Data Visualization

Data visualization transforms numbers into insights. Good visualizations reveal patterns, outliers, and relationships that are invisible in raw data.

Remember: A picture is worth a thousand data points. Visualization is essential for exploratory data analysis (EDA).

Common Chart Types

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Line Chart

Trends over time

Example: Stock prices, temperature

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Bar Chart

Compare categories

Example: Sales by region

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Scatter Plot

Relationships

Example: Height vs weight

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Histogram

Distributions

Example: Age distribution

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Box Plot

Statistical summary

Example: Salary ranges

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Heatmap

Matrix data

Example: Correlation matrix

Matplotlib Basics

Matplotlib is Python's most popular plotting library.

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Output:

Click "Run Code" to see output

Seaborn for Statistical Plots

Seaborn builds on Matplotlib with beautiful statistical visualizations.

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Output:

Click "Run Code" to see output

Exploratory Data Analysis (EDA)

EDA is the process of visually exploring data to understand its structure and patterns.

1. Understand Distributions

Use histograms and box plots to see how data is spread.

2. Find Relationships

Scatter plots and correlation heatmaps reveal connections between variables.

3. Detect Outliers

Box plots and scatter plots help identify unusual data points.

4. Compare Groups

Bar charts and violin plots show differences between categories.

Correlation Heatmap

Visualize relationships between multiple variables at once.

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Output:

Click "Run Code" to see output

Best Practices

✓Choose the right chart for your data type

✓Use clear, descriptive titles and labels

✓Avoid 3D charts (they distort perception)

✓Use color purposefully, not decoratively

✓Start y-axis at zero for bar charts

✓Keep it simple—remove chart junk

Pro Tip: Always visualize your data before modeling. You'll catch errors, understand distributions, and get ideas for feature engineering.