Day 45 - 50 Days of Data Analysis .ipynb

{
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   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "## 1.\n",
    "\n",
    "#### Import the medical_data dataset and view the first 5 rows. Use the info() method to get insights in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "4976db7f",
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   "outputs": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>First Letter</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Gender</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>John</td>\n",
       "      <td>J</td>\n",
       "      <td>Smith</td>\n",
       "      <td>Healthy</td>\n",
       "      <td>Female 27</td>\n",
       "      <td>27.35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mary</td>\n",
       "      <td>M</td>\n",
       "      <td>Jones</td>\n",
       "      <td>Cancer</td>\n",
       "      <td>Female 39</td>\n",
       "      <td>24.29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amy</td>\n",
       "      <td>A</td>\n",
       "      <td>Candy</td>\n",
       "      <td>Heart Disease</td>\n",
       "      <td>Male 51</td>\n",
       "      <td>28.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David</td>\n",
       "      <td>D</td>\n",
       "      <td>Tuss</td>\n",
       "      <td>Diabetes</td>\n",
       "      <td>Female 73</td>\n",
       "      <td>24.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Michael</td>\n",
       "      <td>M</td>\n",
       "      <td>Brookes</td>\n",
       "      <td>Asthma</td>\n",
       "      <td>Male 71</td>\n",
       "      <td>28.21</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  First Name First Letter Last Name       Condition     Gender    BMI\n",
       "0      John             J     Smith       Healthy    Female 27  27.35\n",
       "1      Mary             M     Jones        Cancer    Female 39  24.29\n",
       "2       Amy             A     Candy  Heart Disease     Male 51  28.72\n",
       "3     David             D      Tuss       Diabetes   Female 73  24.66\n",
       "4   Michael             M   Brookes         Asthma     Male 71  28.21"
      ]
     },
     "execution_count": 1,
     "metadata": {},
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    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "\n",
    "df = pd.read_csv(\"medical_data.csv\")\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "13442199",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 10 entries, 0 to 9\n",
      "Data columns (total 6 columns):\n",
      " #   Column        Non-Null Count  Dtype  \n",
      "---  ------        --------------  -----  \n",
      " 0   First Name    10 non-null     object \n",
      " 1   First Letter  10 non-null     object \n",
      " 2   Last Name     10 non-null     object \n",
      " 3   Condition     10 non-null     object \n",
      " 4   Gender        10 non-null     object \n",
      " 5   BMI           10 non-null     float64\n",
      "dtypes: float64(1), object(5)\n",
      "memory usage: 608.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b92db16",
   "metadata": {},
   "source": [
    "## 2. \n",
    "\n",
    "#### What is the IQR ranges of the \"BMI\" column?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2160e023",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The IQR range is 23.71 to 27.66\n"
     ]
    }
   ],
   "source": [
    "lower_limit = df['BMI'].quantile(q = 0.25)\n",
    "upper_limit = df['BMI'].quantile(q = 0.75)\n",
    "\n",
    "print(f'The IQR range is {lower_limit:.2f} to {upper_limit:.2f}')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "feb3c5d1",
   "metadata": {},
   "source": [
    "## 3.\n",
    "\n",
    "#### You must have noticed that there are two columns with names. As part of feature engineering, you are required to create a copy of your DataFrame (question 1). Combine the \"First Name\" column and the \"Last Name\" column into one column called \"Names.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "e41d8d50",
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>First Name</th>\n",
       "      <th>First Letter</th>\n",
       "      <th>Last Name</th>\n",
       "      <th>Condition</th>\n",
       "      <th>Gender</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>John</td>\n",
       "      <td>J</td>\n",
       "      <td>Smith</td>\n",
       "      <td>Healthy</td>\n",
       "      <td>Female 27</td>\n",
       "      <td>27.35</td>\n",
       "      <td>John Smith</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mary</td>\n",
       "      <td>M</td>\n",
       "      <td>Jones</td>\n",
       "      <td>Cancer</td>\n",
       "      <td>Female 39</td>\n",
       "      <td>24.29</td>\n",
       "      <td>Mary Jones</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amy</td>\n",
       "      <td>A</td>\n",
       "      <td>Candy</td>\n",
       "      <td>Heart Disease</td>\n",
       "      <td>Male 51</td>\n",
       "      <td>28.72</td>\n",
       "      <td>Amy Candy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David</td>\n",
       "      <td>D</td>\n",
       "      <td>Tuss</td>\n",
       "      <td>Diabetes</td>\n",
       "      <td>Female 73</td>\n",
       "      <td>24.66</td>\n",
       "      <td>David Tuss</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Michael</td>\n",
       "      <td>M</td>\n",
       "      <td>Brookes</td>\n",
       "      <td>Asthma</td>\n",
       "      <td>Male 71</td>\n",
       "      <td>28.21</td>\n",
       "      <td>Michael Brookes</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  First Name First Letter Last Name       Condition     Gender    BMI   \n",
       "0      John             J     Smith       Healthy    Female 27  27.35  \\\n",
       "1      Mary             M     Jones        Cancer    Female 39  24.29   \n",
       "2       Amy             A     Candy  Heart Disease     Male 51  28.72   \n",
       "3     David             D      Tuss       Diabetes   Female 73  24.66   \n",
       "4   Michael             M   Brookes         Asthma     Male 71  28.21   \n",
       "\n",
       "              Name  \n",
       "0       John Smith  \n",
       "1       Mary Jones  \n",
       "2        Amy Candy  \n",
       "3       David Tuss  \n",
       "4  Michael Brookes  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
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    }
   ],
   "source": [
    "df_copy = df.copy()\n",
    "\n",
    "# concatenate the two columns into one column\n",
    "df_copy['Name'] = df_copy[\"First Name\"].str.cat(df_copy[\"Last Name\"], sep='')\n",
    "df_copy.head() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6abb7659",
   "metadata": {},
   "source": [
    "## 4. \n",
    "\n",
    "#### Now that you have combined the two columns, drop columns \"First Name\" and \"Last Name\" from the DataFrame and change the order of the columns to: [Name, First Letter, BMI, Gender, Condition]."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "4abf37b0",
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>First Letter</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>John Smith</td>\n",
       "      <td>J</td>\n",
       "      <td>27.35</td>\n",
       "      <td>Female 27</td>\n",
       "      <td>Healthy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mary Jones</td>\n",
       "      <td>M</td>\n",
       "      <td>24.29</td>\n",
       "      <td>Female 39</td>\n",
       "      <td>Cancer</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Amy Candy</td>\n",
       "      <td>A</td>\n",
       "      <td>28.72</td>\n",
       "      <td>Male 51</td>\n",
       "      <td>Heart Disease</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>David Tuss</td>\n",
       "      <td>D</td>\n",
       "      <td>24.66</td>\n",
       "      <td>Female 73</td>\n",
       "      <td>Diabetes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Michael Brookes</td>\n",
       "      <td>M</td>\n",
       "      <td>28.21</td>\n",
       "      <td>Male 71</td>\n",
       "      <td>Asthma</td>\n",
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      "text/plain": [
       "              Name First Letter    BMI     Gender       Condition\n",
       "0       John Smith            J  27.35  Female 27       Healthy  \n",
       "1       Mary Jones            M  24.29  Female 39        Cancer  \n",
       "2        Amy Candy            A  28.72    Male 51  Heart Disease \n",
       "3       David Tuss            D  24.66  Female 73       Diabetes \n",
       "4  Michael Brookes            M  28.21    Male 71         Asthma "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Drop the two columns\n",
    "df_copy.drop(columns=([\"First Name\",\"Last Name\"]), axis=1, inplace=True)\n",
    "\n",
    "# using iloc to change columns order\n",
    "df_copy = df_copy.iloc[:, [4, 0, 3, 2, 1]]\n",
    "df_copy.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2b746b32",
   "metadata": {},
   "source": [
    "## 5.\n",
    "#### The \"Gender\" column is combined with the age. Your task is to create a new column for age by separating age from the \"Gender\" column. Once you do that, make the condition column the last column of the DataFrame. Your columns' order must be: [Name, First Letter, BMI, Gender, Age, Condition]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "7a75fdfe",
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   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Name</th>\n",
       "      <th>First Letter</th>\n",
       "      <th>BMI</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Age</th>\n",
       "      <th>Condition</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Jessica kil</td>\n",
       "      <td>J</td>\n",
       "      <td>27.76</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>Diabetes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Mike Storey</td>\n",
       "      <td>M</td>\n",
       "      <td>23.51</td>\n",
       "      <td>Female</td>\n",
       "      <td>28</td>\n",
       "      <td>Asthma</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Sarah Mary</td>\n",
       "      <td>S</td>\n",
       "      <td>26.78</td>\n",
       "      <td>Male</td>\n",
       "      <td>39</td>\n",
       "      <td>Heart Disease</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>Chris Derick</td>\n",
       "      <td>C</td>\n",
       "      <td>21.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>Healthy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>Emily Cat</td>\n",
       "      <td>E</td>\n",
       "      <td>21.62</td>\n",
       "      <td>Male</td>\n",
       "      <td>61</td>\n",
       "      <td>Asthma</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Name First Letter    BMI  Gender Age       Condition\n",
       "5   Jessica kil            J  27.76    Male  61       Diabetes \n",
       "6   Mike Storey            M  23.51  Female  28         Asthma \n",
       "7    Sarah Mary            S  26.78    Male  39  Heart Disease \n",
       "8  Chris Derick            C  21.50    Male  61        Healthy \n",
       "9     Emily Cat            E  21.62    Male  61         Asthma "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Creating two columns from the Gender column using the split method\n",
    "df_copy[['Gender', 'Age']] = df_copy.Gender.str.split(\" \", expand=True)\n",
    "\n",
    "# using iloc to change columns order\n",
    "df_copy = df_copy.iloc[:, [0, 1, 2, 3, 5, 4]]\n",
    "df_copy.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "44c507b8",
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