commit4

parent 1c4fcd48
...@@ -14,7 +14,7 @@ ...@@ -14,7 +14,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 6, "execution_count": 7,
"metadata": { "metadata": {
"scrolled": true "scrolled": true
}, },
...@@ -107,7 +107,7 @@ ...@@ -107,7 +107,7 @@
"4 5 1585 41.5 5.15" "4 5 1585 41.5 5.15"
] ]
}, },
"execution_count": 6, "execution_count": 7,
"metadata": {}, "metadata": {},
"output_type": "execute_result" "output_type": "execute_result"
} }
...@@ -118,7 +118,6 @@ ...@@ -118,7 +118,6 @@
"import pandas as pd\n", "import pandas as pd\n",
"import matplotlib.pyplot as plt\n", "import matplotlib.pyplot as plt\n",
"import numpy as np\n", "import numpy as np\n",
"plt.rcParams[\"figure.figsize\"] = (18,9)\n",
"data = pd.read_csv('https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv')\n", "data = pd.read_csv('https://raw.githubusercontent.com/vincentarelbundock/Rdatasets/master/csv/HistData/Wheat.csv')\n",
"data = data.dropna()\n", "data = data.dropna()\n",
"data.head()" "data.head()"
...@@ -160,7 +159,7 @@ ...@@ -160,7 +159,7 @@
}, },
{ {
"cell_type": "code", "cell_type": "code",
"execution_count": 7, "execution_count": 8,
"metadata": {}, "metadata": {},
"outputs": [ "outputs": [
{ {
...@@ -193,6 +192,157 @@ ...@@ -193,6 +192,157 @@
"ax1.grid()\n", "ax1.grid()\n",
"ax1.set_ylabel('Weekly Wages (in shillings)', color='g')\n", "ax1.set_ylabel('Weekly Wages (in shillings)', color='g')\n",
"ax2.set_ylabel('the Price of Wheat for Each Quarter (in shillings)', color='b')\n", "ax2.set_ylabel('the Price of Wheat for Each Quarter (in shillings)', color='b')\n",
"plt.rcParams[\"figure.figsize\"] = (18,9)\n",
"plt.minorticks_on()\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Step 2 : Improve the units and the data presentation\n",
"We would like to make our graph more relatable by changing the unit 'shillings' into 'pounds' which is the current currency in England. Moreover we will now present the wages in poundsterlings per month, which is more common to describe a salary (we consider 1 month = 4 weeks). Lastly, the notion of a 'Quarter' is also not fammiliar these days, therefore we will change the unit of the wheat's weight into metric system kilograms (kg). To achieve of all these conversions we simply apply the following operations."
]
},
{
"cell_type": "code",
"execution_count": 24,
"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>0</th>\n",
" <td>1</td>\n",
" <td>1565</td>\n",
" <td>0.301471</td>\n",
" <td>1.000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>1570</td>\n",
" <td>0.330882</td>\n",
" <td>1.010</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>1575</td>\n",
" <td>0.308824</td>\n",
" <td>1.016</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>1580</td>\n",
" <td>0.360294</td>\n",
" <td>1.024</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>1585</td>\n",
" <td>0.305147</td>\n",
" <td>1.030</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" Unnamed: 0 Year Wheat Wages\n",
"0 1 1565 0.301471 1.000\n",
"1 2 1570 0.330882 1.010\n",
"2 3 1575 0.308824 1.016\n",
"3 4 1580 0.360294 1.024\n",
"4 5 1585 0.305147 1.030"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"datanewcopy = data.copy()\n",
"datanew = data.copy()\n",
"datanew['Wheat'] = datanewcopy['Wheat'] / (6.8 * 20)\n",
"datanew['Wages'] = datanewcopy['Wages'] / 20 * 4\n",
"datanew.head()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Then we plot again the same graph with the newly converted data, and also we need to change the labels."
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 1296x648 with 2 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"xax = datanew['Year']\n",
"yaxwages = datanew['Wages']\n",
"yaxwheat = datanew['Wheat']\n",
"\n",
"fig,ax1 = plt.subplots()\n",
"\n",
"ax2 = ax1.twinx()\n",
"ax1.plot(xax, yaxwages, 'g-')\n",
"ax1.set_ylim([0,8])\n",
"ax2.plot(xax, yaxwheat, 'b-')\n",
"ax2.set_ylim([0,8])\n",
"\n",
"ax1.set(xlabel='Year', title='The Price of Wheat per Kg & \\nMonthly Wages of Labor')\n",
"ax1.grid()\n",
"ax1.set_ylabel('Monthly Wages (in poundsterling)', color='g')\n",
"ax2.set_ylabel('Price of Wheat per Kg (in poundsterling)', color='b')\n",
" \n",
"plt.rcParams[\"figure.figsize\"] = (18,9)\n",
"plt.minorticks_on()\n", "plt.minorticks_on()\n",
"plt.show()" "plt.show()"
] ]
...@@ -201,7 +351,7 @@ ...@@ -201,7 +351,7 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"### Step 2 : Change the units and improve the graph" "### Step 3 : Improve the graph"
] ]
}, },
{ {
......
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