James McDonald

  Chapter 3:  Handling and Cleaning Data with Python Libraries This past week in BALT 4363 , I learned about handling and cleaning data with python libraries. This is a very important topic to understand. During this semester, I have learned how to create nice visuals using RStudio and Python. Creating visuals is not highly difficult, the difficulty comes from organizing data to be able to create them. The trickiest part of this is cleaning the data to be able to create better visuals. Pandas Pandas is a library that provides easy, high performance data structures and data analysis tools. It is very useful for handling large datasets by offering flexible data manipulation tools. Inside of pandas, there are two primary data structures: Series and DataFrame. Series is a one dimensional array, DataFrame is a two dimensional data structure. NumPy NumPy (Numerical Python) is a library for Python that adds support for large arrays and matrices, while also having a large collection o...

Chapter 2: Python Data Manipulation In data science, visualizations are seen as one of the most important things to know how to do. This allows for easy ways to analyze data, recognize potential trends, predict where trends are going, etc. While this is very easy to do with data sets that are clean, set up nicely and organized, it can sometimes be very tricky if you have a data set that is not organized in a way that you want it. This is where data manipulation comes in. Using Python data manipulation, you can organize data how you want to read it. Here are some examples of this manipulation: For and While Loops Loops allow you to repeat code. FOR loops are used fore when you want to repeat the code a specific amount of time, and While loops are for when you want to execute code as long as a condition runs as true. Conditional Statements Conditional statements are a very simple form of code that allow you to execute different codes depending on a condition. These are by using IF or i...