diff --git a/exercise_2.ipynb b/exercise_2.ipynb
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@@ -0,0 +1,1794 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Exercise 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as p"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 2. Cyclists"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Load the files and merge the data into a single data frame."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# reading data from file\n",
+    "df1 = p.read_csv('cyclists-helsinki.csv',\n",
+    "           # field separator character\n",
+    "           sep=\",\",\n",
+    "           # use column 0 as row names\n",
+    "           index_col=0,\n",
+    "           # use row 0 as column names\n",
+    "           header=0)\n",
+    "\n",
+    "# reading data from file\n",
+    "df2 = p.read_csv('cyclists-espoo.csv',\n",
+    "           # field separator character\n",
+    "           sep=\",\",\n",
+    "           # use column 0 as row names\n",
+    "           index_col=0,\n",
+    "           # use row 0 as column names\n",
+    "           header=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "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>Eteläesplanadi</th>\n",
+       "      <th>Kaivokatu</th>\n",
+       "      <th>Kuusisaarentie</th>\n",
+       "      <th>Merikannontie</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-01-01</th>\n",
+       "      <td>129.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-01-02</th>\n",
+       "      <td>526.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-01-03</th>\n",
+       "      <td>546.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-01-04</th>\n",
+       "      <td>259.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-01-05</th>\n",
+       "      <td>230.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Eteläesplanadi  Kaivokatu  Kuusisaarentie  Merikannontie\n",
+       "date                                                                \n",
+       "2014-01-01           129.0        NaN             NaN            NaN\n",
+       "2014-01-02           526.0        NaN             NaN            NaN\n",
+       "2014-01-03           546.0        NaN             NaN            NaN\n",
+       "2014-01-04           259.0        NaN             NaN            NaN\n",
+       "2014-01-05           230.0        NaN             NaN            NaN"
+      ]
+     },
+     "execution_count": 60,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df1.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "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>Gallen-Kallela</th>\n",
+       "      <th>Länsiväylä</th>\n",
+       "      <th>Länsituulenkuja</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>date</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2014-01-03</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-01-04</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-01-05</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-01-06</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-01-07</th>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Gallen-Kallela  Länsiväylä  Länsituulenkuja\n",
+       "date                                                   \n",
+       "2014-01-03             NaN         NaN              NaN\n",
+       "2014-01-04             NaN         NaN              NaN\n",
+       "2014-01-05             NaN         NaN              NaN\n",
+       "2014-01-06             NaN         NaN              NaN\n",
+       "2014-01-07             NaN         NaN              NaN"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# DataFrame to be written to file\n",
+    "df = p.merge(df1, df2, on='date', how='outer')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "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>Eteläesplanadi</th>\n",
+       "      <th>Kaivokatu</th>\n",
+       "      <th>Kuusisaarentie</th>\n",
+       "      <th>Merikannontie</th>\n",
+       "      <th>Gallen-Kallela</th>\n",
+       "      <th>Länsiväylä</th>\n",
+       "      <th>Länsituulenkuja</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>date</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>2014-01-01</th>\n",
+       "      <td>129.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-01-02</th>\n",
+       "      <td>526.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-01-03</th>\n",
+       "      <td>546.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-01-04</th>\n",
+       "      <td>259.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2014-01-05</th>\n",
+       "      <td>230.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-07-27</th>\n",
+       "      <td>1939.0</td>\n",
+       "      <td>3120.0</td>\n",
+       "      <td>1405.0</td>\n",
+       "      <td>2180.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-07-28</th>\n",
+       "      <td>1775.0</td>\n",
+       "      <td>2706.0</td>\n",
+       "      <td>1185.0</td>\n",
+       "      <td>1801.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-07-29</th>\n",
+       "      <td>1657.0</td>\n",
+       "      <td>2770.0</td>\n",
+       "      <td>1201.0</td>\n",
+       "      <td>1876.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-07-30</th>\n",
+       "      <td>1397.0</td>\n",
+       "      <td>2058.0</td>\n",
+       "      <td>1123.0</td>\n",
+       "      <td>1659.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-07-31</th>\n",
+       "      <td>1269.0</td>\n",
+       "      <td>1919.0</td>\n",
+       "      <td>1347.0</td>\n",
+       "      <td>2125.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>NaN</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>3134 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Eteläesplanadi  Kaivokatu  Kuusisaarentie  Merikannontie  \\\n",
+       "date                                                                   \n",
+       "2014-01-01           129.0        NaN             NaN            NaN   \n",
+       "2014-01-02           526.0        NaN             NaN            NaN   \n",
+       "2014-01-03           546.0        NaN             NaN            NaN   \n",
+       "2014-01-04           259.0        NaN             NaN            NaN   \n",
+       "2014-01-05           230.0        NaN             NaN            NaN   \n",
+       "...                    ...        ...             ...            ...   \n",
+       "2022-07-27          1939.0     3120.0          1405.0         2180.0   \n",
+       "2022-07-28          1775.0     2706.0          1185.0         1801.0   \n",
+       "2022-07-29          1657.0     2770.0          1201.0         1876.0   \n",
+       "2022-07-30          1397.0     2058.0          1123.0         1659.0   \n",
+       "2022-07-31          1269.0     1919.0          1347.0         2125.0   \n",
+       "\n",
+       "            Gallen-Kallela  Länsiväylä  Länsituulenkuja  \n",
+       "date                                                     \n",
+       "2014-01-01             NaN         NaN              NaN  \n",
+       "2014-01-02             NaN         NaN              NaN  \n",
+       "2014-01-03             NaN         NaN              NaN  \n",
+       "2014-01-04             NaN         NaN              NaN  \n",
+       "2014-01-05             NaN         NaN              NaN  \n",
+       "...                    ...         ...              ...  \n",
+       "2022-07-27             NaN         NaN              NaN  \n",
+       "2022-07-28             NaN         NaN              NaN  \n",
+       "2022-07-29             NaN         NaN              NaN  \n",
+       "2022-07-30             NaN         NaN              NaN  \n",
+       "2022-07-31             NaN         NaN              NaN  \n",
+       "\n",
+       "[3134 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- For how many days were observations made in total? <br>\n",
+    "3134 days"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- How many observation days were there for each street?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Eteläesplanadi     3072\n",
+       "Kaivokatu          1722\n",
+       "Kuusisaarentie     2640\n",
+       "Merikannontie      2843\n",
+       "Gallen-Kallela     1459\n",
+       "Länsiväylä         1460\n",
+       "Länsituulenkuja    2209\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[:].count(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Eteläesplanadi     3072\n",
+      "Kaivokatu          1722\n",
+      "Kuusisaarentie     2640\n",
+      "Merikannontie      2843\n",
+      "Gallen-Kallela     1459\n",
+      "Länsiväylä         1460\n",
+      "Länsituulenkuja    2209\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "obs = df.notna().sum(axis=0)\n",
+    "print(obs)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- On how many days were all streets observed simultaneously?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1400\n"
+     ]
+    }
+   ],
+   "source": [
+    "all_observed_days = df.notna().all(axis=1).sum()\n",
+    "print(all_observed_days)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Which street was the busiest in terms of the total number of cyclists?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Eteläesplanadi     3677532.0\n",
+       "Kaivokatu          3675674.0\n",
+       "Kuusisaarentie     2823869.0\n",
+       "Merikannontie      4839490.0\n",
+       "Gallen-Kallela     1107151.0\n",
+       "Länsiväylä         1324149.0\n",
+       "Länsituulenkuja    2037502.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[:].sum(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Merikannontie 4839490.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "street = df[:].sum(axis=0).idxmax()\n",
+    "amount = df[:].sum(axis=0).max()\n",
+    "print(street, amount)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Filter out the dates which have one or more missing values. Does this affect your conclusion about the busiest street? Why or why not?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "filtered_df = df.dropna()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "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>Eteläesplanadi</th>\n",
+       "      <th>Kaivokatu</th>\n",
+       "      <th>Kuusisaarentie</th>\n",
+       "      <th>Merikannontie</th>\n",
+       "      <th>Gallen-Kallela</th>\n",
+       "      <th>Länsiväylä</th>\n",
+       "      <th>Länsituulenkuja</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>date</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>2018-06-17</th>\n",
+       "      <td>1944.0</td>\n",
+       "      <td>2776.0</td>\n",
+       "      <td>1973.0</td>\n",
+       "      <td>3736.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>803.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-06-19</th>\n",
+       "      <td>2533.0</td>\n",
+       "      <td>3770.0</td>\n",
+       "      <td>2076.0</td>\n",
+       "      <td>2842.0</td>\n",
+       "      <td>1145.0</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>1075.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-06-21</th>\n",
+       "      <td>2125.0</td>\n",
+       "      <td>3167.0</td>\n",
+       "      <td>1519.0</td>\n",
+       "      <td>2365.0</td>\n",
+       "      <td>911.0</td>\n",
+       "      <td>1279.0</td>\n",
+       "      <td>999.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-06-22</th>\n",
+       "      <td>519.0</td>\n",
+       "      <td>707.0</td>\n",
+       "      <td>314.0</td>\n",
+       "      <td>599.0</td>\n",
+       "      <td>176.0</td>\n",
+       "      <td>193.0</td>\n",
+       "      <td>275.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2018-06-23</th>\n",
+       "      <td>1052.0</td>\n",
+       "      <td>1431.0</td>\n",
+       "      <td>1029.0</td>\n",
+       "      <td>1823.0</td>\n",
+       "      <td>681.0</td>\n",
+       "      <td>704.0</td>\n",
+       "      <td>329.0</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-06-15</th>\n",
+       "      <td>2611.0</td>\n",
+       "      <td>4373.0</td>\n",
+       "      <td>2337.0</td>\n",
+       "      <td>3397.0</td>\n",
+       "      <td>1559.0</td>\n",
+       "      <td>1911.0</td>\n",
+       "      <td>2773.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-06-16</th>\n",
+       "      <td>2569.0</td>\n",
+       "      <td>4438.0</td>\n",
+       "      <td>2212.0</td>\n",
+       "      <td>3223.0</td>\n",
+       "      <td>1490.0</td>\n",
+       "      <td>1671.0</td>\n",
+       "      <td>2579.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-06-17</th>\n",
+       "      <td>2400.0</td>\n",
+       "      <td>3971.0</td>\n",
+       "      <td>1827.0</td>\n",
+       "      <td>2979.0</td>\n",
+       "      <td>1347.0</td>\n",
+       "      <td>1531.0</td>\n",
+       "      <td>2674.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-06-18</th>\n",
+       "      <td>600.0</td>\n",
+       "      <td>979.0</td>\n",
+       "      <td>342.0</td>\n",
+       "      <td>566.0</td>\n",
+       "      <td>259.0</td>\n",
+       "      <td>275.0</td>\n",
+       "      <td>1110.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2022-06-19</th>\n",
+       "      <td>1317.0</td>\n",
+       "      <td>2134.0</td>\n",
+       "      <td>1486.0</td>\n",
+       "      <td>2487.0</td>\n",
+       "      <td>1258.0</td>\n",
+       "      <td>1301.0</td>\n",
+       "      <td>1928.0</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1400 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "            Eteläesplanadi  Kaivokatu  Kuusisaarentie  Merikannontie  \\\n",
+       "date                                                                   \n",
+       "2018-06-17          1944.0     2776.0          1973.0         3736.0   \n",
+       "2018-06-19          2533.0     3770.0          2076.0         2842.0   \n",
+       "2018-06-21          2125.0     3167.0          1519.0         2365.0   \n",
+       "2018-06-22           519.0      707.0           314.0          599.0   \n",
+       "2018-06-23          1052.0     1431.0          1029.0         1823.0   \n",
+       "...                    ...        ...             ...            ...   \n",
+       "2022-06-15          2611.0     4373.0          2337.0         3397.0   \n",
+       "2022-06-16          2569.0     4438.0          2212.0         3223.0   \n",
+       "2022-06-17          2400.0     3971.0          1827.0         2979.0   \n",
+       "2022-06-18           600.0      979.0           342.0          566.0   \n",
+       "2022-06-19          1317.0     2134.0          1486.0         2487.0   \n",
+       "\n",
+       "            Gallen-Kallela  Länsiväylä  Länsituulenkuja  \n",
+       "date                                                     \n",
+       "2018-06-17             0.0         0.0            803.0  \n",
+       "2018-06-19          1145.0         0.0           1075.0  \n",
+       "2018-06-21           911.0      1279.0            999.0  \n",
+       "2018-06-22           176.0       193.0            275.0  \n",
+       "2018-06-23           681.0       704.0            329.0  \n",
+       "...                    ...         ...              ...  \n",
+       "2022-06-15          1559.0      1911.0           2773.0  \n",
+       "2022-06-16          1490.0      1671.0           2579.0  \n",
+       "2022-06-17          1347.0      1531.0           2674.0  \n",
+       "2022-06-18           259.0       275.0           1110.0  \n",
+       "2022-06-19          1258.0      1301.0           1928.0  \n",
+       "\n",
+       "[1400 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered_df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 71,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Kaivokatu 3016688.0\n"
+     ]
+    }
+   ],
+   "source": [
+    "street = filtered_df[:].sum(axis=0).idxmax()\n",
+    "amount = filtered_df[:].sum(axis=0).max()\n",
+    "print(street, amount)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 72,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Eteläesplanadi     1531870.0\n",
+       "Kaivokatu          3016688.0\n",
+       "Kuusisaarentie     1500513.0\n",
+       "Merikannontie      2445090.0\n",
+       "Gallen-Kallela     1054062.0\n",
+       "Länsiväylä         1262780.0\n",
+       "Länsituulenkuja    1471640.0\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 72,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filtered_df[:].sum(axis=0)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 3. Human heights"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Create a histogram and a density plot of the following two sets of data points, which contain human heights measured in centimeters."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0    170\n",
+      "1    192\n",
+      "2    184\n",
+      "3    168\n",
+      "4    176\n",
+      "5    181\n",
+      "6    163\n",
+      "dtype: int64 0     170\n",
+      "1     170\n",
+      "2     170\n",
+      "3     170\n",
+      "4     192\n",
+      "5     192\n",
+      "6     192\n",
+      "7     192\n",
+      "8     184\n",
+      "9     184\n",
+      "10    184\n",
+      "11    184\n",
+      "12    168\n",
+      "13    168\n",
+      "14    168\n",
+      "15    168\n",
+      "16    176\n",
+      "17    176\n",
+      "18    176\n",
+      "19    176\n",
+      "20    181\n",
+      "21    181\n",
+      "22    181\n",
+      "23    181\n",
+      "24    163\n",
+      "25    163\n",
+      "26    163\n",
+      "27    163\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "s1 = p.Series((170, 192, 184, 168, 176, 181, 163))\n",
+    "s2 = p.Series((170, 170, 170, 170, 192, 192, 192, 192, 184, 184, 184, 184, 168, 168, 168, 168, 176, 176, 176, 176, 181,\n",
+    "181, 181, 181, 163, 163, 163, 163))\n",
+    "print(s1, s2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 74,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: ylabel='Frequency'>"
+      ]
+     },
+     "execution_count": 74,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "bins = (160, 170, 180, 190, 200)\n",
+    "p.DataFrame(s1).plot(kind = \"hist\", bins = bins)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 75,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: ylabel='Frequency'>"
+      ]
+     },
+     "execution_count": 75,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p.DataFrame(s2).plot(kind = \"hist\", bins = bins)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: ylabel='Density'>"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p.DataFrame(s1).plot(kind = \"density\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 77,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: ylabel='Density'>"
+      ]
+     },
+     "execution_count": 77,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "p.DataFrame(s2).plot(kind = \"density\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Based on the plots, would you consider the distributions to be normal? How confident are you about your\n",
+    "conclusion? "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "First one could be considered as normal but second one not, because it has two peaks. The first one is also a little bit skewed to the left, but I think it is close enough. We can't be that confident tough because the sample size on the first is very little."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- The data sets contain similar values, but your conclusions may differ. Can you explain such a difference in\n",
+    "your results?"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "I think the difference is due to the fact that the second series cover more values than the first one. "
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 4. World temperature"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# reading data from file\n",
+    "dft = p.read_csv('temperature.txt',\n",
+    "           sep = \"\\s+\", skiprows=2\n",
+    "           # use column 0 as row names\n",
+    "           #index_col=0\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "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>No_Smoothing</th>\n",
+       "      <th>Lowess(5)</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1880</td>\n",
+       "      <td>-0.17</td>\n",
+       "      <td>-0.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1881</td>\n",
+       "      <td>-0.09</td>\n",
+       "      <td>-0.13</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>1882</td>\n",
+       "      <td>-0.11</td>\n",
+       "      <td>-0.17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>1883</td>\n",
+       "      <td>-0.17</td>\n",
+       "      <td>-0.20</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>1884</td>\n",
+       "      <td>-0.28</td>\n",
+       "      <td>-0.24</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>139</th>\n",
+       "      <td>2019</td>\n",
+       "      <td>0.98</td>\n",
+       "      <td>0.93</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>140</th>\n",
+       "      <td>2020</td>\n",
+       "      <td>1.01</td>\n",
+       "      <td>0.95</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>141</th>\n",
+       "      <td>2021</td>\n",
+       "      <td>0.85</td>\n",
+       "      <td>0.97</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>142</th>\n",
+       "      <td>2022</td>\n",
+       "      <td>0.89</td>\n",
+       "      <td>0.99</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>143</th>\n",
+       "      <td>2023</td>\n",
+       "      <td>1.17</td>\n",
+       "      <td>1.01</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>144 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     Year  No_Smoothing  Lowess(5)\n",
+       "0    1880         -0.17      -0.09\n",
+       "1    1881         -0.09      -0.13\n",
+       "2    1882         -0.11      -0.17\n",
+       "3    1883         -0.17      -0.20\n",
+       "4    1884         -0.28      -0.24\n",
+       "..    ...           ...        ...\n",
+       "139  2019          0.98       0.93\n",
+       "140  2020          1.01       0.95\n",
+       "141  2021          0.85       0.97\n",
+       "142  2022          0.89       0.99\n",
+       "143  2023          1.17       1.01\n",
+       "\n",
+       "[144 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dft"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "-  Calculate the mean and the median of the data."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "0.0657638888888889"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dft[\"No_Smoothing\"].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 81,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "-0.045"
+      ]
+     },
+     "execution_count": 81,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dft[\"No_Smoothing\"].median()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Create a histogram and a density plot for the pre-2000 measurements. Does the variable seem to be\n",
+    "normally distributed?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 82,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: ylabel='Frequency'>"
+      ]
+     },
+     "execution_count": 82,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pre_2000 = dft.loc[dft.Year < 2000].No_Smoothing\n",
+    "#no_smoothing = pre_2000[\"No_Smoothing\"]\n",
+    "\n",
+    "pre_2000.plot(kind = \"hist\", bins = 20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: ylabel='Density'>"
+      ]
+     },
+     "execution_count": 83,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "pre_2000.plot(kind = \"density\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The variable seems to be normally distributed but a little bit skewed to the left, becuase both of the plots are bell-shaped an there are only one peak."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Create a histogram and a density plot for the measurements from year 2000 onwards. Does the variable\n",
+    "seem to be normally distributed?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: ylabel='Frequency'>"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "aft_2000 = dft[dft[\"Year\"] >= 2000]\n",
+    "no_smoothing2 = aft_2000[\"No_Smoothing\"]\n",
+    "\n",
+    "no_smoothing2.plot(kind = \"hist\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 85,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: ylabel='Density'>"
+      ]
+     },
+     "execution_count": 85,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "no_smoothing2.plot(kind = \"density\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "The variable doesn't seem to be normally distributed, because it is not symmetrical and not bell-shaped."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 5. Electric bikes (continues)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- What types are the variables? (Consider as many categorisations as possible.) <Br>\n",
+    "• Check that the data types and values in the data you have loaded match the variable types. Fix if needed."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dfb = p.read_csv(\"bikes.data.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 87,
+   "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>ticket</th>\n",
+       "      <th>cost</th>\n",
+       "      <th>month</th>\n",
+       "      <th>location_from</th>\n",
+       "      <th>location_to</th>\n",
+       "      <th>duration</th>\n",
+       "      <th>distance</th>\n",
+       "      <th>assistance</th>\n",
+       "      <th>energy_used</th>\n",
+       "      <th>energy_collected</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>single</td>\n",
+       "      <td>0.35</td>\n",
+       "      <td>9</td>\n",
+       "      <td>MICROTEKNIA</td>\n",
+       "      <td>PUIJONLAAKSO</td>\n",
+       "      <td>411.0</td>\n",
+       "      <td>2150</td>\n",
+       "      <td>1</td>\n",
+       "      <td>19.0</td>\n",
+       "      <td>2.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>single</td>\n",
+       "      <td>1.20</td>\n",
+       "      <td>5</td>\n",
+       "      <td>SATAMA</td>\n",
+       "      <td>KEILANKANTA</td>\n",
+       "      <td>1411.0</td>\n",
+       "      <td>7130</td>\n",
+       "      <td>1</td>\n",
+       "      <td>53.8</td>\n",
+       "      <td>15.3</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>savonia</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>9</td>\n",
+       "      <td>TASAVALLANKATU</td>\n",
+       "      <td>NEULAMÄKI</td>\n",
+       "      <td>1308.0</td>\n",
+       "      <td>5420</td>\n",
+       "      <td>1</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>9.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>savonia</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>10</td>\n",
+       "      <td>TORI</td>\n",
+       "      <td>KAUPPAKATU</td>\n",
+       "      <td>1036.0</td>\n",
+       "      <td>1180</td>\n",
+       "      <td>1</td>\n",
+       "      <td>6.5</td>\n",
+       "      <td>2.1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>single</td>\n",
+       "      <td>0.30</td>\n",
+       "      <td>9</td>\n",
+       "      <td>TORI</td>\n",
+       "      <td>TORI</td>\n",
+       "      <td>319.0</td>\n",
+       "      <td>1120</td>\n",
+       "      <td>1</td>\n",
+       "      <td>13.7</td>\n",
+       "      <td>1.2</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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1769</th>\n",
+       "      <td>savonia</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>10</td>\n",
+       "      <td>KAUPPAKATU</td>\n",
+       "      <td>TORI</td>\n",
+       "      <td>836.0</td>\n",
+       "      <td>960</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8.0</td>\n",
+       "      <td>2.7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1770</th>\n",
+       "      <td>single</td>\n",
+       "      <td>0.20</td>\n",
+       "      <td>7</td>\n",
+       "      <td>TORI</td>\n",
+       "      <td>SATAMA</td>\n",
+       "      <td>199.0</td>\n",
+       "      <td>930</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3.7</td>\n",
+       "      <td>3.6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1771</th>\n",
+       "      <td>season</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>7</td>\n",
+       "      <td>TORI</td>\n",
+       "      <td>TORI</td>\n",
+       "      <td>61.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0.0</td>\n",
+       "      <td>0.0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1772</th>\n",
+       "      <td>savonia</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>9</td>\n",
+       "      <td>MICROTEKNIA</td>\n",
+       "      <td>PUIJONLAAKSO</td>\n",
+       "      <td>610.0</td>\n",
+       "      <td>2460</td>\n",
+       "      <td>1</td>\n",
+       "      <td>36.5</td>\n",
+       "      <td>6.9</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1773</th>\n",
+       "      <td>season</td>\n",
+       "      <td>0.00</td>\n",
+       "      <td>8</td>\n",
+       "      <td>PUIJONLAAKSO</td>\n",
+       "      <td>KAUPPAKATU</td>\n",
+       "      <td>478.0</td>\n",
+       "      <td>2250</td>\n",
+       "      <td>1</td>\n",
+       "      <td>8.1</td>\n",
+       "      <td>13.8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>1774 rows × 10 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       ticket  cost  month   location_from   location_to  duration  distance  \\\n",
+       "0      single  0.35      9     MICROTEKNIA  PUIJONLAAKSO     411.0      2150   \n",
+       "1      single  1.20      5          SATAMA   KEILANKANTA    1411.0      7130   \n",
+       "2     savonia  0.00      9  TASAVALLANKATU     NEULAMÄKI    1308.0      5420   \n",
+       "3     savonia  0.00     10            TORI    KAUPPAKATU    1036.0      1180   \n",
+       "4      single  0.30      9            TORI          TORI     319.0      1120   \n",
+       "...       ...   ...    ...             ...           ...       ...       ...   \n",
+       "1769  savonia  0.00     10      KAUPPAKATU          TORI     836.0       960   \n",
+       "1770   single  0.20      7            TORI        SATAMA     199.0       930   \n",
+       "1771   season  0.00      7            TORI          TORI      61.0         0   \n",
+       "1772  savonia  0.00      9     MICROTEKNIA  PUIJONLAAKSO     610.0      2460   \n",
+       "1773   season  0.00      8    PUIJONLAAKSO    KAUPPAKATU     478.0      2250   \n",
+       "\n",
+       "      assistance  energy_used  energy_collected  \n",
+       "0              1         19.0               2.7  \n",
+       "1              1         53.8              15.3  \n",
+       "2              1         43.0               9.9  \n",
+       "3              1          6.5               2.1  \n",
+       "4              1         13.7               1.2  \n",
+       "...          ...          ...               ...  \n",
+       "1769           1          8.0               2.7  \n",
+       "1770           1          3.7               3.6  \n",
+       "1771           1          0.0               0.0  \n",
+       "1772           1         36.5               6.9  \n",
+       "1773           1          8.1              13.8  \n",
+       "\n",
+       "[1774 rows x 10 columns]"
+      ]
+     },
+     "execution_count": 87,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfb"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 88,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ticket               object\n",
+       "cost                float64\n",
+       "month                 int64\n",
+       "location_from        object\n",
+       "location_to          object\n",
+       "duration            float64\n",
+       "distance              int64\n",
+       "assistance            int64\n",
+       "energy_used         float64\n",
+       "energy_collected    float64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 88,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dfb.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "- Variable       |     Description <Br>\n",
+    "\n",
+    "ticket |                 ticket type <Br>\n",
+    "    - Categorial <Br>\n",
+    "    - Ordinal <Br>\n",
+    "    - Discrete <Br>\n",
+    "    \n",
+    "cost            |        paid fee in euros <Br>\n",
+    "    - Quantitative <Br>\n",
+    "    - Discrete <Br>\n",
+    "\n",
+    "month            |       calendar month during which the trip was made<Br>\n",
+    "    - Categorial <Br>\n",
+    "        - Ordinal <Br>\n",
+    "    - Discrete <Br>\n",
+    "\n",
+    "location_from   |        start location of the trip<Br>\n",
+    "    - Categorial <Br>\n",
+    "    - Discrete <Br>\n",
+    "    - nominal <Br>\n",
+    "\n",
+    "location_to     |        end location of the trip<Br>\n",
+    "    - Categorial <Br>\n",
+    "    - Discrete <Br>\n",
+    "    - Nominal <Br>\n",
+    "\n",
+    "duration        |        travel time in seconds<Br>\n",
+    "    - Quantitative <Br>\n",
+    "    - Continuous <Br>\n",
+    "\n",
+    "distance        |        travel distance in meters<Br>\n",
+    "    - Quantitative <Br>\n",
+    "    - Continuous <Br>\n",
+    "\n",
+    "assistance energy_used | status of electric assistance (0 = disabled, 1 = enabled)<Br>\n",
+    "    - Indicator variable <Br>\n",
+    "\n",
+    "energy_used          |   energy consumed by the bike in watt-hours<Br>\n",
+    "    - Quantitative <Br>\n",
+    "    - Continuous <Br>\n",
+    "    \n",
+    "energy_collected    |    energy collected by the bike in watt-hours<Br>\n",
+    "    - Quantitative <Br>\n",
+    "    - Continuous <Br>"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}