For most of my panda related learning and projects In this Repository I will focus on how to walk through many of the fundamental concepts to use the Python Pandas Data Science Library We start off by installing pandas and loading in an example csv. We then look at different ways to read the data. Read a column, rows, specific cell, etc. Also ways to read data based on conditioning. We then move into some more advanced ways to sort & filter data. We look at making conditional changes to our data. We also start doing aggregate stats using the groupby function. We also load very large data set into python by chunks
I Merged the 12 months of sales data files into a single csv using Excel, Power Query and Python pandas. Cleaned the merged data files to answer business questions. I augmented the data with additional columns to suit our questions such as:
- What was the best month for sales? How much was earned that month?
- Which City had the highest number of sales?
- What time should we display advertisements to maximize the likelihood of the customer’s buying a product?
- What products are most often sold together?
- counting pairs of items that occur together in Orders
- What product sold the most? Why do you think it sold the most?