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q01_load_data/build.py

@@ -1,7 +1,15 @@
+# %load q01_load_data/build.py
 import pandas as pd
 import numpy as np
 from sklearn.model_selection import train_test_split
 
+path ='data/elecdemand.csv'
+
+def q01_load_data(path):
+    data = pd.read_csv(path)
+    data['Datetime']=pd.to_datetime(data['Datetime'])
+    return data.shape, data
+
+# q01_load_data(path)
 
 
-

q02_data_splitter/build.py

@@ -1,7 +1,16 @@
+# %load q02_data_splitter/build.py
 import pandas as pd
 import numpy as np
 from sklearn.model_selection import TimeSeriesSplit
 from greyatomlib.time_series_day_02_project.q01_load_data.build import q01_load_data
 
+path = 'data/elecdemand.csv'
+
+def q02_data_splitter(path):
+    np.random.seed(9)
+    shape,data = q01_load_data(path)
+    tscv = TimeSeriesSplit(n_splits=2)
+    split_data = list(tscv.split(data))
+    return split_data
+
 
-

q03_time_plot/build.py

@@ -1,7 +1,19 @@
+# %load q03_time_plot/build.py
 import pandas as pd
 import numpy as np
 import matplotlib.pyplot as plt
 from greyatomlib.time_series_day_02_project.q01_load_data.build import q01_load_data
 plt.switch_backend('agg')
 
+path = 'data/elecdemand.csv'
+
+def q03_time_plot(path):
+    shp,df=q01_load_data(path)
+    plt.figure(figsize=(16, 6))
+    plt.plot(df['Datetime'], df['Demand'])
+    plt.xlabel('Time')
+    plt.ylabel('Demand')
+    plt.title('Electricity Demand in Australia for a year')
+    plt.show()
+