ETC-example.ipynb

Kernel: Python 3 (Anaconda)

import pandas as pd

netatmo = pd.read_csv('NetAtmo_2016.csv', parse_dates=True, index_col=1)

netatmo.columns

Index(['Timestamp', 'Temperature', 'Humidity', 'CO2', 'Noise', 'Pressure'], dtype='object')

%matplotlib inline
netatmo['Temperature'].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7fba207da0f0>

pd.read_csv('ETC Data 439L.csv', parse_dates=True, index_col=0).diff(1)['House (kWH)'].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7fb9fbfa25f8>

netatmo['Temperature'].resample('M').mean()

Timezone : America/Los_Angeles
2016-02-29    20.365464
2016-03-31    21.709408
2016-04-30    21.852277
2016-05-31    22.487452
2016-06-30    23.529825
2016-07-31    23.555051
2016-08-31    23.367346
2016-09-30    23.583787
2016-10-31    21.918393
2016-11-30    21.998396
2016-12-31    24.477415
Freq: M, Name: Temperature, dtype: float64