From 7485e14887b27ef77e37b352728d538faf7f58b0 Mon Sep 17 00:00:00 2001
From: Claudio Maggioni <maggicl@usi.ch>
Date: Tue, 21 Mar 2023 18:21:11 +0100
Subject: [PATCH] hw1: code for ex3 done

---
 Assignment1/Assignment1.ipynb                 | 519 ++++++++++++++++--
 Assignment1/datasets/market_value_decline.csv |  16 +
 2 files changed, 486 insertions(+), 49 deletions(-)
 create mode 100644 Assignment1/datasets/market_value_decline.csv

diff --git a/Assignment1/Assignment1.ipynb b/Assignment1/Assignment1.ipynb
index a6212ef..003fbb9 100644
--- a/Assignment1/Assignment1.ipynb
+++ b/Assignment1/Assignment1.ipynb
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 1,
+   "execution_count": 31,
    "id": "fcf3beb9",
    "metadata": {},
    "outputs": [],
@@ -34,7 +34,8 @@
     "import seaborn as sns\n",
     "import matplotlib.pyplot as plt\n",
     "import bokeh\n",
-    "import ftfy"
+    "import ftfy\n",
+    "import matplotlib as mpl"
    ]
   },
   {
@@ -51,7 +52,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 3,
    "id": "a0af6847",
    "metadata": {},
    "outputs": [
@@ -61,7 +62,7 @@
        "('Ü', 'sloppy-windows-1252')"
       ]
      },
-     "execution_count": 2,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -73,7 +74,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 4,
    "id": "22ce9426",
    "metadata": {},
    "outputs": [
@@ -270,7 +271,7 @@
        "4  2016-03-31 00:00:00             0       60437  2016-04-06 10:17:21  "
       ]
      },
-     "execution_count": 3,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -283,7 +284,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 5,
    "id": "a332b6a5",
    "metadata": {},
    "outputs": [
@@ -296,23 +297,23 @@
        " 'offerType': ['str'],\n",
        " 'price': ['int64'],\n",
        " 'abtest': ['str'],\n",
-       " 'vehicleType': ['nan', 'str'],\n",
+       " 'vehicleType': ['str', 'nan'],\n",
        " 'yearOfRegistration': ['int64'],\n",
-       " 'gearbox': ['nan', 'str'],\n",
+       " 'gearbox': ['str', 'nan'],\n",
        " 'powerPS': ['int64'],\n",
-       " 'model': ['nan', 'str'],\n",
+       " 'model': ['str', 'nan'],\n",
        " 'kilometer': ['int64'],\n",
        " 'monthOfRegistration': ['int64'],\n",
-       " 'fuelType': ['nan', 'str'],\n",
+       " 'fuelType': ['str', 'nan'],\n",
        " 'brand': ['str'],\n",
-       " 'notRepairedDamage': ['nan', 'str'],\n",
+       " 'notRepairedDamage': ['str', 'nan'],\n",
        " 'dateCreated': ['str'],\n",
        " 'nrOfPictures': ['int64'],\n",
        " 'postalCode': ['int64'],\n",
        " 'lastSeen': ['str']}"
       ]
      },
-     "execution_count": 4,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -330,7 +331,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 6,
    "id": "11bfa9a2",
    "metadata": {},
    "outputs": [
@@ -371,7 +372,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 7,
    "id": "f1c539c4",
    "metadata": {},
    "outputs": [
@@ -412,7 +413,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 8,
    "id": "86074e70",
    "metadata": {},
    "outputs": [
@@ -609,7 +610,7 @@
        "4  2016-03-31 00:00:00             0       60437  2016-04-06 10:17:21  "
       ]
      },
-     "execution_count": 7,
+     "execution_count": 8,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -652,7 +653,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 9,
    "id": "8b6f9ce3",
    "metadata": {},
    "outputs": [
@@ -682,7 +683,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 10,
    "id": "98f8d101",
    "metadata": {},
    "outputs": [
@@ -707,7 +708,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 11,
    "id": "f300f49d",
    "metadata": {},
    "outputs": [
@@ -726,7 +727,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 12,
    "id": "923c5354",
    "metadata": {},
    "outputs": [],
@@ -737,7 +738,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 13,
    "id": "4b847b1f",
    "metadata": {},
    "outputs": [],
@@ -748,7 +749,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 14,
    "id": "bf1f417d",
    "metadata": {},
    "outputs": [
@@ -778,7 +779,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -790,7 +791,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 15,
    "id": "919e692f",
    "metadata": {},
    "outputs": [],
@@ -835,7 +836,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 16,
    "id": "7cc5c90f",
    "metadata": {},
    "outputs": [
@@ -887,27 +888,25 @@
    "metadata": {},
    "source": [
     "### 2.2\n",
-    "\n"
+    "\n",
+    "To compare the range of prices between car brands, I choose to plot the distribution of car prices for each car brand. To achieve this, I choose to use a variant of the box plot called boxen plot, which ditches whiskers in favour of showing octiles, 16-tiles and so on with coloured rectangles similar to the inner quartiles with exponentially smaller heights.\n",
+    "\n",
+    "From the plot we can see that the `mercedes_benz` car type has the highest median price, and it also has the most right skewed price distribution out of all car brands. `volvo` has the second-highest average and also a skewed price distribution. Both `lancia` and `fiat` are instead more uniformly distributed towards lower prices, while `alfa_romeo` has a similar distribution however with some skewing towards the expensive side. [`trabant`](https://www.youtube.com/watch?v=npMKIUTa3uI) is the cheapest car type.\n",
+    "\n",
+    "I choose to use a box-plot style graph as it is an effective representation to show some salient characterististics for one-dimensional distributions, such as the median and the quartiles (25% percentile, 75% percentile). I choose a `boxenplot` in particular to better capture the right-skewedness of some distributions with the additional percentiles considered by the octiles (87.5%), 16-tiles (93.75%) and so on exponentially."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 32,
    "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": [
-       "<Figure size 1500x800 with 1 Axes>"
+       "<Figure size 1800x800 with 1 Axes>"
       ]
      },
      "metadata": {},
@@ -915,27 +914,25 @@
     }
    ],
    "source": [
-    "brands = ['mercedes_benz', 'fiat', 'volvo', 'alfa_romeo', 'lancia']\n",
+    "brands = ['mercedes_benz', 'fiat', 'volvo', 'alfa_romeo', 'lancia', 'trabant']\n",
     "\n",
     "df_price = df_used \\\n",
-    "    .loc[df_used.brand.isin(brands), ['brand', 'price']]\n",
+    "    .loc[df_used.brand.isin(brands), ['brand', 'price']] \\\n",
+    "    .sort_values('brand', ascending=True)\n",
     "\n",
     "sns.set_theme(palette=\"hls\")\n",
     "\n",
     "# Initialize the matplotlib figure\n",
-    "f, ax = plt.subplots(figsize=(15, 8))\n",
+    "f, ax = plt.subplots(figsize=(18, 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",
+    "ax.set(ylabel=\"\", xlim=[0, 100000], xticks=range(0, 105001, 5000),\n",
     "       xlabel=\"Distribution of prices per vehicle type and fuel type\")\n",
     "       \n",
     "sns.despine(offset=10, trim=True)"
@@ -967,11 +964,75 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 130,
    "id": "eb956ed4",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "df_m = pd.read_csv(\"./datasets/market_value_decline.csv\").rename(columns={\n",
+    "       'Unnamed: 0': 'bank',\n",
+    "       'market_value_2007': '2007',\n",
+    "       'market_value_2009': '2009'\n",
+    "})\n",
+    "\n",
+    "df_mkt = df_m\n",
+    "df_mkt[\"diff\"] = 100 * (df_mkt['2009'] - df_mkt['2007']) / df_mkt['2007']\n",
+    "df_mkt = df_mkt.sort_values(['diff'], ascending=False)\n",
+    "\n",
+    "# sort source DF according to new order by diff\n",
+    "df_m = df_m.reindex(df_mkt.index)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "4a29684b",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_mval = pd.melt(df_m.loc[:, ['bank', '2007', '2009']], id_vars=['bank'], var_name='year', value_name='market_value')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 136,
+   "id": "d3d58d25",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1500x800 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "sns.set_theme(palette=\"hls\")\n",
+    "\n",
+    "# Initialize the matplotlib figure\n",
+    "f, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 8), sharey=True)\n",
+    "\n",
+    "ax2.xaxis.set_major_formatter(mpl.ticker.FuncFormatter(lambda x, pos: '%.0fB' % (x)))\n",
+    "ax1.xaxis.set_major_formatter(mpl.ticker.FuncFormatter(lambda x, pos: '%.0f%%' % (x)))\n",
+    "\n",
+    "sns.barplot(y=\"bank\", x=\"diff\", data=df_mkt, ax=ax1, color=sns.xkcd_rgb[\"purplish red\"])\n",
+    "sns.barplot(\n",
+    "    data=df_mval, ax=ax2,\n",
+    "    x=\"market_value\", y=\"bank\", hue=\"year\",\n",
+    "    palette=[sns.xkcd_rgb[\"prussian blue\"], sns.xkcd_rgb[\"sienna\"]]\n",
+    ")\n",
+    "\n",
+    "# Add a legend and informative axis label\n",
+    "ax2.set(ylabel=\"Institution\", xlim=[0, 300],\n",
+    "       xlabel=\"Market value\")\n",
+    "ax1.set(ylabel=\"Institution\", xlim=[-1, 0], xticks=range(-100, 1, 10),\n",
+    "       xlabel=\"Market value decrease\")\n",
+    "sns.despine(left=True, bottom=True)"
+   ]
   },
   {
    "attachments": {},
@@ -996,11 +1057,371 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 62,
    "id": "b4fde7e4",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "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>airport_name</th>\n",
+       "      <th>city</th>\n",
+       "      <th>country</th>\n",
+       "      <th>IATA</th>\n",
+       "      <th>ICAO</th>\n",
+       "      <th>latitude</th>\n",
+       "      <th>longitude</th>\n",
+       "      <th>altitude</th>\n",
+       "      <th>timezone</th>\n",
+       "      <th>DST</th>\n",
+       "      <th>tz_database_timezone</th>\n",
+       "      <th>type</th>\n",
+       "      <th>source</th>\n",
+       "      <th>flights_planned</th>\n",
+       "      <th>flights_cancelled</th>\n",
+       "      <th>flights_delayed</th>\n",
+       "      <th>delay_duration</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>ID</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>1600</th>\n",
+       "      <td>Bar Yehuda Airfield</td>\n",
+       "      <td>Metzada</td>\n",
+       "      <td>Israel</td>\n",
+       "      <td>MTZ</td>\n",
+       "      <td>LLMZ</td>\n",
+       "      <td>31.328199</td>\n",
+       "      <td>35.388599</td>\n",
+       "      <td>-1266</td>\n",
+       "      <td>2</td>\n",
+       "      <td>E</td>\n",
+       "      <td>Asia/Jerusalem</td>\n",
+       "      <td>airport</td>\n",
+       "      <td>OurAirports</td>\n",
+       "      <td>62</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>32.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1595</th>\n",
+       "      <td>Ein Yahav Airfield</td>\n",
+       "      <td>Eyn-yahav</td>\n",
+       "      <td>Israel</td>\n",
+       "      <td>EIY</td>\n",
+       "      <td>LLEY</td>\n",
+       "      <td>30.621700</td>\n",
+       "      <td>35.203300</td>\n",
+       "      <td>-164</td>\n",
+       "      <td>2</td>\n",
+       "      <td>E</td>\n",
+       "      <td>Asia/Jerusalem</td>\n",
+       "      <td>airport</td>\n",
+       "      <td>OurAirports</td>\n",
+       "      <td>56</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>24.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7646</th>\n",
+       "      <td>Jacqueline Cochran Regional Airport</td>\n",
+       "      <td>Palm Springs</td>\n",
+       "      <td>United States</td>\n",
+       "      <td>TRM</td>\n",
+       "      <td>KTRM</td>\n",
+       "      <td>33.626701</td>\n",
+       "      <td>-116.160004</td>\n",
+       "      <td>-115</td>\n",
+       "      <td>-8</td>\n",
+       "      <td>A</td>\n",
+       "      <td>America/Los_Angeles</td>\n",
+       "      <td>airport</td>\n",
+       "      <td>OurAirports</td>\n",
+       "      <td>60</td>\n",
+       "      <td>0</td>\n",
+       "      <td>7</td>\n",
+       "      <td>28.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4357</th>\n",
+       "      <td>Atyrau Airport</td>\n",
+       "      <td>Atyrau</td>\n",
+       "      <td>Kazakhstan</td>\n",
+       "      <td>GUW</td>\n",
+       "      <td>UATG</td>\n",
+       "      <td>47.121899</td>\n",
+       "      <td>51.821400</td>\n",
+       "      <td>-72</td>\n",
+       "      <td>5</td>\n",
+       "      <td>U</td>\n",
+       "      <td>Asia/Oral</td>\n",
+       "      <td>airport</td>\n",
+       "      <td>OurAirports</td>\n",
+       "      <td>71</td>\n",
+       "      <td>0</td>\n",
+       "      <td>9</td>\n",
+       "      <td>35.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2151</th>\n",
+       "      <td>Ramsar Airport</td>\n",
+       "      <td>Ramsar</td>\n",
+       "      <td>Iran</td>\n",
+       "      <td>RZR</td>\n",
+       "      <td>OINR</td>\n",
+       "      <td>36.909901</td>\n",
+       "      <td>50.679600</td>\n",
+       "      <td>-70</td>\n",
+       "      <td>3.5</td>\n",
+       "      <td>E</td>\n",
+       "      <td>Asia/Tehran</td>\n",
+       "      <td>airport</td>\n",
+       "      <td>OurAirports</td>\n",
+       "      <td>62</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6</td>\n",
+       "      <td>47.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3039</th>\n",
+       "      <td>Lengpui Airport</td>\n",
+       "      <td>Aizwal</td>\n",
+       "      <td>India</td>\n",
+       "      <td>AJL</td>\n",
+       "      <td>VELP</td>\n",
+       "      <td>23.840599</td>\n",
+       "      <td>92.619698</td>\n",
+       "      <td>1398</td>\n",
+       "      <td>5.5</td>\n",
+       "      <td>N</td>\n",
+       "      <td>Asia/Calcutta</td>\n",
+       "      <td>airport</td>\n",
+       "      <td>OurAirports</td>\n",
+       "      <td>118</td>\n",
+       "      <td>0</td>\n",
+       "      <td>23</td>\n",
+       "      <td>38.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1670</th>\n",
+       "      <td>Emmen Air Base</td>\n",
+       "      <td>Emmen</td>\n",
+       "      <td>Switzerland</td>\n",
+       "      <td>EML</td>\n",
+       "      <td>LSME</td>\n",
+       "      <td>47.092444</td>\n",
+       "      <td>8.305184</td>\n",
+       "      <td>1400</td>\n",
+       "      <td>1</td>\n",
+       "      <td>E</td>\n",
+       "      <td>Europe/Zurich</td>\n",
+       "      <td>airport</td>\n",
+       "      <td>OurAirports</td>\n",
+       "      <td>124</td>\n",
+       "      <td>0</td>\n",
+       "      <td>19</td>\n",
+       "      <td>38.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6215</th>\n",
+       "      <td>Long Lellang Airport</td>\n",
+       "      <td>Long Datih</td>\n",
+       "      <td>Malaysia</td>\n",
+       "      <td>LGL</td>\n",
+       "      <td>WBGF</td>\n",
+       "      <td>3.421000</td>\n",
+       "      <td>115.153999</td>\n",
+       "      <td>1400</td>\n",
+       "      <td>8</td>\n",
+       "      <td>N</td>\n",
+       "      <td>Asia/Kuala_Lumpur</td>\n",
+       "      <td>airport</td>\n",
+       "      <td>OurAirports</td>\n",
+       "      <td>126</td>\n",
+       "      <td>0</td>\n",
+       "      <td>18</td>\n",
+       "      <td>32.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7375</th>\n",
+       "      <td>Minaçu Airport</td>\n",
+       "      <td>Minacu</td>\n",
+       "      <td>Brazil</td>\n",
+       "      <td>MQH</td>\n",
+       "      <td>SBMC</td>\n",
+       "      <td>-13.549100</td>\n",
+       "      <td>-48.195301</td>\n",
+       "      <td>1401</td>\n",
+       "      <td>-3</td>\n",
+       "      <td>S</td>\n",
+       "      <td>America/Sao_Paulo</td>\n",
+       "      <td>airport</td>\n",
+       "      <td>OurAirports</td>\n",
+       "      <td>119</td>\n",
+       "      <td>1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>48.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9253</th>\n",
+       "      <td>Bubovice Airport</td>\n",
+       "      <td>Bubovice</td>\n",
+       "      <td>Czech Republic</td>\n",
+       "      <td>\\N</td>\n",
+       "      <td>LKBU</td>\n",
+       "      <td>49.974400</td>\n",
+       "      <td>14.178100</td>\n",
+       "      <td>1401</td>\n",
+       "      <td>1</td>\n",
+       "      <td>E</td>\n",
+       "      <td>Europe/Prague</td>\n",
+       "      <td>airport</td>\n",
+       "      <td>OurAirports</td>\n",
+       "      <td>128</td>\n",
+       "      <td>0</td>\n",
+       "      <td>15</td>\n",
+       "      <td>32.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>6029 rows × 17 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "                             airport_name          city         country IATA  \\\n",
+       "ID                                                                             \n",
+       "1600                  Bar Yehuda Airfield       Metzada          Israel  MTZ   \n",
+       "1595                   Ein Yahav Airfield     Eyn-yahav          Israel  EIY   \n",
+       "7646  Jacqueline Cochran Regional Airport  Palm Springs   United States  TRM   \n",
+       "4357                       Atyrau Airport        Atyrau      Kazakhstan  GUW   \n",
+       "2151                       Ramsar Airport        Ramsar            Iran  RZR   \n",
+       "...                                   ...           ...             ...  ...   \n",
+       "3039                      Lengpui Airport        Aizwal           India  AJL   \n",
+       "1670                       Emmen Air Base         Emmen     Switzerland  EML   \n",
+       "6215                 Long Lellang Airport    Long Datih        Malaysia  LGL   \n",
+       "7375                       Minaçu Airport        Minacu          Brazil  MQH   \n",
+       "9253                     Bubovice Airport      Bubovice  Czech Republic   \\N   \n",
+       "\n",
+       "      ICAO   latitude   longitude  altitude timezone DST tz_database_timezone  \\\n",
+       "ID                                                                              \n",
+       "1600  LLMZ  31.328199   35.388599     -1266        2   E       Asia/Jerusalem   \n",
+       "1595  LLEY  30.621700   35.203300      -164        2   E       Asia/Jerusalem   \n",
+       "7646  KTRM  33.626701 -116.160004      -115       -8   A  America/Los_Angeles   \n",
+       "4357  UATG  47.121899   51.821400       -72        5   U            Asia/Oral   \n",
+       "2151  OINR  36.909901   50.679600       -70      3.5   E          Asia/Tehran   \n",
+       "...    ...        ...         ...       ...      ...  ..                  ...   \n",
+       "3039  VELP  23.840599   92.619698      1398      5.5   N        Asia/Calcutta   \n",
+       "1670  LSME  47.092444    8.305184      1400        1   E        Europe/Zurich   \n",
+       "6215  WBGF   3.421000  115.153999      1400        8   N    Asia/Kuala_Lumpur   \n",
+       "7375  SBMC -13.549100  -48.195301      1401       -3   S    America/Sao_Paulo   \n",
+       "9253  LKBU  49.974400   14.178100      1401        1   E        Europe/Prague   \n",
+       "\n",
+       "         type       source  flights_planned  flights_cancelled  \\\n",
+       "ID                                                               \n",
+       "1600  airport  OurAirports               62                  0   \n",
+       "1595  airport  OurAirports               56                  0   \n",
+       "7646  airport  OurAirports               60                  0   \n",
+       "4357  airport  OurAirports               71                  0   \n",
+       "2151  airport  OurAirports               62                  1   \n",
+       "...       ...          ...              ...                ...   \n",
+       "3039  airport  OurAirports              118                  0   \n",
+       "1670  airport  OurAirports              124                  0   \n",
+       "6215  airport  OurAirports              126                  0   \n",
+       "7375  airport  OurAirports              119                  1   \n",
+       "9253  airport  OurAirports              128                  0   \n",
+       "\n",
+       "      flights_delayed  delay_duration  \n",
+       "ID                                     \n",
+       "1600                9            32.0  \n",
+       "1595                7            24.0  \n",
+       "7646                7            28.0  \n",
+       "4357                9            35.0  \n",
+       "2151                6            47.0  \n",
+       "...               ...             ...  \n",
+       "3039               23            38.0  \n",
+       "1670               19            38.0  \n",
+       "6215               18            32.0  \n",
+       "7375               25            48.0  \n",
+       "9253               15            32.0  \n",
+       "\n",
+       "[6029 rows x 17 columns]"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_air = pd.read_csv(\"./datasets/airports.csv\", index_col='ID')\n",
+    "df_del = pd.read_csv(\"./datasets/airports-delays.csv\", index_col='ID', sep=\";\")\n",
+    "\n",
+    "df_del\n",
+    "#pd.cut(df_del.flights_delayed, range(0, df_del.flights_delayed.max(), 25))\n",
+    "\n",
+    "#df_bycountry = df_del.loc[:, ['country', 'flights_delayed']].groupby('country').sum().sort_values('flights_delayed', ascending=False)\n",
+    "#df_bycountry\n"
+   ]
   },
   {
    "cell_type": "markdown",
diff --git a/Assignment1/datasets/market_value_decline.csv b/Assignment1/datasets/market_value_decline.csv
new file mode 100644
index 0000000..5219bc1
--- /dev/null
+++ b/Assignment1/datasets/market_value_decline.csv
@@ -0,0 +1,16 @@
+,market_value_2007,market_value_2009
+Morgan Stanley,49,16.0
+RBS,120,4.6
+Deutsche Bank,76,10.3
+Credit Agricole,67,17.0
+Societe Generale,80,26.0
+Barclays,91,7.4
+BNP Paribas,108,32.5
+Unicredit,93,26.0
+UBS,116,35.0
+Credit Suisse,75,27.0
+Goldman Sachs,100,35.0
+Santander,116,64.0
+Citigroup,255,19.0
+JP Morgan,165,85.0
+HSBC,215,97.0