Data Visualization with Matplotlib and Seaborn PDF

Objectiv es Chapter 4  Introduction to Matplotlib  Build data visualizations with the Data Visualizations with Matplotlib library Matplotlib...

Objectiv es Chapter 4  Introduction to Matplotlib  Build data visualizations with the Data Visualizations with Matplotlib library Matplotlib  Setting up Matplotlib  Basic Plotting with Matplotlib Lecturer. Ahmed Osman  Customizing Plots  Advanced Plotting Techniques  Build data visualization with Seaborn Lib  Purpose of Data ualization Better analysis Quick action Identifying patterns Finding errors Understanding the story Exploring business insights Understandable presentation  General Encouraging Thoughts A picture is worth a thousand words. Equality Equity General Encouraging Thoughts A picture is worth a thousand words. Data is the new oil, but visualization is the refinery. Graphs and charts are the bridges that connect numbers to decisions. Seeing is understanding. Numbers have an intrinsic beauty, but visualization gives them a soul. Graphs speak louder than numbers. Visualization is the art of making the invisible, visible. Mastering visualization is mastering the language of data. Every graph has a story to tell; you just need the right tools to uncover it. Charts are the storytellers of the data world. Data Visualization We will Focus on Matplotlib and Seaborn Data Visualization Libraries  Graphical representation of data to simplify understanding. Introducti Identifies trends, patterns, and outliers. on to Data Visualizati Aids in storytelling and decision- on making. Key uses: simplifying large datasets, communicating insights, enhancing storytelling. Visualization Libraries Common plots: Foundational Line, Scatter, Matplotlib: library for Bar, plotting. Histogram. Built on Common plots: Matplotlib for Heatmaps, Pair Seaborn: statistical Plots, Box visualizations. Plots.  Line Plot: Displays trends over time. Example: Annual growth in sales. Bar Chart: Visualizes comparisons between Common categories. Example: Average performance by faculty. Types of Histogram: Shows the distribution of data. Visualizatio Example: Exam score distribution. ns Pie Chart: Illustrates proportions within a whole. Example: Attendance status distribution. Scatter Plot: Visualizes relationships between two variables. Example: Activity scores vs. final exam scores. Heatmap (Seaborn): Displays correlations. Example: Faculty and course average scores. Population Growth (Line Chart) GDP by Region (Bar chart) Student Examination Scores (Histogram) Reading Hours vs. Grade (Scatter Plot) Correlation Matrix (Heatmap)  Clean and 1. Prepare Your 2. Set Up Your organize data Steps for Data: using Pandas. Environment: Data Visualizatio Install libraries: pip install 3. Choose the Right Chart Align with the insights you n matplotlib Type: want to convey. seaborn. Add labels, 5. Analyze and 4. Customize titles, and Interpret Your Chart: adjust colors. Insights.  Basic Commands: plt.plot(): Create line plots. plt.bar(): Create bar charts. plt.hist(): Create histograms. plt.scatter(): Create scatter plots. Features of Customization Options: Matplotlib Colors, linestyles, markers, titles, labels, legends. Save Plots: Save in PNG, JPG, or PDF formats using plt.savefig().  Simplified Statistical Plots: Built-in support for Pandas DataFrames. Advanced plots: heatmaps, pair plots, violin plots. Features of Built-in Themes: Seaborn Set global themes for consistent styling. sns.set_theme(style='whitegrid'). Key Commands: sns.barplot(), sns.boxplot(), sns.heatmap(), sns.pairplot(). Best Practices in Data Visualization Know Keep Focus on Maintain Validate Know Your Keep It Focus on Maintain Validate Audience: Simple: Insights: Consistency: Your Data: Tailor Avoid Highlight Use Ensure visualizations overloading trends, consistent accuracy to their visuals with patterns, or styles, colors, before technical unnecessary outliers. and labels. creating knowledge. elements. visualizations. Find the Jupyter Notebook file END