correct graph with 2 y axis

parent 65ea7df1
...@@ -21,6 +21,8 @@ ...@@ -21,6 +21,8 @@
"\n", "\n",
"## Les données\n", "## Les données\n",
"\n", "\n",
"### Sources\n",
"\n",
"W. Playfair n'a pas publié les données numériques brutes de son étude. Néanmoins une version numérisée est diponible [ici][data_url], réalisé par [Vincent Arel-Bundock] et publié sur son site [R datasets][vab r datasets].\n", "W. Playfair n'a pas publié les données numériques brutes de son étude. Néanmoins une version numérisée est diponible [ici][data_url], réalisé par [Vincent Arel-Bundock] et publié sur son site [R datasets][vab r datasets].\n",
"\n", "\n",
"[data_url]: https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv\n", "[data_url]: https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv\n",
...@@ -30,7 +32,21 @@ ...@@ -30,7 +32,21 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 2, "execution_count": 36,
"metadata": {},
"outputs": [],
"source": [
"# import des bibliothèques\n",
"import urllib\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"from os import listdir\n",
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -57,18 +73,12 @@ ...@@ -57,18 +73,12 @@
" '.ipynb_checkpoints']" " '.ipynb_checkpoints']"
] ]
}, },
"execution_count": 2, "execution_count": 1,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"# import des bibliothèques\n",
"import urllib\n",
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"from os import listdir\n",
"\n",
"# téléchargement du fichier\n", "# téléchargement du fichier\n",
"data_url = 'https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv'\n", "data_url = 'https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv'\n",
"filename = 'Wheat.csv'\n", "filename = 'Wheat.csv'\n",
...@@ -88,7 +98,414 @@ ...@@ -88,7 +98,414 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 5, "execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# lecture du fichier\n",
"rawdata = pd.read_csv(filename, index_col=0)"
]
},
{
"cell_type": "code",
"execution_count": 8,
"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>Year</th>\n",
" <th>Wheat</th>\n",
" <th>Wages</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>1565</td>\n",
" <td>41.0</td>\n",
" <td>5.00</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>1570</td>\n",
" <td>45.0</td>\n",
" <td>5.05</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>1575</td>\n",
" <td>42.0</td>\n",
" <td>5.08</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>1580</td>\n",
" <td>49.0</td>\n",
" <td>5.12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>1585</td>\n",
" <td>41.5</td>\n",
" <td>5.15</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Year Wheat Wages\n",
"1 1565 41.0 5.00\n",
"2 1570 45.0 5.05\n",
"3 1575 42.0 5.08\n",
"4 1580 49.0 5.12\n",
"5 1585 41.5 5.15"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rawdata.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Vérification des données\n",
"\n",
"Les données sont vérifiées sur les critères suivants :\n",
"\n",
"* présence de lignes vides\n",
"* rupture de date, ou division non régulière\n",
"\n",
"Il est à noter les données sont enregistrées tous les 5 ans."
]
},
{
"cell_type": "code",
"execution_count": 10,
"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>Year</th>\n",
" <th>Wheat</th>\n",
" <th>Wages</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>51</th>\n",
" <td>1815</td>\n",
" <td>78.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td>1820</td>\n",
" <td>54.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td>1821</td>\n",
" <td>54.0</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Year Wheat Wages\n",
"51 1815 78.0 NaN\n",
"52 1820 54.0 NaN\n",
"53 1821 54.0 NaN"
]
},
"execution_count": 10,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# présence de lignes vides\n",
"rawdata[rawdata.isnull().any(axis=1)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Les 3 dernières lignes sont vides pour la colonne *Wages*, ce qui explique l'arrêt de la ligne rouge et de la surface bleue dans le document originale."
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Années de rupture :\n",
"1820\n"
]
}
],
"source": [
"# rupture de date, ou division non régulière\n",
"print(\"Années de rupture :\")\n",
"expect_delta = 5\n",
"for y1, y2 in zip(rawdata['Year'][:-1], rawdata['Year'][1:]):\n",
" delta_y = y2 - y1\n",
" if delta_y != expect_delta :\n",
" print(y1)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Seule l'année 1820 présente une rupture. En effet l'enregistrement suivant est 1821. Ce qui explique la barre moins large sur le graphique original, pour les années 1820 à 1821.\n",
"\n",
"### Création des unités\n",
"\n",
"Pour rappel les untités originales des données sont les suivantes :"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"wheat_unit = 'Shillings pour un quart de boisseau'\n",
"wages_unit = 'Shillings par semaine'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Reproduction du graphique original\n",
"\n",
"Ci-dessous le graphique original est reproduit à l'aide de `Matplotlib`.\n",
"Pour représenter des dégradés voir ce lien : https://matplotlib.org/3.2.1/gallery/lines_bars_and_markers/gradient_bar.html?highlight=gradient"
]
},
{
"cell_type": "code",
"execution_count": 116,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0,0.5,'Price of the Quarter of Wheat in Shillings')"
]
},
"execution_count": 116,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 1097.28x548.64 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"from matplotlib.ticker import MultipleLocator\n",
"\n",
"# représentation des salaires en courbe rouge avec l'aire sous la courbe bleu\n",
"fig1, ax1 = plt.subplots(1,1)\n",
"\n",
"fig1.set_size_inches(2.54*6, 2.54*3)\n",
"\n",
"ax1.bar(rawdata['Year'][:-2], rawdata['Wheat'][:-2], align='edge', width=5, color='dimgrey')\n",
"ax1.bar(rawdata['Year'][-2:], rawdata['Wheat'][-2:], align='edge', width=1, color='dimgrey')\n",
"\n",
"# graduation proche de l'original, avec les siècles comme majeure\n",
"# et une graduation mineure tous les 5 ans\n",
"# aide sur les ticks locator :\n",
"# https://matplotlib.org/gallery/ticks_and_spines/tick-locators.html#sphx-glr-gallery-ticks-and-spines-tick-locators-py\n",
"ax1.xaxis.set_major_locator(MultipleLocator(100))\n",
"ax1.xaxis.set_minor_locator(MultipleLocator(5))\n",
"\n",
"# l'axe 2 partage l'axe x de l'axe 1\n",
"ax2 = ax1.twinx()\n",
"ax2.fill_between(rawdata['Year'], rawdata['Wages'])\n",
"ax2.plot(rawdata['Year'], rawdata['Wages'], 'r', linewidth=2)\n",
"\n",
"# les deux axes ont les mêmes limites, pour reproduire l'original\n",
"myylim = ax1.get_ylim()\n",
"ax2.set_ylim(myylim)\n",
"\n",
"# les marges de l'axe x sont diminuées\n",
"ax1.set_xlim([int(rawdata['Year'][0:1]), int(rawdata['Year'][-1:]+5)])\n",
"\n",
"ax1.grid(True, which='both')\n",
"\n",
"ax1.set_title(\"\"\"Chart Showing at One View\n",
"the Price of the Quarter of Wheat, and Wages of Labour\n",
"by the Week, from 1565 to 1821\"\"\")\n",
"\n",
"ax1.set_xlabel('5 Years each division')\n",
"ax2.set_ylabel('Price of the Quarter of Wheat in Shillings')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Graphique avec deux axes des ordonnées\n",
"Le graphique précédent est repris et amélioré avec deux axes distincts cette fois-ci.\n",
"Le jeu de couleur est changé pour un affichage plus lisible."
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"Text(0.5,1,'Chart Showing at One View\\nthe Price of the Quarter of Wheat, and Wages of Labour\\nby the Week, from 1565 to 1821')"
]
},
"execution_count": 137,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 1097.28x548.64 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig2, ax11 = plt.subplots(1,1)\n",
"\n",
"fig2.set_size_inches(2.54*6, 2.54*3)\n",
"\n",
"# === représentation du prix du blé === #\n",
"ax11.bar(rawdata['Year'][:-2], rawdata['Wheat'][:-2], align='edge', width=5, color='lightgrey')\n",
"ax11.bar(rawdata['Year'][-2:], rawdata['Wheat'][-2:], align='edge', width=1, color='lightgrey')\n",
"\n",
"# graduation proche de l'original, avec les siècles comme majeure\n",
"# et une graduation mineure tous les 5 ans\n",
"ax11.xaxis.set_major_locator(MultipleLocator(100))\n",
"ax11.xaxis.set_minor_locator(MultipleLocator(5))\n",
"# grille\n",
"ax11.grid(True, which='both')\n",
"\n",
"ax11.set_xlabel('5 Years each division')\n",
"ax11.set_ylabel('Price of the Quarter of Wheat in Shillings')\n",
"\n",
"# les marges de l'axe x sont diminuées\n",
"ax11.set_xlim([int(rawdata['Year'][0:1]), int(rawdata['Year'][-1:]+5)])\n",
"\n",
"# === représentation du salaire === #\n",
"# l'axe 2 partage l'axe x de l'axe 1\n",
"ax12 = ax11.twinx()\n",
"ax12.plot(rawdata['Year'], rawdata['Wages'], 'r', linewidth=2)\n",
"\n",
"# les deux axes ont des limites différentes pour montrer que les axes sont différents\n",
"myylim = ax11.get_ylim()\n",
"ax12.set_ylim([0,myylim[1]/2])\n",
"\n",
"ax12.set_ylabel('Wages in Shillings/Week')\n",
"ax12.yaxis.label.set_color('r')\n",
"ax12.tick_params(axis='y', colors='r')\n",
"\n",
"ax11.set_title(\"\"\"Chart Showing at One View\n",
"the Price of the Quarter of Wheat, and Wages of Labour\n",
"by the Week, from 1565 to 1821\"\"\")\n"
]
},
{
"cell_type": "code",
"execution_count": 61,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"53 1821\n",
"Name: Year, dtype: int64"
]
},
"execution_count": 61,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"rawdata['Year'][-1:]"
]
},
{
"cell_type": "code",
"execution_count": 63,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -497,15 +914,12 @@ ...@@ -497,15 +914,12 @@
"53 1821 54.0 NaN" "53 1821 54.0 NaN"
] ]
}, },
"execution_count": 5, "execution_count": 63,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
], ],
"source": [ "source": [
"# lecture du fichier\n",
"\n",
"rawdata = pd.read_csv(filename, index_col=0)\n",
"rawdata" "rawdata"
] ]
}, },
......
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