These six Pandas functions power most data wrangling: read_csv to load data, info to inspect, isnull for missing values, drop to clean, groupby to summarize, and apply to customize logic. Master these and you control your dataset.

📕 https://ebokify.com/python

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#TIL about #querychat (https://posit-dev.github.io/querychat/), it is simply amazing to get quickly a #Shiny app that allows people to explore data using a chatbot interface. There are #Python and #RStats implementations.

#LLM #AI #Data #EDA #DataWrangling

Turning NASA Wake-up Calls into data

For a while back then I was into space flight again. Scientists, science communicators, and engineers were all excited for a new era of rocket launches and the potential unification of the human race as we look towards the future.

During that time I discovered Colin Fries’ work in the NASA History Division to document all NASA “Wake-up calls”. A wake-up call is simply a piece of music used to wake astronauts on missions, a different piece of music, daily, for the duration of the flight.

Take, for example, the last Space Shuttle mission (Space Transportation System) STS-135; it was in flight for 13 days, and the wake-up call on day one was Coldplay’s Viva la Vida, while on day 13 it was Kate Smith singing God Bless America.

As a huge music buff who has the radio or music television on 18 hours a day, I really wanted to delve into this further. While Colin’s work is great, it’s just a PDF file (@wtfpdf). A PDF is not an ideal file format for querying data and gleaning new insights. So, while I wanted to explore it, I first decided to turn it into a true dataset. The result was a set of resources, a website, a JSON, a CSV, and an SQLite database which are each more functional and more maintainable over time.

Lets take a look at the results and https://nasawakeupcalls.github.io below!