Week 5: The End Of Data Frame And Transition Into Statistical Analysis

Hey guys! Thank you for waiting for my late post update. 

Previously, I have been talking about how Data Frame is useful for representing tabular data such, as demonstrated by the image above. For this past week, I have successfully learned more about Data Frame by implementing its specific functions and methods in Python using Jupyter notebook. However, some challenges that I faced was importing a module called “Pandas” as this module was necessary for me to import in a data frame that I can use to organize my selected CSV file. I fixed this problem by installing Anaconda Navigator which allowed me to import the Pandas into Jupyter notebook so I can use the module within my program. From this experience, I learned more about how to run functions within my Terminal specifically to open and install online software. 

The next few weeks, I will be moving into the third phase of my project which is statistical analysis. As you can already see from the image above, I have already started on this task as learning Data Frame simultaneously taught me how to use the describe function to print out the statistical information on parts of my CSV FIle such as average, standard deviation, count, etc. Since I have never taken any form of statistics class, I will be doing research with my online mentor on both Descriptive Statiscs and Inferential Statistics where I learn topics such as stat variaiton, stat quartiles and percentiles, and stat standard deviation which will all be crucial for the next steps of my project. Some other topics that I plan on learning for the next few weeks include Linear Regression topics (p value, r squared, etc). 

Stay tuned for my next week updates! 

References:

• Naveen. “Install Anaconda & Run Pandas On Jupyter Notebook.” Spark By {Examples}, 29 Aug. 2021, Https://Sparkbyexamples.Com/Pandas/Install-Anaconda-Run-Pandas-Jupyter-Notebook/.