Python for AI (Part 4): Visualizing Data with Matplotlib
Introduction
This is Part 4 in your Python-for-AI journey! And if you’ve got the hang of Pandas from Part 3, you’re all set to tackle Matplotlib —Python’s workhorse for data visualization. In AI, data visualization allows you to discover patterns, identify outliers, and communicate your findings. Building on your Java skills and using your knowledge of Python background, this tutorial is where we get down & dirty with practical examples.
What is Matplotlib and Why Use It?
Matplotlib generates line graphs, bar charts, histograms, etc. It is critical for understanding datasets and model results in AI.
- Plot data distributions to check for outliers.
- Visualize trends (e.g., temperature over time).
- Show model performance (e.g., accuracy over iterations).
To get started, install Matplotlib:
pip install matplotlib
Then import it (we’ll use Pandas too, since it pairs nicely):
import matplotlib.pyplot as plt import pandas as pd import numpy as np
Basic Plotting: Line Plot
Let’s start with a simple line plot using the student data from Part 3:
# Data from Part 3
data = {
'Name': ['Alice', 'Bob', 'Charlie', 'Dana'],
'Math': [85, 92, 78, 95],
'Science': [88, 85, 90, 92]
}
df = pd.DataFrame(data)
# Plot Math scores
plt.plot(df['Name'], df['Math'], marker='o', label='Math')
plt.plot(df['Name'], df['Science'], marker='s', label='Science')
plt.title('Student Exam Scores')
plt.xlabel('Student')
plt.ylabel('Score')
plt.legend()
plt.grid(True)
plt.show()
What’s Happening:
plt.plot()creates a line with points (marker='o'for circles,'s'for squares).title,xlabel,ylabellabel the plot.legendshows which line is which.gridadds a background grid.
Output: A line graph comparing Math and Science scores across students.
This plots Math and Science scores with markers and a legend—great for tracking trends.
Bar Chart: Comparing Categories
Bar charts excel at comparisons. Plot average scores:
# Add Average column
df['Average'] = (df['Math'] + df['Science']) / 2
# Bar chart
plt.bar(df['Name'], df['Average'], color=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728'])
plt.title('Average Scores by Student')
plt.xlabel('Student')
plt.ylabel('Average Score')
plt.show()
What’s Happening:
plt.bar()makes bars, with custom colors for each student.- No lines, just bars—great for comparisons.
Output: A colorful bar chart showing each student’s average score.
Each bar gets a unique color—perfect for quick visual insights.
Scatter Plot: Exploring Relationships
Scatter plots reveal relationships between variables. Let’s plot Math vs. Science scores:
plt.scatter(df['Math'], df['Science'], color='purple', s=100, alpha=0.6)
plt.title('Math vs. Science Scores')
plt.xlabel('Math Score')
plt.ylabel('Science Score')
for i, name in enumerate(df['Name']):
plt.annotate(name, (df['Math'][i], df['Science'][i]), xytext=(5, 5), textcoords='offset points')
plt.show()
What’s Happening:
plt.scatter()plots points (sfor size,alphafor transparency).annotatelabels each point with the student’s name.
Output: A scatter plot showing if Math and Science scores correlate, with names next to points.
Points are labeled with names—ideal for spotting correlations in AI data.
Subplots: Multiple Views
In AI, you often compare multiple aspects. Subplots let you do this in one figure. Subplots compare data side by side:
# 1 row, 2 columns
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4))
# Plot 1: Math scores
ax1.plot(df['Name'], df['Math'], 'b-o')
ax1.set_title('Math Scores')
ax1.set_xlabel('Student')
ax1.set_ylabel('Score')
# Plot 2: Science scores
ax2.plot(df['Name'], df['Science'], 'g-s')
ax2.set_title('Science Scores')
ax2.set_xlabel('Student')
ax2.set_ylabel('Score')
# Adjust spacing
plt.tight_layout()
plt.show()
What’s Happening:
subplots()creates two side-by-side plots.- Each
axobject is a separate plot with its own settings.
Output: Two line plots comparing Math and Science scores side by side.
Two plots in one—handy for multi-angle analysis in AI.
Time Series: Real-World AI Example
Imagine you’re analyzing daily temperatures for an AI weather model. Visualize daily temperatures for a weather model:
# Weather data
weather = pd.DataFrame({
'Day': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'],
'Temp': [22, 25, 19, 28, 24]
})
plt.plot(weather['Day'], weather['Temp'], 'r-^', label='Temperature')
plt.title('Daily Temperatures')
plt.xlabel('Day')
plt.ylabel('Temperature (°C)')
plt.legend()
plt.xticks(rotation=45) # Rotate x-axis labels
plt.show()
What’s Happening:
- A time series plot with triangle markers (
'^') and rotated labels for readability. - Each
axobject is a separate plot with its own settings.
Output: A plot showing temperature trends over days—perfect for spotting patterns.
Rotated labels keep it readable—great for time-based AI tasks.
Histogram: Data Distribution
In AI, histograms help you understand data spread. Let’s plot all scores:
all_scores = pd.concat([df['Math'], df['Science']])
plt.hist(all_scores, bins=5, color='skyblue', edgecolor='black')
plt.title('Distribution of All Scores')
plt.xlabel('Score')
plt.ylabel('Frequency')
plt.show()
What’s Happening:
plt.hist()bins scores into 5 groups.edgecoloroutlines bars for clarity.
Output: A histogram showing how scores are distributed—useful for checking normality in AI datasets.
This helps check data normality—a must for AI preprocessing.
Try It Yourself: An Exercise
Use this weather data:
weather = pd.DataFrame({
'Day': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'],
'Temp': [22, 25, 19, 28, 24],
'Rain': [0.1, 0.0, 0.3, 0.2, 0.0]
})
Tasks:
- Create a bar chart of temperatures.
- Add a second bar chart for rainfall using dual axes (hint:
plt.twinx()). - Label everything clearly.
Try it, then check the solution!
Solution
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| # Import required libraries (assumed to be imported earlier in the script) | |
| import pandas as pd | |
| import matplotlib.pyplot as plt | |
| # Step 1: Create a DataFrame with weather data | |
| # - 'Day' column lists days of the week (categorical x-axis data) | |
| # - 'Temp' column holds temperature values in Celsius (numerical data for first y-axis) | |
| # - 'Rain' column holds rainfall values in millimeters (numerical data for second y-axis) | |
| weather = pd.DataFrame({ | |
| 'Day': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'], | |
| 'Temp': [22, 25, 19, 28, 24], | |
| 'Rain': [0.1, 0.0, 0.3, 0.2, 0.0] | |
| }) | |
| # Step 2: Initialize a figure and primary axes | |
| # - `plt.subplots()` creates a figure (fig) and one set of axes (ax1) | |
| # - `fig` controls the overall plot, while `ax1` is for the temperature bars | |
| fig, ax1 = plt.subplots() | |
| # Step 3: Plot temperature bars on the primary axes (ax1) | |
| # - `ax1.bar()` creates bars with 'Day' on x-axis and 'Temp' on y-axis | |
| # - `color='lightblue'` sets a distinct color for temperature bars | |
| # - `label='Temp'` names this data for the legend | |
| ax1.bar(weather['Day'], weather['Temp'], color='lightblue', label='Temp') | |
| # Step 4: Label the x-axis (shared by both plots) | |
| # - `set_xlabel('Day')` labels the x-axis as days of the week | |
| ax1.set_xlabel('Day') | |
| # Step 5: Label the primary y-axis (temperature) | |
| # - `set_ylabel()` names the y-axis for temperature, with units (°C) | |
| # - `color='blue'` matches the label color to the bars for clarity | |
| ax1.set_ylabel('Temperature (°C)', color='blue') | |
| # Step 6: Customize the primary y-axis ticks | |
| # - `tick_params()` sets the y-axis tick labels to blue, aligning with the bars and label | |
| # - `axis='y'` specifies we’re modifying the y-axis (not x) | |
| ax1.tick_params(axis='y', labelcolor='blue') | |
| # Step 7: Create a secondary y-axis sharing the same x-axis | |
| # - `ax1.twinx()` generates `ax2`, a second axes overlaid on `ax1` | |
| # - This allows plotting rainfall on a separate y-axis while sharing the 'Day' x-axis | |
| ax2 = ax1.twinx() | |
| # Step 8: Plot rainfall bars on the secondary axes (ax2) | |
| # - `ax2.bar()` creates bars for 'Rain' on the same 'Day' x-axis | |
| # - `color='lightgreen'` distinguishes rainfall bars from temperature | |
| # - `alpha=0.6` adds transparency to avoid fully overlapping temperature bars | |
| # - `label='Rain'` names this data for the legend | |
| ax2.bar(weather['Day'], weather['Rain'], color='lightgreen', alpha=0.6, label='Rain') | |
| # Step 9: Label the secondary y-axis (rainfall) | |
| # - `set_ylabel()` names the y-axis for rainfall, with units (mm) | |
| # - `color='green'` matches the label to the rainfall bars | |
| ax2.set_ylabel('Rainfall (mm)', color='green') | |
| # Step 10: Customize the secondary y-axis ticks | |
| # - `tick_params()` sets the y-axis tick labels to green, aligning with the bars and label | |
| ax2.tick_params(axis='y', labelcolor='green') | |
| # Step 11: Add a title to the plot | |
| # - `plt.title()` sets a descriptive title at the top of the figure | |
| # - Applies to the entire plot (both axes) | |
| plt.title('Temperature and Rainfall by Day') | |
| # Step 12: Add a legend for both datasets | |
| # - `fig.legend()` creates a combined legend for `ax1` (Temp) and `ax2` (Rain) | |
| # - `loc='upper center'` places it above the plot | |
| # - `bbox_to_anchor=(0.5, -0.05)` shifts it below the plot area (centered) | |
| # - `ncol=2` arranges labels side by side (Temp | Rain) | |
| fig.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), ncol=2) | |
| # Step 13: Display the plot | |
| # - `plt.show()` renders the figure with both bar charts and dual axes | |
| plt.show() |
This dual-axis plot compares temperature and rainfall elegantly.
Code Demo
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| # Import required libraries (assumed to be imported earlier in the script) | |
| import matplotlib.pyplot as plt | |
| import pandas as pd | |
| # Basic Plotting: Line Plot | |
| # Let’s start with a simple line plot using the student data from Part 3: | |
| data = { | |
| 'Name': ['Alice', 'Bob', 'Charlie', 'Dana'], | |
| 'Math': [85, 92, 78, 95], | |
| 'Science': [88, 85, 90, 92] | |
| } | |
| df = pd.DataFrame(data) | |
| # Plot Math scores | |
| plt.plot(df['Name'], df['Math'], marker='o', label='Math') | |
| plt.plot(df['Name'], df['Science'], marker='s', label='Science') | |
| plt.title('Student Exam Scores') | |
| plt.xlabel('Student') | |
| plt.ylabel('Score') | |
| plt.legend() | |
| plt.grid(True) | |
| plt.show() | |
| # Bar Chart: Comparing Categories | |
| # For categorical data, bar charts shine. Let’s plot average scores: | |
| # Add Average column | |
| df['Average'] = (df['Math'] + df['Science']) / 2 | |
| # Bar chart | |
| plt.bar(df['Name'], df['Average'], color=['#1f77b4', '#ff7f0e', '#2ca02c', '#d62728']) | |
| plt.title('Average Scores by Student') | |
| plt.xlabel('Student') | |
| plt.ylabel('Average Score') | |
| plt.show() | |
| # Scatter Plot: Exploring Relationships | |
| # Scatter plots reveal relationships between variables. | |
| # Let’s plot Math vs. Science scores: | |
| plt.scatter(df['Math'], df['Science'], color='purple', s=100, alpha=0.6) | |
| plt.title('Math vs. Science Scores') | |
| plt.xlabel('Math Score') | |
| plt.ylabel('Science Score') | |
| for i, name in enumerate(df['Name']): | |
| plt.annotate(name, (df['Math'][i], df['Science'][i]), xytext=(5, 5), textcoords='offset points') | |
| plt.show() | |
| # Subplots: Multiple Views | |
| # In AI, you often compare multiple aspects. | |
| # Subplots let you do this in one figure: | |
| fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(10, 4)) # 1 row, 2 columns | |
| # Plot 1: Math scores | |
| ax1.plot(df['Name'], df['Math'], 'b-o') | |
| ax1.set_title('Math Scores') | |
| ax1.set_xlabel('Student') | |
| ax1.set_ylabel('Score') | |
| # Plot 2: Science scores | |
| ax2.plot(df['Name'], df['Science'], 'g-s') | |
| ax2.set_title('Science Scores') | |
| ax2.set_xlabel('Student') | |
| ax2.set_ylabel('Score') | |
| plt.tight_layout() # Adjust spacing | |
| plt.show() | |
| # Real-World AI Example: Time Series | |
| # Imagine you’re analyzing daily temperatures for an AI weather model: | |
| # Weather data | |
| weather = pd.DataFrame({ | |
| 'Day': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'], | |
| 'Temp': [22, 25, 19, 28, 24] | |
| }) | |
| plt.plot(weather['Day'], weather['Temp'], 'r-^', label='Temperature') | |
| plt.title('Daily Temperatures') | |
| plt.xlabel('Day') | |
| plt.ylabel('Temperature (°C)') | |
| plt.legend() | |
| plt.xticks(rotation=45) # Rotate x-axis labels | |
| plt.show() | |
| # Histogram: Data Distribution | |
| # In AI, histograms help you understand data spread. | |
| # Let’s plot all scores: | |
| all_scores = pd.concat([df['Math'], df['Science']]) | |
| plt.hist(all_scores, bins=5, color='skyblue', edgecolor='black') | |
| plt.title('Distribution of All Scores') | |
| plt.xlabel('Score') | |
| plt.ylabel('Frequency') | |
| plt.show() | |
| # Exercise | |
| # 1. Create a bar chart of temperatures. | |
| # 2. Add a second bar chart for rainfall using dual axes (hint: plt.twinx()). | |
| # 3. Label everything clearly. | |
| # Solution: | |
| # Step 1: Create a DataFrame with weather data | |
| # - 'Day' column lists days of the week (categorical x-axis data) | |
| # - 'Temp' column holds temperature values in Celsius (numerical data for first y-axis) | |
| # - 'Rain' column holds rainfall values in millimeters (numerical data for second y-axis) | |
| weather = pd.DataFrame({ | |
| 'Day': ['Mon', 'Tue', 'Wed', 'Thu', 'Fri'], | |
| 'Temp': [22, 25, 19, 28, 24], | |
| 'Rain': [0.1, 0.0, 0.3, 0.2, 0.0] | |
| }) | |
| # Step 2: Initialize a figure and primary axes | |
| # - `plt.subplots()` creates a figure (fig) and one set of axes (ax1) | |
| # - `fig` controls the overall plot, while `ax1` is for the temperature bars | |
| fig, ax1 = plt.subplots() | |
| # Step 3: Plot temperature bars on the primary axes (ax1) | |
| # - `ax1.bar()` creates bars with 'Day' on x-axis and 'Temp' on y-axis | |
| # - `color='lightblue'` sets a distinct color for temperature bars | |
| # - `label='Temp'` names this data for the legend | |
| ax1.bar(weather['Day'], weather['Temp'], color='lightblue', label='Temp') | |
| # Step 4: Label the x-axis (shared by both plots) | |
| # - `set_xlabel('Day')` labels the x-axis as days of the week | |
| ax1.set_xlabel('Day') | |
| # Step 5: Label the primary y-axis (temperature) | |
| # - `set_ylabel()` names the y-axis for temperature, with units (°C) | |
| # - `color='blue'` matches the label color to the bars for clarity | |
| ax1.set_ylabel('Temperature (°C)', color='blue') | |
| # Step 6: Customize the primary y-axis ticks | |
| # - `tick_params()` sets the y-axis tick labels to blue, aligning with the bars and label | |
| # - `axis='y'` specifies we’re modifying the y-axis (not x) | |
| ax1.tick_params(axis='y', labelcolor='blue') | |
| # Step 7: Create a secondary y-axis sharing the same x-axis | |
| # - `ax1.twinx()` generates `ax2`, a second axes overlaid on `ax1` | |
| # - This allows plotting rainfall on a separate y-axis while sharing the 'Day' x-axis | |
| ax2 = ax1.twinx() | |
| # Step 8: Plot rainfall bars on the secondary axes (ax2) | |
| # - `ax2.bar()` creates bars for 'Rain' on the same 'Day' x-axis | |
| # - `color='lightgreen'` distinguishes rainfall bars from temperature | |
| # - `alpha=0.6` adds transparency to avoid fully overlapping temperature bars | |
| # - `label='Rain'` names this data for the legend | |
| ax2.bar(weather['Day'], weather['Rain'], color='lightgreen', alpha=0.6, label='Rain') | |
| # Step 9: Label the secondary y-axis (rainfall) | |
| # - `set_ylabel()` names the y-axis for rainfall, with units (mm) | |
| # - `color='green'` matches the label to the rainfall bars | |
| ax2.set_ylabel('Rainfall (mm)', color='green') | |
| # Step 10: Customize the secondary y-axis ticks | |
| # - `tick_params()` sets the y-axis tick labels to green, aligning with the bars and label | |
| ax2.tick_params(axis='y', labelcolor='green') | |
| # Step 11: Add a title to the plot | |
| # - `plt.title()` sets a descriptive title at the top of the figure | |
| # - Applies to the entire plot (both axes) | |
| plt.title('Temperature and Rainfall by Day') | |
| # Step 12: Add a legend for both datasets | |
| # - `fig.legend()` creates a combined legend for `ax1` (Temp) and `ax2` (Rain) | |
| # - `loc='upper center'` places it above the plot | |
| # - `bbox_to_anchor=(0.5, -0.05)` shifts it below the plot area (centered) | |
| # - `ncol=2` arranges labels side by side (Temp | Rain) | |
| fig.legend(loc='upper center', bbox_to_anchor=(0.5, -0.05), ncol=2) | |
| # Step 13: Display the plot | |
| # - `plt.show()` renders the figure with both bar charts and dual axes | |
| plt.show() |
Next Steps
You’re now a Matplotlib pro! Then do some advanced plotting with Seaborn or start using it for machine learning with scikit-learn. Try out this exercise — change colors or add grids — then tell me what comes next!
Conclusion
Explore AI with Data Visualization in Matplotlib Basics You’ve trained yourself to plot, compare, and analyze data—essential Java-to-Python mapping to gear your expertise. Stay course and you are headed for the path of AI success!
