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+In [1]:
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+import pandas as pd
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+In [21]:
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+df = pd.read_csv('../../data/total_watt.csv', header=None)
+df.columns=['datetime','consumption']
+df = df.set_index('datetime')
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+In [35]:
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+%matplotlib inline
+plt.figure()
+df.plot(title='Energy Consumption for every 30 min', figsize=(16, 6))
+plt.xticks(rotation=30)
+plt.ylabel('Energy Consumption, W')
+plt.xlabel('Date')
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+Out[35]:
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+In [87]:
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+import datetime as dt
+df = pd.read_csv('../../data/total_watt.csv', header=None, parse_dates=[0], \
+ date_parser=lambda x: pd.datetime.strptime(x, '%Y-%m-%d %H:%M:%S').strftime('%Y-%m-%d'))
+df.columns=['datetime','consumption']
+plt.figure()
+df = df.groupby(by=df.datetime.dt.date).sum()
+df.plot.bar(title='Energy Consumption per day', figsize=(16, 6))
+plt.xticks(rotation=30)
+plt.ylabel('Energy Consumption, W')
+plt.xlabel('Date')
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+Out[87]:
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+In [143]:
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+# Plot clusters
+labels = KMeans(3, random_state=0).fit_predict(df)
+plt.figure()
+plt.scatter(df.index, df.consumption, c=labels, s=50, cmap='viridis')
+plt.title('Clustering Energy Consumption')
+plt.figsize=(16, 6)
+plt.xticks(rotation=30)
+plt.ylabel('Energy Consumption, W')
+plt.xlabel('Date')
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+Out[143]:
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+In [ ]:
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