Week 2: BALT 4396 - Cleaning Data with Python Libraries

-

 Chapter 3: Handling and Cleaning Data with Python Libraries


Data cleaning is an essential part of data analysis. Python libraries such as Pandas and NumPy help simplify the process. Pandas is a very powerful library, providing high-performance and very simple data structures and data analysis tools for Python. The two key data structures in Pandas are:
 
Series: A one-dimensional labeled array that can hold any data type.
DataFrame: A two-dimensional labeled data structure that has columns of potentially different data types.
 
Pandas can import data from multiple sources. Some of these sources are CSV, Excel, JSON, and SQL. The most common methods for using Pandas are read_csv(), read_excel(), and read_json().
 
Some of the ways that Pandas is able to help with data cleaning are by handling missing data, removing duplicates, renaming columns, and replacing values. 
 
I am new to Python, but I have taken a class on Java. Python has always been a very tricky code for me, as I have seen, but I have not used it. However, after reading this chapter, it does seem a little less tricky. Knowing now how to use these data cleaning tools makes Python look a little bit more user-friendly. Pandas and NumPy are very simple to use and extremely helpful for anyone using Python. I am very excited to read more about this book so I can gain more experience with Python.

Comments

Post a Comment

Popular posts from this blog

Week 5 - BALT 4363 - Probability and Statistics for Data Science

-
Image
Probability and Statistics for Data Science  (image created using Canva AI) This week I decided to write my blog on the chapter we went over. I absolutely love everything to do with data science. However, there are about three main points that are my favorite. Visualizations top the chart, then analyzing statistics, and then probability. During this chapter, we went over both probability and statistics, so I loved the reading.  The first topic covered was descriptive statistics. Some common descriptive statistics are mean, median, modem standard deviation, etc. These are statistics that we have seen since a young age, and continue to be a prominent statistic as we grow older and get in higher levels of math, in either school or the workplace. All of these statistics are highly important and they can all give us important information. Such as average age of employees at your company, or the median value of a certain dataset. We then move on to probability. Probability distribut...
Read more

Week 6 - Coding through Python and AI

-
Image
 Coding using Python and AI     In today's modern era, coding has become an easier thing to learn. Python is the preferred programming language for AI development. Python offers a user friendly syntax and a system of libraries and frameworks used for AI and machine learning projects. Coding with Python and AI opens doors to endless possibilities for creativity and innovation. This could be through developing chatbots, games, predictive analytics, and many other ideas. Python allows you to turn your ideas into reality. Last week I dove into coding a game and uploading it onto a blog post. By using Python and AI, I was able to do this in under an hour. Whether you are new to coding, or are experienced, there is a ton to learn from Python and AI. Diving into the world of coding gives you such an exciting adventure. With Python as your guide, and AI assisting you, you will be able to create almost anything that you want.
Read more