starting moviepy exploration

Author: Kamalabot

movie_py_explored/example_vids/businessman.mp4

@@ -1,5 +1,24 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "9ba332e3-06a7-4fc6-a266-c8a3455f54f4",
+   "metadata": {},
+   "source": [
+    "🔷 Matplotlib – Data Visualization\n",
+    "Applications:\n",
+    "\n",
+    "Exploratory Data Analysis (EDA): Visualizing trends, outliers, and patterns.\n",
+    "\n",
+    "Reports & Dashboards: Generating plots for reports in finance, healthcare, marketing.\n",
+    "\n",
+    "Scientific Visualization: Graphing equations, simulations, experiment data.\n",
+    "\n",
+    "Education: Visual aids for teaching math, stats, and data concepts.\n",
+    "\n",
+    "Custom Plotting Tools: Used as a backend for other plotting libraries (e.g., Seaborn)."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 4,

movie_py_explored/example_vids/procrastinate.mp4

@@ -1,5 +1,45 @@
 {
  "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "bb5690e3-b533-4029-bf75-6957e29b4296",
+   "metadata": {},
+   "source": [
+    "🔷 NumPy – Numerical Computing\n",
+    "\n",
+    "Applications:\n",
+    "\n",
+    "Scientific Computing: Fast linear algebra, Fourier transforms, and random number generation.\n",
+    "\n",
+    "Machine Learning: Preprocessing large datasets and feeding them into models (e.g., with TensorFlow, PyTorch).\n",
+    "\n",
+    "Signal & Image Processing: Efficient pixel-level operations on arrays/images.\n",
+    "\n",
+    "Simulation: Physics, chemistry, and financial simulations using matrix ops.\n",
+    "\n",
+    "Robotics & Control Systems: State estimation, control matrices, Kalman filters."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "3c4bc4df-110c-41b1-900b-19be246e0cde",
+   "metadata": {},
+   "source": [
+    "🔶 Pandas – Data Analysis & Manipulation\n",
+    "\n",
+    "Applications:\n",
+    "\n",
+    "Data Cleaning: Handling missing data, renaming, filtering, type conversion.\n",
+    "\n",
+    "Data Transformation: Grouping, aggregating, merging, pivoting datasets.\n",
+    "\n",
+    "Time Series Analysis: Working with timestamps, date-indexed data, rolling windows.\n",
+    "\n",
+    "Business Analytics: Sales data, financial reports, customer behavior insights.\n",
+    "\n",
+    "ETL Pipelines: Extract-Transform-Load in data engineering tasks.\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 2,