Pandas Data Cleaning Assignment.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Problem Statement\n",
    "\n",
    "It happens all the time: someone gives you data containing malformed strings,\n",
    "Python, lists and missing data. How do you tidy it up so you can get on with the\n",
    "analysis?\n",
    "Take this monstrosity as the DataFrame to use in the following puzzles:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'From_To': ['LoNDon_paris', 'MAdrid_miLAN',\n",
    "'londON_StockhOlm','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": 100,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "1. Some values in the the FlightNumber column are missing. These numbers are\n",
    "meant to increase by 10 with each row so 10055 and 10075 need to be put in\n",
    "place. Fill in these missing numbers and make the column an integer column\n",
    "(instead of a float column)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\manis\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n",
      "C:\\Users\\manis\\Anaconda3\\lib\\site-packages\\ipykernel_launcher.py:2: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  \n"
     ]
    }
   ],
   "source": [
    "df[\"FlightNumber\"][1] = 10055\n",
    "df[\"FlightNumber\"][3] = 10075\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>LoNDon_paris</td>\n",
       "      <td>10045</td>\n",
       "      <td>[23, 47]</td>\n",
       "      <td>KLM(!)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\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",
       "      <td>2</td>\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",
       "      <td>3</td>\n",
       "      <td>Budapest_PaRis</td>\n",
       "      <td>10075</td>\n",
       "      <td>[13]</td>\n",
       "      <td>12. Air France</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\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": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.astype({\"FlightNumber\": int})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. The From_To column would be better as two separate columns! Split each\n",
    "string on the underscore delimiter _ to give a new temporary DataFrame with\n",
    "the correct values. Assign the correct column names to this temporary\n",
    "DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "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</th>\n",
       "      <th>To</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>LoNDon</td>\n",
       "      <td>paris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>MAdrid</td>\n",
       "      <td>miLAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>londON</td>\n",
       "      <td>StockhOlm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Budapest</td>\n",
       "      <td>PaRis</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Brussels</td>\n",
       "      <td>londOn</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "       From         To\n",
       "0    LoNDon      paris\n",
       "1    MAdrid      miLAN\n",
       "2    londON  StockhOlm\n",
       "3  Budapest      PaRis\n",
       "4  Brussels     londOn"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df = pd.DataFrame()\n",
    "\n",
    "temp_df[[\"From\",\"To\"]] = df[\"From_To\"].str.split(\"_\",expand = True)\n",
    "temp_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Notice how the capitalisation of the city names is all mixed up in this\n",
    "temporary DataFrame. Standardise the strings so that only the first letter is\n",
    "uppercase (e.g. \"londON\" should become \"London\".)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "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</th>\n",
       "      <th>To</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>London</td>\n",
       "      <td>Paris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Madrid</td>\n",
       "      <td>Milan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>London</td>\n",
       "      <td>Stockholm</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Budapest</td>\n",
       "      <td>Paris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>Brussels</td>\n",
       "      <td>London</td>\n",
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      "text/plain": [
       "       From         To\n",
       "0    London      Paris\n",
       "1    Madrid      Milan\n",
       "2    London  Stockholm\n",
       "3  Budapest      Paris\n",
       "4  Brussels     London"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_df[\"From\"] = temp_df[\"From\"].str.capitalize() \n",
    "temp_df[\"To\"] = temp_df[\"To\"].str.capitalize()\n",
    "temp_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4. Delete the From_To column from df and attach the temporary DataFrame\n",
    "from the previous questions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>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",
       "      <td>0</td>\n",
       "      <td>10045.0</td>\n",
       "      <td>[23, 47]</td>\n",
       "      <td>KLM(!)</td>\n",
       "      <td>London</td>\n",
       "      <td>Paris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>10055.0</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",
       "      <td>2</td>\n",
       "      <td>10065.0</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",
       "      <td>3</td>\n",
       "      <td>10075.0</td>\n",
       "      <td>[13]</td>\n",
       "      <td>12. Air France</td>\n",
       "      <td>Budapest</td>\n",
       "      <td>Paris</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>10085.0</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",
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      ],
      "text/plain": [
       "   FlightNumber  RecentDelays              Airline      From         To\n",
       "0       10045.0      [23, 47]               KLM(!)    London      Paris\n",
       "1       10055.0            []    <Air France> (12)    Madrid      Milan\n",
       "2       10065.0  [24, 43, 87]  (British Airways. )    London  Stockholm\n",
       "3       10075.0          [13]       12. Air France  Budapest      Paris\n",
       "4       10085.0      [67, 32]          \"Swiss Air\"  Brussels     London"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(\"From_To\", axis = 1, inplace = True)\n",
    "df[\"From\"] = temp_df[\"From\"]\n",
    "df[\"To\"] =temp_df[\"To\"]\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "5. In the RecentDelays column, the values have been entered into the\n",
    "DataFrame as a list. We would like each first value in its own column, each\n",
    "second value in its own column, and so on. If there isn't an Nth value, the value\n",
    "should be NaN.\n",
    "\n",
    "Expand the Series of lists into a DataFrame named delays, rename the columns\n",
    "delay_1, delay_2, etc. and replace the unwanted RecentDelays column in df\n",
    "with delays.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>24.0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>87.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>67.0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "   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": 111,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "delays = pd.DataFrame(df[\"RecentDelays\"].to_list(), columns = (\"delay_1\",\"delay_2\",\"delay_3\"))\n",
    "delays"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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