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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Excel to Python\n",
+    "\n",
+    "-   Importing\n",
+    "-   Plotting\n",
+    "-   Statistical analysis\n",
+    "\n",
+    "## **How Excel Translates to Python**\n",
+    "\n",
+    "Here’s how common Excel functionalities map to Python:\n",
+    "\n",
+    "| **Excel Feature**       | **Python Equivalent**                                    |\n",
+    "|----------------------|--------------------------------------------------|\n",
+    "| Formulas (SUM, AVERAGE) | `numpy`, `pandas` (`df.sum()`, `df.mean()`)              |\n",
+    "| Sorting & Filtering     | `pandas.sort_values()`, `df[df['col'] > value]`          |\n",
+    "| Conditional Formatting  | `matplotlib` for highlighting                            |\n",
+    "| Pivot Tables            | `pandas.pivot_table()`                                   |\n",
+    "| Charts & Graphs         | `matplotlib`, `seaborn`, `plotly`                        |\n",
+    "| Regression Analysis     | `scipy.stats.linregress`, `sklearn.linear_model`         |\n",
+    "| Solver/Optimization     | `scipy.optimize`                                         |\n",
+    "| VBA Macros              | Python scripting with `openpyxl`, `pandas`, or `xlwings` |\n",
+    "\n",
+    "## Statistical functions\n",
+    "\n",
+    "#### SUM\n",
+    "\n",
+    "Built-in:\n",
+    "\n",
+    "``` python\n",
+    "my_array = [1, 2, 3, 4, 5]\n",
+    "total = sum(my_array)\n",
+    "print(total)  # Output: 15\n",
+    "```\n",
+    "\n",
+    "Numpy:\n",
+    "\n",
+    "``` python\n",
+    "import numpy as np\n",
+    "\n",
+    "my_array = np.array([1, 2, 3, 4, 5])\n",
+    "total = np.sum(my_array)\n",
+    "print(total)  # Output: 15\n",
+    "```\n",
+    "\n",
+    "### Average\n",
+    "\n",
+    "Built-in:\n",
+    "\n",
+    "``` python\n",
+    "my_array = [1, 2, 3, 4, 5]\n",
+    "average = sum(my_array) / len(my_array)\n",
+    "print(average)  # Output: 3.0\n",
+    "```\n",
+    "\n",
+    "Numpy:\n",
+    "\n",
+    "``` python\n",
+    "import numpy as np\n",
+    "\n",
+    "my_array = np.array([1, 2, 3, 4, 5])\n",
+    "average = np.mean(my_array)\n",
+    "print(average)  # Output: 3.0\n",
+    "```\n",
+    "\n",
+    "## Plotting\n",
+    "\n",
+    "We can use the package *matplotlib* to plot our graphs in python.\n",
+    "Matplotlib provides data visualization tools for the Scientific Python\n",
+    "Ecosystem. You can make very professional looking figures with this\n",
+    "tool.\n",
+    "\n",
+    "Here is a section from the matplotlib documentation page that you can\n",
+    "run in python.\n",
+    "\n",
+    "``` python\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig, ax = plt.subplots()             # Create a figure containing a single Axes.\n",
+    "ax.plot([1, 2, 3, 4], [1, 4, 2, 3])  # Plot some data on the Axes.\n",
+    "plt.show()                           # Show the figure.\n",
+    "```\n",
+    "\n",
+    "Check out the documentation pages for a [simple\n",
+    "example](https://matplotlib.org/stable/users/explain/quick_start.html#a-simple-example)\n",
+    "or more information on the types of plots you came create\n",
+    "[here](https://matplotlib.org/stable/plot_types/index.html)."
+   ],
+   "id": "3e04cd8a-c731-494a-bf4f-dfe745a8a487"
+  }
+ ],
+ "nbformat": 4,
+ "nbformat_minor": 5,
+ "metadata": {}
+}