Master Matplotlib: 40+ Python Plotting Techniques from Basics to Advanced

1. Import

Import the Matplotlib library using the conventional alias:

import matplotlib.pyplot as plt

2. Basic Charts

Examples include plotting a sine curve, line with markers, scatter plot, pie‑style area chart, error bars, bar charts, horizontal bars, histograms, stacked histograms, and 2‑D histograms.

x = np.linspace(0, 10, 30)
plt.plot(x, np.sin(x))
plt.plot(x, np.sin(x), '-o')
plt.scatter(x, np.sin(x))
plt.errorbar(x, y, yerr=dy, fmt='.k')
plt.bar(x, y, tick_label=label)
plt.barh(x, y, tick_label=label)
plt.hist(data)
plt.hist(data, bins=30, histtype='stepfilled', density=True)
plt.hist2d(x, y, bins=30)

3. Custom Plot Elements

Set line style, axis limits, labels, titles, grids, reference lines, shaded regions, annotations, and text.

plt.plot(x, np.sin(x), '--')
plt.ylim(-1.5, 1.5)
plt.xlabel('variable x')
plt.ylabel('value y')
plt.title('三角函数')
plt.grid()
plt.axhline(y=0.8, ls='--', c='r')
plt.axvspan(4, 6, facecolor='r', alpha=0.3)
plt.axhspan(-0.2, 0.2, facecolor='y', alpha=0.3)
plt.text(3.2, 0, 'sin(x)', weight='bold', color='r')
plt.annotate('maximum', xy=(np.pi/2, 1), xytext=(np.pi/2+1, 1), weight='bold', color='r', arrowprops=dict(arrowstyle='->', connectionstyle='arc3', color='r'))

4. Custom Legends

Create legends, position them, split into columns, and display selective entries.

ax.legend()
ax.legend(loc='upper left', frameon=False)
ax.legend(loc='lower center', ncol=2, frameon=False)
ax.legend(lines[:2], ['first', 'second'])

5. Color Maps

Show color bars, change colormaps, and create discrete color levels.

plt.imshow(I)
plt.colorbar()
plt.imshow(I, cmap='gray')
plt.imshow(I, cmap=plt.cm.get_cmap('Blues', 6))
plt.clim(-1, 1)

6. Multiple Subplots

Define subplot positions, share axes, create grids of subplots, and adjust layout.

ax1 = plt.axes()
ax2 = plt.axes([0.65, 0.65, 0.2, 0.2])
fig = plt.figure()
ax1 = fig.add_axes([0.1, 0.5, 0.8, 0.4], ylim=(-1.2, 1.2))
ax2 = fig.add_axes([0.1, 0.1, 0.8, 0.4], ylim=(-1.2, 1.2))
for i in range(1, 7):
    plt.subplot(2, 3, i)
    plt.text(0.5, 0.5, str((2, 3, i)), fontsize=18, ha='center')
fig, ax = plt.subplots(2, 3, sharex='col', sharey='row')

7. 3‑D Plots

Create a 3‑D canvas and plot a helix or scatter points.

from mpl_toolkits import mplot3d
fig = plt.figure()
ax = plt.axes(projection='3d')
zline = np.linspace(0, 15, 1000)
xline = np.sin(zline)
yline = np.cos(zline)
ax.plot3D(xline, yline, zline)
ax.scatter3D(xdata, ydata, zdata, c=zdata, cmap='Greens')

8. Pokémon Dataset Visualizations

Various charts (stacked bar, grouped bar, stacked area, shared‑axis line plots, multi‑line plots, scatter with color encoding, histograms, pie chart, bar chart, correlation heatmap) illustrate Pokémon attributes such as HP, Attack, Defense, Type, and overall scores.

# Example stacked bar
plt.bar(ind, defense, bottom=attack+hp, label='Defense')
plt.bar(ind, attack, bottom=hp, label='Attack')
plt.bar(ind, hp, label='HP')
# Grouped bar
plt.bar(ind, pokemon_hp, width, label='HP')
plt.bar(ind + width, pokemon_attack, width, label='Attack')
# Scatter with color
plt.scatter(x, y, c=colors, alpha=0.5)
# Correlation heatmap
import seaborn as sns
sns.heatmap(corr, annot=True)