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{
"cells": [
{
"cell_type": "code",
"execution_count": 49,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"['01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12', '13', '14', '15', '16', '01', '02', '03', '04', '05', '06', '07', '08', '09', '10', '11', '12', '01', '02', '03']\n"
]
}
],
"source": [
"## Projet Maman 2\n",
"\n",
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"donnees = pd.read_csv('inria_41016_session02_grade_report_2019-06-03-0808.csv')"
]
},
{
"cell_type": "code",
"execution_count": 81,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" Type Num Id Label\n",
"0 Quiz 283 01 Module 1\n",
"1 Quiz 268 02 Module 1\n",
"2 Quiz 285 03 Module 1\n",
"3 Quiz 259 04 Module 1\n",
"4 Quiz 234 05 Module 1\n",
"5 Quiz 190 06 Module 2\n",
"6 Quiz 188 07 Module 2\n",
"7 Quiz 181 08 Module 2\n",
"8 Quiz 165 09 Module 2\n",
"9 Quiz 149 10 Module 2\n",
"10 Quiz 136 11 Module 2\n",
"11 Quiz 129 12 Module 3\n",
"12 Quiz 127 13 Module 3\n",
"13 Quiz 93 14 Module 4\n",
"14 Quiz 80 15 Module 4\n",
"15 Quiz 74 16 Module 4\n",
"16 QuizP 126 01 Jupiter\n",
"17 QuizP 95 02 R\n",
"18 QuizP 77 03 OrgMode\n",
"19 QuizP 77 04 Jupiter\n",
"20 QuizP 53 05 R\n",
"21 QuizP 43 06 OrgMode\n",
"22 QuizP 70 07 Jupiter\n",
"23 QuizP 45 08 R\n",
"24 QuizP 35 09 OrgMode\n",
"25 QuizP 69 10 Jupiter\n",
"26 QuizP 43 11 R\n",
"27 QuizP 36 12 OrgMode\n",
"28 Exercices 195 01 Module 1\n",
"29 Exercices 117 02 Module 2\n",
"30 Exercices 85 03 Module 3\n"
]
}
],
"source": [
"## Tableau\n",
"\n",
"Type_init= list(donnees.columns[3:19])+list(donnees.columns[20:32])+list(donnees.columns[34:37])\n",
"Type=[i.split()[0] for i in Type_init]\n",
"Id=[i.split()[1] for i in Type_init]\n",
"Num=[(sum(donnees.loc[:,type]>0)) for type in Type_init]\n",
"Label=[\"Module 1\",\"Module 1\",\"Module 1\",\"Module 1\",\"Module 1\",\"Module 2\",\"Module 2\",\"Module 2\",\"Module 2\",\"Module 2\",\"Module 2\",\"Module 3\",\"Module 3\",\"Module 4\",\"Module 4\",\"Module 4\",\"Jupiter\",\"R\",\"OrgMode\",\"Jupiter\",\"R\",\"OrgMode\",\"Jupiter\",\"R\",\"OrgMode\",\"Jupiter\",\"R\",\"OrgMode\",\"Module 1\",\"Module 2\",\"Module 3\"]\n",
"tableau=pd.DataFrame({'Type':Type,\"Id\":Id,\"Num\":Num,\"Label\":Label})\n",
"col=[\"Type\",\"Num\",\"Id\",\"Label\"]\n",
"tableau = tableau.loc[:, col]\n",
"print(tableau)"
]
},
{
"cell_type": "code",
"execution_count": 114,
"metadata": {},
"outputs": [
{
"ename": "TypeError",
"evalue": "cannot compare a dtyped [object] array with a scalar of type [bool]",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36mna_op\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m 900\u001b[0m \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 901\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 902\u001b[0m \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m 132\u001b[0m \u001b[0mxor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbool_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'xor'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'^'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 133\u001b[0;31m rand_=bool_method(lambda x, y: operator.and_(y, x),\n\u001b[0m\u001b[1;32m 134\u001b[0m names('rand_'), op('&')),\n",
"\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for &: 'str' and 'str'",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36mna_op\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m 918\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbool\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 919\u001b[0;31m \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlib\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mscalar_binop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 920\u001b[0m \u001b[0;32mexcept\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32mpandas/_libs/lib.pyx\u001b[0m in \u001b[0;36mpandas._libs.lib.scalar_binop\u001b[0;34m()\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36m<lambda>\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m 132\u001b[0m \u001b[0mxor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mbool_method\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0moperator\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mxor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'xor'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mop\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'^'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 133\u001b[0;31m rand_=bool_method(lambda x, y: operator.and_(y, x),\n\u001b[0m\u001b[1;32m 134\u001b[0m names('rand_'), op('&')),\n",
"\u001b[0;31mTypeError\u001b[0m: unsupported operand type(s) for &: 'bool' and 'str'",
"\nDuring handling of the above exception, another exception occurred:\n",
"\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-114-fc729485d06d>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m \u001b[0mtableau\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mtableau\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"Type\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m\"QuizP\"\u001b[0m \u001b[0;34m&\u001b[0m \u001b[0mtableau\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"Label\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m==\u001b[0m\u001b[0;34m\"Jupiter\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"Num\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 15\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"QuizP\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 16\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshow\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36mwrapper\u001b[0;34m(self, other)\u001b[0m\n\u001b[1;32m 952\u001b[0m is_integer_dtype(np.asarray(other)) else fill_bool)\n\u001b[1;32m 953\u001b[0m return filler(self._constructor(\n\u001b[0;32m--> 954\u001b[0;31m \u001b[0mna_op\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 955\u001b[0m index=self.index)).__finalize__(self)\n\u001b[1;32m 956\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;32m/opt/conda/lib/python3.6/site-packages/pandas/core/ops.py\u001b[0m in \u001b[0;36mna_op\u001b[0;34m(x, y)\u001b[0m\n\u001b[1;32m 922\u001b[0m \u001b[0;34m\"with a scalar of type [{type}]\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 923\u001b[0m ).format(dtype=x.dtype, type=type(y).__name__)\n\u001b[0;32m--> 924\u001b[0;31m \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 925\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 926\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mTypeError\u001b[0m: cannot compare a dtyped [object] array with a scalar of type [bool]"
]
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1080x144 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"## Graphs\n",
"\n",
"plt.figure(1,figsize=(15,2))\n",
"plt.subplot(1,3,1)\n",
"tableau.loc[tableau.loc[:,\"Type\"]==\"Exercices\",\"Num\"].plot()\n",
"plt.title(\"Exercices\")\n",
"\n",
"\n",
"plt.subplot(1,3,2)\n",
"tableau.loc[tableau.loc[:,\"Type\"]==\"Quiz\",\"Num\"].plot()\n",
"plt.title(\"Quiz\")\n",
"\n",
"plt.subplot(1,3,3)\n",
"tableau.loc[tableau.loc[:,\"Type\"]==\"QuizP\" & tableau.loc[:,\"Label\"]==\"Jupiter\",\"Num\"].plot()\n",
"plt.title(\"QuizP\")\n",
"plt.show()"
]
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Projet Maman 2/inria_41016_session02_grade_report_2019-06-03-0808.csv 0 → 100644
View file @ 2850adb2
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