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mooc-rr
Commit 9f52fbc2 authored by agatheS's avatar agatheS
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module2/exo4/exercice.ipynb
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...@@ -27,6 +27,8 @@ ...@@ -27,6 +27,8 @@
], ],
"source": [ "source": [
"import os\n", "import os\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"mypath=os.getcwd()\n", "mypath=os.getcwd()\n",
"print(mypath)" "print(mypath)"
] ]
...@@ -52,13 +54,15 @@ ...@@ -52,13 +54,15 @@
" 'exercice_fr.ipynb',\n", " 'exercice_fr.ipynb',\n",
" 'exercice.ipynb',\n", " 'exercice.ipynb',\n",
" 'exercice_fr.Rmd',\n", " 'exercice_fr.Rmd',\n",
" 'spokes_calc.ipynb',\n",
" 'exercice_python_fr.org',\n", " 'exercice_python_fr.org',\n",
" 'exercice_R_en.org',\n", " 'exercice_R_en.org',\n",
" 'exercice_R_fr.org',\n", " 'exercice_R_fr.org',\n",
" 'exercice_en.Rmd',\n", " 'exercice_en.Rmd',\n",
" 'exercice_en.ipynb',\n", " 'exercice_en.ipynb',\n",
" '.ipynb_checkpoints',\n", " '.ipynb_checkpoints',\n",
" 'donnees.csv']" " 'donnees.csv',\n",
" 'Untitled.ipynb']"
] ]
}, },
"execution_count": 2, "execution_count": 2,
...@@ -156,9 +160,9 @@ ...@@ -156,9 +160,9 @@
" <th>date</th>\n", " <th>date</th>\n",
" <th>sport</th>\n", " <th>sport</th>\n",
" <th>durée</th>\n", " <th>durée</th>\n",
" <th>FC moy</th>\n", " <th>FCmoy</th>\n",
" <th>FC max</th>\n", " <th>FCmax</th>\n",
" <th>intensité ressentie</th>\n", " <th>intensitéressentie</th>\n",
" <th>Unnamed: 7</th>\n", " <th>Unnamed: 7</th>\n",
" </tr>\n", " </tr>\n",
" </thead>\n", " </thead>\n",
...@@ -223,19 +227,19 @@ ...@@ -223,19 +227,19 @@
"</div>" "</div>"
], ],
"text/plain": [ "text/plain": [
" Unnamed: 0 date sport durée FC moy FC max \\\n", " Unnamed: 0 date sport durée FCmoy FCmax \\\n",
"0 NaN ----- ----- ------ ------ ------- \n", "0 NaN ----- ----- ------ ------ ------- \n",
"1 NaN 18/03/2020 vélo 1:09:16 128 176 \n", "1 NaN 18/03/2020 vélo 1:09:16 128 176 \n",
"2 NaN 19/03/2020 vélo 2:29:58 151 188 \n", "2 NaN 19/03/2020 vélo 2:29:58 151 188 \n",
"3 NaN 20/03/2020 vélo 0:44:05 144 176 \n", "3 NaN 20/03/2020 vélo 0:44:05 144 176 \n",
"4 NaN 25/03/2020 crossfit 0:51:25 128 182 \n", "4 NaN 25/03/2020 crossfit 0:51:25 128 182 \n",
"\n", "\n",
" intensité ressentie Unnamed: 7 \n", " intensitéressentie Unnamed: 7 \n",
"0 ------------------- NaN \n", "0 ------------------- NaN \n",
"1 facile NaN \n", "1 facile NaN \n",
"2 mod+ NaN \n", "2 mod+ NaN \n",
"3 facile NaN \n", "3 facile NaN \n",
"4 mod+ NaN " "4 mod+ NaN "
] ]
}, },
"execution_count": 6, "execution_count": 6,
...@@ -278,9 +282,9 @@ ...@@ -278,9 +282,9 @@
" <th>date</th>\n", " <th>date</th>\n",
" <th>sport</th>\n", " <th>sport</th>\n",
" <th>durée</th>\n", " <th>durée</th>\n",
" <th>FC moy</th>\n", " <th>FCmoy</th>\n",
" <th>FC max</th>\n", " <th>FCmax</th>\n",
" <th>intensité ressentie</th>\n", " <th>intensitéressentie</th>\n",
" </tr>\n", " </tr>\n",
" </thead>\n", " </thead>\n",
" <tbody>\n", " <tbody>\n",
...@@ -289,8 +293,8 @@ ...@@ -289,8 +293,8 @@
" <td>18/03/2020</td>\n", " <td>18/03/2020</td>\n",
" <td>vélo</td>\n", " <td>vélo</td>\n",
" <td>1:09:16</td>\n", " <td>1:09:16</td>\n",
" <td>128</td>\n", " <td>128.0</td>\n",
" <td>176</td>\n", " <td>176.0</td>\n",
" <td>facile</td>\n", " <td>facile</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
...@@ -298,8 +302,8 @@ ...@@ -298,8 +302,8 @@
" <td>19/03/2020</td>\n", " <td>19/03/2020</td>\n",
" <td>vélo</td>\n", " <td>vélo</td>\n",
" <td>2:29:58</td>\n", " <td>2:29:58</td>\n",
" <td>151</td>\n", " <td>151.0</td>\n",
" <td>188</td>\n", " <td>188.0</td>\n",
" <td>mod+</td>\n", " <td>mod+</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
...@@ -307,8 +311,8 @@ ...@@ -307,8 +311,8 @@
" <td>20/03/2020</td>\n", " <td>20/03/2020</td>\n",
" <td>vélo</td>\n", " <td>vélo</td>\n",
" <td>0:44:05</td>\n", " <td>0:44:05</td>\n",
" <td>144</td>\n", " <td>144.0</td>\n",
" <td>176</td>\n", " <td>176.0</td>\n",
" <td>facile</td>\n", " <td>facile</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
...@@ -316,8 +320,8 @@ ...@@ -316,8 +320,8 @@
" <td>25/03/2020</td>\n", " <td>25/03/2020</td>\n",
" <td>crossfit</td>\n", " <td>crossfit</td>\n",
" <td>0:51:25</td>\n", " <td>0:51:25</td>\n",
" <td>128</td>\n", " <td>128.0</td>\n",
" <td>182</td>\n", " <td>182.0</td>\n",
" <td>mod+</td>\n", " <td>mod+</td>\n",
" </tr>\n", " </tr>\n",
" <tr>\n", " <tr>\n",
...@@ -325,8 +329,8 @@ ...@@ -325,8 +329,8 @@
" <td>26/03/2020</td>\n", " <td>26/03/2020</td>\n",
" <td>vélo</td>\n", " <td>vélo</td>\n",
" <td>0:45:29</td>\n", " <td>0:45:29</td>\n",
" <td>162</td>\n", " <td>162.0</td>\n",
" <td>193</td>\n", " <td>193.0</td>\n",
" <td>mod++</td>\n", " <td>mod++</td>\n",
" </tr>\n", " </tr>\n",
" </tbody>\n", " </tbody>\n",
...@@ -334,12 +338,12 @@ ...@@ -334,12 +338,12 @@
"</div>" "</div>"
], ],
"text/plain": [ "text/plain": [
" date sport durée FC moy FC max intensité ressentie \n", " date sport durée FCmoy FCmax intensitéressentie\n",
"0 18/03/2020 vélo 1:09:16 128 176 facile \n", "0 18/03/2020 vélo 1:09:16 128.0 176.0 facile\n",
"1 19/03/2020 vélo 2:29:58 151 188 mod+ \n", "1 19/03/2020 vélo 2:29:58 151.0 188.0 mod+\n",
"2 20/03/2020 vélo 0:44:05 144 176 facile \n", "2 20/03/2020 vélo 0:44:05 144.0 176.0 facile\n",
"3 25/03/2020 crossfit 0:51:25 128 182 mod+ \n", "3 25/03/2020 crossfit 0:51:25 128.0 182.0 mod+\n",
"4 26/03/2020 vélo 0:45:29 162 193 mod++ " "4 26/03/2020 vélo 0:45:29 162.0 193.0 mod++"
] ]
}, },
"execution_count": 7, "execution_count": 7,
...@@ -363,15 +367,18 @@ ...@@ -363,15 +367,18 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"[['18/03/2020 ' 'vélo ' '1:09:16 ' 128 176 'facile ']\n", "[['18/03/2020' 'vélo' '1:09:16' 128.0 176.0 'facile']\n",
" ['19/03/2020 ' 'vélo ' '2:29:58 ' 151 188 'mod+ ']\n", " ['19/03/2020' 'vélo' '2:29:58' 151.0 188.0 'mod+']\n",
" ['20/03/2020 ' 'vélo ' '0:44:05 ' 144 176 'facile ']\n", " ['20/03/2020' 'vélo' '0:44:05' 144.0 176.0 'facile']\n",
" ['25/03/2020 ' 'crossfit ' '0:51:25 ' 128 182 'mod+ ']\n", " ['25/03/2020' 'crossfit' '0:51:25' 128.0 182.0 'mod+']\n",
" ['26/03/2020 ' 'vélo ' '0:45:29 ' 162 193 'mod++ ']\n", " ['26/03/2020' 'vélo' '0:45:29' 162.0 193.0 'mod++']\n",
" ['30/03/2020 ' 'cap ' '0:39:04 ' 158 189 'mod++ ']\n", " ['30/03/2020' 'cap' '0:39:04' 158.0 189.0 'mod++']\n",
" ['30/03/2020 ' 'crossfit ' '0:29:14 ' 130 169 'mod+ ']\n", " ['30/03/2020' 'crossfit' '0:29:14' 130.0 169.0 'mod+']\n",
" ['31/03/2020 ' 'vélo ' '0:41:52 ' 156 181 'mod+ ']\n", " ['31/03/2020' 'vélo' '0:41:52' 156.0 181.0 'mod+']\n",
" ['01/04/2020 ' 'vélo ' '0:39:06 ' 168 190 'mod++ ']]\n" " ['01/04/2020' 'vélo' '0:39:06' 168.0 190.0 'mod++']\n",
" ['04/04/2020' 'slack' '1:30:00' nan nan 'facile']\n",
" ['05/04/2020' 'vélo' '1:03:41' 152.0 189.0 'mod++']\n",
" ['05/04/2020' 'slack' '1:00:00' nan nan 'facile']]\n"
] ]
} }
], ],
...@@ -389,7 +396,7 @@ ...@@ -389,7 +396,7 @@
"name": "stdout", "name": "stdout",
"output_type": "stream", "output_type": "stream",
"text": [ "text": [
"9\n" "12\n"
] ]
} }
], ],
...@@ -406,9 +413,10 @@ ...@@ -406,9 +413,10 @@
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"array(['18/03/2020 ', '19/03/2020 ', '20/03/2020 ', '25/03/2020 ',\n", "array(['18/03/2020', '19/03/2020', '20/03/2020', '25/03/2020',\n",
" '26/03/2020 ', '30/03/2020 ', '30/03/2020 ', '31/03/2020 ',\n", " '26/03/2020', '30/03/2020', '30/03/2020', '31/03/2020',\n",
" '01/04/2020 '], dtype=object)" " '01/04/2020', '04/04/2020', '05/04/2020', '05/04/2020'],\n",
" dtype=object)"
] ]
}, },
"execution_count": 10, "execution_count": 10,
...@@ -428,7 +436,7 @@ ...@@ -428,7 +436,7 @@
{ {
"data": { "data": {
"text/plain": [ "text/plain": [
"'18/03/2020 '" "'18/03/2020'"
] ]
}, },
"execution_count": 11, "execution_count": 11,
...@@ -442,7 +450,7 @@ ...@@ -442,7 +450,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 16, "execution_count": 12,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -451,14 +459,201 @@ ...@@ -451,14 +459,201 @@
"datetime.datetime(2020, 3, 18, 0, 0)" "datetime.datetime(2020, 3, 18, 0, 0)"
] ]
}, },
"execution_count": 16, "execution_count": 12,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"from datetime import datetime, date, time, timezone\n", "from datetime import datetime, date, time, timezone\n",
"datetime.strptime(mat[0,0],\"%d/%m/%Y \")" "datetime.strptime(mat[0,0],\"%d/%m/%Y\")"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'datetime.time'>\n",
"13:55:26\n"
]
}
],
"source": [
"time_str = '13:55:26'\n",
"time_object = datetime.strptime(time_str, '%H:%M:%S').time()\n",
"print(type(time_object))\n",
"print(time_object)"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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vTHKE7ddK2jXJO21vSnJY19mw/Ky0Me79JJ2dZFPXQYp794DH5p/mFYvj/kUVXq7mOIzEhRSwgBVV3P1FOHiU+vNi5y4F99kkd9t+i5pFI6z+W5qzJb1Z0hVJbrS9v36x/gDYxooaKsFo2b4+ySH987+8Q82e+LlJnttxtLL6K3mz9cm7gPlWxAIcjM1D/a8nSPr7JFeqOf8LFsn2wbavk3SDpJtsb7D9rK5zYXmiuPFofL+/EvVUSZ+2/RjxM7VU75f0+iT7JVmrZnolq3oxEEMlWLL+ubePk/TNJN+yvY+kg5N8vuNo5dj+RpJDhz0GSBQ3sCzYvkLNWSv/qf/QGZKmkry4u1RYrvhYCywPr1azvuDy/m0vSaxAxUArajogsBz1Tyh1bpLXdZ0FNbDHDXQsyUOSnt11DtTBHjewPFxn++OSLpV039yDSS7vLhKWK4obWB6eKOkubXu6gKgZ7wa2QXEDy8NOks6au2qL7T01+JwwAGPcwDIx/1JbP9YEXWoLo0VxA8vDTv29bEmTd6ktjBY/GMDyMNGX2sJosXISWCYm+VJbGC2KGwCKYYwbAIqhuAGgGIobAIqhuAGgmP8HkdrICLoDdiUAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"sport=mat[:,1]\n",
"pd.Series(sport).value_counts().plot('bar')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.lines.Line2D at 0x7f4983c805f8>]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"dates = mat[:,0]\n",
"dates_list = [datetime.strptime(date, '%d/%m/%Y').date() for date in dates]\n",
"plt.plot(dates)"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"mat[:,0]=dates_list"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[datetime.date(2020, 3, 18) datetime.date(2020, 3, 19)\n",
" datetime.date(2020, 3, 20) datetime.date(2020, 3, 25)\n",
" datetime.date(2020, 3, 26) datetime.date(2020, 3, 30)\n",
" datetime.date(2020, 3, 30) datetime.date(2020, 3, 31)\n",
" datetime.date(2020, 4, 1) datetime.date(2020, 4, 4)\n",
" datetime.date(2020, 4, 5) datetime.date(2020, 4, 5)]\n"
]
}
],
"source": [
"print(mat[:,0])"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [],
"source": [
"durees=mat[:,2]\n",
"mat[:,2]=[datetime.strptime(time, '%H:%M:%S').time() for time in durees]"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(dates,durees)\n",
"plt.gcf().autofmt_xdate()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[datetime.date(2020, 3, 18), 'vélo', datetime.time(1, 9, 16),\n",
" 128.0, 176.0, 'facile'],\n",
" [datetime.date(2020, 3, 19), 'vélo', datetime.time(2, 29, 58),\n",
" 151.0, 188.0, 'mod+'],\n",
" [datetime.date(2020, 3, 20), 'vélo', datetime.time(0, 44, 5),\n",
" 144.0, 176.0, 'facile'],\n",
" [datetime.date(2020, 3, 25), 'crossfit', datetime.time(0, 51, 25),\n",
" 128.0, 182.0, 'mod+'],\n",
" [datetime.date(2020, 3, 26), 'vélo', datetime.time(0, 45, 29),\n",
" 162.0, 193.0, 'mod++'],\n",
" [datetime.date(2020, 3, 30), 'cap', datetime.time(0, 39, 4),\n",
" 158.0, 189.0, 'mod++'],\n",
" [datetime.date(2020, 3, 30), 'crossfit', datetime.time(0, 29, 14),\n",
" 130.0, 169.0, 'mod+'],\n",
" [datetime.date(2020, 3, 31), 'vélo', datetime.time(0, 41, 52),\n",
" 156.0, 181.0, 'mod+'],\n",
" [datetime.date(2020, 4, 1), 'vélo', datetime.time(0, 39, 6),\n",
" 168.0, 190.0, 'mod++'],\n",
" [datetime.date(2020, 4, 4), 'slack', datetime.time(1, 30), nan,\n",
" nan, 'facile'],\n",
" [datetime.date(2020, 4, 5), 'vélo', datetime.time(1, 3, 41),\n",
" 152.0, 189.0, 'mod++'],\n",
" [datetime.date(2020, 4, 5), 'slack', datetime.time(1, 0), nan,\n",
" nan, 'facile']], dtype=object)"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"mat"
] ]
}, },
{ {
......
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