A practical guide to Python visualization
Answer: Matplotlib is Python’s foundational 2D plotting library, created by John Hunter in 2003. It provides:
pyplot (MATLAB-style) and powerful object-oriented APIimport numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
print(f"Matplotlib version: {mpl.__version__}")
print(f"Available backends: {mpl.rcsetup.all_backends[:5]}...")Matplotlib version: 3.9.4
Available backends: ['gtk3agg', 'gtk3cairo', 'gtk4agg', 'gtk4cairo', 'macosx']...If Matplotlib is missing, install it with:
pip install matplotlibAnswer: Every Matplotlib visualization consists of:
fig, ax = plt.subplots(figsize=(8, 5))
# Demonstrate figure anatomy
ax.set_title("Figure Anatomy", fontsize=14, fontweight="bold")
ax.set_xlabel("X-axis (horizontal)")
ax.set_ylabel("Y-axis (vertical)")
ax.text(0.5, 0.5, "This is the Axes\n(plot area)", ha="center", va="center",
fontsize=12, bbox=dict(boxstyle="round", facecolor="lightblue"))
ax.set_xlim(0, 1)
ax.set_ylim(0, 1)
ax.grid(alpha=0.3)
# Add annotations showing parts
ax.annotate("Title", xy=(0.5, 1.02), xycoords="axes fraction", ha="center", fontsize=10, color="red")
ax.annotate("Spines", xy=(0, 0.5), xycoords="axes fraction", ha="right", fontsize=9, color="green")
fig.text(0.02, 0.5, "Figure boundary →", rotation=90, va="center", fontsize=9, color="purple")
plt.tight_layout()
plt.show()
plt (pyplot) and the object-oriented API?Answer:
| Aspect | pyplot (plt) |
Object-Oriented (OO) |
|---|---|---|
| Style | MATLAB-like, stateful | Pythonic, explicit |
| Use case | Quick plots, exploration | Complex figures, automation |
| Control | Limited | Full control over every element |
| Reusability | Harder to reuse | Easy to create functions |
# pyplot style (quick but less control)
plt.figure(figsize=(6, 3))
plt.plot([1, 2, 3], [1, 4, 9])
plt.title("Pyplot Style")
plt.show()
# Object-oriented style (recommended for complex work)
fig, ax = plt.subplots(figsize=(6, 3))
ax.plot([1, 2, 3], [1, 4, 9])
ax.set_title("Object-Oriented Style")
plt.show()
Answer: Use plt.plot() with x and y arrays, then add labels and show.
x = np.linspace(0, 10, 100)
y = np.sin(x)
plt.figure(figsize=(8, 4))
plt.plot(x, y, color="royalblue", linewidth=2)
plt.title("Sine Wave", fontsize=14)
plt.xlabel("x")
plt.ylabel("sin(x)")
plt.grid(alpha=0.3)
plt.show()
Answer: Matplotlib offers extensive customization:
Line styles: - (solid), -- (dashed), -. (dash-dot), : (dotted)
Markers: o (circle), s (square), ^ (triangle), D (diamond), * (star), +, x
Colors: Named colors, hex codes, RGB tuples, or color cycle shortcuts (C0, C1, etc.)
fig, axs = plt.subplots(1, 3, figsize=(14, 4))
x = np.linspace(0, 2, 20)
# Line styles
for i, ls in enumerate(['-', '--', '-.', ':']):
axs[0].plot(x, x + i*0.5, linestyle=ls, linewidth=2, label=f"'{ls}'")
axs[0].set_title("Line Styles")
axs[0].legend()
axs[0].grid(alpha=0.3)
# Markers
markers = ['o', 's', '^', 'D', '*', 'p', 'h']
for i, m in enumerate(markers):
axs[1].plot(x[::2], (x[::2] + i*0.3), marker=m, linestyle='-', markersize=8, label=f"'{m}'")
axs[1].set_title("Markers")
axs[1].legend(ncol=2, fontsize=8)
axs[1].grid(alpha=0.3)
# Colors
colors = ['#e63946', '#457b9d', '#2a9d8f', '#f4a261', '#264653']
for i, c in enumerate(colors):
axs[2].bar(i, i+1, color=c, label=c)
axs[2].set_title("Custom Colors (Hex)")
axs[2].legend(fontsize=8)
plt.tight_layout()
plt.show()
Answer: Plot each line with a label parameter, then call plt.legend().
t = np.linspace(0, 2 * np.pi, 200)
plt.figure(figsize=(9, 4))
plt.plot(t, np.sin(t), label="sin(t)", linewidth=2)
plt.plot(t, np.cos(t), label="cos(t)", linewidth=2, linestyle="--")
plt.plot(t, np.sin(t) * np.cos(t), label="sin(t)·cos(t)", linewidth=2, linestyle="-.")
plt.title("Trigonometric Functions Comparison")
plt.xlabel("t (radians)")
plt.ylabel("Value")
plt.legend(loc="upper right")
plt.grid(alpha=0.25)
plt.axhline(y=0, color='k', linewidth=0.5)
plt.show()
Answer: Use xlim(), ylim(), xticks(), yticks() or their OO equivalents.
x = np.linspace(0, 10, 100)
y = np.exp(-x/3) * np.sin(2*x)
fig, ax = plt.subplots(figsize=(9, 4))
ax.plot(x, y, color="#2a9d8f", linewidth=2)
# Custom limits
ax.set_xlim(0, 8)
ax.set_ylim(-0.6, 1.0)
# Custom ticks
ax.set_xticks([0, 2, 4, 6, 8])
ax.set_xticklabels(['0', '2π/5', '4π/5', '6π/5', '8π/5'])
ax.set_yticks([-0.5, 0, 0.5, 1.0])
ax.set_title("Custom Axis Limits and Tick Labels")
ax.set_xlabel("Phase")
ax.set_ylabel("Amplitude")
ax.grid(alpha=0.3)
plt.show()
Answer: Use savefig() with the desired file extension. Matplotlib detects format from filename.
fig, ax = plt.subplots(figsize=(7, 4))
ax.plot(np.random.randn(50).cumsum(), color="steelblue", linewidth=2)
ax.set_title("Random Walk")
ax.grid(alpha=0.3)
# Save in multiple formats (uncomment to save)
# fig.savefig("plot.png", dpi=300, bbox_inches="tight") # Raster, web-friendly
# fig.savefig("plot.pdf", bbox_inches="tight") # Vector, publication
# fig.savefig("plot.svg", bbox_inches="tight") # Vector, scalable
# fig.savefig("plot.eps", bbox_inches="tight") # Vector, LaTeX compatible
plt.show()
print("Supported formats:", fig.canvas.get_supported_filetypes())
Supported formats: {'eps': 'Encapsulated Postscript', 'jpg': 'Joint Photographic Experts Group', 'jpeg': 'Joint Photographic Experts Group', 'pdf': 'Portable Document Format', 'pgf': 'PGF code for LaTeX', 'png': 'Portable Network Graphics', 'ps': 'Postscript', 'raw': 'Raw RGBA bitmap', 'rgba': 'Raw RGBA bitmap', 'svg': 'Scalable Vector Graphics', 'svgz': 'Scalable Vector Graphics', 'tif': 'Tagged Image File Format', 'tiff': 'Tagged Image File Format', 'webp': 'WebP Image Format'}Answer: Use grid() with axis, which, and linestyle parameters.
fig, axs = plt.subplots(1, 3, figsize=(14, 4))
x = np.linspace(0, 10, 100)
y = np.sin(x)
# Default grid
axs[0].plot(x, y)
axs[0].grid(True)
axs[0].set_title("Default Grid")
# Only horizontal grid
axs[1].plot(x, y)
axs[1].grid(axis='y', alpha=0.5)
axs[1].set_title("Horizontal Only")
# Major and minor grid
axs[2].plot(x, y)
axs[2].grid(which='major', linestyle='-', alpha=0.7)
axs[2].grid(which='minor', linestyle=':', alpha=0.4)
axs[2].minorticks_on()
axs[2].set_title("Major + Minor Grid")
plt.tight_layout()
plt.show()
Answer: Use axhline(), axvline(), hlines(), and vlines().
fig, ax = plt.subplots(figsize=(9, 5))
x = np.linspace(0, 10, 100)
y = np.sin(x)
ax.plot(x, y, linewidth=2, label="sin(x)")
# Reference lines
ax.axhline(y=0, color='k', linestyle='-', linewidth=0.8, label="y=0")
ax.axhline(y=0.5, color='red', linestyle='--', alpha=0.7, label="y=0.5 threshold")
ax.axvline(x=np.pi, color='green', linestyle=':', linewidth=2, label="x=π")
# Span regions
ax.axhspan(-0.2, 0.2, alpha=0.2, color='yellow', label="Safe zone")
ax.axvspan(6, 8, alpha=0.15, color='blue', label="Highlight region")
ax.set_xlim(0, 10)
ax.legend(loc="upper right", fontsize=9)
ax.set_title("Reference Lines and Spans")
ax.grid(alpha=0.3)
plt.show()
Create a simple line plot of \(y = x^2\) for x ∈ [0, 10].
Requirements: - Blue solid line with linewidth=2 - Title: “Quadratic Function” - X and Y axis labels - Grid enabled
Plot \(y = x\), \(y = x^2\), and \(y = x^3\) on the same figure for x ∈ [0, 3].
Requirements: - Different colors for each line - Line labels and legend - Add grid with alpha=0.3
Create a figure showing \(\sin(x)\), \(\sin(2x)\), and \(\sin(3x)\) for x ∈ [0, 2π].
Requirements: - Different line styles (solid, dashed, dotted) - Custom colors (not default) - Legend positioned at ‘upper right’ - Add horizontal line at y=0 - Custom x-ticks at [0, π/2, π, 3π/2, 2π] with labels [‘0’, ‘π/2’, ‘π’, ‘3π/2’, ‘2π’]
Plot \(y = e^{-x} \cdot \cos(2\pi x)\) for x ∈ [0, 5].
Requirements: - Highlight the region where |y| < 0.2 using axhspan - Add vertical lines at x = 1, 2, 3, 4 using axvline - Mark the envelope curves \(\pm e^{-x}\) with dashed lines - Proper annotations explaining what each element represents
Create a plot showing three exponential decay curves: \(e^{-x}\), \(e^{-2x}\), and \(e^{-0.5x}\) for x ∈ [0, 5].
Requirements: 1. Different line styles and colors for each curve 2. A horizontal line at y=0.1 (threshold) 3. Vertical line where \(e^{-x}\) crosses the threshold (x = ln(10) ≈ 2.303) 4. Annotation pointing to the intersection 5. LaTeX labels: \(e^{-x}\), \(e^{-2x}\), \(e^{-0.5x}\) 6. Legend with title “Decay Rates” 7. Save as both PNG (300 DPI) and SVG
Answer: Use bar() for vertical and barh() for horizontal bars.
categories = ['Python', 'JavaScript', 'Java', 'C++', 'Go']
values = [85, 72, 68, 45, 52]
colors = ['#264653', '#2a9d8f', '#e9c46a', '#f4a261', '#e76f51']
fig, axs = plt.subplots(1, 2, figsize=(12, 4))
# Vertical bars
axs[0].bar(categories, values, color=colors, edgecolor='white', linewidth=1.5)
axs[0].set_title("Programming Language Popularity")
axs[0].set_ylabel("Score")
axs[0].set_ylim(0, 100)
# Add value labels on bars
for i, (cat, val) in enumerate(zip(categories, values)):
axs[0].text(i, val + 2, str(val), ha='center', fontweight='bold')
# Horizontal bars
axs[1].barh(categories, values, color=colors, edgecolor='white', linewidth=1.5)
axs[1].set_title("Horizontal Bar Chart")
axs[1].set_xlabel("Score")
axs[1].set_xlim(0, 100)
axs[1].invert_yaxis() # Top category first
plt.tight_layout()
plt.show()
Answer: For grouped bars, offset the x positions. For stacked, use the bottom parameter.
categories = ['Q1', 'Q2', 'Q3', 'Q4']
product_a = [20, 35, 30, 35]
product_b = [25, 32, 34, 20]
product_c = [15, 25, 28, 30]
x = np.arange(len(categories))
width = 0.25
fig, axs = plt.subplots(1, 2, figsize=(13, 5))
# Grouped bars
bars1 = axs[0].bar(x - width, product_a, width, label='Product A', color='#264653')
bars2 = axs[0].bar(x, product_b, width, label='Product B', color='#2a9d8f')
bars3 = axs[0].bar(x + width, product_c, width, label='Product C', color='#e9c46a')
axs[0].set_xlabel('Quarter')
axs[0].set_ylabel('Sales')
axs[0].set_title('Grouped Bar Chart: Quarterly Sales')
axs[0].set_xticks(x)
axs[0].set_xticklabels(categories)
axs[0].legend()
axs[0].grid(axis='y', alpha=0.3)
# Stacked bars
axs[1].bar(categories, product_a, label='Product A', color='#264653')
axs[1].bar(categories, product_b, bottom=product_a, label='Product B', color='#2a9d8f')
axs[1].bar(categories, product_c, bottom=np.array(product_a)+np.array(product_b),
label='Product C', color='#e9c46a')
axs[1].set_xlabel('Quarter')
axs[1].set_ylabel('Total Sales')
axs[1].set_title('Stacked Bar Chart: Quarterly Sales')
axs[1].legend()
axs[1].grid(axis='y', alpha=0.3)
plt.tight_layout()
plt.show()
Answer: Use the s (size) and c (color) parameters with colormaps.
np.random.seed(42)
n = 100
x = np.random.randn(n)
y = x + np.random.randn(n) * 0.5
colors = np.random.rand(n)
sizes = np.abs(np.random.randn(n)) * 200 + 50
fig, axs = plt.subplots(1, 2, figsize=(13, 5))
# Basic scatter
axs[0].scatter(x, y, alpha=0.7, edgecolors='white', linewidth=0.5)
axs[0].set_title("Basic Scatter Plot")
axs[0].set_xlabel("X")
axs[0].set_ylabel("Y")
axs[0].grid(alpha=0.3)
# Scatter with size and color mapping
scatter = axs[1].scatter(x, y, c=colors, s=sizes, cmap='viridis',
alpha=0.7, edgecolors='white', linewidth=0.5)
axs[1].set_title("Scatter with Color & Size Mapping")
axs[1].set_xlabel("X")
axs[1].set_ylabel("Y")
axs[1].grid(alpha=0.3)
plt.colorbar(scatter, ax=axs[1], label="Color Value")
plt.tight_layout()
plt.show()
Answer: Use hist() with parameters like bins, density, histtype, alpha.
np.random.seed(42)
data1 = np.random.normal(0, 1, 1000)
data2 = np.random.normal(2, 1.5, 1000)
fig, axs = plt.subplots(2, 2, figsize=(12, 9))
# Basic histogram
axs[0, 0].hist(data1, bins=30, color='steelblue', edgecolor='white', alpha=0.8)
axs[0, 0].set_title("Basic Histogram")
axs[0, 0].set_xlabel("Value")
axs[0, 0].set_ylabel("Frequency")
# Density histogram (normalized)
axs[0, 1].hist(data1, bins=30, density=True, color='#2a9d8f', edgecolor='white', alpha=0.8)
axs[0, 1].set_title("Density Histogram (Normalized)")
axs[0, 1].set_xlabel("Value")
axs[0, 1].set_ylabel("Density")
# Overlapping histograms
axs[1, 0].hist(data1, bins=30, alpha=0.6, label='Distribution 1', color='#264653')
axs[1, 0].hist(data2, bins=30, alpha=0.6, label='Distribution 2', color='#e76f51')
axs[1, 0].set_title("Overlapping Histograms")
axs[1, 0].legend()
# Step histogram
axs[1, 1].hist(data1, bins=30, histtype='step', linewidth=2, label='Step', color='#264653')
axs[1, 1].hist(data1, bins=30, histtype='stepfilled', alpha=0.3, label='Filled', color='#264653')
axs[1, 1].set_title("Step Histogram Types")
axs[1, 1].legend()
for ax in axs.flat:
ax.grid(alpha=0.3)
plt.tight_layout()
plt.show()
Answer: Use pie() with explode for emphasis and a white center circle for donuts.
labels = ['Python', 'JavaScript', 'Java', 'C++', 'Others']
sizes = [35, 25, 20, 12, 8]
colors = ['#264653', '#2a9d8f', '#e9c46a', '#f4a261', '#e76f51']
explode = (0.05, 0, 0, 0, 0) # Explode Python slice
fig, axs = plt.subplots(1, 2, figsize=(13, 5))
# Basic pie chart
axs[0].pie(sizes, explode=explode, labels=labels, colors=colors, autopct='%1.1f%%',
shadow=False, startangle=90, textprops={'fontsize': 10})
axs[0].set_title("Pie Chart: Language Usage", fontsize=12)
# Donut chart
wedges, texts, autotexts = axs[1].pie(sizes, colors=colors, autopct='%1.1f%%',
startangle=90, pctdistance=0.75,
textprops={'fontsize': 9, 'color': 'white'})
# Create donut by adding white circle
centre_circle = plt.Circle((0, 0), 0.50, fc='white')
axs[1].add_patch(centre_circle)
axs[1].legend(wedges, labels, title="Languages", loc="center left", bbox_to_anchor=(0.9, 0.5))
axs[1].set_title("Donut Chart: Language Usage", fontsize=12)
plt.tight_layout()
plt.show()
plt.subplots()?Answer: subplots(nrows, ncols) returns a figure and array of axes.
fig, axs = plt.subplots(2, 3, figsize=(14, 8))
x = np.linspace(0, 2*np.pi, 100)
# Different plots in each subplot
axs[0, 0].plot(x, np.sin(x), 'b-')
axs[0, 0].set_title("sin(x)")
axs[0, 1].plot(x, np.cos(x), 'r--')
axs[0, 1].set_title("cos(x)")
axs[0, 2].plot(x, np.tan(x), 'g-.')
axs[0, 2].set_ylim(-5, 5)
axs[0, 2].set_title("tan(x) (clipped)")
axs[1, 0].bar(['A', 'B', 'C'], [3, 7, 5], color=['#264653', '#2a9d8f', '#e9c46a'])
axs[1, 0].set_title("Bar Chart")
axs[1, 1].scatter(np.random.rand(30), np.random.rand(30), c=np.random.rand(30), cmap='viridis')
axs[1, 1].set_title("Scatter Plot")
axs[1, 2].hist(np.random.randn(200), bins=15, color='steelblue', edgecolor='white')
axs[1, 2].set_title("Histogram")
# Add super title and common labels
fig.suptitle("Multiple Subplot Demonstration", fontsize=14, fontweight='bold')
fig.supxlabel("X-axis")
fig.supylabel("Y-axis")
plt.tight_layout()
plt.show()
Answer: Use sharex=True or sharey=True parameters.
fig, axs = plt.subplots(2, 2, figsize=(10, 8), sharex='col', sharey='row')
np.random.seed(42)
data = [np.random.randn(100) * scale + mean for scale, mean in [(1, 0), (1.5, 2), (0.5, -1), (2, 1)]]
for i, ax in enumerate(axs.flat):
ax.hist(data[i], bins=20, alpha=0.7, color=f'C{i}')
ax.set_title(f"Distribution {i+1}")
# Only outer axes have labels due to sharing
for ax in axs[-1, :]:
ax.set_xlabel("Value")
for ax in axs[:, 0]:
ax.set_ylabel("Frequency")
fig.suptitle("Shared Axes: Columns share X, Rows share Y", fontsize=12)
plt.tight_layout()
plt.show()
Answer: Use text() for static text and annotate() for text with arrows.
fig, ax = plt.subplots(figsize=(10, 6))
x = np.linspace(0, 10, 100)
y = np.sin(x) * np.exp(-x/10)
ax.plot(x, y, 'b-', linewidth=2)
# Find maximum
max_idx = np.argmax(y)
max_x, max_y = x[max_idx], y[max_idx]
# Annotation with arrow
ax.annotate(f'Maximum\n({max_x:.2f}, {max_y:.2f})',
xy=(max_x, max_y),
xytext=(max_x + 2, max_y + 0.3),
fontsize=10,
arrowprops=dict(arrowstyle='->', color='red', lw=1.5),
bbox=dict(boxstyle='round,pad=0.3', facecolor='lightyellow', edgecolor='orange'))
# Simple text
ax.text(7, 0.2, "Damped oscillation", fontsize=11, style='italic',
bbox=dict(boxstyle='round', facecolor='lightblue', alpha=0.5))
# Mathematical text using LaTeX
ax.text(5, -0.4, r'$y = \sin(x) \cdot e^{-x/10}$', fontsize=12)
ax.set_title("Annotations and Text Placement")
ax.set_xlabel("x")
ax.set_ylabel("y")
ax.grid(alpha=0.3)
ax.axhline(y=0, color='k', linewidth=0.5)
plt.show()
Answer: Use errorbar() with xerr and/or yerr parameters.
# Experimental data with uncertainties
x = np.array([1, 2, 3, 4, 5])
y = np.array([2.1, 3.9, 6.2, 7.8, 10.1])
y_err = np.array([0.3, 0.4, 0.5, 0.3, 0.6])
x_err = np.array([0.1, 0.1, 0.15, 0.1, 0.2])
fig, axs = plt.subplots(1, 3, figsize=(14, 4))
# Symmetric y-error bars
axs[0].errorbar(x, y, yerr=y_err, fmt='o-', capsize=4, capthick=2,
color='#264653', ecolor='#e76f51', markersize=8)
axs[0].set_title("Y Error Bars")
axs[0].grid(alpha=0.3)
# Both x and y error bars
axs[1].errorbar(x, y, xerr=x_err, yerr=y_err, fmt='s', capsize=3,
color='#2a9d8f', ecolor='gray', markersize=8)
axs[1].set_title("X and Y Error Bars")
axs[1].grid(alpha=0.3)
# Asymmetric error bars
y_err_asym = np.array([[0.2, 0.3, 0.2, 0.25, 0.3], # Lower errors
[0.4, 0.5, 0.6, 0.35, 0.7]]) # Upper errors
axs[2].errorbar(x, y, yerr=y_err_asym, fmt='D-', capsize=4,
color='#e9c46a', ecolor='#264653', markersize=8)
axs[2].set_title("Asymmetric Error Bars")
axs[2].grid(alpha=0.3)
for ax in axs:
ax.set_xlabel("X")
ax.set_ylabel("Y")
plt.tight_layout()
plt.show()
Answer: Use boxplot() or the more flexible bxp().
np.random.seed(42)
data = [np.random.normal(0, std, 100) for std in [1, 1.5, 2, 0.8]]
labels = ['Group A', 'Group B', 'Group C', 'Group D']
fig, axs = plt.subplots(1, 2, figsize=(13, 5))
# Basic box plot
bp = axs[0].boxplot(data, labels=labels, patch_artist=True)
colors = ['#264653', '#2a9d8f', '#e9c46a', '#e76f51']
for patch, color in zip(bp['boxes'], colors):
patch.set_facecolor(color)
patch.set_alpha(0.7)
axs[0].set_title("Box Plot: Distribution Comparison")
axs[0].set_ylabel("Value")
axs[0].grid(axis='y', alpha=0.3)
# Horizontal box plot with notch
bp2 = axs[1].boxplot(data, labels=labels, patch_artist=True, vert=False, notch=True)
for patch, color in zip(bp2['boxes'], colors):
patch.set_facecolor(color)
patch.set_alpha(0.7)
axs[1].set_title("Horizontal Notched Box Plot")
axs[1].set_xlabel("Value")
axs[1].grid(axis='x', alpha=0.3)
plt.tight_layout()
plt.show()
Answer: Use violinplot() to show both the distribution shape and summary statistics.
np.random.seed(42)
data = [np.concatenate([np.random.normal(0, 1, 50), np.random.normal(3, 0.5, 30)]),
np.random.normal(1, 1.5, 100),
np.random.exponential(1, 100),
np.random.uniform(-2, 2, 100)]
fig, axs = plt.subplots(1, 2, figsize=(13, 5))
# Basic violin plot
vp = axs[0].violinplot(data, showmeans=True, showmedians=True)
for i, body in enumerate(vp['bodies']):
body.set_facecolor(f'C{i}')
body.set_alpha(0.7)
axs[0].set_xticks([1, 2, 3, 4])
axs[0].set_xticklabels(['Bimodal', 'Normal', 'Exponential', 'Uniform'])
axs[0].set_title("Violin Plot: Different Distributions")
axs[0].set_ylabel("Value")
axs[0].grid(alpha=0.3)
# Half violin with box plot overlay
parts = axs[1].violinplot(data, showextrema=False)
for i, body in enumerate(parts['bodies']):
body.set_facecolor(f'C{i}')
body.set_alpha(0.7)
# Add box plots on top
bp = axs[1].boxplot(data, widths=0.15)
axs[1].set_xticks([1, 2, 3, 4])
axs[1].set_xticklabels(['Bimodal', 'Normal', 'Exponential', 'Uniform'])
axs[1].set_title("Violin + Box Plot Overlay")
axs[1].set_ylabel("Value")
axs[1].grid(alpha=0.3)
plt.tight_layout()
plt.show()
Answer: Chart selection depends on your data and the story you want to tell:
| Data Type | Chart | Use Case |
|---|---|---|
| Trend over time | Line | Time series, continuous change |
| Category comparison | Bar | Discrete categories, rankings |
| Relationship | Scatter | Correlation, clusters |
| Distribution | Histogram, Box, Violin | Spread, outliers, shape |
| Part of whole | Pie, Stacked Bar | Proportions (use sparingly) |
| 2D density | Heatmap, Contour | Matrix data, correlations |
| Hierarchical | Treemap, Sunburst | Nested categories |
fig, axs = plt.subplots(2, 3, figsize=(14, 9))
np.random.seed(42)
# Time series → Line
dates = np.arange(30)
values = np.cumsum(np.random.randn(30)) + 50
axs[0, 0].plot(dates, values, 'o-', color='#264653', markersize=4)
axs[0, 0].set_title("Trend → Line Chart")
axs[0, 0].set_xlabel("Day")
axs[0, 0].fill_between(dates, values, alpha=0.2)
# Categories → Bar
cats = ['A', 'B', 'C', 'D']
vals = [25, 40, 30, 45]
axs[0, 1].bar(cats, vals, color=['#264653', '#2a9d8f', '#e9c46a', '#e76f51'])
axs[0, 1].set_title("Comparison → Bar Chart")
# Correlation → Scatter
x = np.random.randn(50)
y = 0.7*x + np.random.randn(50)*0.5
axs[0, 2].scatter(x, y, alpha=0.7, c='#2a9d8f', edgecolors='white')
axs[0, 2].set_title("Relationship → Scatter")
# Distribution → Histogram
data = np.random.randn(500)
axs[1, 0].hist(data, bins=25, color='#264653', edgecolor='white', alpha=0.8)
axs[1, 0].set_title("Distribution → Histogram")
# Spread → Box plot
data = [np.random.randn(100)*s + m for s, m in [(1, 0), (0.5, 2), (1.5, 1)]]
axs[1, 1].boxplot(data, labels=['Low var', 'High mean', 'High var'], patch_artist=True)
axs[1, 1].set_title("Spread → Box Plot")
# Proportion → Pie (use sparingly!)
sizes = [35, 25, 20, 20]
axs[1, 2].pie(sizes, labels=['A', 'B', 'C', 'D'], autopct='%1.0f%%',
colors=['#264653', '#2a9d8f', '#e9c46a', '#e76f51'])
axs[1, 2].set_title("Proportion → Pie (careful!)")
for ax in axs.flat:
if ax != axs[1, 2]:
ax.grid(alpha=0.3)
plt.tight_layout()
plt.show()
Create a bar chart comparing sales across 5 products: [‘Laptop’, ‘Phone’, ‘Tablet’, ‘Watch’, ‘Earbuds’] with values [150, 280, 95, 120, 200].
Requirements: - Different color for each bar - Add value labels on top of each bar - Title and axis labels - Grid on y-axis only
Generate 50 random points (x from uniform [0, 10], y = 2x + noise).
Requirements: - Scatter plot with alpha=0.7 - Different marker size based on y-value - Add a trend line (y = 2x) - Legend distinguishing data vs. trend
Compare performance metrics across 4 teams for 3 quarters.
Requirements: - Grouped bars (3 groups of 4 bars each) - Error bars representing standard deviation - Different colors per quarter - Rotated x-tick labels - Legend outside the plot area
Generate 1000 samples from a normal distribution (mean=75, std=10).
Requirements: - Histogram with 30 bins - Overlay a density curve (kernel density estimate or theoretical normal) - Vertical lines showing mean and ±1σ, ±2σ - Text annotation showing mean and standard deviation values - Different colors for each region (within 1σ, between 1σ-2σ, beyond 2σ)
Create a 2×2 subplot panel analyzing mock business data:
Requirements: 1. Top-left: Monthly revenue time series (line chart with fill_between showing growth area) 2. Top-right: Revenue by product category (horizontal bar chart, sorted by value) 3. Bottom-left: Revenue vs. expenses scatter plot with: - Color representing profit margin - Size representing transaction volume - Colorbar showing profit scale 4. Bottom-right: Distribution of daily transactions (histogram with overlaid density curve)
Add: - Super title: “Business Analytics Dashboard” - Consistent color scheme across all panels - Proper annotations for key insights (e.g., best month, highest margin product)
| Function | Use Case |
|---|---|
plot() |
Line charts, time series |
scatter() |
Relationships, clusters |
bar() / barh() |
Categorical comparisons |
hist() |
Distributions |
pie() |
Part of whole (sparingly) |
boxplot() |
Statistical summaries |
violinplot() |
Distribution shape |
errorbar() |
Data with uncertainty |
fill_between() |
Confidence bands |
imshow() / pcolormesh() |
Heatmaps, images |