<jupyter_start><jupyter_text>M00 PhysComp – Intro to Physical Computing Exercise: Processing Environmental Data (BME680)**Date:** 01/04/2019**Author:** Cisco • A C R O B O T I C 🦿**Contact Info:**  @[acrobotic](https://twitter.com/acrobotic) (Twitter) • <EMAIL>**Module Link:** [https://github.com/acrobotic/EduKits](https://github.com/acrobotic/EduKits)**Support:** [https://discord.gg/hbGxaa](https://discord.gg/hbGxaa)**Filename:** process_data_bme680.ipynb**Language:** Python 3 (Jupyter Notebook) OverviewThe goal of this exercise is to process and visualize locally stored data collected from an Environmental Sensor. List of MaterialsNone. Hardware SetupNone.     Software SetupEnsure you've followed the steps in `get_data_bme680.ipynb` and that the file `bme680.csv` exists using the *Jupyter* navigator from within your browser. * Include all the libraries we'll need for processing the data<jupyter_code>from pandas import read_csv
from matplotlib import pyplot<jupyter_output><empty_output><jupyter_text>* Read the csv data from the file<jupyter_code># Load Environmental Data using read_csv
series = read_csv('data/bme680.csv', header=0, parse_dates=[0,1], index_col='time (GMT)', squeeze=True)
print(type(series))
print(series.head())<jupyter_output><class 'pandas.core.frame.DataFrame'>
                          date  temperature (C)  humidity (%)  pressure (kPa)
time (GMT)                                                                   
2020-04-16 00:45:19 2020-04-16            27.21        31.889          986.70
2020-04-16 00:45:20 2020-04-16            27.05        34.058          986.68
2020-04-16 00:45:21 2020-04-16            26.98        34.028          986.66
2020-04-16 00:45:22 2020-04-16            26.96        33.997          986.66
2020-04-16 00:45:23 2020-04-16            26.95        33.920          986.67<jupyter_text>* Example of accessing the data rows for a specific measurement, that is, the value entries for that measurement<jupyter_code>series['temperature (C)']<jupyter_output><empty_output><jupyter_text>* Example plot of the timeseries data<jupyter_code>series['temperature (C)'].plot(linewidth=0.5);<jupyter_output><empty_output>
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