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3019d5179c63106eb2a5b22f6f97b24a
mooc-rr
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2fb4bcb0
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2fb4bcb0
authored
Jan 11, 2024
by
3019d5179c63106eb2a5b22f6f97b24a
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exercice.ipynb
module2/exo3/exercice.ipynb
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module2/exo3/exercice.ipynb
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2fb4bcb0
{
"cells": [],
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(14.113000000000001, 2.8, 23.4, 14.5, 4.334094455301447)"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"\n",
"import numpy as np\n",
"\n",
"# Data set\n",
"data = [14.0, 7.6, 11.2, 12.8, 12.5, 9.9, 14.9, 9.4, 16.9, 10.2, 14.9, 18.1, 7.3, 9.8, \n",
" 10.9, 12.2, 9.9, 2.9, 2.8, 15.4, 15.7, 9.7, 13.1, 13.2, 12.3, 11.7, 16.0, 12.4, \n",
" 17.9, 12.2, 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, \n",
" 14.4, 15.7, 14.0, 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, \n",
" 13.5, 6.3, 6.4, 17.6, 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, \n",
" 19.6, 21.7, 11.3, 15.0, 14.3, 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, \n",
" 16.8, 15.8, 20.4, 15.8, 22.4, 16.2, 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, \n",
" 8.9, 21.0]\n",
"\n",
"# Calculating the required statistics\n",
"mean = np.mean(data)\n",
"minimum = np.min(data)\n",
"maximum = np.max(data)\n",
"median = np.median(data)\n",
"std_deviation = np.std(data, ddof=1) # using ddof=1 for sample standard deviation\n",
"\n",
"mean, minimum, maximum, median, std_deviation"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 864x432 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"\n",
"# Sorting data for sequence plot\n",
"sorted_data = sorted(data)\n",
"\n",
"# Creating sequence plot\n",
"plt.figure(figsize=(12, 6))\n",
"\n",
"plt.subplot(1, 2, 1)\n",
"plt.plot(sorted_data, marker='o')\n",
"plt.title('Sequence Plot')\n",
"plt.xlabel('Index')\n",
"plt.ylabel('Value')\n",
"\n",
"# Creating histogram\n",
"plt.subplot(1, 2, 2)\n",
"plt.hist(data, bins=20, color='blue', edgecolor='black')\n",
"plt.title('Histogram')\n",
"plt.xlabel('Value')\n",
"plt.ylabel('Frequency')\n",
"\n",
"plt.tight_layout()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
...
...
@@ -16,10 +115,9 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.
3
"
"version": "3.6.
4
"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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