09-student-alcohol-consumption.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 1. Import the necessary libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 2. Import the dataset from this address.\n",
    "\n",
    "#### Step 3. Assign it to a variable called df."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>...</th>\n",
       "      <th>famrel</th>\n",
       "      <th>freetime</th>\n",
       "      <th>goout</th>\n",
       "      <th>Dalc</th>\n",
       "      <th>Walc</th>\n",
       "      <th>health</th>\n",
       "      <th>absences</th>\n",
       "      <th>G1</th>\n",
       "      <th>G2</th>\n",
       "      <th>G3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>18</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>at_home</td>\n",
       "      <td>teacher</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GP</td>\n",
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       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>...</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "      <td>14</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>...</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 33 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob  ...  \\\n",
       "0     GP   F   18       U     GT3       A     4     4  at_home   teacher  ...   \n",
       "1     GP   F   17       U     GT3       T     1     1  at_home     other  ...   \n",
       "2     GP   F   15       U     LE3       T     1     1  at_home     other  ...   \n",
       "3     GP   F   15       U     GT3       T     4     2   health  services  ...   \n",
       "4     GP   F   16       U     GT3       T     3     3    other     other  ...   \n",
       "\n",
       "  famrel freetime  goout  Dalc  Walc health absences  G1  G2  G3  \n",
       "0      4        3      4     1     1      3        6   5   6   6  \n",
       "1      5        3      3     1     1      3        4   5   5   6  \n",
       "2      4        3      2     2     3      3       10   7   8  10  \n",
       "3      3        2      2     1     1      5        2  15  14  15  \n",
       "4      4        3      2     1     2      5        4   6  10  10  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://raw.githubusercontent.com/guipsamora/pandas_exercises/master/04_Apply/Students_Alcohol_Consumption/student-mat.csv'\n",
    "df = pd.read_csv(url)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 4. For the purpose of this exercise slice the dataframe from 'school' until the 'guardian' column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
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       "      <th>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>reason</th>\n",
       "      <th>guardian</th>\n",
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       "  </thead>\n",
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       "      <td>GP</td>\n",
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       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>at_home</td>\n",
       "      <td>teacher</td>\n",
       "      <td>course</td>\n",
       "      <td>mother</td>\n",
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       "      <th>1</th>\n",
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       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>at_home</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>mother</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>15</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>health</td>\n",
       "      <td>services</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>16</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>3</td>\n",
       "      <td>other</td>\n",
       "      <td>other</td>\n",
       "      <td>home</td>\n",
       "      <td>father</td>\n",
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      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu     Mjob      Fjob  \\\n",
       "0     GP   F   18       U     GT3       A     4     4  at_home   teacher   \n",
       "1     GP   F   17       U     GT3       T     1     1  at_home     other   \n",
       "2     GP   F   15       U     LE3       T     1     1  at_home     other   \n",
       "3     GP   F   15       U     GT3       T     4     2   health  services   \n",
       "4     GP   F   16       U     GT3       T     3     3    other     other   \n",
       "\n",
       "   reason guardian  \n",
       "0  course   mother  \n",
       "1  course   father  \n",
       "2   other   mother  \n",
       "3    home   mother  \n",
       "4    home   father  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = df.loc[:, 'school':'guardian']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 5. Create a lambda function that will capitalize strings."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "capitalizer = lambda x: x.capitalize()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 6. Capitalize both Mjob and Fjob"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Mjob'] = df['Mjob'].apply(capitalizer)\n",
    "df['Fjob'] = df['Fjob'].apply(capitalizer)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 7. Print the last elements of the data set."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "      <th>school</th>\n",
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       "      <th>390</th>\n",
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       "      <td>LE3</td>\n",
       "      <td>A</td>\n",
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       "      <td>other</td>\n",
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       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>18</td>\n",
       "      <td>R</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Services</td>\n",
       "      <td>Other</td>\n",
       "      <td>course</td>\n",
       "      <td>mother</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>19</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Other</td>\n",
       "      <td>At_home</td>\n",
       "      <td>course</td>\n",
       "      <td>father</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "    school sex  age address famsize Pstatus  Medu  Fedu      Mjob      Fjob  \\\n",
       "390     MS   M   20       U     LE3       A     2     2  Services  Services   \n",
       "391     MS   M   17       U     LE3       T     3     1  Services  Services   \n",
       "392     MS   M   21       R     GT3       T     1     1     Other     Other   \n",
       "393     MS   M   18       R     LE3       T     3     2  Services     Other   \n",
       "394     MS   M   19       U     LE3       T     1     1     Other   At_home   \n",
       "\n",
       "     reason guardian  \n",
       "390  course    other  \n",
       "391  course   mother  \n",
       "392  course    other  \n",
       "393  course   mother  \n",
       "394  course   father  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 8. Create a function called majority that returns a boolean value to a new column called legal_drinker (Consider majority as older than 17 years old)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>school</th>\n",
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       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
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       "      <th>Mjob</th>\n",
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       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>Services</td>\n",
       "      <td>Services</td>\n",
       "      <td>course</td>\n",
       "      <td>mother</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>392</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>21</td>\n",
       "      <td>R</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>course</td>\n",
       "      <td>other</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>393</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>18</td>\n",
       "      <td>R</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>Services</td>\n",
       "      <td>Other</td>\n",
       "      <td>course</td>\n",
       "      <td>mother</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>MS</td>\n",
       "      <td>M</td>\n",
       "      <td>19</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Other</td>\n",
       "      <td>At_home</td>\n",
       "      <td>course</td>\n",
       "      <td>father</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    school sex  age address famsize Pstatus  Medu  Fedu      Mjob      Fjob  \\\n",
       "390     MS   M   20       U     LE3       A     2     2  Services  Services   \n",
       "391     MS   M   17       U     LE3       T     3     1  Services  Services   \n",
       "392     MS   M   21       R     GT3       T     1     1     Other     Other   \n",
       "393     MS   M   18       R     LE3       T     3     2  Services     Other   \n",
       "394     MS   M   19       U     LE3       T     1     1     Other   At_home   \n",
       "\n",
       "     reason guardian  legal_drinker  \n",
       "390  course    other           True  \n",
       "391  course   mother          False  \n",
       "392  course    other           True  \n",
       "393  course   mother           True  \n",
       "394  course   father           True  "
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['legal_drinker'] = df['age'] > 17\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Step 10. Multiply every number of the dataset by 10."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "def times10(x):\n",
    "    if type(x) is int:\n",
    "        return 10 * x\n",
    "    return x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>school</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>address</th>\n",
       "      <th>famsize</th>\n",
       "      <th>Pstatus</th>\n",
       "      <th>Medu</th>\n",
       "      <th>Fedu</th>\n",
       "      <th>Mjob</th>\n",
       "      <th>Fjob</th>\n",
       "      <th>reason</th>\n",
       "      <th>guardian</th>\n",
       "      <th>legal_drinker</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>180</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>At_home</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>course</td>\n",
       "      <td>mother</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>170</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>At_home</td>\n",
       "      <td>Other</td>\n",
       "      <td>course</td>\n",
       "      <td>father</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>150</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>At_home</td>\n",
       "      <td>Other</td>\n",
       "      <td>other</td>\n",
       "      <td>mother</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>150</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>40</td>\n",
       "      <td>20</td>\n",
       "      <td>Health</td>\n",
       "      <td>Services</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>160</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>home</td>\n",
       "      <td>father</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>GP</td>\n",
       "      <td>M</td>\n",
       "      <td>160</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>40</td>\n",
       "      <td>30</td>\n",
       "      <td>Services</td>\n",
       "      <td>Other</td>\n",
       "      <td>reputation</td>\n",
       "      <td>mother</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>GP</td>\n",
       "      <td>M</td>\n",
       "      <td>160</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>T</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>GP</td>\n",
       "      <td>F</td>\n",
       "      <td>170</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>A</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>Other</td>\n",
       "      <td>Teacher</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>GP</td>\n",
       "      <td>M</td>\n",
       "      <td>150</td>\n",
       "      <td>U</td>\n",
       "      <td>LE3</td>\n",
       "      <td>A</td>\n",
       "      <td>30</td>\n",
       "      <td>20</td>\n",
       "      <td>Services</td>\n",
       "      <td>Other</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>GP</td>\n",
       "      <td>M</td>\n",
       "      <td>150</td>\n",
       "      <td>U</td>\n",
       "      <td>GT3</td>\n",
       "      <td>T</td>\n",
       "      <td>30</td>\n",
       "      <td>40</td>\n",
       "      <td>Other</td>\n",
       "      <td>Other</td>\n",
       "      <td>home</td>\n",
       "      <td>mother</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  school sex  age address famsize Pstatus  Medu  Fedu      Mjob      Fjob  \\\n",
       "0     GP   F  180       U     GT3       A    40    40   At_home   Teacher   \n",
       "1     GP   F  170       U     GT3       T    10    10   At_home     Other   \n",
       "2     GP   F  150       U     LE3       T    10    10   At_home     Other   \n",
       "3     GP   F  150       U     GT3       T    40    20    Health  Services   \n",
       "4     GP   F  160       U     GT3       T    30    30     Other     Other   \n",
       "5     GP   M  160       U     LE3       T    40    30  Services     Other   \n",
       "6     GP   M  160       U     LE3       T    20    20     Other     Other   \n",
       "7     GP   F  170       U     GT3       A    40    40     Other   Teacher   \n",
       "8     GP   M  150       U     LE3       A    30    20  Services     Other   \n",
       "9     GP   M  150       U     GT3       T    30    40     Other     Other   \n",
       "\n",
       "       reason guardian  legal_drinker  \n",
       "0      course   mother           True  \n",
       "1      course   father          False  \n",
       "2       other   mother          False  \n",
       "3        home   mother          False  \n",
       "4        home   father          False  \n",
       "5  reputation   mother          False  \n",
       "6        home   mother          False  \n",
       "7        home   mother          False  \n",
       "8        home   mother          False  \n",
       "9        home   mother          False  "
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.applymap(times10).head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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