9.8 Shuffling and Random Sampling.ipynb

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   "source": [
    "![title](img/cover4.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<center><em>Copyright! This material is protected, please do not copy or distribute.    by:Taher Assaf</em></center>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "<h1 align=\"center\">Udemy course : Python Bootcamp for Data Science 2021 Numpy Pandas & Seaborn</h1> \n",
    "\n",
    "***"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9.8 Shuffling and Random Sampling"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "First we import numpy and pandas library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we create a simple series 10 integers using numpy function **random.randint()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1     8\n",
       "2    17\n",
       "3     4\n",
       "4    16\n",
       "5     5\n",
       "6     7\n",
       "7    15\n",
       "8     4\n",
       "9    12\n",
       "dtype: int32"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1 = pd.Series(np.random.randint(20, size =10))\n",
    "ser1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can randomly shuffle the values in this series using the function **sample()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1     8\n",
       "8     4\n",
       "4    16\n",
       "3     4\n",
       "9    12\n",
       "6     7\n",
       "0    10\n",
       "7    15\n",
       "2    17\n",
       "5     5\n",
       "dtype: int32"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1.sample(frac=1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The index can be sorted again after the shuffling using the function **reset_index()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     4\n",
       "1     8\n",
       "2    15\n",
       "3     4\n",
       "4    17\n",
       "5     5\n",
       "6    16\n",
       "7    12\n",
       "8    10\n",
       "9     7\n",
       "dtype: int32"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1 = ser1.sample(frac=1).reset_index(drop=True)\n",
    "ser1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To take a random sample or a random subset of your dataset we change the argument **frac** into the desired fraction, here we choose 50% of the data: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2    15\n",
       "8    10\n",
       "9     7\n",
       "3     4\n",
       "7    12\n",
       "dtype: int32"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample1 = ser1.sample(frac= .5)\n",
    "sample1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     4\n",
       "1    10\n",
       "2     5\n",
       "3     8\n",
       "4     4\n",
       "dtype: int32"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample1 = ser1.sample(frac =0.5).reset_index(drop=True)\n",
    "sample1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we read a dataframe from a csv file using the function **pd.read_csv()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "hide_input": false
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       "      <td>1. Male</td>\n",
       "      <td>1. Never Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>1. &lt; HS Grad</td>\n",
       "      <td>75.043154</td>\n",
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       "      <td>1. Never Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>4. College Grad</td>\n",
       "      <td>70.476020</td>\n",
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       "      <td>154.685293</td>\n",
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       "      <td>1. White</td>\n",
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       "      <td>75.043154</td>\n",
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       "      <th>2995</th>\n",
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       "      <td>44</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>2. Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>3. Some College</td>\n",
       "      <td>154.685293</td>\n",
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       "      <td>2007</td>\n",
       "      <td>30</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>2. Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>2. HS Grad</td>\n",
       "      <td>99.689464</td>\n",
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       "      <td>27</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>2. Married</td>\n",
       "      <td>2. Black</td>\n",
       "      <td>1. &lt; HS Grad</td>\n",
       "      <td>66.229408</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2998</th>\n",
       "      <td>2005</td>\n",
       "      <td>27</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>1. Never Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>3. Some College</td>\n",
       "      <td>87.981033</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2999</th>\n",
       "      <td>2009</td>\n",
       "      <td>55</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>5. Separated</td>\n",
       "      <td>1. White</td>\n",
       "      <td>2. HS Grad</td>\n",
       "      <td>90.481913</td>\n",
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       "      year  age      sex            maritl      race        education  \\\n",
       "0     2006   18  1. Male  1. Never Married  1. White     1. < HS Grad   \n",
       "1     2004   24  1. Male  1. Never Married  1. White  4. College Grad   \n",
       "2     2003   45  1. Male        2. Married  1. White  3. Some College   \n",
       "3     2003   43  1. Male        2. Married  3. Asian  4. College Grad   \n",
       "4     2005   50  1. Male       4. Divorced  1. White       2. HS Grad   \n",
       "...    ...  ...      ...               ...       ...              ...   \n",
       "2995  2008   44  1. Male        2. Married  1. White  3. Some College   \n",
       "2996  2007   30  1. Male        2. Married  1. White       2. HS Grad   \n",
       "2997  2005   27  1. Male        2. Married  2. Black     1. < HS Grad   \n",
       "2998  2005   27  1. Male  1. Never Married  1. White  3. Some College   \n",
       "2999  2009   55  1. Male      5. Separated  1. White       2. HS Grad   \n",
       "\n",
       "            wage  \n",
       "0      75.043154  \n",
       "1      70.476020  \n",
       "2     130.982177  \n",
       "3     154.685293  \n",
       "4      75.043154  \n",
       "...          ...  \n",
       "2995  154.685293  \n",
       "2996   99.689464  \n",
       "2997   66.229408  \n",
       "2998   87.981033  \n",
       "2999   90.481913  \n",
       "\n",
       "[3000 rows x 7 columns]"
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data/ex3.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To select a random sample with 20% of this dataframe, we set the fraction argument to be **frac = 0.2**: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
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       "      <td>1. Male</td>\n",
       "      <td>2. Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>2. HS Grad</td>\n",
       "      <td>82.682399</td>\n",
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       "      <th>2801</th>\n",
       "      <td>2007</td>\n",
       "      <td>45</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>2. Married</td>\n",
       "      <td>1. White</td>\n",
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       "      <th>1823</th>\n",
       "      <td>2006</td>\n",
       "      <td>22</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>1. Never Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>4. College Grad</td>\n",
       "      <td>114.475713</td>\n",
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       "      <td>2004</td>\n",
       "      <td>49</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>1. Never Married</td>\n",
       "      <td>4. Other</td>\n",
       "      <td>1. &lt; HS Grad</td>\n",
       "      <td>87.981033</td>\n",
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       "      <td>1. Male</td>\n",
       "      <td>2. Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>4. College Grad</td>\n",
       "      <td>92.036323</td>\n",
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       "      <td>2005</td>\n",
       "      <td>45</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>4. Divorced</td>\n",
       "      <td>1. White</td>\n",
       "      <td>2. HS Grad</td>\n",
       "      <td>94.072715</td>\n",
       "    </tr>\n",
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       "      <th>1850</th>\n",
       "      <td>2003</td>\n",
       "      <td>27</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>5. Separated</td>\n",
       "      <td>1. White</td>\n",
       "      <td>3. Some College</td>\n",
       "      <td>95.230713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2442</th>\n",
       "      <td>2004</td>\n",
       "      <td>40</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>2. Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>2. HS Grad</td>\n",
       "      <td>75.355679</td>\n",
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       "      <th>2280</th>\n",
       "      <td>2004</td>\n",
       "      <td>58</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>2. Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>3. Some College</td>\n",
       "      <td>148.413159</td>\n",
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      "text/plain": [
       "      year  age      sex            maritl      race           education  \\\n",
       "2713  2008   57  1. Male        2. Married  1. White          2. HS Grad   \n",
       "2801  2007   45  1. Male        2. Married  1. White     4. College Grad   \n",
       "1823  2006   22  1. Male  1. Never Married  1. White     4. College Grad   \n",
       "358   2009   41  1. Male       4. Divorced  1. White  5. Advanced Degree   \n",
       "2482  2004   49  1. Male  1. Never Married  4. Other        1. < HS Grad   \n",
       "...    ...  ...      ...               ...       ...                 ...   \n",
       "763   2006   31  1. Male        2. Married  1. White     4. College Grad   \n",
       "1128  2005   45  1. Male       4. Divorced  1. White          2. HS Grad   \n",
       "1850  2003   27  1. Male      5. Separated  1. White     3. Some College   \n",
       "2442  2004   40  1. Male        2. Married  1. White          2. HS Grad   \n",
       "2280  2004   58  1. Male        2. Married  1. White     3. Some College   \n",
       "\n",
       "            wage  \n",
       "2713   82.682399  \n",
       "2801  130.983694  \n",
       "1823  114.475713  \n",
       "358    38.605915  \n",
       "2482   87.981033  \n",
       "...          ...  \n",
       "763    92.036323  \n",
       "1128   94.072715  \n",
       "1850   95.230713  \n",
       "2442   75.355679  \n",
       "2280  148.413159  \n",
       "\n",
       "[600 rows x 7 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample2 = data.sample(frac =0.2)\n",
    "sample2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also here the index can be sorted using the function **reset_index()**:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
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       "      <th>0</th>\n",
       "      <td>2008</td>\n",
       "      <td>23</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>2. Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>4. College Grad</td>\n",
       "      <td>36.673704</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2005</td>\n",
       "      <td>54</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>2. Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>2. HS Grad</td>\n",
       "      <td>158.173662</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2003</td>\n",
       "      <td>57</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>2. Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>4. College Grad</td>\n",
       "      <td>268.266292</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2007</td>\n",
       "      <td>50</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>1. Never Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>2. HS Grad</td>\n",
       "      <td>50.411923</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2009</td>\n",
       "      <td>50</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>1. Never Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>5. Advanced Degree</td>\n",
       "      <td>157.700528</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>595</th>\n",
       "      <td>2007</td>\n",
       "      <td>59</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>2. Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>4. College Grad</td>\n",
       "      <td>145.805163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>596</th>\n",
       "      <td>2004</td>\n",
       "      <td>63</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>2. Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>2. HS Grad</td>\n",
       "      <td>134.705375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>597</th>\n",
       "      <td>2003</td>\n",
       "      <td>49</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>1. Never Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>4. College Grad</td>\n",
       "      <td>130.982177</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>598</th>\n",
       "      <td>2006</td>\n",
       "      <td>28</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>5. Separated</td>\n",
       "      <td>1. White</td>\n",
       "      <td>1. &lt; HS Grad</td>\n",
       "      <td>90.481913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>2006</td>\n",
       "      <td>29</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>2. Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>3. Some College</td>\n",
       "      <td>108.390640</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>600 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     year  age      sex            maritl      race           education  \\\n",
       "0    2008   23  1. Male        2. Married  1. White     4. College Grad   \n",
       "1    2005   54  1. Male        2. Married  1. White          2. HS Grad   \n",
       "2    2003   57  1. Male        2. Married  1. White     4. College Grad   \n",
       "3    2007   50  1. Male  1. Never Married  1. White          2. HS Grad   \n",
       "4    2009   50  1. Male  1. Never Married  1. White  5. Advanced Degree   \n",
       "..    ...  ...      ...               ...       ...                 ...   \n",
       "595  2007   59  1. Male        2. Married  1. White     4. College Grad   \n",
       "596  2004   63  1. Male        2. Married  1. White          2. HS Grad   \n",
       "597  2003   49  1. Male  1. Never Married  1. White     4. College Grad   \n",
       "598  2006   28  1. Male      5. Separated  1. White        1. < HS Grad   \n",
       "599  2006   29  1. Male        2. Married  1. White     3. Some College   \n",
       "\n",
       "           wage  \n",
       "0     36.673704  \n",
       "1    158.173662  \n",
       "2    268.266292  \n",
       "3     50.411923  \n",
       "4    157.700528  \n",
       "..          ...  \n",
       "595  145.805163  \n",
       "596  134.705375  \n",
       "597  130.982177  \n",
       "598   90.481913  \n",
       "599  108.390640  \n",
       "\n",
       "[600 rows x 7 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample3 = data.sample(frac =0.2).reset_index(drop=True)\n",
    "sample3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also select a random sample based on the number of rows rather than fraction, here we choose a sybset of 100 rows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "hide_input": false
   },
   "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>year</th>\n",
       "      <th>age</th>\n",
       "      <th>sex</th>\n",
       "      <th>maritl</th>\n",
       "      <th>race</th>\n",
       "      <th>education</th>\n",
       "      <th>wage</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2003</td>\n",
       "      <td>45</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>2. Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>4. College Grad</td>\n",
       "      <td>133.232351</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2007</td>\n",
       "      <td>43</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>2. Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>2. HS Grad</td>\n",
       "      <td>65.110854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2006</td>\n",
       "      <td>51</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>2. Married</td>\n",
       "      <td>2. Black</td>\n",
       "      <td>2. HS Grad</td>\n",
       "      <td>94.072715</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2003</td>\n",
       "      <td>20</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>1. Never Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>2. HS Grad</td>\n",
       "      <td>75.355679</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2004</td>\n",
       "      <td>54</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>2. Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>2. HS Grad</td>\n",
       "      <td>94.072715</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>95</th>\n",
       "      <td>2008</td>\n",
       "      <td>54</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>2. Married</td>\n",
       "      <td>3. Asian</td>\n",
       "      <td>2. HS Grad</td>\n",
       "      <td>95.230713</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96</th>\n",
       "      <td>2007</td>\n",
       "      <td>32</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>2. Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>1. &lt; HS Grad</td>\n",
       "      <td>65.110854</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97</th>\n",
       "      <td>2009</td>\n",
       "      <td>57</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>2. Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>5. Advanced Degree</td>\n",
       "      <td>109.833986</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98</th>\n",
       "      <td>2004</td>\n",
       "      <td>33</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>2. Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>4. College Grad</td>\n",
       "      <td>100.086742</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>2003</td>\n",
       "      <td>40</td>\n",
       "      <td>1. Male</td>\n",
       "      <td>2. Married</td>\n",
       "      <td>1. White</td>\n",
       "      <td>2. HS Grad</td>\n",
       "      <td>182.020621</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>100 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    year  age      sex            maritl      race           education  \\\n",
       "0   2003   45  1. Male        2. Married  1. White     4. College Grad   \n",
       "1   2007   43  1. Male        2. Married  1. White          2. HS Grad   \n",
       "2   2006   51  1. Male        2. Married  2. Black          2. HS Grad   \n",
       "3   2003   20  1. Male  1. Never Married  1. White          2. HS Grad   \n",
       "4   2004   54  1. Male        2. Married  1. White          2. HS Grad   \n",
       "..   ...  ...      ...               ...       ...                 ...   \n",
       "95  2008   54  1. Male        2. Married  3. Asian          2. HS Grad   \n",
       "96  2007   32  1. Male        2. Married  1. White        1. < HS Grad   \n",
       "97  2009   57  1. Male        2. Married  1. White  5. Advanced Degree   \n",
       "98  2004   33  1. Male        2. Married  1. White     4. College Grad   \n",
       "99  2003   40  1. Male        2. Married  1. White          2. HS Grad   \n",
       "\n",
       "          wage  \n",
       "0   133.232351  \n",
       "1    65.110854  \n",
       "2    94.072715  \n",
       "3    75.355679  \n",
       "4    94.072715  \n",
       "..         ...  \n",
       "95   95.230713  \n",
       "96   65.110854  \n",
       "97  109.833986  \n",
       "98  100.086742  \n",
       "99  182.020621  \n",
       "\n",
       "[100 rows x 7 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sample4 = data.sample(n=100).reset_index(drop=True)\n",
    "sample4"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***\n",
    "\n",
    "<h1 align=\"center\">Thank You</h1> \n",
    "\n",
    "***"
   ]
  }
 ],
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