From 85af121cc0763464bef228252220a11e2e682540 Mon Sep 17 00:00:00 2001
From: Claudio Maggioni <maggicl@usi.ch>
Date: Tue, 28 Mar 2023 14:00:00 +0200
Subject: [PATCH] hw1: done ex1-ex4

---
 Assignment1/Assignment1.ipynb | 50 ++++++++++++++++++++++++-----------
 1 file changed, 34 insertions(+), 16 deletions(-)

diff --git a/Assignment1/Assignment1.ipynb b/Assignment1/Assignment1.ipynb
index f46aa6e..349e1c3 100644
--- a/Assignment1/Assignment1.ipynb
+++ b/Assignment1/Assignment1.ipynb
@@ -297,16 +297,16 @@
        " '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",
@@ -962,6 +962,25 @@
     "![Banks%20-%20market%20cap.png](attachment:Banks%20-%20market%20cap.png)"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "139bb76f",
+   "metadata": {},
+   "source": [
+    "<!-- List all the problems that you detect in the design of this graph with respect to the quantive message the graph is supposed to deliver. -->\n",
+    "The given graph is not suited to show quantitative values because it relies on the areas of two-dimensional objects to convey the magnitude of the values it shows. Humans are not suited to understand at a glance the difference between the areas of two objects, instead being capable to grasp much one-dimensional differences, like length. This is why the use of a bar chart would have been more suited to show these quantitative values.\n",
+    "\n",
+    "The given graph does not provide a convenient way to show the market decline of each bank. As we are required to mentally compute the difference of market value before and after the stock market crash, it is hard to compare the banks to see which one has lost the least or the most value. \n",
+    "\n",
+    "<!-- How would you compare both the remaining market value of each bank after the loss caused by the crisis and their decline? -->\n",
+    "As we want to convey that JP Morgan has one of the lowest relative market value differences, I would plot directly this difference as another bar chart.\n",
+    "\n",
+    "<!-- Is there a better solution to achieve these two goals? -->\n",
+    "<!-- Propose and implement a different graph that delivers effectively the message. -->\n",
+    "We can implement a better graph with a table lens bar chart showing both the relative market value decrease for each bank and the pre- and post-market collapse market values. The left side shows the former message (i.e. fulfilling the main purpose), while the right side shows the latter message (i.e. fulfilling the secondary purpose)."
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": 17,
@@ -969,8 +988,6 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# TODO: text explaination\n",
-    "\n",
     "df_m = pd.read_csv(\"./datasets/market_value_decline.csv\").rename(columns={\n",
     "       'Unnamed: 0': 'bank',\n",
     "       'market_value_2007': '2007',\n",
@@ -1062,7 +1079,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 41,
+   "execution_count": 20,
    "id": "b4fde7e4",
    "metadata": {},
    "outputs": [],
@@ -1074,7 +1091,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 42,
+   "execution_count": 21,
    "id": "25391739",
    "metadata": {},
    "outputs": [],
@@ -1120,7 +1137,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 43,
+   "execution_count": 22,
    "id": "f8906707",
    "metadata": {},
    "outputs": [
@@ -1230,7 +1247,7 @@
        "Angola                 51.666667            0.0  "
       ]
      },
-     "execution_count": 43,
+     "execution_count": 22,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1258,7 +1275,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 40,
+   "execution_count": 23,
    "id": "a677ce07",
    "metadata": {},
    "outputs": [
@@ -1400,7 +1417,7 @@
        "          Cameroon              339     84.750000  "
       ]
      },
-     "execution_count": 40,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1425,13 +1442,13 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 24,
    "id": "a29b8c2f",
    "metadata": {},
    "outputs": [
     {
      "data": {
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       "text/plain": [
        "<Figure size 1500x800 with 1 Axes>"
       ]
@@ -1452,6 +1469,7 @@
     "sns.set_theme(style=\"ticks\", palette=\"pastel\")\n",
     "\n",
     "# Draw a nested boxplot to show bills by day and time\n",
+    "ax1.set(xlim=[0, 700])\n",
     "sns.boxplot(x=\"# of flights\", y=\"continent\",\n",
     "            hue=\"Kind of flight\", palette=[\"m\", \"g\"],\n",
     "            data=df_4_3)\n",
@@ -1494,7 +1512,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 25,
    "id": "386875c8",
    "metadata": {},
    "outputs": [],