Three Ways of Drawing

Matplotlib behaves differently depending on where you run your code: Jupyter Notebook, Python files, or the terminal.

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Jupyter Notebook

In Jupyter notebooks, plots render automatically inline.

import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 2*np.pi, 100)
y = np.sin(x)

plt.plot(x, y)
# No plt.show() needed - displays automatically

Key points:

• Neither plt.show() nor plt.draw() are required
• Plots appear below the cell
• Use %matplotlib inline for static images
• Use %matplotlib notebook for interactive plots

Magic Commands

%matplotlib inline    # Static images (default in modern Jupyter)
%matplotlib notebook  # Interactive (zoom, pan)
%matplotlib widget    # Interactive in JupyterLab

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Python File (.py)

When running Python scripts, you must explicitly display plots.

# script.py
import matplotlib.pyplot as plt
import numpy as np

x = np.linspace(0, 2*np.pi, 100)
y = np.sin(x)

plt.plot(x, y)
plt.show()  # Required - opens interactive window

Key points:

• plt.show() starts an event loop
• Opens one or more interactive windows
• Script blocks until windows are closed
• Looks for all active figure objects

Running the Script

python script.py

A window will open displaying the plot. The script continues after you close the window.

---

Terminal (Interactive Python)

In an interactive Python session, use plt.draw() for updates.

>>> import matplotlib.pyplot as plt
>>> import numpy as np
>>> 
>>> plt.ion()  # Turn on interactive mode
>>> x = np.linspace(0, 10, 100)
>>> plt.plot(x, np.sin(x))
>>> plt.draw()  # Updates the figure

Key points:

• plt.ion() enables interactive mode
• plt.draw() updates the figure without blocking
• plt.ioff() disables interactive mode
• Useful for dynamic updates

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Comparison

Environment	Display Method	Blocking	Interactive
Jupyter	Automatic	No	Optional
Python file	plt.show()	Yes	Yes
Terminal	plt.draw()	No	Yes

---

plt.show() vs plt.draw()

plt.show()

• Starts the event loop
• Blocks execution until window closed
• Displays all pending figures
• Used in scripts

plt.plot([1, 2, 3])
plt.show()  # Blocks here
print("After show")  # Runs after window closed

plt.draw()

• Redraws the current figure
• Non-blocking
• Used for animations and updates
• Requires interactive mode

plt.ion()
plt.plot([1, 2, 3])
plt.draw()  # Non-blocking
print("Continues immediately")

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Best Practices

1. Jupyter: Let automatic display work; use magic commands for interactivity
2. Scripts: Always end with plt.show()
3. Interactive: Use plt.ion() and plt.draw() for dynamic updates
4. Saving: Use plt.savefig() before plt.show() in scripts

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Key Takeaways

• Jupyter: automatic display, no show() needed
• Python files: plt.show() required, blocks until closed
• Terminal: plt.draw() for non-blocking updates
• plt.ion() enables interactive mode
• Choose the right approach for your environment

---

Runnable Example: matplotlib_cheatsheet.py

"""
MATPLOTLIB QUICK REFERENCE CHEAT SHEET
======================================

This is a condensed reference for quick lookup while coding.
For detailed explanations, see the full course files.
"""

# ============================================================================
# IMPORTS
# ============================================================================

import matplotlib.pyplot as plt
import numpy as np

# ============================================================================
# TWO PLOTTING STYLES
# ============================================================================

# MATLAB-Style (Simple, for quick plots)

if __name__ == "__main__":
    plt.plot(x, y)
    plt.xlabel('X Label')
    plt.ylabel('Y Label')
    plt.title('Title')
    plt.show()

    # OOP-Style (Recommended for complex plots)
    fig, ax = plt.subplots()
    ax.plot(x, y)
    ax.set_xlabel('X Label')
    ax.set_ylabel('Y Label')
    ax.set_title('Title')
    plt.show()

    # ============================================================================
    # METHOD NAME DIFFERENCES
    # ============================================================================

    """
    MATLAB-style              OOP-style
    -------------            ----------------
    plt.xlabel()      →      ax.set_xlabel()
    plt.ylabel()      →      ax.set_ylabel()
    plt.title()       →      ax.set_title()
    plt.xlim()        →      ax.set_xlim()
    plt.ylim()        →      ax.set_ylim()
    plt.xticks()      →      ax.set_xticks()
    plt.yticks()      →      ax.set_yticks()
    plt.legend()      →      ax.legend()        # SAME
    plt.grid()        →      ax.grid()          # SAME
    plt.plot()        →      ax.plot()          # SAME
    """

    # ============================================================================
    # CREATING SUBPLOTS
    # ============================================================================

    # Single plot
    fig, ax = plt.subplots()

    # Multiple plots (2 rows, 3 columns)
    fig, axes = plt.subplots(2, 3, figsize=(12, 8))
    # axes.shape = (2, 3)
    # Access: axes[row, col]

    # With shared axes
    fig, axes = plt.subplots(2, 1, sharex=True)

    # Flatten for easy iteration
    axes_flat = axes.flatten()

    # ============================================================================
    # PLOT() - LINE PLOTS
    # ============================================================================

    # Basic
    ax.plot(x, y)

    # With format string: '[marker][line][color]'
    ax.plot(x, y, 'ro-')   # Red circles with solid line
    ax.plot(x, y, 'b--')   # Blue dashed line
    ax.plot(x, y, 'g:s')   # Green dotted with squares

    # With keyword arguments
    ax.plot(x, y, 
            color='red',              # or 'r' or '#FF0000'
            linestyle='--',           # or '--'
            linewidth=2,              # or lw
            marker='o',
            markersize=8,             # or ms
            markerfacecolor='blue',   # or mfc
            markeredgecolor='black',  # or mec
            alpha=0.7,
            label='My Line')

    # Multiple lines
    ax.plot(x, y1, 'r-', label='Line 1')
    ax.plot(x, y2, 'b--', label='Line 2')
    ax.legend()

    # ============================================================================
    # HIST() - HISTOGRAMS
    # ============================================================================

    # Basic histogram
    n, bins, patches = ax.hist(data, bins=30)

    # With explicit bin edges (can be unequal!)
    bins = [0, 1, 2, 5, 10]
    n, bins, patches = ax.hist(data, bins=bins)

    # Probability density (area = 1.0)
    n, bins, patches = ax.hist(data, bins=30, density=True)

    # Customization
    ax.hist(data,
            bins=30,                  # int or array
            density=False,            # False=counts, True=density
            cumulative=False,         # False or True
            histtype='bar',           # 'bar', 'step', 'stepfilled'
            color='blue',
            edgecolor='black',
            alpha=0.7,
            label='My Data')

    # Return values
    n       # Array of counts or densities (length = num_bins)
    bins    # Array of bin edges (length = num_bins + 1)
    patches # List of bar objects

    # Key formula for density
    # density[i] = count[i] / (N × bin_width[i])

    # ============================================================================
    # COLORS
    # ============================================================================

    # Short codes
    'r' 'g' 'b' 'c' 'm' 'y' 'k' 'w'

    # Full names
    'red' 'green' 'blue' 'cyan' 'magenta' 'yellow' 'black' 'white'

    # Hex codes
    '#FF0000'  # Red
    '#00FF00'  # Green
    '#0000FF'  # Blue

    # RGB tuple (0-1)
    (1.0, 0.0, 0.0)  # Red

    # RGBA tuple (0-1, with alpha)
    (1.0, 0.0, 0.0, 0.5)  # Semi-transparent red

    # ============================================================================
    # LINE STYLES
    # ============================================================================

    '-'   # Solid
    '--'  # Dashed
    ':'   # Dotted
    '-.'  # Dash-dot
    ''    # No line

    # ============================================================================
    # MARKERS
    # ============================================================================

    '.'  # Point
    'o'  # Circle
    's'  # Square
    '^'  # Triangle up
    'v'  # Triangle down
    '*'  # Star
    '+'  # Plus
    'x'  # X
    'D'  # Diamond

    # ============================================================================
    # CUSTOMIZATION
    # ============================================================================

    # Labels and title
    ax.set_xlabel('X Label', fontsize=12)
    ax.set_ylabel('Y Label', fontsize=12)
    ax.set_title('Title', fontsize=14, fontweight='bold')

    # Limits
    ax.set_xlim(0, 10)
    ax.set_ylim(-1, 1)

    # Ticks
    ax.set_xticks([0, 2, 4, 6, 8, 10])
    ax.set_xticklabels(['0', '2', '4', '6', '8', '10'])

    # Grid
    ax.grid(True)
    ax.grid(True, alpha=0.3, linestyle='--')

    # Legend
    ax.legend()
    ax.legend(loc='upper right', fontsize=12, framealpha=0.9)

    # Spines (hide top and right)
    ax.spines['top'].set_visible(False)
    ax.spines['right'].set_visible(False)

    # ============================================================================
    # TEXT AND ANNOTATIONS
    # ============================================================================

    # Simple text
    ax.text(5, 0.5, 'My text', fontsize=12)

    # Text with box
    ax.text(5, 0.5, 'Text', bbox=dict(boxstyle='round', facecolor='wheat'))

    # Annotation with arrow
    ax.annotate('Point of interest',
                xy=(5, 0.5),      # Point to annotate
                xytext=(6, 0.8),  # Text position
                arrowprops=dict(arrowstyle='->'))

    # ============================================================================
    # FIGURE AND AXES
    # ============================================================================

    # Figure size
    fig, ax = plt.subplots(figsize=(10, 6))  # Width, height in inches

    # DPI (resolution)
    fig, ax = plt.subplots(dpi=150)

    # Tight layout (auto-adjust spacing)
    plt.tight_layout()

    # Save figure
    plt.savefig('figure.png', dpi=300, bbox_inches='tight')

    # ============================================================================
    # GRIDSPEC (COMPLEX LAYOUTS)
    # ============================================================================

    import matplotlib.gridspec as gridspec

    fig = plt.figure(figsize=(12, 8))
    gs = gridspec.GridSpec(3, 3, figure=fig)

    # Span multiple cells
    ax1 = fig.add_subplot(gs[0, :])    # Row 0, all columns
    ax2 = fig.add_subplot(gs[1:, 0])   # Rows 1-2, column 0
    ax3 = fig.add_subplot(gs[1, 1:])   # Row 1, columns 1-2

    # ============================================================================
    # COMMON PATTERNS
    # ============================================================================

    # Single plot with customization
    fig, ax = plt.subplots(figsize=(10, 6))
    ax.plot(x, y, 'b-', linewidth=2, label='Data')
    ax.set_xlabel('X', fontsize=12)
    ax.set_ylabel('Y', fontsize=12)
    ax.set_title('My Plot', fontsize=14, fontweight='bold')
    ax.legend()
    ax.grid(True, alpha=0.3)
    plt.tight_layout()
    plt.show()

    # Histogram with density and PDF overlay
    fig, ax = plt.subplots(figsize=(10, 6))
    n, bins, patches = ax.hist(data, bins=30, density=True, alpha=0.7, label='Data')
    ax.plot(x_pdf, pdf, 'r-', linewidth=3, label='Theory')
    ax.set_xlabel('Value', fontsize=12)
    ax.set_ylabel('Probability Density', fontsize=12)
    ax.legend()
    plt.show()

    # Multiple subplots
    fig, axes = plt.subplots(2, 2, figsize=(12, 10))
    for i, ax in enumerate(axes.flatten()):
        ax.plot(x, y[i])
        ax.set_title(f'Plot {i+1}')
    plt.tight_layout()
    plt.show()

    # ============================================================================
    # DEBUGGING CHECKLIST
    # ============================================================================

    """
    Common Mistakes:
    1. Using plt.xlabel() instead of ax.set_xlabel() in OOP style
    2. Forgetting that axes[row, col] (not axes[col, row])
    3. Not unpacking hist() return values when needed
    4. Confusing density=True (area=1) with density=False (counts)
    5. Accessing axes[2, 1] when shape is (2, 2) (out of bounds)
    6. Not calling plt.show() at the end
    7. Forgetting that bins array has length = num_bins + 1
    """

    # ============================================================================
    # QUICK DIAGNOSIS
    # ============================================================================

    """
    Error: 'AxesSubplot' object has no attribute 'xlabel'
    Fix: Use ax.set_xlabel() not ax.xlabel()

    Error: IndexError: index 2 is out of bounds for axis 0 with size 2
    Fix: Check axes.shape, remember axes[row, col]

    Issue: Histogram looks weird
    Check: bins parameter, try different values

    Issue: Want to overlay PDF but scales don't match
    Fix: Use density=True in hist()

    Issue: Can't access individual subplots
    Fix: Use axes.flatten() to convert to 1D array
    """

    print("Cheat sheet loaded! Use as quick reference while coding.")