<jupyter_start><jupyter_text>Exploratory plots<jupyter_code>import datetime
import calendar
import pprint
import json
import numpy as np
import pandas as pd
from sklearn.metrics import mean_squared_error
import matplotlib.pyplot as plt
from matplotlib import rcParams
rcParams['figure.figsize'] = 12, 4<jupyter_output><empty_output><jupyter_text>Load project<jupyter_code>project_folder = '../../datasets/radon-small/'
df = pd.read_csv(project_folder + 'flow1.csv', parse_dates=['time'])
flow = df.set_index('time')['flow'].fillna(0)
flow.head()<jupyter_output><empty_output><jupyter_text>Helper functions<jupyter_code>def plot_days(start_day, end_day):
    """Plot series for specific data range"""
    df = flow[pd.Timestamp(start_day): pd.Timestamp(end_day)]
    df.plot()
    plt.show()<jupyter_output><empty_output><jupyter_text>Plot the whole series<jupyter_code>flow.plot()
plt.show()<jupyter_output><empty_output><jupyter_text>From the plot it looks that we might have some outliers here. But it is also possible that the bigger values are correct responses to the rain we need to look closer to this data.  Explore november 2013<jupyter_code>plot_days('2013-11-01', '2013-12-01')<jupyter_output><empty_output><jupyter_text>Lets looks closer to the "standard" day and the day with outlier And now some days with anomalies<jupyter_code>plot_days('2013-11-01', '2013-11-03')
plot_days('2013-11-02T18:00', '2013-11-02T20:00')<jupyter_output><empty_output><jupyter_text>It looks that probably here we have some reading problem which results in outlier  Lets check some flow in Spring<jupyter_code>plot_days('2014-06-01', '2014-07-01')
plot_days('2014-06-11', '2014-06-13')
plot_days('2014-06-17', '2014-06-18')<jupyter_output><empty_output>
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