the last 3 rows of the DataFrame
+# TO DO: Print the last 3 rows of the DataFrame
+print(weather_data.tail(3))
 
 
 # In[3] Visualisation
@@ -39,47 +35,49 @@
 weather_data_ordered = weather_data.sort_values(by='Formatted Date')
 
 weather_data_ordered = weather_data_ordered.reset_index(drop=True)
+# Drop categorical columns
+weather_data_ordered = weather_data_ordered.drop(
+    columns=['Summary', 'Precip Type', 'Loud Cover', 'Daily Summary'])
+
 weather_data_ordered.plot(
     x='Formatted Date', y=['Temperature (C)'], color='red', figsize=(15, 8))
 
-# TODO: Plot Temperature (C) V.S the Date using only the data from 2006
-
+# TO DO: Plot Temperature (C) V.S the Date using only the data from 2006
+weather_data_ordered.head(8759).plot(x='Formatted Date', y=['Temperature (C)'], color='red')
 
 # -----------------------------------------------------------------------------
 weather_data_ordered.plot(
     subplots=True, x='Formatted Date', y=['Temperature (C)', 'Humidity'],
     figsize=(15, 8))
-
-# TODO: Plot different combinations of the variables, for different years
+# TO DO: Plot different combinations of the variables, for different years
 
 
 # -----------------------------------------------------------------------------
 
+# In[4] Data summarization and aggregation
+
+# Weather data by year
+weather_data_by_year = aggregate_by_year(
+    weather_data_ordered, 'Formatted Date')
+
+# TO DO: Create and use a function to get the average
+#       of the weather data by month
+
 
-# In[4] Correlations
+# In[5] Data Analysis and Interpretation
 plot_correlations(weather_data_ordered, size=15)
 cmap_category, cmap_list = ('Plot gradiends convention', ['viridis', ])
 plot_color_gradients(cmap_category, cmap_list)
 
 weather_correlations = weather_data_ordered.corr()
-weather_corr_temp_humidity = weather_data_ordered['Temperature (C)'].corr(
+weather_data_ordered['Temperature (C)'].corr(
     weather_data_ordered['Humidity'])
 
-# TODO: Get the correlation for different combinations of variables.
+# TO DO: Get the correlation for different combinations of variables.
 #       Contrast them with the weather_correlations dataframe
 
 
-# In[5] Data summarization and aggregation
-
-# Weather data by year
-weather_data_by_year = aggregate_by_year(
-    weather_data_ordered, 'Formatted Date')
-
-# TODO: Create and use a function to get the average
-#       of the weather data by month
-
-
-# In[6] Lineal regression
+# In[6] Data Modeling and Prediction
 
 # Get data subsets for the model
 X_train, X_test, Y_train, Y_test = train_test_split(
@@ -103,6 +101,6 @@
 plt.legend()
 plt.show()
 
-# TODO: Using the coefficients predict the temperature for a
+# TO DO: Using the coefficients predict the temperature for a
 #       given level of humidity using the 'predicted_temperature' function
 #       available in 'utils'