Assignment04.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#1\n",
    "test = pd.read_csv(\"test.csv\")\n",
    "myDF = pd.DataFrame(test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\n",
       "\n",
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       "        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>ID</th>\n",
       "      <th>Range</th>\n",
       "      <th>Value</th>\n",
       "      <th>Type</th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>Model</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>192</td>\n",
       "      <td>50</td>\n",
       "      <td>F</td>\n",
       "      <td>NXEYEM</td>\n",
       "      <td>EX85</td>\n",
       "      <td>[56$]</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>299</td>\n",
       "      <td>21</td>\n",
       "      <td>M</td>\n",
       "      <td>VNFKZD</td>\n",
       "      <td>CT41</td>\n",
       "      <td>[18$]</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>378</td>\n",
       "      <td>28</td>\n",
       "      <td>X</td>\n",
       "      <td>IMGMFY</td>\n",
       "      <td>VN81</td>\n",
       "      <td>[90$]</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  Range  Value Type Zip Code Model   Cost      Continent\n",
       "0  10    192     50    F   NXEYEM  EX85  [56$]           Asia\n",
       "1  20    299     21    M   VNFKZD  CT41  [18$]           Asia\n",
       "2  30    378     28    X   IMGMFY  VN81  [90$]  North America"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#2 print first three rows\n",
    "myDF.iloc[:3,:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Range</th>\n",
       "      <th>Value</th>\n",
       "      <th>Type</th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Continent</th>\n",
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       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>192</td>\n",
       "      <td>50</td>\n",
       "      <td>F</td>\n",
       "      <td>NXEYEM</td>\n",
       "      <td>[56$]</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>20</td>\n",
       "      <td>299</td>\n",
       "      <td>21</td>\n",
       "      <td>M</td>\n",
       "      <td>VNFKZD</td>\n",
       "      <td>[18$]</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>378</td>\n",
       "      <td>28</td>\n",
       "      <td>X</td>\n",
       "      <td>IMGMFY</td>\n",
       "      <td>[90$]</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>40</td>\n",
       "      <td>156</td>\n",
       "      <td>44</td>\n",
       "      <td>Q</td>\n",
       "      <td>RHRHJY</td>\n",
       "      <td>[89$]</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>468</td>\n",
       "      <td>33</td>\n",
       "      <td>J</td>\n",
       "      <td>HPQCMC</td>\n",
       "      <td>[95$]</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>945</th>\n",
       "      <td>9460</td>\n",
       "      <td>136</td>\n",
       "      <td>12</td>\n",
       "      <td>C</td>\n",
       "      <td>HGVBPV</td>\n",
       "      <td>[51$]</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>946</th>\n",
       "      <td>9470</td>\n",
       "      <td>316</td>\n",
       "      <td>46</td>\n",
       "      <td>L</td>\n",
       "      <td>SHBAUD</td>\n",
       "      <td>[33$]</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947</th>\n",
       "      <td>9480</td>\n",
       "      <td>461</td>\n",
       "      <td>46</td>\n",
       "      <td>K</td>\n",
       "      <td>PUZUVO</td>\n",
       "      <td>[12$]</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>948</th>\n",
       "      <td>9490</td>\n",
       "      <td>185</td>\n",
       "      <td>13</td>\n",
       "      <td>N</td>\n",
       "      <td>UMESZO</td>\n",
       "      <td>[39$]</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>949</th>\n",
       "      <td>9500</td>\n",
       "      <td>137</td>\n",
       "      <td>10</td>\n",
       "      <td>W</td>\n",
       "      <td>JOHHVL</td>\n",
       "      <td>[10$]</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>950 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID  Range  Value Type Zip Code   Cost      Continent\n",
       "0      10    192     50    F   NXEYEM  [56$]           Asia\n",
       "1      20    299     21    M   VNFKZD  [18$]           Asia\n",
       "2      30    378     28    X   IMGMFY  [90$]  North America\n",
       "3      40    156     44    Q   RHRHJY  [89$]           Asia\n",
       "4      50    468     33    J   HPQCMC  [95$]         Africa\n",
       "..    ...    ...    ...  ...      ...    ...            ...\n",
       "945  9460    136     12    C   HGVBPV  [51$]  South America\n",
       "946  9470    316     46    L   SHBAUD  [33$]  North America\n",
       "947  9480    461     46    K   PUZUVO  [12$]         Europe\n",
       "948  9490    185     13    N   UMESZO  [39$]  North America\n",
       "949  9500    137     10    W   JOHHVL  [10$]  South America\n",
       "\n",
       "[950 rows x 7 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#3 Delete 'Model' column from the DF temp\n",
    "myDF.drop('Model', inplace=False, axis =1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#4 \n",
    "#Column will not be deleted permanetly without inplace"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Range</th>\n",
       "      <th>Value</th>\n",
       "      <th>Type</th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>Model</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Continent</th>\n",
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       "  </thead>\n",
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       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>192</td>\n",
       "      <td>50</td>\n",
       "      <td>F</td>\n",
       "      <td>NXEYEM</td>\n",
       "      <td>EX85</td>\n",
       "      <td>[56$]</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>378</td>\n",
       "      <td>28</td>\n",
       "      <td>X</td>\n",
       "      <td>IMGMFY</td>\n",
       "      <td>VN81</td>\n",
       "      <td>[90$]</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>468</td>\n",
       "      <td>33</td>\n",
       "      <td>J</td>\n",
       "      <td>HPQCMC</td>\n",
       "      <td>QX90</td>\n",
       "      <td>[95$]</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>70</td>\n",
       "      <td>327</td>\n",
       "      <td>24</td>\n",
       "      <td>F</td>\n",
       "      <td>NLVQPY</td>\n",
       "      <td>JY72</td>\n",
       "      <td>[25$]</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>80</td>\n",
       "      <td>495</td>\n",
       "      <td>50</td>\n",
       "      <td>Z</td>\n",
       "      <td>DLUMAX</td>\n",
       "      <td>DU50</td>\n",
       "      <td>[70$]</td>\n",
       "      <td>Antarctica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  Range  Value Type Zip Code Model   Cost      Continent\n",
       "0  10    192     50    F   NXEYEM  EX85  [56$]           Asia\n",
       "2  30    378     28    X   IMGMFY  VN81  [90$]  North America\n",
       "4  50    468     33    J   HPQCMC  QX90  [95$]         Africa\n",
       "6  70    327     24    F   NLVQPY  JY72  [25$]         Europe\n",
       "7  80    495     50    Z   DLUMAX  DU50  [70$]     Antarctica"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#5 Del row 1,3,5 permanetly \n",
    "myDF.drop([1,3,5], axis=0, inplace=True)\n",
    "myDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "297.53326293558604\n"
     ]
    }
   ],
   "source": [
    "#6 Calculate the mean of 'Range' \n",
    "df_mean = myDF[\"Range\"].mean()\n",
    "print(df_mean)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "50\n",
      "50\n"
     ]
    }
   ],
   "source": [
    "#7 How many 'F' do we have in the 'Type' column? Replace them with 'AAA' PERMANENTLY\n",
    "num_f = myDF[myDF.Type == 'F'].shape[0]\n",
    "print(num_f)\n",
    "print(len(myDF[myDF['Type'] == 'F']))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Range</th>\n",
       "      <th>Value</th>\n",
       "      <th>Type</th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>Model</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Continent</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>192</td>\n",
       "      <td>50</td>\n",
       "      <td>AAA</td>\n",
       "      <td>NXEYEM</td>\n",
       "      <td>EX85</td>\n",
       "      <td>[56$]</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>378</td>\n",
       "      <td>28</td>\n",
       "      <td>X</td>\n",
       "      <td>IMGMFY</td>\n",
       "      <td>VN81</td>\n",
       "      <td>[90$]</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>468</td>\n",
       "      <td>33</td>\n",
       "      <td>J</td>\n",
       "      <td>HPQCMC</td>\n",
       "      <td>QX90</td>\n",
       "      <td>[95$]</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>70</td>\n",
       "      <td>327</td>\n",
       "      <td>24</td>\n",
       "      <td>AAA</td>\n",
       "      <td>NLVQPY</td>\n",
       "      <td>JY72</td>\n",
       "      <td>[25$]</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>80</td>\n",
       "      <td>495</td>\n",
       "      <td>50</td>\n",
       "      <td>Z</td>\n",
       "      <td>DLUMAX</td>\n",
       "      <td>DU50</td>\n",
       "      <td>[70$]</td>\n",
       "      <td>Antarctica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  Range  Value Type Zip Code Model   Cost      Continent\n",
       "0  10    192     50  AAA   NXEYEM  EX85  [56$]           Asia\n",
       "2  30    378     28    X   IMGMFY  VN81  [90$]  North America\n",
       "4  50    468     33    J   HPQCMC  QX90  [95$]         Africa\n",
       "6  70    327     24  AAA   NLVQPY  JY72  [25$]         Europe\n",
       "7  80    495     50    Z   DLUMAX  DU50  [70$]     Antarctica"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myDF.loc[(myDF.Type == 'F'), 'Type'] = 'AAA'\n",
    "myDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>ID</th>\n",
       "      <th>Range</th>\n",
       "      <th>Value</th>\n",
       "      <th>Type</th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>Model</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Continent</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>192</td>\n",
       "      <td>50</td>\n",
       "      <td>AAA</td>\n",
       "      <td>NXEYEM</td>\n",
       "      <td>EX85</td>\n",
       "      <td>56</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>378</td>\n",
       "      <td>28</td>\n",
       "      <td>X</td>\n",
       "      <td>IMGMFY</td>\n",
       "      <td>VN81</td>\n",
       "      <td>90</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>468</td>\n",
       "      <td>33</td>\n",
       "      <td>J</td>\n",
       "      <td>HPQCMC</td>\n",
       "      <td>QX90</td>\n",
       "      <td>95</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>70</td>\n",
       "      <td>327</td>\n",
       "      <td>24</td>\n",
       "      <td>AAA</td>\n",
       "      <td>NLVQPY</td>\n",
       "      <td>JY72</td>\n",
       "      <td>25</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>80</td>\n",
       "      <td>495</td>\n",
       "      <td>50</td>\n",
       "      <td>Z</td>\n",
       "      <td>DLUMAX</td>\n",
       "      <td>DU50</td>\n",
       "      <td>70</td>\n",
       "      <td>Antarctica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID  Range  Value Type Zip Code Model  Cost      Continent\n",
       "0  10    192     50  AAA   NXEYEM  EX85    56           Asia\n",
       "2  30    378     28    X   IMGMFY  VN81    90  North America\n",
       "4  50    468     33    J   HPQCMC  QX90    95         Africa\n",
       "6  70    327     24  AAA   NLVQPY  JY72    25         Europe\n",
       "7  80    495     50    Z   DLUMAX  DU50    70     Antarctica"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#8 Get rid of the [, $ and ] from the ‘Cost’ column permanently and display the first 5 rows. What is the type of ‘Cost’ column after this\n",
    "myDF['Cost'] = myDF['Cost'].str.replace('[$]','')\n",
    "myDF['Cost'] = myDF['Cost'].apply(lambda x: x.replace('[','').replace(']', ''))\n",
    "#9 Convert the string columns to int\n",
    "myDF['Cost'] = myDF['Cost'].astype(int)\n",
    "myDF.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Continent\n",
       "Africa           54.741259\n",
       "Antarctica       54.706897\n",
       "Asia             54.279070\n",
       "Australia        56.838028\n",
       "Europe           53.258993\n",
       "North America    55.006993\n",
       "South America    53.162963\n",
       "Name: Cost, dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#10 Calculate the average of ‘Cost’ column. Then, calculate the average of ‘Cost’ for each ‘Continent’\n",
    "\n",
    "myDF.groupby('Continent')['Cost'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        vertical-align: top;\n",
       "    }\n",
       "\n",
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       "        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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>143.0</td>\n",
       "      <td>54.741259</td>\n",
       "      <td>25.961432</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.00</td>\n",
       "      <td>54.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antarctica</th>\n",
       "      <td>116.0</td>\n",
       "      <td>54.706897</td>\n",
       "      <td>24.880590</td>\n",
       "      <td>10.0</td>\n",
       "      <td>35.00</td>\n",
       "      <td>59.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>129.0</td>\n",
       "      <td>54.279070</td>\n",
       "      <td>26.109737</td>\n",
       "      <td>10.0</td>\n",
       "      <td>33.00</td>\n",
       "      <td>54.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>142.0</td>\n",
       "      <td>56.838028</td>\n",
       "      <td>26.057121</td>\n",
       "      <td>10.0</td>\n",
       "      <td>36.25</td>\n",
       "      <td>56.5</td>\n",
       "      <td>81.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>139.0</td>\n",
       "      <td>53.258993</td>\n",
       "      <td>26.185054</td>\n",
       "      <td>10.0</td>\n",
       "      <td>29.00</td>\n",
       "      <td>54.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North America</th>\n",
       "      <td>143.0</td>\n",
       "      <td>55.006993</td>\n",
       "      <td>25.664821</td>\n",
       "      <td>10.0</td>\n",
       "      <td>33.50</td>\n",
       "      <td>56.0</td>\n",
       "      <td>78.5</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South America</th>\n",
       "      <td>135.0</td>\n",
       "      <td>53.162963</td>\n",
       "      <td>25.724780</td>\n",
       "      <td>10.0</td>\n",
       "      <td>31.00</td>\n",
       "      <td>55.0</td>\n",
       "      <td>74.5</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               count       mean        std   min    25%   50%   75%   max\n",
       "Continent                                                                \n",
       "Africa         143.0  54.741259  25.961432  11.0  34.00  54.0  78.0  97.0\n",
       "Antarctica     116.0  54.706897  24.880590  10.0  35.00  59.0  74.0  97.0\n",
       "Asia           129.0  54.279070  26.109737  10.0  33.00  54.0  74.0  99.0\n",
       "Australia      142.0  56.838028  26.057121  10.0  36.25  56.5  81.0  97.0\n",
       "Europe         139.0  53.258993  26.185054  10.0  29.00  54.0  73.0  99.0\n",
       "North America  143.0  55.006993  25.664821  10.0  33.50  56.0  78.5  99.0\n",
       "South America  135.0  53.162963  25.724780  10.0  31.00  55.0  74.5  98.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#11 Calculate the min, max and standard deviation of the ‘Cost’ column, group by each ‘Continent’, all in one command.\n",
    "myDF.groupby('Continent')['Cost'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th colspan=\"8\" halign=\"left\">ID</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Range</th>\n",
       "      <th>...</th>\n",
       "      <th colspan=\"2\" halign=\"left\">Value</th>\n",
       "      <th colspan=\"8\" halign=\"left\">Cost</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>...</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
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       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Continent</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Africa</th>\n",
       "      <td>143.0</td>\n",
       "      <td>4797.692308</td>\n",
       "      <td>2614.387519</td>\n",
       "      <td>50.0</td>\n",
       "      <td>2680.0</td>\n",
       "      <td>4900.0</td>\n",
       "      <td>7115.0</td>\n",
       "      <td>9450.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>282.475524</td>\n",
       "      <td>...</td>\n",
       "      <td>42.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>54.741259</td>\n",
       "      <td>25.961432</td>\n",
       "      <td>11.0</td>\n",
       "      <td>34.00</td>\n",
       "      <td>54.0</td>\n",
       "      <td>78.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Antarctica</th>\n",
       "      <td>116.0</td>\n",
       "      <td>4569.310345</td>\n",
       "      <td>2936.313786</td>\n",
       "      <td>80.0</td>\n",
       "      <td>1887.5</td>\n",
       "      <td>4455.0</td>\n",
       "      <td>7330.0</td>\n",
       "      <td>9370.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>288.698276</td>\n",
       "      <td>...</td>\n",
       "      <td>39.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>116.0</td>\n",
       "      <td>54.706897</td>\n",
       "      <td>24.880590</td>\n",
       "      <td>10.0</td>\n",
       "      <td>35.00</td>\n",
       "      <td>59.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Asia</th>\n",
       "      <td>129.0</td>\n",
       "      <td>4334.031008</td>\n",
       "      <td>2683.431665</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2040.0</td>\n",
       "      <td>4040.0</td>\n",
       "      <td>6680.0</td>\n",
       "      <td>9390.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>284.914729</td>\n",
       "      <td>...</td>\n",
       "      <td>36.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>129.0</td>\n",
       "      <td>54.279070</td>\n",
       "      <td>26.109737</td>\n",
       "      <td>10.0</td>\n",
       "      <td>33.00</td>\n",
       "      <td>54.0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Australia</th>\n",
       "      <td>142.0</td>\n",
       "      <td>4677.323944</td>\n",
       "      <td>2793.945560</td>\n",
       "      <td>120.0</td>\n",
       "      <td>2242.5</td>\n",
       "      <td>4590.0</td>\n",
       "      <td>6992.5</td>\n",
       "      <td>9410.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>305.894366</td>\n",
       "      <td>...</td>\n",
       "      <td>42.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>142.0</td>\n",
       "      <td>56.838028</td>\n",
       "      <td>26.057121</td>\n",
       "      <td>10.0</td>\n",
       "      <td>36.25</td>\n",
       "      <td>56.5</td>\n",
       "      <td>81.0</td>\n",
       "      <td>97.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Europe</th>\n",
       "      <td>139.0</td>\n",
       "      <td>5078.848921</td>\n",
       "      <td>2623.991892</td>\n",
       "      <td>70.0</td>\n",
       "      <td>3065.0</td>\n",
       "      <td>4970.0</td>\n",
       "      <td>7295.0</td>\n",
       "      <td>9480.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>320.338129</td>\n",
       "      <td>...</td>\n",
       "      <td>42.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>139.0</td>\n",
       "      <td>53.258993</td>\n",
       "      <td>26.185054</td>\n",
       "      <td>10.0</td>\n",
       "      <td>29.00</td>\n",
       "      <td>54.0</td>\n",
       "      <td>73.0</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>North America</th>\n",
       "      <td>143.0</td>\n",
       "      <td>4906.783217</td>\n",
       "      <td>2841.216413</td>\n",
       "      <td>30.0</td>\n",
       "      <td>2375.0</td>\n",
       "      <td>4820.0</td>\n",
       "      <td>7580.0</td>\n",
       "      <td>9490.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>299.832168</td>\n",
       "      <td>...</td>\n",
       "      <td>42.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>143.0</td>\n",
       "      <td>55.006993</td>\n",
       "      <td>25.664821</td>\n",
       "      <td>10.0</td>\n",
       "      <td>33.50</td>\n",
       "      <td>56.0</td>\n",
       "      <td>78.5</td>\n",
       "      <td>99.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>South America</th>\n",
       "      <td>135.0</td>\n",
       "      <td>4963.851852</td>\n",
       "      <td>2656.186804</td>\n",
       "      <td>90.0</td>\n",
       "      <td>2910.0</td>\n",
       "      <td>5070.0</td>\n",
       "      <td>6975.0</td>\n",
       "      <td>9500.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>298.422222</td>\n",
       "      <td>...</td>\n",
       "      <td>41.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>135.0</td>\n",
       "      <td>53.162963</td>\n",
       "      <td>25.724780</td>\n",
       "      <td>10.0</td>\n",
       "      <td>31.00</td>\n",
       "      <td>55.0</td>\n",
       "      <td>74.5</td>\n",
       "      <td>98.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>7 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                  ID                                                           \\\n",
       "               count         mean          std    min     25%     50%     75%   \n",
       "Continent                                                                       \n",
       "Africa         143.0  4797.692308  2614.387519   50.0  2680.0  4900.0  7115.0   \n",
       "Antarctica     116.0  4569.310345  2936.313786   80.0  1887.5  4455.0  7330.0   \n",
       "Asia           129.0  4334.031008  2683.431665   10.0  2040.0  4040.0  6680.0   \n",
       "Australia      142.0  4677.323944  2793.945560  120.0  2242.5  4590.0  6992.5   \n",
       "Europe         139.0  5078.848921  2623.991892   70.0  3065.0  4970.0  7295.0   \n",
       "North America  143.0  4906.783217  2841.216413   30.0  2375.0  4820.0  7580.0   \n",
       "South America  135.0  4963.851852  2656.186804   90.0  2910.0  5070.0  6975.0   \n",
       "\n",
       "                       Range              ... Value         Cost             \\\n",
       "                  max  count        mean  ...   75%   max  count       mean   \n",
       "Continent                                 ...                                 \n",
       "Africa         9450.0  143.0  282.475524  ...  42.0  50.0  143.0  54.741259   \n",
       "Antarctica     9370.0  116.0  288.698276  ...  39.0  50.0  116.0  54.706897   \n",
       "Asia           9390.0  129.0  284.914729  ...  36.0  50.0  129.0  54.279070   \n",
       "Australia      9410.0  142.0  305.894366  ...  42.0  50.0  142.0  56.838028   \n",
       "Europe         9480.0  139.0  320.338129  ...  42.0  50.0  139.0  53.258993   \n",
       "North America  9490.0  143.0  299.832168  ...  42.0  50.0  143.0  55.006993   \n",
       "South America  9500.0  135.0  298.422222  ...  41.0  50.0  135.0  53.162963   \n",
       "\n",
       "                                                         \n",
       "                     std   min    25%   50%   75%   max  \n",
       "Continent                                                \n",
       "Africa         25.961432  11.0  34.00  54.0  78.0  97.0  \n",
       "Antarctica     24.880590  10.0  35.00  59.0  74.0  97.0  \n",
       "Asia           26.109737  10.0  33.00  54.0  74.0  99.0  \n",
       "Australia      26.057121  10.0  36.25  56.5  81.0  97.0  \n",
       "Europe         26.185054  10.0  29.00  54.0  73.0  99.0  \n",
       "North America  25.664821  10.0  33.50  56.0  78.5  99.0  \n",
       "South America  25.724780  10.0  31.00  55.0  74.5  98.0  \n",
       "\n",
       "[7 rows x 32 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#12 Calculate the min, max and standard deviation of all numerical columns, group by each ‘Continent’, all in one command.\n",
    "myDF.groupby('Continent').describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f8c329a50d0>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#13 Illustrate the mean of all numerical columns, group by the ‘Continent’, using a ‘bar’ chart.\n",
    "\n",
    "barDF=myDF.groupby('Continent').mean()\n",
    "barDF.plot.bar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Africa           143\n",
       "North America    143\n",
       "Australia        142\n",
       "Europe           139\n",
       "South America    135\n",
       "Asia             129\n",
       "Antarctica       116\n",
       "Name: Continent, dtype: int64"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#14 Count the number of each continent in the ‘Continent’ column.\n",
    "myDF['Continent'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "#15 Show the percentage of each continent in the ‘Continent’ column.\n",
    "#Cannot perform % operation on a string"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#16 Show the unique values that we have in the ‘Continent’ column. Then show the number of unique values that we have in the ‘Continent’ column.\n",
    "\n",
    "myDF['Continent'].unique()\n",
    "len(myDF['Continent'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35    20\n",
       "56    20\n",
       "73    18\n",
       "54    17\n",
       "97    17\n",
       "      ..\n",
       "77     5\n",
       "92     5\n",
       "94     5\n",
       "42     3\n",
       "24     2\n",
       "Name: Cost, Length: 90, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#17 Use the value_count method for the ‘Cost’ column and describe the output as a comment in the next cell, using the # for the comment.\n",
    "myDF['Cost'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AxesSubplot(0.125,0.125;0.775x0.755)\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#18 & 19 Plot a histogram for the ‘Cost’ column.Try to change the number of bins to 50 to show the result more accurately.\n",
    "\n",
    "plot = myDF['Cost'].plot.hist(bins=50, alpha = 0.5)\n",
    "print(plot)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ID           0\n",
       "Range        0\n",
       "Value        0\n",
       "Type         2\n",
       "Zip Code     9\n",
       "Model        0\n",
       "Cost         0\n",
       "Continent    0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#20 \n",
    "\n",
    "myDF['Zip Code'].isnull().sum()\n",
    "len(myDF['Zip Code']) - myDF.count()\n",
    "myDF['Type'].isnull().sum()\n",
    "#There are 9 null/missing values in the 'Zip Code' column and 2 null/missing values in the 'Type' column\n",
    "myDF.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>ID</th>\n",
       "      <th>Range</th>\n",
       "      <th>Value</th>\n",
       "      <th>Type</th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>Model</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>192</td>\n",
       "      <td>50</td>\n",
       "      <td>AAA</td>\n",
       "      <td>NXEYEM</td>\n",
       "      <td>EX85</td>\n",
       "      <td>56</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>30</td>\n",
       "      <td>378</td>\n",
       "      <td>28</td>\n",
       "      <td>X</td>\n",
       "      <td>IMGMFY</td>\n",
       "      <td>VN81</td>\n",
       "      <td>90</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>50</td>\n",
       "      <td>468</td>\n",
       "      <td>33</td>\n",
       "      <td>J</td>\n",
       "      <td>HPQCMC</td>\n",
       "      <td>QX90</td>\n",
       "      <td>95</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>70</td>\n",
       "      <td>327</td>\n",
       "      <td>24</td>\n",
       "      <td>AAA</td>\n",
       "      <td>NLVQPY</td>\n",
       "      <td>JY72</td>\n",
       "      <td>25</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>80</td>\n",
       "      <td>495</td>\n",
       "      <td>50</td>\n",
       "      <td>Z</td>\n",
       "      <td>DLUMAX</td>\n",
       "      <td>DU50</td>\n",
       "      <td>70</td>\n",
       "      <td>Antarctica</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>945</th>\n",
       "      <td>9460</td>\n",
       "      <td>136</td>\n",
       "      <td>12</td>\n",
       "      <td>C</td>\n",
       "      <td>HGVBPV</td>\n",
       "      <td>NG88</td>\n",
       "      <td>51</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>946</th>\n",
       "      <td>9470</td>\n",
       "      <td>316</td>\n",
       "      <td>46</td>\n",
       "      <td>L</td>\n",
       "      <td>SHBAUD</td>\n",
       "      <td>IE12</td>\n",
       "      <td>33</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>947</th>\n",
       "      <td>9480</td>\n",
       "      <td>461</td>\n",
       "      <td>46</td>\n",
       "      <td>K</td>\n",
       "      <td>PUZUVO</td>\n",
       "      <td>HB82</td>\n",
       "      <td>12</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>948</th>\n",
       "      <td>9490</td>\n",
       "      <td>185</td>\n",
       "      <td>13</td>\n",
       "      <td>N</td>\n",
       "      <td>UMESZO</td>\n",
       "      <td>WJ90</td>\n",
       "      <td>39</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>949</th>\n",
       "      <td>9500</td>\n",
       "      <td>137</td>\n",
       "      <td>10</td>\n",
       "      <td>W</td>\n",
       "      <td>JOHHVL</td>\n",
       "      <td>QE45</td>\n",
       "      <td>10</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>936 rows × 8 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       ID  Range  Value Type Zip Code Model  Cost      Continent\n",
       "0      10    192     50  AAA   NXEYEM  EX85    56           Asia\n",
       "2      30    378     28    X   IMGMFY  VN81    90  North America\n",
       "4      50    468     33    J   HPQCMC  QX90    95         Africa\n",
       "6      70    327     24  AAA   NLVQPY  JY72    25         Europe\n",
       "7      80    495     50    Z   DLUMAX  DU50    70     Antarctica\n",
       "..    ...    ...    ...  ...      ...   ...   ...            ...\n",
       "945  9460    136     12    C   HGVBPV  NG88    51  South America\n",
       "946  9470    316     46    L   SHBAUD  IE12    33  North America\n",
       "947  9480    461     46    K   PUZUVO  HB82    12         Europe\n",
       "948  9490    185     13    N   UMESZO  WJ90    39  North America\n",
       "949  9500    137     10    W   JOHHVL  QE45    10  South America\n",
       "\n",
       "[936 rows x 8 columns]"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#21 \n",
    "myDF.dropna() # There are 936 rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False    945\n",
       "True       2\n",
       "Name: Type, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#22 Show the statistics for the ‘Type’ column using the value_count method, including the NaN values in the list. How many missing values do we have?\n",
    "myDF['Type'].isna().value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      AAA\n",
       "2        X\n",
       "4        J\n",
       "6      AAA\n",
       "7        Z\n",
       "      ... \n",
       "945      C\n",
       "946      L\n",
       "947      K\n",
       "948      N\n",
       "949      W\n",
       "Name: Type, Length: 947, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#23 Replace the ‘Type’ column missing values with ‘XXX’ and show the result again.\n",
    "myDF['Type'].fillna('XXX')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(936, 8)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#24 Drop the rows that have any missing value permanently and show the number of rows after running the drop command.\n",
    "myDF.dropna(inplace=True)\n",
    "myDF.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6        70\n",
       "7        80\n",
       "8        90\n",
       "9       100\n",
       "10      110\n",
       "       ... \n",
       "945    9460\n",
       "946    9470\n",
       "947    9480\n",
       "948    9490\n",
       "949    9500\n",
       "Name: ID, Length: 933, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#25 Set the ‘ID’ column as the new index and delete the index name. Then show the first 5 rows of the DF. Show the value of the cell that its ID is 70 and its column header is ‘Value’.\n",
    "myDF.set_index('ID')\n",
    "myDF['ID'][3:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#26Show the index, columns and the shape of the current DF. Bring back the ID column to the DF and reset the index. Again, show the index, columns and the shape of the current DF.\n",
    "\n",
    "myDF.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Int64Index([  0,   2,   4,   6,   7,   8,   9,  10,  11,  12,\n",
       "            ...\n",
       "            940, 941, 942, 943, 944, 945, 946, 947, 948, 949],\n",
       "           dtype='int64', length=936)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#26 Show the index, columns and the shape of the current DF. Bring back the ID column to the DF and reset the index. Again, show the index, columns and the shape of the current DF.\n",
    "myDF.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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>index</th>\n",
       "      <th>ID</th>\n",
       "      <th>Range</th>\n",
       "      <th>Value</th>\n",
       "      <th>Type</th>\n",
       "      <th>Zip Code</th>\n",
       "      <th>Model</th>\n",
       "      <th>Cost</th>\n",
       "      <th>Continent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>192</td>\n",
       "      <td>50</td>\n",
       "      <td>AAA</td>\n",
       "      <td>NXEYEM</td>\n",
       "      <td>EX85</td>\n",
       "      <td>56</td>\n",
       "      <td>Asia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>30</td>\n",
       "      <td>378</td>\n",
       "      <td>28</td>\n",
       "      <td>X</td>\n",
       "      <td>IMGMFY</td>\n",
       "      <td>VN81</td>\n",
       "      <td>90</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>50</td>\n",
       "      <td>468</td>\n",
       "      <td>33</td>\n",
       "      <td>J</td>\n",
       "      <td>HPQCMC</td>\n",
       "      <td>QX90</td>\n",
       "      <td>95</td>\n",
       "      <td>Africa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>70</td>\n",
       "      <td>327</td>\n",
       "      <td>24</td>\n",
       "      <td>AAA</td>\n",
       "      <td>NLVQPY</td>\n",
       "      <td>JY72</td>\n",
       "      <td>25</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>80</td>\n",
       "      <td>495</td>\n",
       "      <td>50</td>\n",
       "      <td>Z</td>\n",
       "      <td>DLUMAX</td>\n",
       "      <td>DU50</td>\n",
       "      <td>70</td>\n",
       "      <td>Antarctica</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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>931</th>\n",
       "      <td>945</td>\n",
       "      <td>9460</td>\n",
       "      <td>136</td>\n",
       "      <td>12</td>\n",
       "      <td>C</td>\n",
       "      <td>HGVBPV</td>\n",
       "      <td>NG88</td>\n",
       "      <td>51</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>932</th>\n",
       "      <td>946</td>\n",
       "      <td>9470</td>\n",
       "      <td>316</td>\n",
       "      <td>46</td>\n",
       "      <td>L</td>\n",
       "      <td>SHBAUD</td>\n",
       "      <td>IE12</td>\n",
       "      <td>33</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>933</th>\n",
       "      <td>947</td>\n",
       "      <td>9480</td>\n",
       "      <td>461</td>\n",
       "      <td>46</td>\n",
       "      <td>K</td>\n",
       "      <td>PUZUVO</td>\n",
       "      <td>HB82</td>\n",
       "      <td>12</td>\n",
       "      <td>Europe</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>934</th>\n",
       "      <td>948</td>\n",
       "      <td>9490</td>\n",
       "      <td>185</td>\n",
       "      <td>13</td>\n",
       "      <td>N</td>\n",
       "      <td>UMESZO</td>\n",
       "      <td>WJ90</td>\n",
       "      <td>39</td>\n",
       "      <td>North America</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>935</th>\n",
       "      <td>949</td>\n",
       "      <td>9500</td>\n",
       "      <td>137</td>\n",
       "      <td>10</td>\n",
       "      <td>W</td>\n",
       "      <td>JOHHVL</td>\n",
       "      <td>QE45</td>\n",
       "      <td>10</td>\n",
       "      <td>South America</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>936 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     index    ID  Range  Value Type Zip Code Model  Cost      Continent\n",
       "0        0    10    192     50  AAA   NXEYEM  EX85    56           Asia\n",
       "1        2    30    378     28    X   IMGMFY  VN81    90  North America\n",
       "2        4    50    468     33    J   HPQCMC  QX90    95         Africa\n",
       "3        6    70    327     24  AAA   NLVQPY  JY72    25         Europe\n",
       "4        7    80    495     50    Z   DLUMAX  DU50    70     Antarctica\n",
       "..     ...   ...    ...    ...  ...      ...   ...   ...            ...\n",
       "931    945  9460    136     12    C   HGVBPV  NG88    51  South America\n",
       "932    946  9470    316     46    L   SHBAUD  IE12    33  North America\n",
       "933    947  9480    461     46    K   PUZUVO  HB82    12         Europe\n",
       "934    948  9490    185     13    N   UMESZO  WJ90    39  North America\n",
       "935    949  9500    137     10    W   JOHHVL  QE45    10  South America\n",
       "\n",
       "[936 rows x 9 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "myDF.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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