The first column is the draw number, columns N1-L2 are the drawn numbers and lucky stars (by the order they were drawn), the Jackpot column is the jackpot in Euros and the Wins column tell us how many bets hit the jackpot. In this tutorial, we’ll use Python and xlwings with Excel to clean up a data set and then generate some graphs to visualize which numbers win the EuroMillions most frequently. (Note, though, that our data set denominates winnings in Pounds, not Euros). The largest jackpot ever won was €190 million. In order to win the jackpot, participants must correctly choose all drawn numbers and lucky stars. The data available at that link should be updated with the latest information up to whenever you’re reading this post, but in case it’s unavailable, here’s a CSV file with the data from that link as of September 20.Įuromillions is a multinational lottery available in some European countries, specifically in Andorra, Austria, Belgium, France (including overseas regions and collectivities), Ireland, the Isle of Man, Liechtenstein, Luxembourg, Monaco, Portugal, Spain, Switzerland and the United Kingdom ( source).Īs of this writing, the draw consists of five numbers from a pool of 50 numbers (numbered 1 through 50) and two numbers called lucky stars from a pool of 12 numbers. This data set was downloaded from this link and it contains all the EuroMillions draws up to, and including, the 20th of September. We will be working with a data set that contains information regarding the draws of an European lottery called EuroMillions. In order to follow this post you should be familiar with basic Python concepts (objects, methods, attributes, functions) and Python’s syntax and have an intermediate knowledge of Excel and VBA.
Make excel for mac faster how to#
In this xlwings tutorial, we’ll walk through how to use Python in Excel to execute and use some common operations like deleting rows based on certain criteria, using Excel functions and formulas, autofilling, creating sheets, charts, etc. But no matter how strongly you prefer working with Python, at the end of the day there is sometimes a need to present your findings or share your data using Excel.īut that doesn’t mean can’t still enjoy some of the efficiencies of Python! In fact, using a library called xlwings, you can use Python to make working in Excel faster.
Make excel for mac faster code#
At Dataquest, we generally recommend working with code for quite a few reasons, and many of our data science courses are aimed at teaching efficient coding for data analysis and data science.
xlsx") df = Parallel(n_jobs=-1, verbose=10)(delayed(loop)(file_number) for file_number in range(10)) df = pd.concat(df, ignore_index=True) end = time.time() print("Excel//:", end - start) > 13.Excel is very, very pervasive in today’s businesses. start = time.time() def loop(file_number): return pd.read_excel(f"Dummy.
Make excel for mac faster update#
Compared to our pickle code from above, we only need to update the loop function.
You can also use joblib to parallelize this. Let’s imagine that you received excel files and that you have no other choice but to load them as is. pickle and, each time you receive a new file, you can load it once as a. Example: Imagine that you use transactional monthly data (each month you load a new month of data). The first time you open a file, save it as a pickle so that you will be able to load the pickle version directly next time.
In general, it is much faster to work with pickle files than csv files.
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