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mooc-rr
Commit d23b2740 authored by 082aa0aa9507b80b621099010e33de9d's avatar 082aa0aa9507b80b621099010e33de9d
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module2/exo2/exercice.ipynb
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...@@ -2,7 +2,7 @@ ...@@ -2,7 +2,7 @@
"cells": [ "cells": [
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 15,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -11,6 +11,7 @@ ...@@ -11,6 +11,7 @@
"text": [ "text": [
"Mean: 14.113000000000001\n", "Mean: 14.113000000000001\n",
"Standard_variation: 4.312369534258399\n", "Standard_variation: 4.312369534258399\n",
"4.31\n",
"Min: 2.8\n", "Min: 2.8\n",
"Median: 14.5\n", "Median: 14.5\n",
"Max: 23.4\n" "Max: 23.4\n"
...@@ -21,23 +22,88 @@ ...@@ -21,23 +22,88 @@
"import numpy as np\n", "import numpy as np\n",
"import pandas as pd\n", "import pandas as pd\n",
"\n", "\n",
"\n",
"#input dataset\n", "#input dataset\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, 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, 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, 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, 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, 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, 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, 8.9, 21.0]\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, 10.9,\n",
" 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, 17.9, 12.2,\n",
" 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0,\n",
" 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6,\n",
" 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3,\n",
" 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2,\n",
" 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]\n",
"\n", "\n",
"#convert to array\n",
"data_1 = np.array(data)\n",
"#mean\n", "#mean\n",
"print(\"Mean:\", np.mean(data))\n", "print(\"Mean:\", np.mean(data_1))\n",
"\n", "\n",
"#standard_variation\n", "#standard_variation\n",
"print(\"Standard_variation:\", np.std(data))\n", "print(\"Standard_variation:\", np.std(data_1))\n",
"a = round(np.std(data_1),2)\n",
"print(a)\n",
"\n", "\n",
"#minimum\n", "#minimum\n",
"print(\"Min:\", np.min(data))\n", "print(\"Min:\", np.min(data_1))\n",
"\n", "\n",
"#median\n", "#median\n",
"print(\"Median:\", np.median(data))\n", "print(\"Median:\", np.median(data_1))\n",
"\n", "\n",
"#maximum\n", "#maximum\n",
"print(\"Max:\", np.max(data))" "print(\"Max:\", np.max(data_1))"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Mean: 14.113000000000001\n",
"Standard Deviation: 4.312369534258399\n",
"Minimum: 2.8\n",
"Median: 14.5\n",
"Maximum: 23.4\n"
]
}
],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"\n",
"# Input the dataset\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, 10.9,\n",
" 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, 17.9, 12.2,\n",
" 16.2, 18.7, 8.9, 11.9, 12.1, 14.6, 12.1, 4.7, 3.9, 16.9, 16.8, 11.3, 14.4, 15.7, 14.0,\n",
" 13.6, 18.0, 13.6, 19.9, 13.7, 17.0, 20.5, 9.9, 12.5, 13.2, 16.1, 13.5, 6.3, 6.4, 17.6,\n",
" 19.1, 12.8, 15.5, 16.3, 15.2, 14.6, 19.1, 14.4, 21.4, 15.1, 19.6, 21.7, 11.3, 15.0, 14.3,\n",
" 16.8, 14.0, 6.8, 8.2, 19.9, 20.4, 14.6, 16.4, 18.7, 16.8, 15.8, 20.4, 15.8, 22.4, 16.2,\n",
" 20.3, 23.4, 12.1, 15.5, 15.4, 18.4, 15.7, 10.2, 8.9, 21.0]\n",
"\n",
"# Convert to a numpy array\n",
"np_data = np.array(data)\n",
"\n",
"# Compute the mean\n",
"mean_value = np.mean(np_data)\n",
"print(f\"Mean: {mean_value}\")\n",
"\n",
"# Compute the standard deviation\n",
"std_deviation = np.std(np_data)\n",
"print(f\"Standard Deviation: {std_deviation}\")\n",
"\n",
"# Compute the minimum\n",
"min_value = np.min(np_data)\n",
"print(f\"Minimum: {min_value}\")\n",
"\n",
"# Compute the median\n",
"median_value = np.median(np_data)\n",
"print(f\"Median: {median_value}\")\n",
"\n",
"# Compute the maximum\n",
"max_value = np.max(np_data)\n",
"print(f\"Maximum: {max_value}\")\n"
] ]
}, },
{ {
......
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