If you are an electrical engineer, and want to know how to use Python to get data from the internet and display it, this post is for you.
(This is the second part of a series. By the end we’ll have written a Python script to display a chart of electricity market prices.
- All you need to analyse the electricity market pt 1
- All you need to analyse the electricity market pt 2
- All you need to analyse the electricity market pt 3
- All you need to analyse the electricity market final
Australian electricity prices are high – let’s analyse
Previously I mentioned that the electricity prices have gone through the roof (more than doubled) post introduction of the carbon tax in Australia.
|Very high electricity prices [Larger Size]|
We’re using these high prices as an excuse to learn how to download and display price data from the internet. This code will work wherever you are in the world.
Using Python to extract a zipped file
Last time, we downloaded a zipped file filled with electricity prices from the energy market operator’s website (read about it here if you want to catch up). Here is the final code from that post:
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In this post we are going to open a zip file containing CSV files and read the
data contained within. To begin, we open the zipped file and get a list of the
file names inside it. We’ve used Python’s
zipfile module and its
class to turn the zip file we downloaded into something that we can use.
A listing of all the filenames in the zip file can be produced using the
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There was just one CSV file inside the zipped file we just opened. We will now
add some code to open it and convert it to Python lists using the
module included with Python. Here is a quick refresh on the CSV module.
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csv.reader creates an object that has special functions to effortlessly
return the CSV price data as Python lists. Printing the object,
print prices_csv_reader, shows how the
csv module has read in the file:
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Look carefully at that output, notice how
csv.reader has detected all the
commas and correctly separated all of the columns and rows. All the data is
stored in a list of rows. Now that we have the data in this form, extracting
useful data from this structure can be achieved using the usual Python
In the first two posts of this series, we’ve:
- downloaded a zipped file using
- unzipped the file to get the CSV using
- got ready to read the csv file contents using
In the next blog post we’ll take a closer look at the CSV file using Python. In particular, we will manipulate the CSV file to extract only the data that is of interest to us.
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