Learning Python on Google Cloud

If you have programmed before, then you may be familiar with the concept of libraries. Think of libraries as prepackaged code that we don't have write (i.e. someone already wrote it for us) in order to accomplish common tasks that we would need Python to do. Basically, you don't have to invent the wheel if it exists.

In this section we're going to cover a module/library for visualizing our data! The python module is matplotlib and it's the foundation for other charting libraries in Python.

Working with text in Python is something you don't realize you need until you do. This is where the built in functions (or methods) available in Python strings come into handy. Although you may not immediately identify a use case for yourself with Python strings, I'm confident you will find them useful and will soon apply them.

Let's take a moment to apply our cumulative knowledge in this exercise. Cumulative? Yes, so, you'll likely draw from concepts in lesson 1 to solve for this practice problem.

List comprehension in Python is one of the most elegant ways to essentially leverage loop logic within a single line of code. Applying list comprehension whenever possible will make you a creative data scientist, analyst and yes, programmer!

Functions in Python are a must have. This is where you can create your own custom logic that may not already be available in other modules / libraries. In this video, we'll create 2 types of functions. One that takes no arguments (i.e. parameters) and another that takes one parameter.

Functions can be used to display messages after they are executed, but to get even more value from functions, you can have your function return a value that can be stored in a variable as we'll see in the next video.

In this video, we'll create an example where we are passing more than one argument to our function and using "return" so that our function returns a value based on the functions logic that can be used elsewhere or stored in a variable to be used later.

Time to flex our brain, use our cumulative knowledge coupled with what we learned about functions and really begin feeling like a data scientist / rockstar analyst!

In this video we are taking a pause to reflect on what we've learned. In Lesson 1, we learned the fundamentals of Python. In Lesson 2, we covered how we can leverage ready to use code in modules and ways to get creative with Python, through list comprehension and functions.

In Lesson 3, we'll begin to explore some of the core tools that are used in Data Science and Data Analysis.

In this video, we take a look at the similarities and differences between lists and NumPy arrays and preview some of the power of NumPy Arrays

This video provides an introduction to some really great builtin functions available in Numpy arrays such as the generation of normal distributed data, random values & random value selection, linspace, and more.

We dive right into the pandas library (a.k.a. module) in this course by reading in data hosted on a server! We'll profile our data just to get an understanding of how much and the type of data we're dealing with, preview our data and learn about .iloc, an index based slicer that enables us to look through our data.

In this video, we'll explore what we can do with the pandas describe method and user our indexes within the data frame to extract means, percentiles, and other useful summary values for use in our analysis.

Let's use several of the new concepts we've learned in lesson 3 and begin analyzing and slicing through our data!

We'll tackle one of the key functions that you want to have when analyzing any set of data, and that is sorting data. Learn how to sort data in pandas.

In this lesson, we take a look at filters. You won't find many references to filters, but you'd be surprised how valuable and useful they are for managing your data!

Since we are working on the cloud, we need to talk about how we go about exporting our work to a file we can use, ON THE CLOUD! This is cool, because this is something you don't typically find in Python courses.

If you are an AWS shop who uses Redshift for their databases, then you are in luck! In this tutorial, we cover how to connect to Postgres, the foundation for Redshift. Connecting to Redshift and Postgres is virtually identical! So whether you are an open-source shop or techie shop, we'll get you connected to your data!

This video covers how to get data from your database and into your pandas Data Frame. Working with flat files is cool, but most of our data is stored in databases.

Aggregate functions are core to data analysis. With pandas groupby method, you can compute counts, unique counts, means, mins, max and more.

Column names may sometime be cryptic. Let's look at how to rename our columns in Pandas to something more suitable for analysis and understanding.

But can pandas join tables like SQL? Of course. In this video we look at the parallels between pandas merge method and joins in SQL. No need to toggle back and forth between your database and Python, when you can join data together with pandas!

After joining, we can be left with columns that we don't necessarily need for our final outputs. Learn a technique that allows you to filter down the columns you need for your final results.

Apply is the crux to performing sophisticated manipulations to your data. With apply, we can leverage the power of many methods and functions, including ones that we write for the purpose of deriving new outputs or transforming our information.

So we've learned some pretty cool things with Pandas, such as joins, filtering and apply. Let's flex our Python muscles and put it all together!

As the saying goes, a picture is worth a thousand words. In the same spirit, charts can communicate so much with so little. Pandas leverages matplotlib for chart creation. So you don't have to leave the comfort of pandas to generate some meaningful charts about your data!

Hey everyone, I'm very happy that you completed this course and want to wish you the best as you continue to grow yourself and your career. Before you go, lend me a few more minutes of your time so I can share some recommendations on where to go with Python and recommend some good books that have helped me with my overall development.  Thanks and I'll see you in my future courses!