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Commit bcba5662 authored by 1e4d23f374d1dc1ab421f4aca9eae828's avatar 1e4d23f374d1dc1ab421f4aca9eae828
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module3/exo3/exercice_en.ipynb
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...@@ -13,12 +13,11 @@ ...@@ -13,12 +13,11 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 3, "execution_count": 29,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "source": [
"%matplotlib inline\n", "%matplotlib inline\n",
"# %matplotlib widget \n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"import pandas as pd" "import pandas as pd"
] ]
...@@ -34,477 +33,13 @@ ...@@ -34,477 +33,13 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 28,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [],
{
"data": {
"text/html": [
"<div>\n",
"<style scoped>\n",
" .dataframe tbody tr th:only-of-type {\n",
" vertical-align: middle;\n",
" }\n",
"\n",
" .dataframe tbody tr th {\n",
" vertical-align: top;\n",
" }\n",
"\n",
" .dataframe thead th {\n",
" text-align: right;\n",
" }\n",
"</style>\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>rownames</th>\n",
" <th>Year</th>\n",
" <th>Wheat</th>\n",
" <th>Wages</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>1565</td>\n",
" <td>41.0</td>\n",
" <td>5.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1570</td>\n",
" <td>45.0</td>\n",
" <td>5.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1575</td>\n",
" <td>42.0</td>\n",
" <td>5.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1580</td>\n",
" <td>49.0</td>\n",
" <td>5.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>1585</td>\n",
" <td>41.5</td>\n",
" <td>5.15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>6</td>\n",
" <td>1590</td>\n",
" <td>47.0</td>\n",
" <td>5.25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>7</td>\n",
" <td>1595</td>\n",
" <td>64.0</td>\n",
" <td>5.54</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>8</td>\n",
" <td>1600</td>\n",
" <td>27.0</td>\n",
" <td>5.61</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>9</td>\n",
" <td>1605</td>\n",
" <td>33.0</td>\n",
" <td>5.69</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>10</td>\n",
" <td>1610</td>\n",
" <td>32.0</td>\n",
" <td>5.78</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>11</td>\n",
" <td>1615</td>\n",
" <td>33.0</td>\n",
" <td>5.94</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>12</td>\n",
" <td>1620</td>\n",
" <td>35.0</td>\n",
" <td>6.01</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>13</td>\n",
" <td>1625</td>\n",
" <td>33.0</td>\n",
" <td>6.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>14</td>\n",
" <td>1630</td>\n",
" <td>45.0</td>\n",
" <td>6.22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>15</td>\n",
" <td>1635</td>\n",
" <td>33.0</td>\n",
" <td>6.30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>16</td>\n",
" <td>1640</td>\n",
" <td>39.0</td>\n",
" <td>6.37</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>17</td>\n",
" <td>1645</td>\n",
" <td>53.0</td>\n",
" <td>6.45</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>18</td>\n",
" <td>1650</td>\n",
" <td>42.0</td>\n",
" <td>6.50</td>\n",
" </tr>\n",
" <tr>\n",
" <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",
" <tr>\n",
" <th>50</th>\n",
" <td>51</td>\n",
" <td>1815</td>\n",
" <td>78.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>52</td>\n",
" <td>1820</td>\n",
" <td>54.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>53</td>\n",
" <td>1821</td>\n",
" <td>54.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" rownames 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\n",
"50 51 1815 78.0 NaN\n",
"51 52 1820 54.0 NaN\n",
"52 53 1821 54.0 NaN"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [ "source": [
"raw_data = pd.read_csv(data_url)\n", "raw_data = pd.read_csv(data_url, usecols=[1, 2, 3])\n",
"raw_data" "# raw_data.rename(columns = {'Wheat':'Wheat Price(shillings/quarter)', 'Wages':'Wages(shillings/week)'}, inplace=True)\n",
"# raw_data"
] ]
}, },
{ {
...@@ -528,10 +63,37 @@ ...@@ -528,10 +63,37 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": null, "execution_count": 27,
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [
"source": [] {
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f9bc2ca5cc0>"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
},
{
"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.figure()\n",
"raw_data['Wheat'].plot()\n",
"raw_data['Wages'].plot()"
]
}, },
{ {
"cell_type": "markdown", "cell_type": "markdown",
......
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