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1661d26ea203f8f0ee929edaef91bf91
mooc-rr
Commits
5a491bf0
Commit
5a491bf0
authored
Nov 11, 2020
by
1661d26ea203f8f0ee929edaef91bf91
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Untitled.ipynb
module3/exo3/Untitled.ipynb
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exercice-Copy1.ipynb
module3/exo3/exercice-Copy1.ipynb
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exercice.ipynb
module3/exo3/exercice.ipynb
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module3/exo3/Untitled.ipynb
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5a491bf0
{
"cells": [],
"metadata": {},
"nbformat": 4,
"nbformat_minor": 4
}
module3/exo3/exercice-Copy1.ipynb
0 → 100644
View file @
5a491bf0
{
"cells": [
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"data_url = \"https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv\""
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
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],
"text/plain": [
" Unnamed: 0 Year Wheat Wages\n",
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"2 3 1575 42.0 5.08\n",
"3 4 1580 49.0 5.12\n",
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"13 14 1630 45.0 6.22\n",
"14 15 1635 33.0 6.30\n",
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"18 19 1655 40.5 6.60\n",
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"25 26 1690 40.0 8.00\n",
"26 27 1695 50.0 8.50\n",
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"29 30 1710 44.0 11.00\n",
"30 31 1715 33.0 11.75\n",
"31 32 1720 29.0 12.50\n",
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"33 34 1730 26.0 13.30\n",
"34 35 1735 32.0 13.60\n",
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"36 37 1745 27.5 14.50\n",
"37 38 1750 31.0 15.00\n",
"38 39 1755 35.5 15.70\n",
"39 40 1760 31.0 16.50\n",
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"41 42 1770 47.0 18.50\n",
"42 43 1775 44.0 19.50\n",
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]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data = pd.read_csv(data_url)\n",
"raw_data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
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" <th>18</th>\n",
" <td>19</td>\n",
" <td>1655</td>\n",
" <td>40.5</td>\n",
" <td>6.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>20</td>\n",
" <td>1660</td>\n",
" <td>46.5</td>\n",
" <td>6.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>21</td>\n",
" <td>1665</td>\n",
" <td>32.0</td>\n",
" <td>6.80</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>22</td>\n",
" <td>1670</td>\n",
" <td>37.0</td>\n",
" <td>6.90</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>23</td>\n",
" <td>1675</td>\n",
" <td>43.0</td>\n",
" <td>7.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>24</td>\n",
" <td>1680</td>\n",
" <td>35.0</td>\n",
" <td>7.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>25</td>\n",
" <td>1685</td>\n",
" <td>27.0</td>\n",
" <td>7.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>26</td>\n",
" <td>1690</td>\n",
" <td>40.0</td>\n",
" <td>8.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>27</td>\n",
" <td>1695</td>\n",
" <td>50.0</td>\n",
" <td>8.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>28</td>\n",
" <td>1700</td>\n",
" <td>30.0</td>\n",
" <td>9.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>29</td>\n",
" <td>1705</td>\n",
" <td>32.0</td>\n",
" <td>10.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>30</td>\n",
" <td>1710</td>\n",
" <td>44.0</td>\n",
" <td>11.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td>31</td>\n",
" <td>1715</td>\n",
" <td>33.0</td>\n",
" <td>11.75</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td>32</td>\n",
" <td>1720</td>\n",
" <td>29.0</td>\n",
" <td>12.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td>33</td>\n",
" <td>1725</td>\n",
" <td>39.0</td>\n",
" <td>13.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td>34</td>\n",
" <td>1730</td>\n",
" <td>26.0</td>\n",
" <td>13.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td>35</td>\n",
" <td>1735</td>\n",
" <td>32.0</td>\n",
" <td>13.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td>36</td>\n",
" <td>1740</td>\n",
" <td>27.0</td>\n",
" <td>14.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td>37</td>\n",
" <td>1745</td>\n",
" <td>27.5</td>\n",
" <td>14.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td>38</td>\n",
" <td>1750</td>\n",
" <td>31.0</td>\n",
" <td>15.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td>39</td>\n",
" <td>1755</td>\n",
" <td>35.5</td>\n",
" <td>15.70</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td>40</td>\n",
" <td>1760</td>\n",
" <td>31.0</td>\n",
" <td>16.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td>41</td>\n",
" <td>1765</td>\n",
" <td>43.0</td>\n",
" <td>17.60</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td>42</td>\n",
" <td>1770</td>\n",
" <td>47.0</td>\n",
" <td>18.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td>43</td>\n",
" <td>1775</td>\n",
" <td>44.0</td>\n",
" <td>19.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td>44</td>\n",
" <td>1780</td>\n",
" <td>46.0</td>\n",
" <td>21.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td>45</td>\n",
" <td>1785</td>\n",
" <td>42.0</td>\n",
" <td>23.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td>46</td>\n",
" <td>1790</td>\n",
" <td>47.5</td>\n",
" <td>25.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td>47</td>\n",
" <td>1795</td>\n",
" <td>76.0</td>\n",
" <td>27.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td>48</td>\n",
" <td>1800</td>\n",
" <td>79.0</td>\n",
" <td>28.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td>49</td>\n",
" <td>1805</td>\n",
" <td>81.0</td>\n",
" <td>29.50</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td>50</td>\n",
" <td>1810</td>\n",
" <td>99.0</td>\n",
" <td>30.00</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Year Wheat Wages\n",
"0 1 1565 41.0 5.00\n",
"1 2 1570 45.0 5.05\n",
"2 3 1575 42.0 5.08\n",
"3 4 1580 49.0 5.12\n",
"4 5 1585 41.5 5.15\n",
"5 6 1590 47.0 5.25\n",
"6 7 1595 64.0 5.54\n",
"7 8 1600 27.0 5.61\n",
"8 9 1605 33.0 5.69\n",
"9 10 1610 32.0 5.78\n",
"10 11 1615 33.0 5.94\n",
"11 12 1620 35.0 6.01\n",
"12 13 1625 33.0 6.12\n",
"13 14 1630 45.0 6.22\n",
"14 15 1635 33.0 6.30\n",
"15 16 1640 39.0 6.37\n",
"16 17 1645 53.0 6.45\n",
"17 18 1650 42.0 6.50\n",
"18 19 1655 40.5 6.60\n",
"19 20 1660 46.5 6.75\n",
"20 21 1665 32.0 6.80\n",
"21 22 1670 37.0 6.90\n",
"22 23 1675 43.0 7.00\n",
"23 24 1680 35.0 7.30\n",
"24 25 1685 27.0 7.60\n",
"25 26 1690 40.0 8.00\n",
"26 27 1695 50.0 8.50\n",
"27 28 1700 30.0 9.00\n",
"28 29 1705 32.0 10.00\n",
"29 30 1710 44.0 11.00\n",
"30 31 1715 33.0 11.75\n",
"31 32 1720 29.0 12.50\n",
"32 33 1725 39.0 13.00\n",
"33 34 1730 26.0 13.30\n",
"34 35 1735 32.0 13.60\n",
"35 36 1740 27.0 14.00\n",
"36 37 1745 27.5 14.50\n",
"37 38 1750 31.0 15.00\n",
"38 39 1755 35.5 15.70\n",
"39 40 1760 31.0 16.50\n",
"40 41 1765 43.0 17.60\n",
"41 42 1770 47.0 18.50\n",
"42 43 1775 44.0 19.50\n",
"43 44 1780 46.0 21.00\n",
"44 45 1785 42.0 23.00\n",
"45 46 1790 47.5 25.50\n",
"46 47 1795 76.0 27.50\n",
"47 48 1800 79.0 28.50\n",
"48 49 1805 81.0 29.50\n",
"49 50 1810 99.0 30.00"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data = raw_data.dropna().copy()\n",
"data"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"ename": "NameError",
"evalue": "name 'data' is not defined",
"output_type": "error",
"traceback": [
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
"\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)",
"\u001b[0;32m<ipython-input-1-6d195a5f7a99>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Year'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0my1\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Wages'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0my2\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Wheat'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'r'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlabel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'Wages'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 5\u001b[0m \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfill_between\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'b'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0malpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0.5\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
"\u001b[0;31mNameError\u001b[0m: name 'data' is not defined"
]
}
],
"source": [
"x=data['Year']\n",
"y1=data['Wages']\n",
"y2=data['Wheat']\n",
"plt.plot(x, y1, color='r', label = 'Wages')\n",
"plt.fill_between(x, -1, y1, color='b', alpha=0.5)\n",
"plt.bar(x,y2, color='k', label = 'Wheat')\n",
"plt.xticks(x, rotation=90, size =20)\n",
"plt.grid()\n",
"plt.legend()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.4"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
module3/exo3/exercice.ipynb
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