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rwcitek/categorical-scatter-plot.ipynb

Last active April 5, 2025 21:05
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Categorical scatter plot
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categorical-scatter-plot.ipynb
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"import pandas as pd\n",
"import matplotlib.pyplot as plt\n"
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"# Create the DataFrame from your sample data\n",
"data = {'X': ['a', 'a', 'b', 'b', 'c'],\n",
" 'Y': [1, 2, 1, 4, 3]}\n",
"df = pd.DataFrame(data)\n",
"df"
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"metadata": {},
"execution_count": 2
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"cell_type": "code",
"source": [
"# Create the scatter plot\n",
"plt.figure(figsize=(8, 6)) # Optional: Adjust the size of the plot\n",
"plt.scatter(df['X'], df['Y'])\n",
"\n",
"\n",
"# Add labels and title\n",
"plt.xlabel('X')\n",
"plt.ylabel('Y')\n",
"plt.title('Scatter Plot of Y vs. X')\n",
"plt.ylim(0,5)\n",
"\n",
"# Display the plot\n",
"plt.grid(True) # Optional: Add a grid for better readability\n",
"plt.show()"
],
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\n"
},
"metadata": {}
}
]
},
{
"cell_type": "code",
"source": [],
"metadata": {
"id": "moFEab2QpqE8"
},
"execution_count": null,
"outputs": []
}
]
}
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