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‎Matplotlib_Visualization_with_Python.ipynb‎

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"metadata": {
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"1️⃣ Subplots\n",
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"Subplots help you show multiple plots in a single figure."
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],
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"metadata": {
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"id": "8QerCujUbhen"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"\n",
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"x = np.linspace(0, 10, 100)\n",
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"\n",
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"# Create subplots (2 rows, 2 columns)\n",
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"fig, axes = plt.subplots(2, 2, figsize=(10, 8))\n",
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"\n",
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"# Plot on each subplot\n",
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"axes[0, 0].plot(x, np.sin(x), 'r') # Red sine wave\n",
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"axes[0, 0].set_title('Sine Wave')\n",
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"\n",
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"axes[0, 1].plot(x, np.cos(x), 'g') # Green cosine wave\n",
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"axes[0, 1].set_title('Cosine Wave')\n",
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"\n",
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"axes[1, 0].scatter(x, np.random.rand(100), color='blue')\n",
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"axes[1, 0].set_title('Random Scatter')\n",
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"\n",
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"axes[1, 1].hist(np.random.randn(1000), bins=30, color='purple')\n",
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"axes[1, 1].set_title('Histogram')\n",
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"\n",
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"plt.tight_layout() # Adjust layout to avoid overlapping\n",
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"plt.show()\n"
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],
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"metadata": {
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"id": "a8gvjqs3beJ4"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"2️⃣ Customizing Plots (Colors, Markers, and Styles)\n",
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"You can customize your plots using different colors, markers, and line styles."
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],
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"metadata": {
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"id": "uE0drRzsbu05"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"x = np.linspace(0, 10, 100)\n",
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"y = np.sin(x)\n",
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"\n",
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"# Customize line style, color, and marker\n",
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"plt.plot(x, y, linestyle='--', color='orange', marker='o', markersize=5, label='Sine')\n",
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"plt.title('Customized Plot')\n",
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"plt.xlabel('X-axis')\n",
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"plt.ylabel('Y-axis')\n",
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"plt.legend()\n",
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"plt.grid(True) # Add grid lines\n",
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"plt.show()\n"
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],
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"metadata": {
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"id": "wMZj1Wpmbvq_"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"**Line Styles**: '-', '--', '-.', ':'\n",
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"\n",
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"**Colors**: 'r', 'g', 'b', 'orange', etc.\n",
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"\n",
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"**Markers**: 'o', 's' (square), 'x', '*', 'D' (diamond), etc."
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],
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"metadata": {
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"id": "thWnwKGMb0x_"
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}
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},
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{
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"cell_type": "markdown",
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"source": [
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"3️⃣ Saving Plots as Images\n",
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"You can save plots as PNG, PDF, SVG, and more using savefig()."
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],
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"metadata": {
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"id": "4YtyHAMScF13"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"x = np.linspace(0, 10, 100)\n",
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"y = np.sin(x)\n",
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"\n",
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"plt.plot(x, y)\n",
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"plt.title('Plot to be Saved')\n",
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"plt.xlabel('X-axis')\n",
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"plt.ylabel('Y-axis')\n",
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"\n",
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"# Save the plot as a PNG image\n",
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"plt.savefig('saved_plot.png', dpi=300, bbox_inches='tight') # High resolution\n",
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"plt.show()\n"
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],
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"metadata": {
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"id": "tcexyk37b1X4"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"4️⃣ Animated Plots\n",
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"Matplotlib supports animations using FuncAnimation from matplotlib.animation. Here’s a simple animation example:"
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],
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"metadata": {
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"id": "0X9C8SFUcJRI"
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}
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},
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{
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"cell_type": "code",
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import numpy as np\n",
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"from matplotlib.animation import FuncAnimation\n",
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"\n",
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"x = np.linspace(0, 10, 100)\n",
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"fig, ax = plt.subplots()\n",
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"line, = ax.plot(x, np.sin(x))\n",
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"\n",
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"# Function to update the plot for each frame\n",
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"def update(frame):\n",
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" line.set_ydata(np.sin(x + frame / 10)) # Change sine wave phase\n",
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" return line,\n",
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"\n",
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"ani = FuncAnimation(fig, update, frames=100, interval=50) # 100 frames, 50 ms interval\n",
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"plt.show()\n"
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],
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"metadata": {
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"id": "_S05SJcUcTJp"
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},
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"execution_count": null,
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"outputs": []
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},
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{
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"cell_type": "markdown",
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"source": [
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"FuncAnimation Parameters:\n",
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"\n",
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"frames: Total number of frames\n",
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"\n",
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"interval: Delay between frames (in milliseconds)\n",
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"\n",
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"update: Function to update the plot\n",
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"\n",
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"You can save the animation as a video using ani.save('animation.mp4')."
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],
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"metadata": {
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"id": "nWMo6laCcXTY"
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}
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},
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{
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"cell_type": "markdown",
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"source": [

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