Co2 Daily

    This series of CO2 data analysis was a part of my effort to interpreting CO2 data from lowcost sensors. The sensors are located in a high-rise building near the center of Hanoi.

    In this post, we will work with ESRL/NOAA data taken daily from several sites over the world. The sample was taken daily using flask sampling. The site was selected to refect various conditions of high photosynthesis, on a tiny island, a reference station in Mauna Loa (Hawaii)...

    Extract Rss Feed From Noaa For Co2 Update

    Experiment with requests and feedparser package to extract update on CO2 concentration from NOAA website

    Exercise with web scraping, manipulate string and convert to Python object such as datetime, float and pack the data into dataframe. Finally, the data is plotted using matplotlib.pyplot

    Embed Images In Jupyter

    Taking a variety of tools to embed images to the Jupyter Notebook. A few are: Using img tag in HTML; converting image to a base64 string representation; using selenium to render map created by folium

    Convert images into base64 string is handy to avoid missing attached files and pack the document into one single file

    Test Lowcost Temperature Sensors

    Comparing temperature sensors (Dallas DS18B20, Microchip MCP9808, Resistance Temperature Detector (RTD) PT-100, Sensirion SHT(3x, 21).

    Cleaning and reformatting complex data for pandas. The data was stored in text file with list of dictionary and the key as another dictionary

    Using seaborn to display data by using its powerful feature hue

    Graph Co2 At Mauna Loa

    Using historical data from NOAA website for Mauna Loa Observatory Station to re-construct the graph of atmospheric CO2 since 1960

    We will use matplotlib with requests to get data, extract data then with pandas to make a dataframe for the final graphing