6_Pandas_Assignment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "conservative-worst",
   "metadata": {},
   "source": [
    "# 6 - Python Assignment  [Questions](https://drive.google.com/file/d/1ISetIhHbrX2_s8aUe5V0yMI4NwzBA32q/view)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "southwest-hospital",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "\n",
    "df = pd.DataFrame({'From_To': ['LoNDon_paris', 'MAdrid_miLAN',\n",
    "'londON_StockhOlm',\n",
    "\n",
    "'Budapest_PaRis', 'Brussels_londOn'],\n",
    "'FlightNumber': [10045, np.nan, 10065, np.nan, 10085],\n",
    "'RecentDelays': [[23, 47], [], [24, 43, 87], [13], [67, 32]],\n",
    "'Airline': ['KLM(!)', '<Air France> (12)', '(British Airways. )',\n",
    "'12. Air France', '\"Swiss Air\"']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "parental-entity",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>From_To</th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>RecentDelays</th>\n",
       "      <th>Airline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LoNDon_paris</td>\n",
       "      <td>10045.0</td>\n",
       "      <td>[23, 47]</td>\n",
       "      <td>KLM(!)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MAdrid_miLAN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>&lt;Air France&gt; (12)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>londON_StockhOlm</td>\n",
       "      <td>10065.0</td>\n",
       "      <td>[24, 43, 87]</td>\n",
       "      <td>(British Airways. )</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Budapest_PaRis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[13]</td>\n",
       "      <td>12. Air France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brussels_londOn</td>\n",
       "      <td>10085.0</td>\n",
       "      <td>[67, 32]</td>\n",
       "      <td>\"Swiss Air\"</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            From_To  FlightNumber  RecentDelays              Airline\n",
       "0      LoNDon_paris       10045.0      [23, 47]               KLM(!)\n",
       "1      MAdrid_miLAN           NaN            []    <Air France> (12)\n",
       "2  londON_StockhOlm       10065.0  [24, 43, 87]  (British Airways. )\n",
       "3    Budapest_PaRis           NaN          [13]       12. Air France\n",
       "4   Brussels_londOn       10085.0      [67, 32]          \"Swiss Air\""
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "imported-deputy",
   "metadata": {},
   "source": [
    "## Solution 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "animated-bangladesh",
   "metadata": {},
   "outputs": [],
   "source": [
    "nan_indexes = df.loc[df['FlightNumber'].isna(),'FlightNumber'].index\n",
    "for i in nan_indexes:\n",
    "    df.loc[i,'FlightNumber'] = df.loc[i-1,'FlightNumber'] + 10\n",
    "df['FlightNumber'] = df['FlightNumber'].astype(np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "interesting-briefs",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>From_To</th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>RecentDelays</th>\n",
       "      <th>Airline</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LoNDon_paris</td>\n",
       "      <td>10045</td>\n",
       "      <td>[23, 47]</td>\n",
       "      <td>KLM(!)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MAdrid_miLAN</td>\n",
       "      <td>10055</td>\n",
       "      <td>[]</td>\n",
       "      <td>&lt;Air France&gt; (12)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>londON_StockhOlm</td>\n",
       "      <td>10065</td>\n",
       "      <td>[24, 43, 87]</td>\n",
       "      <td>(British Airways. )</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Budapest_PaRis</td>\n",
       "      <td>10075</td>\n",
       "      <td>[13]</td>\n",
       "      <td>12. Air France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brussels_londOn</td>\n",
       "      <td>10085</td>\n",
       "      <td>[67, 32]</td>\n",
       "      <td>\"Swiss Air\"</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            From_To  FlightNumber  RecentDelays              Airline\n",
       "0      LoNDon_paris         10045      [23, 47]               KLM(!)\n",
       "1      MAdrid_miLAN         10055            []    <Air France> (12)\n",
       "2  londON_StockhOlm         10065  [24, 43, 87]  (British Airways. )\n",
       "3    Budapest_PaRis         10075          [13]       12. Air France\n",
       "4   Brussels_londOn         10085      [67, 32]          \"Swiss Air\""
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "oriental-database",
   "metadata": {},
   "source": [
    "## Solution 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "extraordinary-timothy",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i,item in enumerate(df['From_To'].str.split('_')):\n",
    "    df.loc[i,'From'],df.loc[i,'To'] = item[0],item[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "faced-double",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>From_To</th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>RecentDelays</th>\n",
       "      <th>Airline</th>\n",
       "      <th>From</th>\n",
       "      <th>To</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LoNDon_paris</td>\n",
       "      <td>10045</td>\n",
       "      <td>[23, 47]</td>\n",
       "      <td>KLM(!)</td>\n",
       "      <td>LoNDon</td>\n",
       "      <td>paris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MAdrid_miLAN</td>\n",
       "      <td>10055</td>\n",
       "      <td>[]</td>\n",
       "      <td>&lt;Air France&gt; (12)</td>\n",
       "      <td>MAdrid</td>\n",
       "      <td>miLAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>londON_StockhOlm</td>\n",
       "      <td>10065</td>\n",
       "      <td>[24, 43, 87]</td>\n",
       "      <td>(British Airways. )</td>\n",
       "      <td>londON</td>\n",
       "      <td>StockhOlm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Budapest_PaRis</td>\n",
       "      <td>10075</td>\n",
       "      <td>[13]</td>\n",
       "      <td>12. Air France</td>\n",
       "      <td>Budapest</td>\n",
       "      <td>PaRis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brussels_londOn</td>\n",
       "      <td>10085</td>\n",
       "      <td>[67, 32]</td>\n",
       "      <td>\"Swiss Air\"</td>\n",
       "      <td>Brussels</td>\n",
       "      <td>londOn</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            From_To  FlightNumber  RecentDelays              Airline  \\\n",
       "0      LoNDon_paris         10045      [23, 47]               KLM(!)   \n",
       "1      MAdrid_miLAN         10055            []    <Air France> (12)   \n",
       "2  londON_StockhOlm         10065  [24, 43, 87]  (British Airways. )   \n",
       "3    Budapest_PaRis         10075          [13]       12. Air France   \n",
       "4   Brussels_londOn         10085      [67, 32]          \"Swiss Air\"   \n",
       "\n",
       "       From         To  \n",
       "0    LoNDon      paris  \n",
       "1    MAdrid      miLAN  \n",
       "2    londON  StockhOlm  \n",
       "3  Budapest      PaRis  \n",
       "4  Brussels     londOn  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "caring-dress",
   "metadata": {},
   "source": [
    "## Solution 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "clinical-forum",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['From'] = df['From'].str.capitalize()\n",
    "df['To'] = df['To'].str.capitalize()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "known-jerusalem",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>From_To</th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>RecentDelays</th>\n",
       "      <th>Airline</th>\n",
       "      <th>From</th>\n",
       "      <th>To</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>LoNDon_paris</td>\n",
       "      <td>10045</td>\n",
       "      <td>[23, 47]</td>\n",
       "      <td>KLM(!)</td>\n",
       "      <td>London</td>\n",
       "      <td>Paris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MAdrid_miLAN</td>\n",
       "      <td>10055</td>\n",
       "      <td>[]</td>\n",
       "      <td>&lt;Air France&gt; (12)</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>Milan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>londON_StockhOlm</td>\n",
       "      <td>10065</td>\n",
       "      <td>[24, 43, 87]</td>\n",
       "      <td>(British Airways. )</td>\n",
       "      <td>London</td>\n",
       "      <td>Stockholm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Budapest_PaRis</td>\n",
       "      <td>10075</td>\n",
       "      <td>[13]</td>\n",
       "      <td>12. Air France</td>\n",
       "      <td>Budapest</td>\n",
       "      <td>Paris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brussels_londOn</td>\n",
       "      <td>10085</td>\n",
       "      <td>[67, 32]</td>\n",
       "      <td>\"Swiss Air\"</td>\n",
       "      <td>Brussels</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            From_To  FlightNumber  RecentDelays              Airline  \\\n",
       "0      LoNDon_paris         10045      [23, 47]               KLM(!)   \n",
       "1      MAdrid_miLAN         10055            []    <Air France> (12)   \n",
       "2  londON_StockhOlm         10065  [24, 43, 87]  (British Airways. )   \n",
       "3    Budapest_PaRis         10075          [13]       12. Air France   \n",
       "4   Brussels_londOn         10085      [67, 32]          \"Swiss Air\"   \n",
       "\n",
       "       From         To  \n",
       "0    London      Paris  \n",
       "1    Madrid      Milan  \n",
       "2    London  Stockholm  \n",
       "3  Budapest      Paris  \n",
       "4  Brussels     London  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "listed-ivory",
   "metadata": {},
   "source": [
    "## Solution 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "spiritual-martin",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.drop('From_To',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "sophisticated-worst",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>RecentDelays</th>\n",
       "      <th>Airline</th>\n",
       "      <th>From</th>\n",
       "      <th>To</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10045</td>\n",
       "      <td>[23, 47]</td>\n",
       "      <td>KLM(!)</td>\n",
       "      <td>London</td>\n",
       "      <td>Paris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10055</td>\n",
       "      <td>[]</td>\n",
       "      <td>&lt;Air France&gt; (12)</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>Milan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10065</td>\n",
       "      <td>[24, 43, 87]</td>\n",
       "      <td>(British Airways. )</td>\n",
       "      <td>London</td>\n",
       "      <td>Stockholm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10075</td>\n",
       "      <td>[13]</td>\n",
       "      <td>12. Air France</td>\n",
       "      <td>Budapest</td>\n",
       "      <td>Paris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10085</td>\n",
       "      <td>[67, 32]</td>\n",
       "      <td>\"Swiss Air\"</td>\n",
       "      <td>Brussels</td>\n",
       "      <td>London</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   FlightNumber  RecentDelays              Airline      From         To\n",
       "0         10045      [23, 47]               KLM(!)    London      Paris\n",
       "1         10055            []    <Air France> (12)    Madrid      Milan\n",
       "2         10065  [24, 43, 87]  (British Airways. )    London  Stockholm\n",
       "3         10075          [13]       12. Air France  Budapest      Paris\n",
       "4         10085      [67, 32]          \"Swiss Air\"  Brussels     London"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "leading-cricket",
   "metadata": {},
   "source": [
    "## Solution 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "practical-football",
   "metadata": {},
   "outputs": [],
   "source": [
    "maxx = 0\n",
    "for items in df['RecentDelays']:\n",
    "    if len(items) > maxx:\n",
    "        maxx = len(items)\n",
    "for i in range(maxx):\n",
    "    df['delay_' + str(i+1)] = np.nan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "affected-project",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>FlightNumber</th>\n",
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       "      <th>Airline</th>\n",
       "      <th>From</th>\n",
       "      <th>To</th>\n",
       "      <th>delay_1</th>\n",
       "      <th>delay_2</th>\n",
       "      <th>delay_3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10045</td>\n",
       "      <td>[23, 47]</td>\n",
       "      <td>KLM(!)</td>\n",
       "      <td>London</td>\n",
       "      <td>Paris</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10055</td>\n",
       "      <td>[]</td>\n",
       "      <td>&lt;Air France&gt; (12)</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>Milan</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10065</td>\n",
       "      <td>[24, 43, 87]</td>\n",
       "      <td>(British Airways. )</td>\n",
       "      <td>London</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10075</td>\n",
       "      <td>[13]</td>\n",
       "      <td>12. Air France</td>\n",
       "      <td>Budapest</td>\n",
       "      <td>Paris</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10085</td>\n",
       "      <td>[67, 32]</td>\n",
       "      <td>\"Swiss Air\"</td>\n",
       "      <td>Brussels</td>\n",
       "      <td>London</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FlightNumber  RecentDelays              Airline      From         To  \\\n",
       "0         10045      [23, 47]               KLM(!)    London      Paris   \n",
       "1         10055            []    <Air France> (12)    Madrid      Milan   \n",
       "2         10065  [24, 43, 87]  (British Airways. )    London  Stockholm   \n",
       "3         10075          [13]       12. Air France  Budapest      Paris   \n",
       "4         10085      [67, 32]          \"Swiss Air\"  Brussels     London   \n",
       "\n",
       "   delay_1  delay_2  delay_3  \n",
       "0      NaN      NaN      NaN  \n",
       "1      NaN      NaN      NaN  \n",
       "2      NaN      NaN      NaN  \n",
       "3      NaN      NaN      NaN  \n",
       "4      NaN      NaN      NaN  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "polished-establishment",
   "metadata": {},
   "outputs": [],
   "source": [
    "for index,delay in enumerate(df['RecentDelays']):\n",
    "    for i in range(len(delay)):\n",
    "        df.loc[index,'delay_' + str(i+1)] = delay[i]    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "impaired-arabic",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>FlightNumber</th>\n",
       "      <th>RecentDelays</th>\n",
       "      <th>Airline</th>\n",
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       "      <th>To</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10045</td>\n",
       "      <td>[23, 47]</td>\n",
       "      <td>KLM(!)</td>\n",
       "      <td>London</td>\n",
       "      <td>Paris</td>\n",
       "      <td>23.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10055</td>\n",
       "      <td>[]</td>\n",
       "      <td>&lt;Air France&gt; (12)</td>\n",
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       "      <th>2</th>\n",
       "      <td>10065</td>\n",
       "      <td>[24, 43, 87]</td>\n",
       "      <td>(British Airways. )</td>\n",
       "      <td>London</td>\n",
       "      <td>Stockholm</td>\n",
       "      <td>24.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10075</td>\n",
       "      <td>[13]</td>\n",
       "      <td>12. Air France</td>\n",
       "      <td>Budapest</td>\n",
       "      <td>Paris</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10085</td>\n",
       "      <td>[67, 32]</td>\n",
       "      <td>\"Swiss Air\"</td>\n",
       "      <td>Brussels</td>\n",
       "      <td>London</td>\n",
       "      <td>67.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   FlightNumber  RecentDelays              Airline      From         To  \\\n",
       "0         10045      [23, 47]               KLM(!)    London      Paris   \n",
       "1         10055            []    <Air France> (12)    Madrid      Milan   \n",
       "2         10065  [24, 43, 87]  (British Airways. )    London  Stockholm   \n",
       "3         10075          [13]       12. Air France  Budapest      Paris   \n",
       "4         10085      [67, 32]          \"Swiss Air\"  Brussels     London   \n",
       "\n",
       "   delay_1  delay_2  delay_3  \n",
       "0     23.0     47.0      NaN  \n",
       "1      NaN      NaN      NaN  \n",
       "2     24.0     43.0     87.0  \n",
       "3     13.0      NaN      NaN  \n",
       "4     67.0     32.0      NaN  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  }
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