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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": {}
-}