2017-04-28Fumble.ipynb

Kernel: Python 3 (Anaconda)

import pandas as pd
import numpy as np
column_names = ['Date','PPM/Sec']
netatmo = pd.read_csv('2016Decay.csv', index_col=0, names=column_names)
df = netatmo

df.head()

	PPM/Sec
Date
2016-02-19 13:26:00	NaN
2016-02-19 13:27:00	NaN
2016-02-19 13:27:00	NaN
2016-02-19 13:31:00	NaN
2016-02-19 13:36:00	-5.533333

df.plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7ff4956061d0>

#df.describe()

df = df.replace([np.inf, -np.inf], np.nan)
df.describe()

	PPM/Sec
count	43660.000000
mean	-19.200102
std	53.549855
min	-3449.343333
25%	-17.460833
50%	-8.400000
75%	-4.000000
max	-0.384444

netatmo2 = pd.read_csv('NetAtmo_2016.csv', index_col=1)
netatmo2.head()

	Timestamp	Temperature	Humidity	CO2	Noise	Pressure
Timezone : America/Los_Angeles
2/19/16 13:26	1455917199	18.8	76	NaN	NaN	1015.7
2/19/16 13:27	1455917255	19.2	75	718.0	NaN	1015.7
2/19/16 13:27	1455917257	19.9	73	NaN	NaN	1015.7
2/19/16 13:31	1455917513	20.3	73	337.0	44.0	1015.8
2/19/16 13:36	1455917814	21.2	70	332.0	47.0	1015.7

netatmo2['CO2'].tail()

Timezone : America/Los_Angeles
12/31/16 23:35    483.0
12/31/16 23:40    485.0
12/31/16 23:45    489.0
12/31/16 23:50    475.0
12/31/16 23:55    475.0
Name: CO2, dtype: float64