This course teaches learners how to solve real-world data science tasks using Python Pandas. By analyzing 12 months of sales data, students will learn to clean data, explore business questions, and visualize results using Pandas and Matplotlib. The course covers skills such as dropping NaN values, adding columns, using groupby for aggregate analysis, and plotting graphs. The intended audience for this course includes individuals interested in enhancing their Python Pandas data science skills through practical problem-solving exercises.
Overview
Syllabus
- Intro
- Downloading the Data
- Getting started with the code Jupyter Notebook
Task #1: Merging 12 csvs into a single dataframe
- Read single CSV file
- List all files in a directory
- Concatenating files
- Reading in Updated dataframe
Task #2: Add a Month column
- Parse string in Pandas cell .str
- Drop NaN values from df
- Remove rows based on condition
Task #3: Add a sales column
- Another way to convert a column to numeric ints & floats
Question #1: What was the best month for sales?
- Visualizing our results with bar chart in matplotlib
Question #2: What city sold the most product?
- Add a city column
- Using the .apply method super useful!!
- Why do we use the lambda x ?
- Dropping a column
- Answering the question using groupby
- Plotting our results
Question #3: What time should we display advertisements to maximize the likelihood of purchases?
- Using to_datetime method
- Creating hour & minute columns
- Matplotlib line graph to plot our results
- Interpreting our results
Question #4: What products are most often sold together?
- Finding duplicate values in our DataFrame
- Use transform method to join values from two rows into a single row
- Dropping rows with duplicate values
- Counting pairs of products itertools, collections
Question #5: What product sold the most? Why do you think it did?
- Graphing data
- Overlaying a second Y-axis on existing chart
- Interpreting our results
Taught by
Keith Galli
