Skip to content

M
mooc-rr
Commit fade7ad9 authored by 27823a14859af5a9e21b47f306da5648's avatar 27823a14859af5a9e21b47f306da5648
Browse files
Options

Premiers jets sur l'exercice

parent 9292aa57
Hide whitespace changes
Inline Side-by-side
Showing with 774 additions and 25 deletions
module3/exo3/data_playfairs_wage_wheat.csv 0 → 100644
View file @ fade7ad9
"","Year","Wheat","Wages"
"1",1565,41,5
"2",1570,45,5.05
"3",1575,42,5.08
"4",1580,49,5.12
"5",1585,41.5,5.15
"6",1590,47,5.25
"7",1595,64,5.54
"8",1600,27,5.61
"9",1605,33,5.69
"10",1610,32,5.78
"11",1615,33,5.94
"12",1620,35,6.01
"13",1625,33,6.12
"14",1630,45,6.22
"15",1635,33,6.3
"16",1640,39,6.37
"17",1645,53,6.45
"18",1650,42,6.5
"19",1655,40.5,6.6
"20",1660,46.5,6.75
"21",1665,32,6.8
"22",1670,37,6.9
"23",1675,43,7
"24",1680,35,7.3
"25",1685,27,7.6
"26",1690,40,8
"27",1695,50,8.5
"28",1700,30,9
"29",1705,32,10
"30",1710,44,11
"31",1715,33,11.75
"32",1720,29,12.5
"33",1725,39,13
"34",1730,26,13.3
"35",1735,32,13.6
"36",1740,27,14
"37",1745,27.5,14.5
"38",1750,31,15
"39",1755,35.5,15.7
"40",1760,31,16.5
"41",1765,43,17.6
"42",1770,47,18.5
"43",1775,44,19.5
"44",1780,46,21
"45",1785,42,23
"46",1790,47.5,25.5
"47",1795,76,27.5
"48",1800,79,28.5
"49",1805,81,29.5
"50",1810,99,30
"51",1815,78,NA
"52",1820,54,NA
"53",1821,54,NA
module3/exo3/exercice.ipynb deleted 100644 → 0
View file @ 9292aa57
{
"cells": [],
"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": "3.6.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
module3/exo3/pouvoir_achat_ouvrier_XVI_XX.ipynb 0 → 100644
View file @ fade7ad9
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Le pouvoir d'achat des ouvriers anglais du XVIe au XIXe siècle"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Nous nous proposons ici de reproduire le [graphique](https://fr.wikipedia.org/wiki/William_Playfair#/media/Fichier:Chart_Showing_at_One_View_the_Price_of_the_Quarter_of_Wheat,_and_Wages_of_Labour_by_the_Week,_from_1565_to_1821.png) initialement proposé par William Playfair, avant d'en améliorer certain point, comme la précision sur les unités de prix et une autre approche de la visualisation de ces données.\n",
"\n",
"![graphiqueWiliamFair](https://upload.wikimedia.org/wikipedia/commons/3/3a/Chart_Showing_at_One_View_the_Price_of_the_Quarter_of_Wheat%2C_and_Wages_of_Labour_by_the_Week%2C_from_1565_to_1821.png)"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%matplotlib inline\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"import urllib.request "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Source des données"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Les données sont prises à cette adresse [données](https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv), sur recommendation du sujet du mooc sur la recherche reproductible. Nous vérifions la présence des données dans le répertoire, et ne les téléchargons que si nécessaire."
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"data_url = \"https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv\"\n",
"data_file = \"data_playfairs_wage_wheat.csv\""
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Using local data file\n"
]
},
{
"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>Unnamed: 0</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": [
" 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\n",
"50 51 1815 78.0 NaN\n",
"51 52 1820 54.0 NaN\n",
"52 53 1821 54.0 NaN"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"try :\n",
" with open(data_file):\n",
" print(\"Using local data file\")\n",
"except IOError :\n",
" print(\"Missing data, downloading from {}\".format(data_url))\n",
" urllib.request.urlretrieve(data_url, data_file)\n",
"\n",
"raw_data = pd.read_csv(data_file)\n",
"raw_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Les trois colonnes intéréssantes pour notre étude sont les trois dernières : YEARR, WHEAT, WAGES.\n",
"\n",
"Le tableau suivant montre les unités de chaque colonnes :\n",
"\n",
" | |YEAR |WHEAT|WAGES|\n",
" |:-------:|:-------:|:-------:|:-------:|\n",
" |Traduction| année | blé | salaire|\n",
" |Unité | - | shillings/quart de boisseau | shilling/semaine | \n",
" \n",
"Les conversions se font de la façon suivante :\n",
"\n",
"| Unité ancienne | Unité SI |\n",
"|:-------:|:--------:|\n",
"|1 quart de boisseau | 6.8 kg |"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Remarquons que pour les trois dernières lignes, l'information de salaire est manquante."
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"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>Unnamed: 0</th>\n",
" <th>Year</th>\n",
" <th>Wheat</th>\n",
" <th>Wages</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\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": [
" Unnamed: 0 Year Wheat Wages\n",
"50 51 1815 78.0 NaN\n",
"51 52 1820 54.0 NaN\n",
"52 53 1821 54.0 NaN"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"raw_data[raw_data.isnull().any(axis=1)]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [],
"source": [
"data = raw_data.set_index('Year')"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7fc98d219eb8>"
]
},
"execution_count": 12,
"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": [
"data['Wheat'].plot.bar()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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
}
Markdown is supported
0% or
You are about to add 0 people to the discussion. Proceed with caution.
Finish editing this message first!
Please register or to comment