diff --git a/Assignment1/Assignment1.ipynb b/Assignment1/Assignment1.ipynb
index 17fc5cb..a6212ef 100644
--- a/Assignment1/Assignment1.ipynb
+++ b/Assignment1/Assignment1.ipynb
@@ -31,7 +31,8 @@
"\n",
"import pandas as pd\n",
"import numpy as np\n",
- "import seaborn\n",
+ "import seaborn as sns\n",
+ "import matplotlib.pyplot as plt\n",
"import bokeh\n",
"import ftfy"
]
@@ -295,16 +296,16 @@
" 'offerType': ['str'],\n",
" 'price': ['int64'],\n",
" 'abtest': ['str'],\n",
- " 'vehicleType': ['str', 'nan'],\n",
+ " 'vehicleType': ['nan', 'str'],\n",
" 'yearOfRegistration': ['int64'],\n",
- " 'gearbox': ['str', 'nan'],\n",
+ " 'gearbox': ['nan', 'str'],\n",
" 'powerPS': ['int64'],\n",
- " 'model': ['str', 'nan'],\n",
+ " 'model': ['nan', 'str'],\n",
" 'kilometer': ['int64'],\n",
" 'monthOfRegistration': ['int64'],\n",
- " 'fuelType': ['str', 'nan'],\n",
+ " 'fuelType': ['nan', 'str'],\n",
" 'brand': ['str'],\n",
- " 'notRepairedDamage': ['str', 'nan'],\n",
+ " 'notRepairedDamage': ['nan', 'str'],\n",
" 'dateCreated': ['str'],\n",
" 'nrOfPictures': ['int64'],\n",
" 'postalCode': ['int64'],\n",
@@ -370,7 +371,7 @@
},
{
"cell_type": "code",
- "execution_count": 30,
+ "execution_count": 6,
"id": "f1c539c4",
"metadata": {},
"outputs": [
@@ -411,7 +412,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 7,
"id": "86074e70",
"metadata": {},
"outputs": [
@@ -608,14 +609,15 @@
"4 2016-03-31 00:00:00 0 60437 2016-04-06 10:17:21 "
]
},
- "execution_count": 6,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# I read again the dataset using the information about the column types I found\n",
- "df_used = pd.read_csv(\"./datasets/used_cars_dataset.csv\", encoding=\"windows-1252\", dtype={'dateCrawled': str,\n",
+ "df_used = pd.read_csv(\"./datasets/used_cars_dataset.csv\", encoding=\"windows-1252\", dtype={\n",
+ " 'dateCrawled': str,\n",
" 'name': pd.StringDtype(),\n",
" 'seller': pd.StringDtype(),\n",
" 'offerType': pd.StringDtype(),\n",
@@ -650,7 +652,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 8,
"id": "8b6f9ce3",
"metadata": {},
"outputs": [
@@ -680,7 +682,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 9,
"id": "98f8d101",
"metadata": {},
"outputs": [
@@ -705,7 +707,7 @@
},
{
"cell_type": "code",
- "execution_count": 9,
+ "execution_count": 10,
"id": "f300f49d",
"metadata": {},
"outputs": [
@@ -724,7 +726,7 @@
},
{
"cell_type": "code",
- "execution_count": 10,
+ "execution_count": 11,
"id": "923c5354",
"metadata": {},
"outputs": [],
@@ -735,7 +737,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 12,
"id": "4b847b1f",
"metadata": {},
"outputs": [],
@@ -746,7 +748,7 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 13,
"id": "bf1f417d",
"metadata": {},
"outputs": [
@@ -776,7 +778,7 @@
"dtype: int64"
]
},
- "execution_count": 22,
+ "execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
@@ -788,7 +790,7 @@
},
{
"cell_type": "code",
- "execution_count": 31,
+ "execution_count": 14,
"id": "919e692f",
"metadata": {},
"outputs": [],
@@ -798,6 +800,7 @@
]
},
{
+ "attachments": {},
"cell_type": "markdown",
"id": "47a3929f",
"metadata": {},
@@ -810,17 +813,133 @@
"\n",
""
]
},
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "e2ae928d",
+ "metadata": {},
+ "source": [
+ "### 2.1\n",
+ "\n",
+ "By interpreting the following requirement:\n",
+ "\n",
+ "> on average the price of diesel is greater than the one of benzine\n",
+ "\n",
+ "as meaning that we expect the average price of diesel cars to be greater than the average cars of _benzin_ cars for each car type, I choose to represent the relationship between each car type and the difference of these average values (i.e. $y=E({\\text{diesel}}) - E({\\text{benzin}})$, where a positive value of $y$ would confirm the expectation).\n",
+ "\n",
+ "To represent this relationship I choose to use a simple bar chart plotting these differences. I choose to plot a single series for the difference instead of both series for both fuel types to further focus the reader on the difference and not the values. Additionally, plotting the difference only makes comparing the difference value between car types easier as they are all aligned with the origin."
+ ]
+ },
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"id": "7cc5c90f",
"metadata": {},
- "outputs": [],
- "source": []
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "df_diff = df_used \\\n",
+ " .loc[(df_used.fuelType == 'benzin') | (df_used.fuelType == 'diesel'), ['vehicleType', 'fuelType', 'price']]\\\n",
+ " .groupby(['vehicleType', 'fuelType']) \\\n",
+ " .mean() \\\n",
+ " .sort_values(['vehicleType', 'fuelType'], ascending=[True, True]) \\\n",
+ " .groupby('vehicleType') \\\n",
+ " .diff() \\\n",
+ " .reset_index() \\\n",
+ " .set_index('vehicleType')\n",
+ "\n",
+ "df_diff = df_diff.loc[df_diff.fuelType == 'diesel', ['price']].rename({'price': 'diffPrice'}, axis=1).reset_index()\n",
+ "\n",
+ "sns.set_theme(palette=\"hls\")\n",
+ "\n",
+ "# Initialize the matplotlib figure\n",
+ "f, ax = plt.subplots(figsize=(9, 6))\n",
+ "\n",
+ "# Plot the total crashes\n",
+ "sns.set_color_codes(\"pastel\")\n",
+ "sns.barplot(x=\"vehicleType\", y=\"diffPrice\", data=df_diff,\n",
+ " label=\"avg(diesel) - avg(benzin)\", color=sns.xkcd_rgb[\"ultramarine\"])\n",
+ "\n",
+ "# Add a legend and informative axis label\n",
+ "ax.legend(ncol=2, loc=\"lower right\", frameon=True)\n",
+ "ax.set(ylabel=\"Diesel - benzin difference\", ylim=[-1000, 6000], \n",
+ " xlabel=\"Vehicle type\")\n",
+ "sns.despine(left=True, bottom=True)"
+ ]
+ },
+ {
+ "attachments": {},
+ "cell_type": "markdown",
+ "id": "7cc33371",
+ "metadata": {},
+ "source": [
+ "### 2.2\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 16,
+ "id": "ca97e7c8",
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
+ ]
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ ""
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "brands = ['mercedes_benz', 'fiat', 'volvo', 'alfa_romeo', 'lancia']\n",
+ "\n",
+ "df_price = df_used \\\n",
+ " .loc[df_used.brand.isin(brands), ['brand', 'price']]\n",
+ "\n",
+ "sns.set_theme(palette=\"hls\")\n",
+ "\n",
+ "# Initialize the matplotlib figure\n",
+ "f, ax = plt.subplots(figsize=(15, 8))\n",
+ "\n",
+ "import matplotlib as mpl\n",
+ "mkfunc = lambda x, pos: '%1.0fk' % (x * 1e-3)\n",
+ "mkformatter = mpl.ticker.FuncFormatter(mkfunc)\n",
+ "ax.xaxis.set_major_formatter(mkformatter)\n",
+ "\n",
+ "ax.legend(ncol=2, loc=\"lower right\", frameon=True)\n",
+ "\n",
+ "# Draw a nested boxplot to show bills by day and time\n",
+ "sns.boxenplot(y=\"brand\", x=\"price\", data=df_price)\n",
+ "\n",
+ "ax.set(ylabel=\"\", xlim=[0, 100000], xticks=range(0, 100001, 5000),\n",
+ " xlabel=\"Distribution of prices per vehicle type and fuel type\")\n",
+ " \n",
+ "sns.despine(offset=10, trim=True)"
+ ]
},
{
"attachments": {