CO2 analyzing.ipynb

Kernel: Python 3 (Anaconda)

Wednesday Jan 18th 2017

import pandas as pd

netatmo = pd.read_csv('Jan18th2017.csv', parse_dates=True, index_col=1)

netatmo.columns

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

%matplotlib inline
netatmo['CO2'].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f0db640d2b0>

time2 = '2017-01-18 18:09:00' 
time1 = '2017-01-18 18:24:00'

Wednesday Jan 25th 2017

netatmo2 = pd.read_csv('Jan25th2017.csv', parse_dates=True, index_col=1)

netatmo2.columns

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

%matplotlib inline
netatmo2['CO2'].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f0db6487908>

Wednesday Feb. 8th 2017

netatmo3 = pd.read_csv('Feb8th2017.csv', parse_dates=True, index_col=1)

netatmo2.columns

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

%matplotlib inline
netatmo3['CO2'].plot()

<matplotlib.axes._subplots.AxesSubplot at 0x7f0db42dc9e8>

%matplotlib inline
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
data = pd.read_csv('NetAtmo2017.csv', 
                   index_col=1,      
                   parse_dates=True) 
data[:]

time2 = '2017-01-25 18:09:00' 
time1 = '2017-01-25 19:09:00'
data[time1:time2]