Coding With Python/Data Analytics in Anaconda's Spyder
I attended a Meetup that was taught by Le Wagon out of Mexico City, one of many major world cities that they operate out of, on December 09, 2021 at 11:00 am. The name of the Meetup is: Intro to Python and Data Analytics (in English). I had never been to a tech Meetup before although I have subscribed to tech channels for years; I never really had time to go to one. I only used Meetups to find rock climbing partners and rock climbing. I have to say that although, I had a very difficult time finding a meetup that actually happened, it was definitely worth the wait. Many Meetups just had a host that kept on not showing up or wanting to charge a lot of money for Meetup participation. As a graduated Mathematician, I already knew Python through a Numerical Analysis point of view from my college years. It was the last of the core classes that you take to graduate as a Mathematician. I know that Python is used heavily in the Cybersecurity world but I don’t know that side of Python yet. I also know that Python is not only the most sought-after language in the tech industry; it is also the best paid one. I like Python and I would definitely love using it in my business endeavors. It has many facets t it and is quite versatile and not difficult to use at all. The hardcore and powerful math equations that we used for it in college will blow anyone’s mind away in the world of data mining. I have mainly used Python with panda and matplotlib. Now, I guess that the kids are doing something a little more sophisticated according to Le Wagon.
Now, a new feature in Python that I learned with Le Wagon this week is how to use the library Seaborn. Seaborn apparently is used to create all kinds of fancy plots and data modeling (similar to R). The lecture used matplotlib a bit but not much. It used mainly Seaborn. We also used Anaconda and Jupyter Notebook which I have downloaded in order to have access to their material. We know that in Python, int is integer, float is decimal, the strings can be in single or double quotes, for example: str("") or str(''). Boolean commands return True or False; 1 for True or 0 for False. They are used mainly for if statements and control flows. You can do lists [1, 2, 3,4] and the outcome would be: 1,2,3,4. Objects are stored as variables and you call methods with arguments. Data sourcing is used to extract data from say data banks, clean it and use it in like models. Df means dataframe and some uses are: listings_df (you get a list of items in databases). listing_df can be used in conjuction with (no spaces allowed)
- .shapes
- .types
- .head() to list first 5 rows
- .tail() to list last 5 rows
- .describe()
- .isnull() to look for null values, you get True or False
- .sum()
See image of Hello World on screen using Spyder
See image below of a plot as created by Meetup Presenter using Python's Seaborn
You can find the exercixes yourself by clicking on this link and registering to Le Wagon Coding with Python for Data Analytics yourself.