Set of real world data science tasks completed using the Python Pandas library.
I started by cleaning the data. Tasks during this section include:
Drop NaN values from DataFrame Removing rows based on a condition Change the type of columns (to_numeric, to_datetime, astype) Once I had cleaned up the data a bit, I moved to the data exploration section. In this section I have explore 5 high level business questions related to this data:
What was the best month for sales? How much was earned that month? What city sold the most product? What time should we display advertisemens to maximize the likelihood of customer’s buying product? What products are most often sold together? What product sold the most? Why do you think it sold the most? To answer these questions I have walked through many different pandas & matplotlib methods. They include:
Concatenating multiple csvs together to create a new DataFrame (pd.concat) Adding columns Parsing cells as strings to make new columns (.str) Using the .apply() method Using groupby to perform aggregate analysis Plotting bar charts and lines graphs to visualize the results Labeling the graphs