How to Create and Customize Scatter Plots and Histograms in Matplotlib
Learn how to create scatter plots and histograms in Python using pandas and matplotlib. This beginner-friendly tutorial walks you through loading real-world sales data, preparing it for analysis, and visualizing trends and distributions. You will practice with hands-on exercises and see a mini project example using retail sales data.
Discover the differences between scatter plots and histograms, how to handle missing values, and tips for making your visualizations clear and insightful. By the end, you will be able to plot and interpret your own data, gaining valuable skills for data analysis and business insights.
00:00 Introduction to Python plotting
00:09 Overview of scatter plots and histograms
00:19 Setting up your Python environment
00:40 Importing libraries and suppressing warnings
01:19 Loading and exploring the data set
02:29 Understanding data columns and types
02:51 Converting columns to date format
03:19 Creating a line plot of sales by month
04:01 Introduction to scatter plots
04:31 Building a scatter plot for sales data
05:11 Adding advertising data and advanced scatter plots
06:19 Introduction to histograms
06:51 Creating and customizing histograms
08:09 Comparing multiple columns with histograms
08:56 Checking and cleaning missing data
10:06 Real-world uses of plots in business
10:33 Mini project: finding top sales months
11:00 Highlighting data in scatter plots
11:41 Interactive histogram plotting
12:38 Tips for clearer plots
13:08 Challenge: dynamic scatter plot dot sizes
13:50 Recap and next steps
14:27 Call to action and conclusion
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