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Added updated files in src folder and activities folder #64

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KaziAfrozAlam May 24, 2025
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KaziAfrozAlam May 24, 2025
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7,682 changes: 7,682 additions & 0 deletions activities/1.1-basic-jupyter-notebook.html
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10 changes: 5 additions & 5 deletions activities/1.1-basic-jupyter-notebook.ipynb
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Expand Up @@ -39,13 +39,13 @@
"name": "stdout",
"output_type": "stream",
"text": [
"Hello, JupyterLab!\n"
"Hello, Afroz!\n"
]
}
],
"source": [
"# This is a Python code cell\n",
"print('Hello, JupyterLab!')"
"print('Hello, Afroz!')"
]
},
{
Expand All @@ -71,7 +71,7 @@
"2. Add your name below using Markdown syntax:\n",
"\n",
"### Your Name\n",
"John Doe\n",
"Kazi Afroz Alam\n",
"\n",
"3. Press **Shift + Enter** to render the Markdown."
]
Expand Down Expand Up @@ -145,7 +145,7 @@
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
Expand All @@ -159,7 +159,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.11.3"
"version": "3.12.3"
}
},
"nbformat": 4,
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7 changes: 2 additions & 5 deletions activities/1.2-using-matplotlib.ipynb
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Expand Up @@ -71,7 +71,7 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": null,
"metadata": {},
"outputs": [
{
Expand All @@ -86,19 +86,16 @@
}
],
"source": [
"# Data\n",
"\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()"
]
},
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42 changes: 37 additions & 5 deletions src/1-line-plot.ipynb
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42 changes: 39 additions & 3 deletions src/2-bar-plot.ipynb
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Expand Up @@ -11,16 +11,52 @@
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# TASK: 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."
"# Use different colors for each bar and add a title to the plot.\n",
"import matplotlib.pyplot as plt\n",
"\n",
"categories = ['A', 'B', 'C', 'D']\n",
"values = [5, 7, 3, 9]\n",
"colors = ['red', 'blue', 'green', 'orange'] \n",
"\n",
"plt.bar(categories, values, color=colors)\n",
"\n",
"plt.title('Bar Plot of Categories')\n",
"\n",
"plt.show()\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"name": "python"
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.12.3"
}
},
"nbformat": 4,
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49 changes: 46 additions & 3 deletions src/3-scatter-plot.ipynb
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46 changes: 42 additions & 4 deletions src/4-pie-chart.ipynb
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68 changes: 64 additions & 4 deletions src/5-subplot.ipynb
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36 changes: 32 additions & 4 deletions src/6-histogram.ipynb
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