From e4776132407eae7c22538d80b895fc0f3ca5a888 Mon Sep 17 00:00:00 2001 From: Saim Date: Tue, 31 Dec 2024 11:06:54 +0530 Subject: [PATCH 1/2] Add: Jupyter notebook and matplotlib activites --- activities/1.1-basic-jupyter-notebook.ipynb | 167 ++++++++++++ activities/1.3-using-matplotlib.ipynb | 287 +++++++++++++++++++- 2 files changed, 441 insertions(+), 13 deletions(-) diff --git a/activities/1.1-basic-jupyter-notebook.ipynb b/activities/1.1-basic-jupyter-notebook.ipynb index e69de29b..dba7f486 100644 --- a/activities/1.1-basic-jupyter-notebook.ipynb +++ b/activities/1.1-basic-jupyter-notebook.ipynb @@ -0,0 +1,167 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "f054f174", + "metadata": {}, + "source": [ + "# Welcome to JupyterLab\n", + "\n", + "In this activity, you will get hands-on experience with the basics of JupyterLab. By the end of this notebook, you will be able to:\n", + "\n", + "1. Create and run code cells.\n", + "2. Use Markdown cells for adding text.\n", + "3. Explore JupyterLab's interface and shortcuts.\n", + "4. Save and export your notebooks.\n", + "\n", + "Let’s get started!\n" + ] + }, + { + "cell_type": "markdown", + "id": "0bb6bbdd", + "metadata": {}, + "source": [ + "## 1. Running Code in JupyterLab\n", + "JupyterLab allows you to write and execute Python code interactively. Code cells are the building blocks of notebooks.\n", + "\n", + "### Activity\n", + "Run the Python code below by selecting the cell and pressing **Shift + Enter** (or clicking the Run button in the toolbar)." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f2b4273a", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Hello, JupyterLab!\n" + ] + } + ], + "source": [ + "# This is a Python code cell\n", + "print('Hello, JupyterLab!')" + ] + }, + { + "cell_type": "markdown", + "id": "dde499ca", + "metadata": {}, + "source": [ + "### Task\n", + "1. Modify the code above to print your name.\n", + "2. Run the cell again to see the output.\n" + ] + }, + { + "cell_type": "markdown", + "id": "59d7f0ea", + "metadata": {}, + "source": [ + "## 2. Adding Text with Markdown\n", + "Markdown cells allow you to add text, headings, lists, and more to your notebook.\n", + "\n", + "### Activity\n", + "1. Double-click this cell to edit it.\n", + "2. Add your name below using Markdown syntax:\n", + "\n", + "### Your Name\n", + "John Doe\n", + "\n", + "3. Press **Shift + Enter** to render the Markdown." + ] + }, + { + "cell_type": "markdown", + "id": "b5f27fd8", + "metadata": {}, + "source": [ + "## 3. JupyterLab Interface\n", + "The JupyterLab interface includes several useful components:\n", + "- **File Browser**: Navigate and manage files.\n", + "- **Notebook Tabs**: Work with multiple notebooks or files.\n", + "- **Toolbar**: Run cells, save notebooks, and more.\n", + "\n", + "### Activity\n", + "Explore the interface and try the following:\n", + "1. Locate the File Browser on the left-hand side.\n", + "2. Open a new Python 3 notebook (File > New > Notebook).\n", + "3. Close this new notebook without saving it.\n" + ] + }, + { + "cell_type": "markdown", + "id": "3dc36da6", + "metadata": {}, + "source": [ + "## 4. Keyboard Shortcuts\n", + "Keyboard shortcuts make working in JupyterLab more efficient. Here are some commonly used ones:\n", + "\n", + "- **Shift + Enter**: Run the current cell.\n", + "- **A**: Insert a new cell above.\n", + "- **B**: Insert a new cell below.\n", + "- **DD**: Delete the current cell.\n", + "- **M**: Convert a cell to Markdown.\n", + "- **Y**: Convert a cell to code.\n", + "### Activity\n", + "1. Practice these shortcuts by inserting a new cell below.\n", + "2. Change the cell type to Markdown and add a heading: `# JupyterLab Shortcuts`.\n", + "3. Run the cell to render it." + ] + }, + { + "cell_type": "markdown", + "id": "4a9f2188", + "metadata": {}, + "source": [ + "## 5. Saving and Exporting Notebooks\n", + "You can save your notebook using **Ctrl + S** or the save icon in the toolbar.\n", + "\n", + "Notebooks can also be exported to other formats like HTML or PDF:\n", + "1. Go to **File > Export Notebook As**.\n", + "2. Choose a format (e.g., HTML).\n", + "### Activity\n", + "Save this notebook and export it as an HTML file. Verify that the file is created in the File Browser." + ] + }, + { + "cell_type": "markdown", + "id": "3c27d308", + "metadata": {}, + "source": [ + "# Congratulations!\n", + "You have completed the basics of JupyterLab.\n", + "\n", + "In the next activity, we will explore Python Matplotlib libraries.\n", + "\n", + "Feel free to experiment with JupyterLab further to familiarize yourself with its features." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/activities/1.3-using-matplotlib.ipynb b/activities/1.3-using-matplotlib.ipynb index 7eae0a43..628fcd55 100644 --- a/activities/1.3-using-matplotlib.ipynb +++ b/activities/1.3-using-matplotlib.ipynb @@ -1,13 +1,35 @@ { "cells": [ + { + "cell_type": "markdown", + "id": "89584ec7", + "metadata": {}, + "source": [ + "# Matplotlib Activities \n", + "This notebook contains activities to practice various types of plots and visualizations using Matplotlib." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Activity 1: Basic Line Plot\n", + "### Objective: Create a simple line plot to visualize data.\n", + "#### Instructions:\n", + "1. Create a list of x-values (`[0, 1, 2, 3, 4, 5]`) and a list of corresponding y-values (`[0, 1, 4, 9, 16, 25]`).\n", + "2. Use Matplotlib to plot these values as a line graph.\n", + "3. Label the axes as `x` and `y`.\n", + "4. Give the plot a title: `Basic Line Plot`.\n" + ] + }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -18,20 +40,259 @@ ], "source": [ "import matplotlib.pyplot as plt\n", + "# Data\n", + "x = [0, 1, 2, 3, 4, 5]\n", + "y = [0, 1, 4, 9, 16, 25]\n", + "\n", + "# Create a line plot\n", + "plt.plot(x, y)\n", + "\n", + "# Labeling the axes and the plot\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.title('Basic Line Plot')\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Activity 2: Bar Plot\n", + "### Objective: Create a bar plot to visualize categorical data.\n", + "#### Instructions:\n", + "1. Create a list of categories (`['Category A', 'Category B', 'Category C', 'Category D']`) and corresponding values (`[4, 7, 3, 6]`).\n", + "2. Use Matplotlib to create a bar plot.\n", + "3. Add labels for the x and y axes.\n", + "4. Add a title to the plot: `Bar Plot Example`." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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LBDj54n/++afatGmjm2++WfPmzVOFChW0bds2XXHFFU6WBQAALOFoUHnxxRcVHh6uxMREb9vVV1/tYEUAAMAmjt76+eyzz9SsWTNFR0erYsWKatKkid54440z9s/OzlZmZqbPAgAASi5Hr6js2LFDCQkJGjlypJ566imtWbNGw4cPl9vtVr9+/fL0j4uL0+jRox2oFADgpIgnv3S6hMtW2rgoR1/f0Ssqubm5atq0qcaOHasmTZpo8ODBGjRokKZNm5Zv/1GjRikjI8O7pKenX+SKAQDAxeRoUKlSpYoiIyN92urXr69ff/013/4ej0dlypTxWQAAQMnlaFBp06aNtm7d6tOWkpKiGjVqOFQRAACwiaNB5ZFHHtH333+vsWPHavv27Zo1a5amT5+umJgYJ8sCAACWcDSoNG/eXHPnztV7772nhg0b6vnnn9eUKVPUp08fJ8sCAACWcPSpH0nq3r27unfv7nQZAADAQo5/hT4AAMCZEFQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArOVoUHn22Wflcrl8lnr16jlZEgAAsEiA0wU0aNBAycnJ3vWAAMdLAgAAlnA8FQQEBKhy5cpOlwEAACzk+ByVbdu2qWrVqqpZs6b69OmjX3/99Yx9s7OzlZmZ6bMAAICSy9Gg0rJlSyUlJWn+/PlKSEjQzp071bZtWx05ciTf/nFxcQoLC/Mu4eHhF7liAABwMTkaVLp27aro6Gg1btxYXbp00VdffaXDhw/rww8/zLf/qFGjlJGR4V3S09MvcsUAAOBicnyOyunKli2rOnXqaPv27flu93g88ng8F7kqAADgFMfnqJwuKytLqampqlKlitOlAAAACzgaVB577DEtW7ZMaWlpWrlypW677Tb5+/vrrrvucrIsAABgCUdv/ezevVt33XWXDh06pAoVKujGG2/U999/rwoVKjhZFgAAsISjQeX999938uUBAIDlrJqjAgAAcDqCCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1rAkq48aNk8vl0ogRI5wuBQAAWKLQQSU9PV27d+/2rq9evVojRozQ9OnTz7uINWvW6PXXX1fjxo3P+xgAAKDkKXRQufvuu7VkyRJJ0m+//abOnTtr9erVevrpp/Xcc88VuoCsrCz16dNHb7zxhq644opC7w8AAEquQgeVn376SS1atJAkffjhh2rYsKFWrlypd999V0lJSYUuICYmRlFRUerUqVOh9wUAACVbQGF3OHnypDwejyQpOTlZPXv2lCTVq1dP+/btK9Sx3n//fa1fv15r1qwpUP/s7GxlZ2d71zMzMwv1egAA4NJS6KDSoEEDTZs2TVFRUVq4cKGef/55SdLevXtVvnz5Ah8nPT1dDz/8sBYuXKhSpUoVaJ+4uDiNHj26sCWft4gnv7xorwVfaeOinC4BAGCBQt/6efHFF/X666/rpptu0l133aVrr71WkvTZZ595bwkVxLp167R//341bdpUAQEBCggI0LJly/TKK68oICBAOTk5efYZNWqUMjIyvEt6enphywcAAJeQQl9Ruemmm3Tw4EFlZmb6TH4dPHiwgoODC3ycjh07atOmTT5t999/v+rVq6cnnnhC/v7+efbxeDze204AAKDkK3RQkSRjjNatW6fU1FTdfffdCg0NldvtLlRQCQ0NVcOGDX3aQkJCVL58+TztAADg8lTooLJr1y7deuut+vXXX5Wdna3OnTsrNDRUL774orKzszVt2rTiqBMAAFyGCh1UHn74YTVr1kw//vijz+TZ2267TYMGDbqgYpYuXXpB+wMAgJKl0EHlm2++0cqVK+V2u33aIyIitGfPniIrDAAAoNBP/eTm5ub7RM7u3bsVGhpaJEUBAABI5xFUbrnlFk2ZMsW77nK5lJWVpdjYWHXr1q0oawMAAJe5Qt/6mThxorp06aLIyEgdP35cd999t7Zt26Yrr7xS7733XnHUCAAALlOFDirVq1fXjz/+qPfff18bN25UVlaWBgwYoD59+igoKKg4agQAAJep8/oelYCAAPXt27eoawEAAPBR6KAyc+bMs26/9957z7sYAACA053X96ic7uTJkzp27Jj3m2kJKgAAoKgU+qmfP//802fJysrS1q1bdeONNzKZFgAAFKlCB5X81K5dW+PGjctztQUAAOBCFElQkf6eYLt3796iOhwAAEDh56h89tlnPuvGGO3bt0+vvfaa2rRpU2SFAQAAFDqo9OrVy2fd5XKpQoUK6tChgyZOnFhUdQEAABQ+qOTm5hZHHQAAAHkU2RwVAACAolagKyojR44s8AEnTZp03sUAAACcrkBB5YcffijQwVwu1wUVAwAAcLoCBZUlS5YUdx0AAAB5MEcFAABY67x+PXnt2rX68MMP9euvv+rEiRM+2+bMmVMkhQEAABT6isr777+v1q1ba/PmzZo7d65Onjypn3/+WYsXL1ZYWFhx1AgAAC5ThQ4qY8eO1eTJk/X555/L7Xbr5Zdf1pYtW9S7d29dddVVxVEjAAC4TBU6qKSmpioqKkqS5Ha7dfToUblcLj3yyCOaPn16kRcIAAAuX4UOKldccYWOHDkiSapWrZp++uknSdLhw4d17Nixoq0OAABc1gocVE4Fknbt2mnhwoWSpOjoaD388MMaNGiQ7rrrLnXs2LF4qgQAAJelAj/107hxYzVv3ly9evVSdHS0JOnpp59WYGCgVq5cqdtvv13/+c9/iq1QAABw+SlwUFm2bJkSExMVFxenMWPG6Pbbb9fAgQP15JNPFmd9AADgMlbgWz9t27bVjBkztG/fPr366qtKS0tT+/btVadOHb344ov67bffirNOAABwGSr0ZNqQkBDdf//9WrZsmVJSUhQdHa34+HhdddVV6tmzZ3HUCAAALlMX9BX6tWrV0lNPPaX//Oc/Cg0N1ZdffllUdQEAAJzfV+hL0vLlyzVjxgzNnj1bfn5+6t27twYMGFCUtQEAgMtcoYLK3r17lZSUpKSkJG3fvl2tW7fWK6+8ot69eyskJKS4agQAAJepAgeVrl27Kjk5WVdeeaXuvfde9e/fX3Xr1i3O2gAAwGWuwEElMDBQH3/8sbp37y5/f//irAkAAEBSIYLKZ599Vpx1AAAA5HFBT/0AAAAUJ4IKAACwFkEFAABYi6ACAACsRVABAADWcjSoJCQkqHHjxipTpozKlCmjVq1aad68eU6WBAAALOJoUKlevbrGjRundevWae3aterQoYP+9a9/6eeff3ayLAAAYInz/q2fotCjRw+f9TFjxighIUHff/+9GjRo4FBVAADAFo4GldPl5OToo48+0tGjR9WqVat8+2RnZys7O9u7npmZebHKAwAADnA8qGzatEmtWrXS8ePHVbp0ac2dO1eRkZH59o2Li9Po0aMvcoUALhURT37pdAmXrbRxUU6XgBLK8ad+6tatqw0bNmjVqlUaMmSI+vXrp19++SXfvqNGjVJGRoZ3SU9Pv8jVAgCAi8nxKyput1u1atWSJF1//fVas2aNXn75Zb3++ut5+no8Hnk8notdIgAAcIjjV1T+KTc312ceCgAAuHw5ekVl1KhR6tq1q6666iodOXJEs2bN0tKlS7VgwQInywIAAJZwNKjs379f9957r/bt26ewsDA1btxYCxYsUOfOnZ0sCwAAWMLRoPLmm286+fIAAMBy1s1RAQAAOIWgAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtR4NKXFycmjdvrtDQUFWsWFG9evXS1q1bnSwJAABYxNGgsmzZMsXExOj777/XwoULdfLkSd1yyy06evSok2UBAABLBDj54vPnz/dZT0pKUsWKFbVu3Tq1a9fOoaoAAIAtHA0q/5SRkSFJKleuXL7bs7OzlZ2d7V3PzMy8KHUBAABnWDOZNjc3VyNGjFCbNm3UsGHDfPvExcUpLCzMu4SHh1/kKgEAwMVkTVCJiYnRTz/9pPfff/+MfUaNGqWMjAzvkp6efhErBAAAF5sVt36GDh2qL774QsuXL1f16tXP2M/j8cjj8VzEygAAgJMcDSrGGA0bNkxz587V0qVLdfXVVztZDgAAsIyjQSUmJkazZs3Sp59+qtDQUP3222+SpLCwMAUFBTlZGgAAsICjc1QSEhKUkZGhm266SVWqVPEuH3zwgZNlAQAASzh+6wcAAOBMrHnqBwAA4J8IKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWcjSoLF++XD169FDVqlXlcrn0ySefOFkOAACwjKNB5ejRo7r22msVHx/vZBkAAMBSAU6+eNeuXdW1a1cnSwAAABZzNKgUVnZ2trKzs73rmZmZDlYDAACK2yU1mTYuLk5hYWHeJTw83OmSAABAMbqkgsqoUaOUkZHhXdLT050uCQAAFKNL6taPx+ORx+NxugwAAHCRXFJXVAAAwOXF0SsqWVlZ2r59u3d9586d2rBhg8qVK6errrrKwcoAAIANHA0qa9eu1c033+xdHzlypCSpX79+SkpKcqgqAABgC0eDyk033SRjjJMlAAAAizFHBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFiLoAIAAKxFUAEAANYiqAAAAGsRVAAAgLUIKgAAwFoEFQAAYC2CCgAAsBZBBQAAWIugAgAArEVQAQAA1iKoAAAAaxFUAACAtQgqAADAWgQVAABgLYIKAACwFkEFAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtK4JKfHy8IiIiVKpUKbVs2VKrV692uiQAAGABx4PKBx98oJEjRyo2Nlbr16/Xtddeqy5dumj//v1OlwYAABzmeFCZNGmSBg0apPvvv1+RkZGaNm2agoODNWPGDKdLAwAADnM0qJw4cULr1q1Tp06dvG1+fn7q1KmTvvvuOwcrAwAANghw8sUPHjyonJwcVapUyae9UqVK2rJlS57+2dnZys7O9q5nZGRIkjIzM4ulvtzsY8VyXJxbcY2pxLg6qTjHVWJsncTYllzFMbanjmmMOWdfR4NKYcXFxWn06NF52sPDwx2oBsUpbIrTFaA4MK4lF2NbchXn2B45ckRhYWFn7eNoULnyyivl7++v33//3af9999/V+XKlfP0HzVqlEaOHOldz83N1R9//KHy5cvL5XIVe72XiszMTIWHhys9PV1lypRxuhwUIca2ZGJcSy7GNn/GGB05ckRVq1Y9Z19Hg4rb7db111+vRYsWqVevXpL+Dh+LFi3S0KFD8/T3eDzyeDw+bWXLlr0IlV6aypQpwwejhGJsSybGteRibPM615WUUxy/9TNy5Ej169dPzZo1U4sWLTRlyhQdPXpU999/v9OlAQAAhzkeVO68804dOHBAzzzzjH777Tddd911mj9/fp4JtgAA4PLjeFCRpKFDh+Z7qwfnx+PxKDY2Ns9tMlz6GNuSiXEtuRjbC+cyBXk2CAAAwAGOfzMtAADAmRBUAACAtQgqAADAWgQVAABgLYJKEfjtt980bNgw1axZUx6PR+Hh4erRo4cWLVpU4GMkJSWVqC+v2717t9xutxo2bOh0KReEsf2fZ599Vi6Xy7uEhYWpbdu2WrZsmdOlnRfG1ldmZqaefvpp1atXT6VKlVLlypXVqVMnzZkzp0C/x2ILxvV/Tv/MBgQE6Morr1S7du00ZcoUn9/Ns50VjydfytLS0tSmTRuVLVtW48ePV6NGjXTy5EktWLBAMTEx+f644qXg5MmTCgwMPO/9k5KS1Lt3by1fvlyrVq1Sy5Yti7C6i4OxzatBgwZKTk6WJP3xxx+aMGGCunfvrt27dxf4WyZtwNj6Onz4sG688UZlZGTohRdeUPPmzRUQEKBly5bp//7v/9ShQ4dL4n/cjGtepz6zubm5OnTokJYuXaoXXnhBb7/9tpYuXarQ0NAirrYYGFyQrl27mmrVqpmsrKw82/7880/vP0+cONE0bNjQBAcHm+rVq5shQ4aYI0eOGGOMWbJkiZHks8TGxhpjjDl+/Lh59NFHTdWqVU1wcLBp0aKFWbJkic/rTJ8+3VSvXt0EBQWZXr16mYkTJ5qwsDCfPlOnTjU1a9Y0gYGBpk6dOmbmzJk+2yWZqVOnmh49epjg4GDzzDPPmGuuucaMHz/ep98PP/xgJJlt27ad8Zzk5uaamjVrmvnz55snnnjCDBo06Bxn0U6Mra/Y2Fhz7bXX+rSlp6cbSWb16tVnOIt2Ymx9DRkyxISEhJg9e/bk2XbkyBFz8uTJfPezDePqK7/PrDHGbN682bjdbvP000/nu59tCCoX4NChQ8blcpmxY8ees+/kyZPN4sWLzc6dO82iRYtM3bp1zZAhQ4wxxmRnZ5spU6aYMmXKmH379pl9+/Z5PzQDBw40rVu3NsuXLzfbt28348ePNx6Px6SkpBhjjPn222+Nn5+fGT9+vNm6dauJj4835cqV8/lgzJkzxwQGBpr4+HizdetWM3HiROPv728WL17s7SPJVKxY0cyYMcOkpqaaXbt2mTFjxpjIyEif9zF8+HDTrl27s77XRYsWmcqVK5u//vrLbNq0yYSGhub7Hw6bMbZ5/fM/esePHzfPPfecKVu2rMnIyDjnebIFY+srJyfHXHHFFWbw4MGFOo+2YVzzOlNQMcaYf/3rX6Z+/frnPFc2IKhcgFWrVhlJZs6cOYXe96OPPjLly5f3ricmJuZJ3bt27TL+/v55/srp2LGjGTVqlDHGmDvvvNNERUX5bO/Tp4/PsVq3bp3nqkZ0dLTp1q2bd12SGTFihE+fPXv2GH9/f7Nq1SpjjDEnTpwwV155pUlKSjrre7v77rt9jnXttdeaxMTEs+5jG8Y2r9jYWOPn52dCQkJMSEiIcblcpkyZMmbevHln3MdGjK2v33//3UgykyZNOss7tx/jmtfZgsoTTzxhgoKCzrivTZhMewFMISaYJScnq2PHjqpWrZpCQ0N1zz336NChQzp27NgZ99m0aZNycnJUp04dlS5d2rssW7ZMqampkqStW7eqRYsWPvv9c33z5s1q06aNT1ubNm20efNmn7ZmzZr5rFetWlVRUVGaMWOGJOnzzz9Xdna2oqOjz1jz4cOHNWfOHPXt29fb1rdvX7355ptn3MdGjG3+6tatqw0bNmjDhg1at26dhgwZoujoaK1du/as+9mEsfVVmPNhM8a1cIwxcrlc57XvxcZk2gtQu3ZtuVyuc07QSktLU/fu3TVkyBCNGTNG5cqV07fffqsBAwboxIkTCg4Ozne/rKws+fv7a926dfL39/fZVrp06SJ7H6eEhITkaRs4cKDuueceTZ48WYmJibrzzjvPWK8kzZo1S8ePH/eZPGuMUW5urlJSUlSnTp0ir7s4MLb5c7vdqlWrlne9SZMm+uSTTzRlyhS98847RV53cWBsfVWoUEFly5a9ZCeansK4Fs7mzZt19dVXX2iZFwVXVC5AuXLl1KVLF8XHx+vo0aN5th8+fFiStG7dOuXm5mrixIm64YYbVKdOHe3du9enr9vtVk5Ojk9bkyZNlJOTo/3796tWrVo+S+XKlSX9/RfumjVrfPb753r9+vW1YsUKn7YVK1YoMjLynO+xW7duCgkJUUJCgubPn6/+/fuftf+bb76pRx991PtX94YNG/Tjjz+qbdu23r8ELgWMbcH5+/vrv//973nt6wTG1pefn5/+/e9/6913383z/qS//wf9119/nfM1nca4FtyWLVs0f/583X777ee1/0Xn4G2nEiE1NdVUrlzZREZGmo8//tikpKSYX375xbz88sumXr16xhhjNmzYYCSZKVOmmNTUVDNz5kxTrVo1I8k7E33FihVGkklOTjYHDhwwR48eNcb8fX8zIiLCzJ492+zYscOsWrXKjB071nzxxRfGmP9N3po4caJJSUkx06ZNM+XLlzdly5b11jh37lwTGBhopk6dalJSUryTt06frS7JzJ07N9/3+NRTTxm3233OiVenZqBv3rw5z7apU6eaypUrXzJPDxjD2P5TbGysadCggXeCYUpKinn++eeNJPPWW2+dxxl2DmPr69ChQ6ZevXqmevXq5q233jI///yzSUlJMW+++aapVauWzxMzNmNcfZ3+md2zZ4/ZuHGjeeWVV0zFihVN8+bNvZOEbUdQKQJ79+41MTExpkaNGsbtdptq1aqZnj17+vyLN2nSJFOlShUTFBRkunTpYmbOnOnzwTDGmAcffNCUL1/e53G4EydOmGeeecZERESYwMBAU6VKFXPbbbeZjRs3evebPn26qVatmvdxuBdeeMFUrlzZp8aCPA53pg9GamqqkWReeumls56HoUOH5pmVfsq+ffuMn5+f+fTTT896DNswtv8TGxvr88hmcHCwadSokUlISDjnvjZibH0dPnzYPPnkk6Z27drG7XabSpUqmU6dOpm5c+ea3NzcAh3DBozr/5z+mfX39zflypUzN954o5k8ebI5fvz4Ofe3hcuYEjKTCl6DBg3Sli1b9M033xTJ8b755ht17NhR6enpqlSpUpEcE+eHsS25GNuSiXG9cEymLQEmTJigzp07KyQkRPPmzdNbb72lqVOnXvBxs7OzdeDAAT377LOKjo6+bD4UNmFsSy7GtmRiXIuB05d0cOGio6NNhQoVTKlSpUxkZGSRXYpPTEw0fn5+pmnTpmb37t1FckwUDmNbcjG2JRPjWvS49QMAAKzF48kAAMBaBBUAAGAtggoAALAWQQUAAFiLoAKgxFu6dKlcLpf3a9QBXDoIKgB8/Pbbbxo2bJhq1qwpj8ej8PBw9ejRQ4sWLSrQ/klJSSpbtmzxFllIrVu31r59+xQWFuZ0KQAKiS98A+CVlpamNm3aqGzZsho/frwaNWqkkydPasGCBYqJibkkf2H35MmTcrvd3h+OA3Bp4YoKAK+HHnpILpdLq1ev1u233646deqoQYMGGjlypL7//ntJ0qRJk9SoUSOFhIQoPDxcDz30kLKysiT9fYvl/vvvV0ZGhlwul1wul5599llJf3+z5mOPPaZq1aopJCRELVu21NKlS31e/4033lB4eLiCg4N12223adKkSXmuziQkJOiaa66R2+1W3bp19fbbb/tsd7lcSkhIUM+ePRUSEqIxY8bke+vn22+/Vdu2bRUUFKTw8HANHz7c51d3p06dqtq1a6tUqVKqVKmS7rjjjqI5yQAKx+lvnANgh0OHDhmXy2XGjh171n6TJ082ixcvNjt37jSLFi0ydevWNUOGDDHGGJOdnW2mTJliypQp4/2V5VO/0Dpw4EDTunVrs3z5crN9+3Yzfvx44/F4TEpKijHmf788O378eLN161YTHx9vypUrZ8LCwryvPWfOHBMYGGji4+PN1q1bvb88u3jxYm8fSaZixYpmxowZJjU11ezatcssWbLE50fntm/fbkJCQszkyZNNSkqKWbFihWnSpIm57777jDHGrFmzxvj7+5tZs2aZtLQ0s379evPyyy8X1akGUAgEFQDGGGNWrVplJJk5c+YUar+PPvrIlC9f3ruemJjoEy6MMWbXrl3G39/f7Nmzx6e9Y8eOZtSoUcYYY+68804TFRXls71Pnz4+x2rdurUZNGiQT5/o6GjTrVs377okM2LECJ8+/wwqAwYMMIMHD/bp88033xg/Pz/z3//+18yePduUKVPGZGZmnvsEAChW3PoBIEkyBfw1jeTkZHXs2FHVqlVTaGio7rnnHh06dEjHjh074z6bNm1STk6O6tSpo9KlS3uXZcuWKTU1VZK0detWtWjRwme/f65v3rxZbdq08Wlr06aNNm/e7NPWrFmzs76HH3/8UUlJST61dOnSRbm5udq5c6c6d+6sGjVqqGbNmrrnnnv07rvvnvX9ASg+TKYFIEmqXbu2XC7XWSfMpqWlqXv37hoyZIjGjBmjcuXK6dtvv9WAAQN04sQJBQcH57tfVlaW/P39tW7dOvn7+/tsK126dJG+D0kKCQk56/asrCw98MADGj58eJ5tV111ldxut9avX6+lS5fq66+/1jPPPKNnn31Wa9asse6JJqCk44oKAElSuXLl1KVLF8XHx/tMKj3l8OHDWrdunXJzczVx4kTdcMMNqlOnjvbu3evTz+12Kycnx6etSZMmysnJ0f79+1WrVi2f5dTTOHXr1tWaNWt89vvnev369bVixQqfthUrVigyMrJQ77Vp06b65Zdf8tRSq1Ytud1uSVJAQIA6deqkl156SRs3blRaWpoWL15cqNcBcOEIKgC84uPjlZOToxYtWmj27Nnatm2bNm/erFdeeUWtWrVSrVq1dPLkSb366qvasWOH3n77bU2bNs3nGBEREcrKytKiRYt08OBBHTt2THXq1FGfPn107733as6cOdq5c6dWr16tuLg4ffnll5KkYcOG6auvvtKkSZO0bds2vf7665o3b55cLpf32I8//riSkpKUkJCgbdu2adKkSZozZ44ee+yxQr3PJ554QitXrtTQoUO1YcMGbdu2TZ9++qmGDh0qSfriiy/0yiuvaMOGDdq1a5dmzpyp3Nxc1a1b9wLPMIBCc3qSDAC77N2718TExJgaNWoYt9ttqlWrZnr27GmWLFlijDFm0qRJpkqVKiYoKMh06dLFzJw502eiqjHGPPjgg6Z8+fJGkomNjTXGGHPixAnzzDPPmIiICBMYGGiqVKlibrvtNrNx40bvftOnTzfVqlUzQUFBplevXuaFF14wlStX9qlv6tSppmbNmiYwMNDUqVPHzJw502e7JDN37lyftn9OpjXGmNWrV5vOnTub0qVLm5CQENO4cWMzZswYY8zfE2vbt29vrrjiChMUFGQaN25sPvjggws7sQDOi8uYAs6gA4CLbNCgQdqyZYu++eYbp0sB4BAm0wKwxoQJE9S5c2eFhIRo3rx5euuttzR16lSnywLgIK6oALBG7969tXTpUh05ckQ1a9bUsGHD9OCDDzpdFgAHEVQAAIC1eOoHAABYi6ACAACsRVABAADWIqgAAABrEVQAAIC1CCoAAMBaBBUAAGAtggoAALAWQQUAAFjr/wEUI9kMxGNjPAAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Data\n", + "categories = ['Category A', 'Category B', 'Category C', 'Category D']\n", + "values = [4, 7, 3, 6]\n", + "\n", + "# Create a bar plot\n", + "plt.bar(categories, values)\n", + "\n", + "# Labeling the axes and the plot\n", + "plt.xlabel('Categories')\n", + "plt.ylabel('Values')\n", + "plt.title('Bar Plot Example')\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Activity 3: Scatter Plot\n", + "### Objective: Create a scatter plot to visualize the relationship between two variables.\n", + "#### Instructions:\n", + "1. Create two lists of random values for x and y (e.g., `x = [1, 2, 3, 4, 5]`, `y = [5, 4, 3, 2, 1]`).\n", + "2. Use Matplotlib to plot a scatter plot.\n", + "3. Add labels for the axes and a title: `Scatter Plot Example`." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Data\n", + "x = [1, 2, 3, 4, 5]\n", + "y = [5, 4, 3, 2, 1]\n", + "\n", + "# Create a scatter plot\n", + "plt.scatter(x, y)\n", + "\n", + "# Labeling the axes and the plot\n", + "plt.xlabel('x')\n", + "plt.ylabel('y')\n", + "plt.title('Scatter Plot Example')\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Activity 4: Histogram\n", + "### Objective: Create a histogram to visualize the distribution of a dataset.\n", + "#### Instructions:\n", + "1. Create a list of random values (e.g., `data = [1, 2, 2, 3, 4, 5, 5, 5, 6, 7]`).\n", + "2. Use Matplotlib to create a histogram with 5 bins.\n", + "3. Label the axes and give the plot a title: `Histogram Example`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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evXrKzs4ucf8FBQUqKChwrOfm5rqncAAA4JFMDzctW7bU3r17lZOTo3fffVdxcXHasmVLiQGnrJKSkjRlyhS37Ks0mvz9w3I71u/Zt9MHmF0C4Db83ig//O74fTD9tlTVqlXVvHlzRUREKCkpSWFhYXrhhReK7RscHKzTp087tZ0+fVrBwcEl7n/ixInKyclxLMePH3dr/QAAwLOYHm5+y263O91G+rXIyEht3LjRqS09Pb3EOTqS5O3t7XjU/MoCAACsy9TbUhMnTlT//v3VuHFjXbhwQYsXL9bmzZu1bt06SVJsbKwaNmyopKQkSdKYMWPUo0cPJScna8CAAVqyZIkyMjI0f/58M08DAAB4EFPDzZkzZxQbG6usrCz5+/urQ4cOWrdune68805JUmZmpipV+r/Bpa5du2rx4sX6xz/+oaeeekotWrTQihUr1K5dO7NOAQAAeBhTw83rr79+1c83b95cpG3QoEEaNGjQDaoIAABUdB435wYAAOB6EG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClEG4AAIClmBpukpKSdMstt8jX11dBQUGKiYnRwYMHr7pNWlqabDab01KtWrVyqhgAAHg6U8PNli1blJiYqE8//VTp6em6fPmy7rrrLuXn5191Oz8/P2VlZTmWY8eOlVPFAADA03mZefC1a9c6raelpSkoKEi7du1S9+7dS9zOZrMpODj4RpcHAAAqII+ac5OTkyNJCgwMvGq/vLw8hYaGKiQkRNHR0Tpw4ECJfQsKCpSbm+u0AAAA6/KYcGO32zV27Fh169ZN7dq1K7Ffy5YttXDhQq1cuVKLFi2S3W5X165ddeLEiWL7JyUlyd/f37GEhITcqFMAAAAewGPCTWJiovbv368lS5ZctV9kZKRiY2MVHh6uHj166L333lPdunX16quvFtt/4sSJysnJcSzHjx+/EeUDAAAPYeqcmytGjRqlVatWaevWrWrUqFGZtq1SpYo6duyoI0eOFPu5t7e3vL293VEmAACoAEwduTEMQ6NGjdL777+vjz76SE2bNi3zPgoLC7Vv3z7Vr1//BlQIAAAqGlNHbhITE7V48WKtXLlSvr6+ys7OliT5+/urevXqkqTY2Fg1bNhQSUlJkqRnn31WXbp0UfPmzfXDDz9o5syZOnbsmEaOHGnaeQAAAM9hariZN2+eJKlnz55O7ampqYqPj5ckZWZmqlKl/xtgOn/+vB566CFlZ2erVq1aioiI0Pbt29WmTZvyKhsAAHgwU8ONYRjX7LN582an9dmzZ2v27Nk3qCIAAFDReczTUgAAAO5AuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZCuAEAAJZiarhJSkrSLbfcIl9fXwUFBSkmJkYHDx685nbLly9Xq1atVK1aNbVv316rV68uh2oBAEBFYGq42bJlixITE/Xpp58qPT1dly9f1l133aX8/PwSt9m+fbuGDh2qhIQE7dmzRzExMYqJidH+/fvLsXIAAOCpbIZhGGYXccXZs2cVFBSkLVu2qHv37sX2GTJkiPLz87Vq1SpHW5cuXRQeHq6UlJRrHiM3N1f+/v7KycmRn5+f22q/osnfP3T7PlHUt9MHmF0C4Db83ig//O6ouMry97dHzbnJycmRJAUGBpbYZ8eOHerbt69TW1RUlHbs2FFs/4KCAuXm5jotAADAurzMLuAKu92usWPHqlu3bmrXrl2J/bKzs1WvXj2ntnr16ik7O7vY/klJSZoyZYpbawUAVEyMkpUPs0fIPGbkJjExUfv379eSJUvcut+JEycqJyfHsRw/ftyt+wcAAJ7FI0ZuRo0apVWrVmnr1q1q1KjRVfsGBwfr9OnTTm2nT59WcHBwsf29vb3l7e3ttloBAIBnM3XkxjAMjRo1Su+//74++ugjNW3a9JrbREZGauPGjU5t6enpioyMvFFlAgCACsTUkZvExEQtXrxYK1eulK+vr2PejL+/v6pXry5Jio2NVcOGDZWUlCRJGjNmjHr06KHk5GQNGDBAS5YsUUZGhubPn2/aeQAAAM9h6sjNvHnzlJOTo549e6p+/fqOZenSpY4+mZmZysrKcqx37dpVixcv1vz58xUWFqZ3331XK1asuOokZAAA8Pth6shNab5iZ/PmzUXaBg0apEGDBt2AigAAQEXnMU9LAQAAuAPhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWIpL4ebrr792dx0AAABu4VK4ad68uXr16qVFixbp4sWL7q4JAADAZS6Fm927d6tDhw4aN26cgoOD9cgjj+izzz5zd20AAABl5lK4CQ8P1wsvvKBTp05p4cKFysrK0u2336527dpp1qxZOnv2rLvrBAAAKJXrmlDs5eWl++67T8uXL9eMGTN05MgRjR8/XiEhIYqNjVVWVpa76gQAACiV6wo3GRkZ+stf/qL69etr1qxZGj9+vI4ePar09HSdOnVK0dHR7qoTAACgVLxc2WjWrFlKTU3VwYMHdffdd+vNN9/U3XffrUqVfslKTZs2VVpampo0aeLOWgEAAK7JpXAzb948Pfjgg4qPj1f9+vWL7RMUFKTXX3/9uooDAAAoK5fCzeHDh6/Zp2rVqoqLi3Nl9wAAAC5zac5Namqqli9fXqR9+fLleuONN667KAAAAFe5FG6SkpJUp06dIu1BQUGaNm3adRcFAADgKpfCTWZmppo2bVqkPTQ0VJmZmdddFAAAgKtcCjdBQUH64osvirR//vnnql279nUXBQAA4CqXws3QoUM1evRobdq0SYWFhSosLNRHH32kMWPG6M9//rO7awQAACg1l56Weu655/Ttt9+qT58+8vL6ZRd2u12xsbHMuQEAAKZyKdxUrVpVS5cu1XPPPafPP/9c1atXV/v27RUaGuru+gAAAMrEpXBzxc0336ybb77ZXbUAAABcN5fCTWFhodLS0rRx40adOXNGdrvd6fOPPvrILcUBAACUlUvhZsyYMUpLS9OAAQPUrl072Ww2d9cFAADgEpfCzZIlS7Rs2TLdfffd7q4HAADgurj0KHjVqlXVvHlzd9cCAABw3VwKN08++aReeOEFGYbh7noAAACui0u3pT7++GNt2rRJa9asUdu2bVWlShWnz9977z23FAcAAFBWLoWbgIAA3Xvvve6uBQAA4Lq5FG5SU1PdXQcAAIBbuDTnRpJ+/vlnbdiwQa+++qouXLggSTp16pTy8vLcVhwAAEBZuTRyc+zYMfXr10+ZmZkqKCjQnXfeKV9fX82YMUMFBQVKSUlxd50AAACl4tLIzZgxY9S5c2edP39e1atXd7Tfe++92rhxo9uKAwAAKCuXRm62bdum7du3q2rVqk7tTZo00cmTJ91SGAAAgCtcGrmx2+0qLCws0n7ixAn5+vped1EAAACucinc3HXXXZozZ45j3WazKS8vT5MmTeKVDAAAwFQu3ZZKTk5WVFSU2rRpo4sXL+r+++/X4cOHVadOHb3zzjvurhEAAKDUXAo3jRo10ueff64lS5boiy++UF5enhISEjRs2DCnCcYAAADlzaVwI0leXl4aPny4O2sBAAC4bi6FmzfffPOqn8fGxrpUDAAAwPVyKdyMGTPGaf3y5cv68ccfVbVqVdWoUYNwAwAATOPS01Lnz593WvLy8nTw4EHdfvvtTCgGAACmcvndUr/VokULTZ8+vcioztVs3bpVAwcOVIMGDWSz2bRixYqr9t+8ebNsNluRJTs7+zqrBwAAVuG2cCP9Msn41KlTpe6fn5+vsLAwzZ07t0zHOXjwoLKyshxLUFBQWUsFAAAW5dKcmw8++MBp3TAMZWVl6eWXX1a3bt1KvZ/+/furf//+ZT5+UFCQAgICyrwdAACwPpfCTUxMjNO6zWZT3bp11bt3byUnJ7ujrqsKDw9XQUGB2rVrp8mTJ5cpUAEAAGtzKdzY7XZ311Eq9evXV0pKijp37qyCggItWLBAPXv21H/+8x916tSp2G0KCgpUUFDgWM/NzS2vcgEAgAlc/hI/M7Rs2VItW7Z0rHft2lVHjx7V7Nmz9dZbbxW7TVJSkqZMmVJeJQIAAJO5FG7GjRtX6r6zZs1y5RClduutt+rjjz8u8fOJEyc61Zubm6uQkJAbWhMAADCPS+Fmz5492rNnjy5fvuwYSTl06JAqV67sdHvIZrO5p8qr2Lt3r+rXr1/i597e3vL29r7hdQAAAM/gUrgZOHCgfH199cYbb6hWrVqSfvlivxEjRuiOO+7Qk08+War95OXl6ciRI471b775Rnv37lVgYKAaN26siRMn6uTJk47XPcyZM0dNmzZV27ZtdfHiRS1YsEAfffSR1q9f78ppAAAAC3Ip3CQnJ2v9+vWOYCNJtWrV0tSpU3XXXXeVOtxkZGSoV69ejvUrt4/i4uKUlpamrKwsZWZmOj6/dOmSnnzySZ08eVI1atRQhw4dtGHDBqd9AACA3zeXwk1ubq7Onj1bpP3s2bO6cOFCqffTs2dPGYZR4udpaWlO6xMmTNCECRNKvX8AAPD749I3FN97770aMWKE3nvvPZ04cUInTpzQv//9byUkJOi+++5zd40AAACl5tLITUpKisaPH6/7779fly9f/mVHXl5KSEjQzJkz3VogAABAWbgUbmrUqKFXXnlFM2fO1NGjRyVJzZo1U82aNd1aHAAAQFld14szr7y4skWLFqpZs+ZV588AAACUB5fCzXfffac+ffro5ptv1t13362srCxJUkJCQqmflAIAALgRXAo3TzzxhKpUqaLMzEzVqFHD0T5kyBCtXbvWbcUBAACUlUtzbtavX69169apUaNGTu0tWrTQsWPH3FIYAACAK1waucnPz3casbni+++/51UHAADAVC6FmzvuuMPxSgTpl3dI2e12Pf/883xbMAAAMJVLt6Wef/559enTRxkZGbp06ZImTJigAwcO6Pvvv9cnn3zi7hoBAABKzaWRm3bt2unQoUO6/fbbFR0drfz8fN13333as2ePmjVr5u4aAQAASq3MIzeXL19Wv379lJKSoqeffvpG1AQAAOCyMo/cVKlSRV988cWNqAUAAOC6uXRbavjw4Xr99dfdXQsAAMB1c2lC8c8//6yFCxdqw4YNioiIKPJOqVmzZrmlOAAAgLIqU7j5+uuv1aRJE+3fv1+dOnWSJB06dMipj81mc191AAAAZVSmcNOiRQtlZWVp06ZNkn553cKLL76oevXq3ZDiAAAAyqpMc25++9bvNWvWKD8/360FAQAAXA+XJhRf8duwAwAAYLYyhRubzVZkTg1zbAAAgCcp05wbwzAUHx/veDnmxYsX9eijjxZ5Wuq9995zX4UAAABlUKZwExcX57Q+fPhwtxYDAABwvcoUblJTU29UHQAAAG5xXROKAQAAPA3hBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWArhBgAAWIqp4Wbr1q0aOHCgGjRoIJvNphUrVlxzm82bN6tTp07y9vZW8+bNlZaWdsPrBAAAFYep4SY/P19hYWGaO3duqfp/8803GjBggHr16qW9e/dq7NixGjlypNatW3eDKwUAABWFl5kH79+/v/r371/q/ikpKWratKmSk5MlSa1bt9bHH3+s2bNnKyoq6kaVCQAAKpAKNedmx44d6tu3r1NbVFSUduzYUeI2BQUFys3NdVoAAIB1mTpyU1bZ2dmqV6+eU1u9evWUm5urn376SdWrVy+yTVJSkqZMmVJeJaKcNPn7h2aXAADwUBVq5MYVEydOVE5OjmM5fvy42SUBAIAbqEKN3AQHB+v06dNObadPn5afn1+xozaS5O3tLW9v7/IoDwAAeIAKNXITGRmpjRs3OrWlp6crMjLSpIoAAICnMTXc5OXlae/evdq7d6+kXx713rt3rzIzMyX9ckspNjbW0f/RRx/V119/rQkTJuirr77SK6+8omXLlumJJ54wo3wAAOCBTA03GRkZ6tixozp27ChJGjdunDp27KhnnnlGkpSVleUIOpLUtGlTffjhh0pPT1dYWJiSk5O1YMECHgMHAAAONsMwDLOLKE+5ubny9/dXTk6O/Pz83L5/nuIBAPzefTt9gNv3WZa/vyvUnBsAAIBrIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABLIdwAAABL8YhwM3fuXDVp0kTVqlXTbbfdps8++6zEvmlpabLZbE5LtWrVyrFaAADgyUwPN0uXLtW4ceM0adIk7d69W2FhYYqKitKZM2dK3MbPz09ZWVmO5dixY+VYMQAA8GSmh5tZs2bpoYce0ogRI9SmTRulpKSoRo0aWrhwYYnb2Gw2BQcHO5Z69eqVY8UAAMCTmRpuLl26pF27dqlv376OtkqVKqlv377asWNHidvl5eUpNDRUISEhio6O1oEDB0rsW1BQoNzcXKcFAABYl6nh5ty5cyosLCwy8lKvXj1lZ2cXu03Lli21cOFCrVy5UosWLZLdblfXrl114sSJYvsnJSXJ39/fsYSEhLj9PAAAgOcw/bZUWUVGRio2Nlbh4eHq0aOH3nvvPdWtW1evvvpqsf0nTpyonJwcx3L8+PFyrhgAAJQnLzMPXqdOHVWuXFmnT592aj99+rSCg4NLtY8qVaqoY8eOOnLkSLGfe3t7y9vb+7prBQAAFYOpIzdVq1ZVRESENm7c6Giz2+3auHGjIiMjS7WPwsJC7du3T/Xr179RZQIAgArE1JEbSRo3bpzi4uLUuXNn3XrrrZozZ47y8/M1YsQISVJsbKwaNmyopKQkSdKzzz6rLl26qHnz5vrhhx80c+ZMHTt2TCNHjjTzNAAAgIcwPdwMGTJEZ8+e1TPPPKPs7GyFh4dr7dq1jknGmZmZqlTp/waYzp8/r4ceekjZ2dmqVauWIiIitH37drVp08asUwAAAB7EZhiGYXYR5Sk3N1f+/v7KycmRn5+f2/ff5O8fun2fAABUJN9OH+D2fZbl7+8K97QUAADA1RBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApRBuAACApXhEuJk7d66aNGmiatWq6bbbbtNnn3121f7Lly9Xq1atVK1aNbVv316rV68up0oBAICnMz3cLF26VOPGjdOkSZO0e/duhYWFKSoqSmfOnCm2//bt2zV06FAlJCRoz549iomJUUxMjPbv31/OlQMAAE9kMwzDMLOA2267TbfccotefvllSZLdbldISIgef/xx/f3vfy/Sf8iQIcrPz9eqVascbV26dFF4eLhSUlKuebzc3Fz5+/srJydHfn5+7juR/6/J3z90+z4BAKhIvp0+wO37LMvf36aO3Fy6dEm7du1S3759HW2VKlVS3759tWPHjmK32bFjh1N/SYqKiiqxPwAA+H3xMvPg586dU2FhoerVq+fUXq9ePX311VfFbpOdnV1s/+zs7GL7FxQUqKCgwLGek5Mj6ZcEeCPYC368IfsFAKCiuBF/x17ZZ2luOJkabspDUlKSpkyZUqQ9JCTEhGoAALA+/zk3bt8XLlyQv7//VfuYGm7q1KmjypUr6/Tp007tp0+fVnBwcLHbBAcHl6n/xIkTNW7cOMe63W7X999/r9q1a8tms13nGTjLzc1VSEiIjh8/fkPm81gJ16r0uFalx7UqPa5V2XC9Su9GXSvDMHThwgU1aNDgmn1NDTdVq1ZVRESENm7cqJiYGEm/hI+NGzdq1KhRxW4TGRmpjRs3auzYsY629PR0RUZGFtvf29tb3t7eTm0BAQHuKL9Efn5+/PCXEteq9LhWpce1Kj2uVdlwvUrvRlyra43YXGH6balx48YpLi5OnTt31q233qo5c+YoPz9fI0aMkCTFxsaqYcOGSkpKkiSNGTNGPXr0UHJysgYMGKAlS5YoIyND8+fPN/M0AACAhzA93AwZMkRnz57VM888o+zsbIWHh2vt2rWOScOZmZmqVOn/Hurq2rWrFi9erH/84x966qmn1KJFC61YsULt2rUz6xQAAIAHMT3cSNKoUaNKvA21efPmIm2DBg3SoEGDbnBVZeft7a1JkyYVuQ2GorhWpce1Kj2uVelxrcqG61V6nnCtTP8SPwAAAHcy/fULAAAA7kS4AQAAlkK4AQAAlkK4AQAAlkK4cYOtW7dq4MCBatCggWw2m1asWGF2SR4rKSlJt9xyi3x9fRUUFKSYmBgdPHjQ7LI80rx589ShQwfHF2FFRkZqzZo1ZpdVIUyfPl02m83pyz7xi8mTJ8tmszktrVq1Mrssj3Xy5EkNHz5ctWvXVvXq1dW+fXtlZGSYXZbHadKkSZGfK5vNpsTERFPqIdy4QX5+vsLCwjR37lyzS/F4W7ZsUWJioj799FOlp6fr8uXLuuuuu5Sfn292aR6nUaNGmj59unbt2qWMjAz17t1b0dHROnDggNmlebSdO3fq1VdfVYcOHcwuxWO1bdtWWVlZjuXjjz82uySPdP78eXXr1k1VqlTRmjVr9OWXXyo5OVm1atUyuzSPs3PnTqefqfT0dEky7WtbPOJ7biq6/v37q3///maXUSGsXbvWaT0tLU1BQUHatWuXunfvblJVnmngwIFO6//61780b948ffrpp2rbtq1JVXm2vLw8DRs2TK+99pqmTp1qdjkey8vLq8T38eH/zJgxQyEhIUpNTXW0NW3a1MSKPFfdunWd1qdPn65mzZqpR48eptTDyA1MlZOTI0kKDAw0uRLPVlhYqCVLlig/P7/E96hBSkxM1IABA9S3b1+zS/Fohw8fVoMGDXTTTTdp2LBhyszMNLskj/TBBx+oc+fOGjRokIKCgtSxY0e99tprZpfl8S5duqRFixbpwQcfdPsLqkuLkRuYxm63a+zYserWrRuvzyjBvn37FBkZqYsXL8rHx0fvv/++2rRpY3ZZHmnJkiXavXu3du7caXYpHu22225TWlqaWrZsqaysLE2ZMkV33HGH9u/fL19fX7PL8yhff/215s2bp3Hjxumpp57Szp07NXr0aFWtWlVxcXFml+exVqxYoR9++EHx8fGm1UC4gWkSExO1f/9+7vdfRcuWLbV3717l5OTo3XffVVxcnLZs2ULA+Y3jx49rzJgxSk9PV7Vq1cwux6P9+hZ6hw4ddNtttyk0NFTLli1TQkKCiZV5Hrvdrs6dO2vatGmSpI4dO2r//v1KSUkh3FzF66+/rv79+6tBgwam1cBtKZhi1KhRWrVqlTZt2qRGjRqZXY7Hqlq1qpo3b66IiAglJSUpLCxML7zwgtlleZxdu3bpzJkz6tSpk7y8vOTl5aUtW7boxRdflJeXlwoLC80u0WMFBATo5ptv1pEjR8wuxePUr1+/yD8kWrduzW28qzh27Jg2bNigkSNHmloHIzcoV4Zh6PHHH9f777+vzZs3MzmvjOx2uwoKCswuw+P06dNH+/btc2obMWKEWrVqpb/97W+qXLmySZV5vry8PB09elQPPPCA2aV4nG7duhX5qopDhw4pNDTUpIo8X2pqqoKCgjRgwABT6yDcuEFeXp7Tv3q++eYb7d27V4GBgWrcuLGJlXmexMRELV68WCtXrpSvr6+ys7MlSf7+/qpevbrJ1XmWiRMnqn///mrcuLEuXLigxYsXa/PmzVq3bp3ZpXkcX1/fIvO2atasqdq1azOf6zfGjx+vgQMHKjQ0VKdOndKkSZNUuXJlDR061OzSPM4TTzyhrl27atq0aRo8eLA+++wzzZ8/X/Pnzze7NI9kt9uVmpqquLg4eXmZHC8MXLdNmzYZkooscXFxZpfmcYq7TpKM1NRUs0vzOA8++KARGhpqVK1a1ahbt67Rp08fY/369WaXVWH06NHDGDNmjNlleJwhQ4YY9evXN6pWrWo0bNjQGDJkiHHkyBGzy/JY//u//2u0a9fO8Pb2Nlq1amXMnz/f7JI81rp16wxJxsGDB80uxbAZhmGYE6sAAADcjwnFAADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUgg3AADAUgg3ACyhZ8+eGjt2rNllAPAAhBsAphs4cKD69etX7Gfbtm2TzWbTF198Uc5VAaioCDcATJeQkKD09HSdOHGiyGepqanq3LmzOnToYEJlACoiwg0A0/3hD39Q3bp1lZaW5tSel5en5cuXKyYmRkOHDlXDhg1Vo0YNtW/fXu+8885V92mz2bRixQqntoCAAKdjHD9+XIMHD1ZAQIACAwMVHR2tb7/91j0nBcA0hBsApvPy8lJsbKzS0tL069fdLV++XIWFhRo+fLgiIiL04Ycfav/+/Xr44Yf1wAMP6LPPPnP5mJcvX1ZUVJR8fX21bds2ffLJJ/Lx8VG/fv106dIld5wWAJMQbgB4hAcffFBHjx7Vli1bHG2pqan64x//qNDQUI0fP17h4eG66aab9Pjjj6tfv35atmyZy8dbunSp7Ha7FixYoPbt26t169ZKTU1VZmamNm/e7IYzAmAWwg0Aj9CqVSt17dpVCxculCQdOXJE27ZtU0JCggoLC/Xcc8+pffv2CgwMlI+Pj9atW6fMzEyXj/f555/ryJEj8vX1lY+Pj3x8fBQYGKiLFy/q6NGj7jotACbwMrsAALgiISFBjz/+uObOnavU1FQ1a9ZMPXr00IwZM/TCCy9ozpw5at++vWrWrKmxY8de9faRzWZzusUl/XIr6oq8vDxFRETo7bffLrJt3bp13XdSAMod4QaAxxg8eLDGjBmjxYsX680339Rjjz0mm82mTz75RNHR0Ro+fLgkyW6369ChQ2rTpk2J+6pbt66ysrIc64cPH9aPP/7oWO/UqZOWLl2qoKAg+fn53biTAlDuuC0FwGP4+PhoyJAhmjhxorKyshQfHy9JatGihdLT07V9+3b997//1SOPPKLTp09fdV+9e/fWyy+/rD179igjI0OPPvqoqlSp4vh82LBhqlOnjqKjo7Vt2zZ988032rx5s0aPHl3sI+kAKg7CDQCPkpCQoPPnzysqKkoNGjSQJP3jH/9Qp06dFBUVpZ49eyo4OFgxMTFX3U9ycrJCQkJ0xx136P7779f48eNVo0YNx+c1atTQ1q1b1bhxY913331q3bq1EhISdPHiRUZygArOZvz2pjQAAEAFxsgNAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwFMINAACwlP8HnRZFDddF+MEAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Data\n", + "data = [1, 2, 2, 3, 4, 5, 5, 5, 6, 7]\n", + "\n", + "# Create a histogram\n", + "plt.hist(data, bins=5)\n", + "\n", + "# Labeling the axes and the plot\n", + "plt.xlabel('Value')\n", + "plt.ylabel('Frequency')\n", + "plt.title('Histogram Example')\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Activity 5: Pie Chart\n", + "### Objective: Create a pie chart to visualize the proportions of different categories.\n", + "#### Instructions:\n", + "1. Create a list of categories (`['Apple', 'Banana', 'Cherry', 'Date']`) and their corresponding values (`[10, 15, 7, 5]`).\n", + "2. Use Matplotlib to create a pie chart.\n", + "3. Add a title to the chart: `Fruit Pie Chart`." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Data\n", + "labels = ['Apple', 'Banana', 'Cherry', 'Date']\n", + "sizes = [10, 15, 7, 5]\n", + "\n", + "# Create a pie chart\n", + "plt.pie(sizes, labels=labels, autopct='%1.1f%%')\n", + "\n", + "# Title\n", + "plt.title('Fruit Pie Chart')\n", + "\n", + "# Show the plot\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Activity 6: Multiple Subplots\n", + "### Objective: Create multiple subplots to visualize different types of plots in one figure.\n", + "#### Instructions:\n", + "1. Create a figure with 2 rows and 2 columns of subplots.\n", + "2. In the first subplot, create a line plot.\n", + "3. In the second subplot, create a bar plot.\n", + "4. In the third subplot, create a scatter plot.\n", + "5. In the fourth subplot, create a pie chart." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Data\n", + "x = [0, 1, 2, 3, 4]\n", + "y = [0, 1, 4, 9, 16]\n", + "categories = ['A', 'B', 'C', 'D']\n", + "values = [5, 3, 9, 6]\n", + "labels = ['Apple', 'Banana', 'Cherry', 'Date']\n", + "sizes = [10, 15, 7, 5]\n", + "\n", + "# Create a 2x2 grid of subplots\n", + "fig, axs = plt.subplots(2, 2, figsize=(10, 10))\n", "\n", - "fig, ax = plt.subplots()\n", + "# First subplot: Line plot\n", + "axs[0, 0].plot(x, y)\n", + "axs[0, 0].set_title('Line Plot')\n", "\n", - "fruits = ['apple', 'blueberry', 'cherry', 'orange']\n", - "counts = [40, 100, 30, 55]\n", - "bar_labels = ['red', 'blue', '_red', 'orange']\n", - "bar_colors = ['tab:red', 'tab:blue', 'tab:red', 'tab:orange']\n", + "# Second subplot: Bar plot\n", + "axs[0, 1].bar(categories, values)\n", + "axs[0, 1].set_title('Bar Plot')\n", "\n", - "ax.bar(fruits, counts, label=bar_labels, color=bar_colors)\n", + "# Third subplot: Scatter plot\n", + "axs[1, 0].scatter(x, y)\n", + "axs[1, 0].set_title('Scatter Plot')\n", "\n", - "ax.set_ylabel('fruit supply')\n", - "ax.set_title('Fruit supply by kind and color')\n", - "ax.legend(title='Fruit color')\n", + "# Fourth subplot: Pie chart\n", + "axs[1, 1].pie(sizes, labels=labels, autopct='%1.1f%%')\n", + "axs[1, 1].set_title('Pie Chart')\n", "\n", + "# Show the plots\n", "plt.show()" ] } @@ -52,9 +313,9 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" + "version": "3.11.3" } }, "nbformat": 4, - "nbformat_minor": 2 + "nbformat_minor": 5 } From f1daa8c12d8769fe030a4da66a59cf298eb172e9 Mon Sep 17 00:00:00 2001 From: Saim Date: Tue, 31 Dec 2024 15:02:55 +0530 Subject: [PATCH 2/2] Add: Exercise and instructions. --- README.md | 72 +++- activities/1.2-file-handling-example.ipynb | 0 ...otlib.ipynb => 1.2-using-matplotlib.ipynb} | 0 project/project.ipynb | 307 +++++++----------- 4 files changed, 172 insertions(+), 207 deletions(-) delete mode 100644 activities/1.2-file-handling-example.ipynb rename activities/{1.3-using-matplotlib.ipynb => 1.2-using-matplotlib.ipynb} (100%) diff --git a/README.md b/README.md index 65b9b445..5bae8101 100644 --- a/README.md +++ b/README.md @@ -1,18 +1,68 @@ -## Python & Jupyter Basics +# Student Guide: Jupyter Notebook Activities and Project -This project walks you through some basic activities in Python through some activities and a project. +Welcome! This guide will help you set up your environment, complete the activities, and work on the project exercises. Please follow the steps below carefully. -### Topics +--- -The goal is for you to get familiar with Jupyter Notebooks, and also get familiar with some basic concepts in Python such as: +## 1. Setting Up Codespace for Jupyter Notebook + +### Prerequisites +1. A GitHub account. +2. Basic familiarity with using GitHub and Codespaces. + +### Steps to Create a Codespace +1. **Open Repository**: Navigate to this repository in your GitHub account. +2. **Create Codespace**: Click the `Code` button, select the `Codespaces` tab, and then click `Create codespace on main`. +3. **Install Jupyter Notebook Extension**: + - Once the Codespace is open, go to the **Extensions** view (on the sidebar, look for the square icon with four boxes). + - Search for **Jupyter Notebook** (developed by Microsoft) and install it. +4. **Set Kernel to Python Environment**: + - Open any `.ipynb` file from the repository in the Codespace. + - At the top-right corner of the Jupyter Notebook interface, click on the `Kernel` dropdown. + - Select the Python environment created in your Codespace (e.g., `Python 3 (ipykernel)`). + - This will ensure your notebooks are ready to run Python code. + +--- + +## 2. Activities + +### Overview +Before diving into the project exercises, it's essential to complete the activities. These activities are designed to familiarize you with the basics of Jupyter Notebook and Matplotlib. + +### Available Activities +1. **Basics of Jupyter Notebook**: Learn how to navigate and write basic Python code in Jupyter Notebook. + - File: `activities/1.1-basic-jupyter-notebook.ipynb` +2. **Introduction to Matplotlib**: Explore Matplotlib and create basic visualizations. + - File: `activities/1.2-using-matplotlib.ipynb` + +### Steps to Complete Activities +1. Open the activity files from the `activities` folder in your Codespace. +2. Follow the instructions provided in the notebook cells. +3. Experiment with the examples and try out additional code as needed. + +--- + +## 3. Project: Exercises on Matplotlib + +### Overview +The project file contains a set of exercises related to Matplotlib. These exercises build on the skills you developed in the activities and are meant to test your understanding and creativity. + +### Steps to Complete the Project +1. Open the project file: + - File: `project/project.ipynb` +2. Carefully read the instructions for each exercise. +3. Complete the exercises by writing your solutions in the code cells provided. + +--- + +## 4. Submitting Your Work + +### Submission Guidelines +1. Save your completed project file with all solutions in the `src` folder. + - Recommended filename: `matplotlib_exercises_solution.ipynb`. +2. Ensure all cells are executed, and outputs are visible in the notebook. +3. Push your updated `src` folder to the repository. -- Operations -- Functions -- Conditionals -- Dictionaries -- Lists -- File Handling -- Basic Plotting ### Goal diff --git a/activities/1.2-file-handling-example.ipynb b/activities/1.2-file-handling-example.ipynb deleted file mode 100644 index e69de29b..00000000 diff --git a/activities/1.3-using-matplotlib.ipynb b/activities/1.2-using-matplotlib.ipynb similarity index 100% rename from activities/1.3-using-matplotlib.ipynb rename to activities/1.2-using-matplotlib.ipynb diff --git a/project/project.ipynb b/project/project.ipynb index 2b25814d..4efdf417 100644 --- a/project/project.ipynb +++ b/project/project.ipynb @@ -1,198 +1,113 @@ { - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercise 1: Basic Arithmetic Operations" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO: Calculate the sum of 15 and 30 and print the result" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercise 2: String Manipulation" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO: Convert the string \"hello world\" to uppercase\n", - "string = \"hello world\"\n", - "uppercase_string = __\n", - "print(\"Uppercase String:\", uppercase_string)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercise 3: List Operations" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO: Append the number 10 to the list and print the updated list\n", - "numbers = [1, 2, 3, 4, 5]\n", - "numbers.__(10)\n", - "print(\"Updated List:\", numbers)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercise 4: Dictionary Operations" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO: Add a new key-value pair ('age': 25) to the dictionary and print the updated dictionary\n", - "person = {'name': 'Alice', 'city': 'Wonderland'}\n", - "person[__] = __\n", - "print(\"Updated Dictionary:\", person)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercise 5: Loop through a list" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO: Print each element in the list using a for loop\n", - "fruits = ['apple', 'banana', 'cherry']\n", - "for __ in __:\n", - " print(\"Fruit:\", __)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercise 6: Conditional Statements" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO: Check if the number is even or odd and print the result\n", - "number = __\n", - "if number __ __ == 0:\n", - " print(\"The number is even\")\n", - "else:\n", - " print(\"The number is odd\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercise 7: Function Definition" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO: Define a function that takes two numbers and returns their product\n", - "def multiply(a, b):\n", - " __ __ __ __\n", - "\n", - "# Test the function\n", - "result = multiply(6, 7)\n", - "print(\"Product:\", result)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercise 8: File Handling" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO: Write a string to a file and then read it back\n", - "file_path = 'example.txt'\n", - "# Write the string \"Hello, this is a test file.\" to the file\n", - "\n", - "__\n", - "\n", - "# Read the content of the file and print it\n", - "\n", - "__" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Exercise 9: Plotting with Matplotlib" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "# TODO: Plot a simple line graph using matplotlib\n", - "import matplotlib.pyplot as plt\n", - "\n", - "x = [1, 2, 3, 4, 5]\n", - "y = [2, 3, 5, 7, 11]\n", - "\n", - "# Plot the graph with x and y values\n", - "\n", - "# Set the labels for x-axis and y-axis\n", - "\n", - "# Set the title of the graph\n", - "\n", - "# Display the graph\n" - ] - } - ], - "metadata": { - "language_info": { - "name": "python" - } - }, - "nbformat": 4, - "nbformat_minor": 2 + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 1: Line Plot Customization" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: Create a line plot with x values ranging from 0 to 10 and y values as the square of x.\n", + "# Customize the plot by adding a title, labels for both axes, and a grid." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 2: Bar Plot with Colors" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: Create a bar plot with the following data: categories = ['A', 'B', 'C', 'D'] and values = [5, 7, 3, 9].\n", + "# Use different colors for each bar and add a title to the plot." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 3: Scatter Plot with Annotations" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: Create a scatter plot with x = [1, 2, 3, 4, 5] and y = [2, 4, 6, 8, 10].\n", + "# Annotate each point with its (x, y) value, and set the title as 'Scatter Plot with Annotations'." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 4: Pie Chart with Percentages" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: Create a pie chart with the following data: labels = ['Python', 'Java', 'C++', 'JavaScript'] and sizes = [40, 25, 20, 15].\n", + "# Display the percentages on the chart using autopct." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 5: Subplot Layout" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: Create a 2x2 subplot layout.\n", + "# Plot a line chart in the first subplot, a bar chart in the second, a scatter plot in the third, and a pie chart in the fourth." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Exercise 6: Histogram Customization" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# TODO: Create a histogram for the following data: data = [1, 2, 2, 3, 3, 3, 4, 4, 4, 4, 5].\n", + "# Customize the histogram with a title, labels for the x-axis, and a specific color for the bars." + ] + } + ], + "metadata": { + "language_info": { + "name": "python" + } + }, + "nbformat": 4, + "nbformat_minor": 2 }