notebooks/02_files_and_formats_solutions.ipynb

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    "<center>\n",
    "    <h1>DatatableTon</h1>\n",
    "    💯 datatable exercises\n",
    "    <br>\n",
    "    <br>\n",
    "    <a href='https://github.com/vopani/datatableton/blob/master/LICENSE'>\n",
    "        <img src='https://img.shields.io/badge/license-Apache%202.0-blue.svg?logo=apache'>\n",
    "    </a>\n",
    "    <a href='https://github.com/vopani/datatableton'>\n",
    "        <img src='https://img.shields.io/github/stars/vopani/datatableton?color=yellowgreen&logo=github'>\n",
    "    </a>\n",
    "    <a href='https://twitter.com/vopani'>\n",
    "        <img src='https://img.shields.io/twitter/follow/vopani'>\n",
    "    </a>\n",
    "</center>"
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   "source": [
    "<center>\n",
    "    This is Set 2: Files and Formats (Exercises 11-20) of <b>DatatableTon</b>: <i>💯 datatable exercises</i>\n",
    "    <br>\n",
    "    You can find all the exercises and solutions on <a href=\"https://github.com/vopani/datatableton#exercises-\">GitHub</a>\n",
    "</center>"
   ]
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   "source": [
    "**Prerequisites**\n",
    "* The `datatable` package should be upgraded to the latest version (or v1.0.0+).\n",
    "* Additional packages `numpy`, `pandas` and `pyarrow` should be installed.\n",
    "* Small example values along with the sample dataset [datatableton_sample.zip](https://github.com/vopani/datatableton/blob/main/data/datatableton_sample.zip) will be used for the exercises."
   ]
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     "text": [
      "Requirement already satisfied: pip in /opt/conda/lib/python3.7/site-packages (21.1.2)\r\n",
      "Collecting pip\r\n",
      "  Downloading pip-21.1.3-py3-none-any.whl (1.5 MB)\r\n",
      "\u001b[K     |████████████████████████████████| 1.5 MB 4.3 MB/s \r\n",
      "\u001b[?25hInstalling collected packages: pip\r\n",
      "  Attempting uninstall: pip\r\n",
      "    Found existing installation: pip 21.1.2\r\n",
      "    Uninstalling pip-21.1.2:\r\n",
      "      Successfully uninstalled pip-21.1.2\r\n",
      "Successfully installed pip-21.1.3\r\n",
      "\u001b[33mWARNING: Running pip as root will break packages and permissions. You should install packages reliably by using venv: https://pip.pypa.io/warnings/venv\u001b[0m\r\n",
      "Requirement already satisfied: datatable in /opt/conda/lib/python3.7/site-packages (1.0.0)\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n",
      "Requirement already satisfied: numpy in /opt/conda/lib/python3.7/site-packages (1.19.5)\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n",
      "Requirement already satisfied: pandas in /opt/conda/lib/python3.7/site-packages (1.2.4)\r\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /opt/conda/lib/python3.7/site-packages (from pandas) (2.8.1)\r\n",
      "Requirement already satisfied: pytz>=2017.3 in /opt/conda/lib/python3.7/site-packages (from pandas) (2021.1)\r\n",
      "Requirement already satisfied: numpy>=1.16.5 in /opt/conda/lib/python3.7/site-packages (from pandas) (1.19.5)\r\n",
      "Requirement already satisfied: six>=1.5 in /opt/conda/lib/python3.7/site-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n",
      "Requirement already satisfied: pyarrow in /opt/conda/lib/python3.7/site-packages (4.0.0)\r\n",
      "Requirement already satisfied: numpy>=1.16.6 in /opt/conda/lib/python3.7/site-packages (from pyarrow) (1.19.5)\r\n",
      "\u001b[33mWARNING: Running pip as the 'root' user can result in broken permissions and conflicting behaviour with the system package manager. It is recommended to use a virtual environment instead: https://pip.pypa.io/warnings/venv\u001b[0m\r\n",
      "--2021-07-22 05:39:51--  https://raw.githubusercontent.com/vopani/datatableton/main/data/datatableton_sample.zip\r\n",
      "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.109.133, 185.199.111.133, 185.199.108.133, ...\r\n",
      "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.109.133|:443... connected.\r\n",
      "HTTP request sent, awaiting response... 200 OK\r\n",
      "Length: 3834 (3.7K) [application/zip]\r\n",
      "Saving to: ‘datatableton_sample.zip’\r\n",
      "\r\n",
      "datatableton_sample 100%[===================>]   3.74K  --.-KB/s    in 0s      \r\n",
      "\r\n",
      "2021-07-22 05:39:51 (48.1 MB/s) - ‘datatableton_sample.zip’ saved [3834/3834]\r\n",
      "\r\n"
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   "source": [
    "!python3 -m pip install -U pip\n",
    "!python3 -m pip install -U datatable\n",
    "!python3 -m pip install numpy\n",
    "!python3 -m pip install pandas\n",
    "!python3 -m pip install pyarrow\n",
    "!wget https://raw.githubusercontent.com/vopani/datatableton/main/data/datatableton_sample.zip"
   ]
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   "source": [
    "import datatable as dt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import pyarrow as pa\n",
    "\n",
    "dtable = dt.Frame(f1=range(10), f2=['Y', 'O', 'U', 'C', 'A', 'N', 'D', 'O', 'I', 'T'])\n",
    "dframe = pd.DataFrame({'v1': range(11), 'v2': ['N', 'E', 'V', 'E', 'R', 'G', 'I', 'V', 'E', 'U', 'P']})\n",
    "nparray = np.array([[0, 'C'], [1, 'O'], [2, 'D'], [3, 'E']])\n",
    "patable = pa.Table.from_pandas(dframe)\n",
    "dlist = [range(4), ['D', 'A', 'T', 'A']]\n",
    "ddict = {'x': range(6), 'y': ['P', 'Y', 'T', 'H', 'O', 'N']}\n",
    "dtup = [(0, 'L'), (1, 'E'), (2, 'A'), (3, 'R'), (4, 'N')]"
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   "source": [
    "**Exercise 11: Write `dtable` to a csv named `data.csv`, to a compressed gz csv named `data.gz` and to a jay named `data.jay`**"
   ]
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   "source": [
    "dtable.to_csv('data.csv')\n",
    "dtable.to_csv('data.gz', compression='gzip')\n",
    "dtable.to_jay('data.jay')"
   ]
  },
  {
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   "source": [
    "**Exercise 12: Read `data.csv` and assign it to `data_csv`, read `data.gz` and assign it to `data_gz`, read `data.jay` and assign it to `data_jay`**"
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       "    <tr><td class='row_index'>3</td><td>3</td><td>C</td></tr>\n",
       "    <tr><td class='row_index'>4</td><td>4</td><td>A</td></tr>\n",
       "    <tr><td class='row_index'>5</td><td>5</td><td>N</td></tr>\n",
       "    <tr><td class='row_index'>6</td><td>6</td><td>D</td></tr>\n",
       "    <tr><td class='row_index'>7</td><td>7</td><td>O</td></tr>\n",
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       "    <tr><td class='row_index'>9</td><td>9</td><td>T</td></tr>\n",
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    "data_csv = dt.fread('data.csv')\n",
    "data_csv"
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       "  <div class='footer'>\n",
       "    <div class='frame_dimensions'>10 rows &times; 2 columns</div>\n",
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   "source": [
    "data_gz = dt.fread('data.gz')\n",
    "data_gz"
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       "  <div class='footer'>\n",
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     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_jay = dt.fread('data.jay')\n",
    "data_jay"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19efcee4",
   "metadata": {
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     "start_time": "2021-07-22T05:39:52.287688",
     "status": "completed"
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    "tags": []
   },
   "source": [
    "**Exercise 13: Read data from this URL: [https://raw.githubusercontent.com/vopani/datatableton/main/data/datatableton_sample.csv](https://raw.githubusercontent.com/vopani/datatableton/main/data/datatableton_sample.csv) and assign it to `data_url`**"
   ]
  },
  {
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       "    <tr class='colnames'><td class='row_index'></td><th>timestamp</th><th>user</th><th>age</th><th>product</th><th>category</th><th>price</th><th>quantity</th></tr>\n",
       "    <tr class='coltypes'><td class='row_index'></td><td class='time' title='time64'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='int' title='int32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='str' title='str32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='float' title='float64'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td><td class='int' title='int32'>&#x25AA;&#x25AA;&#x25AA;&#x25AA;</td></tr>\n",
       "  </thead>\n",
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       "    <tr><td class='row_index'>0</td><td>2017-01-01<span class=sp>T</span>13:22:41</td><td>U1</td><td>21</td><td>Eggs</td><td>Dairy</td><td>2.3</td><td>2</td></tr>\n",
       "    <tr><td class='row_index'>1</td><td>2017-05-22<span class=sp>T</span>09:54:21</td><td>U2</td><td>41</td><td>Bread</td><td><span class=na>NA</span></td><td>0.6</td><td>2</td></tr>\n",
       "    <tr><td class='row_index'>2</td><td>2018-11-09<span class=sp>T</span>15:00:01</td><td>U3</td><td><span class=na>NA</span></td><td>Banana</td><td>Fruit</td><td>0.7</td><td>1</td></tr>\n",
       "    <tr><td class='row_index'>3</td><td>2018-12-24<span class=sp>T</span>23:03:33</td><td>U1</td><td>21</td><td>Water</td><td>Drink</td><td>0.2</td><td>1</td></tr>\n",
       "    <tr><td class='row_index'>4</td><td>2019-03-03<span class=sp>T</span>06:21:58</td><td>U4</td><td>19</td><td>Eggs</td><td>Dairy</td><td>2.3</td><td>2</td></tr>\n",
       "    <tr><td class='row_index'>5</td><td>2019-06-17<span class=sp>T</span>16:13:39</td><td>U5</td><td>25</td><td>Grapes</td><td>Fruit</td><td>1.5</td><td>1</td></tr>\n",
       "    <tr><td class='row_index'>6</td><td>2019-07-28<span class=sp>T</span>21:03:11</td><td>U5</td><td>25</td><td>Bread</td><td><span class=na>NA</span></td><td>0.6</td><td>1</td></tr>\n",
       "    <tr><td class='row_index'>7</td><td>2019-12-05<span class=sp>T</span>04:15:42</td><td>U1</td><td>21</td><td>Banana</td><td>Fruit</td><td>0.7</td><td>2</td></tr>\n",
       "    <tr><td class='row_index'>8</td><td>2020-02-02<span class=sp>T</span>03:45:34</td><td>U3</td><td><span class=na>NA</span></td><td>Banana</td><td>Fruit</td><td>0.7</td><td>1</td></tr>\n",
       "    <tr><td class='row_index'>9</td><td>2020-03-05<span class=sp>T</span>07:09:12</td><td>U4</td><td>19</td><td>Grapes</td><td>Fruit</td><td>1.5</td><td>1</td></tr>\n",
       "    <tr><td class='row_index'>10</td><td>2020-03-22<span class=sp>T</span>19:29:38</td><td>U1</td><td>21</td><td>Water</td><td>Drink</td><td>0.2</td><td>1</td></tr>\n",
       "    <tr><td class='row_index'>11</td><td>2020-03-30<span class=sp>T</span>09:44:30</td><td>U4</td><td>19</td><td>Water</td><td>Drink</td><td>0.2</td><td>1</td></tr>\n",
       "    <tr><td class='row_index'>12</td><td>2020-04-01<span class=sp>T</span>13:21:41</td><td>U1</td><td>21</td><td>Banana</td><td>Fruit</td><td>0.7</td><td>2</td></tr>\n",
       "    <tr><td class='row_index'>13</td><td>2020-07-08<span class=sp>T</span>11:45:25</td><td>U2</td><td>41</td><td>Grapes</td><td>Fruit</td><td>1.5</td><td>1</td></tr>\n",
       "    <tr><td class='row_index'>14</td><td>2020-11-19<span class=sp>T</span>18:51:22</td><td>U5</td><td>25</td><td>Water</td><td>Drink</td><td>0.2</td><td>1</td></tr>\n",
       "    <tr><td class='row_index'>15</td><td>2020-12-03<span class=sp>T</span>16:23:48</td><td>U3</td><td><span class=na>NA</span></td><td>Banana</td><td>Fruit</td><td>0.5</td><td>3</td></tr>\n",
       "    <tr><td class='row_index'>16</td><td>2021-02-03<span class=sp>T</span>01:14:40</td><td>U5</td><td>25</td><td>Eggs</td><td>Dairy</td><td>2.1</td><td>4</td></tr>\n",
       "    <tr><td class='row_index'>17</td><td>2021-05-26<span class=sp>T</span>22:42:15</td><td>U3</td><td><span class=na>NA</span></td><td>Bread</td><td><span class=na>NA</span></td><td>0.6</td><td>1</td></tr>\n",
       "    <tr><td class='row_index'>18</td><td>2021-06-14<span class=sp>T</span>15:49:28</td><td>U4</td><td>19</td><td>Eggs</td><td>Dairy</td><td>2.3</td><td>2</td></tr>\n",
       "    <tr><td class='row_index'>19</td><td>2021-07-01<span class=sp>T</span>04:37:31</td><td>U4</td><td>19</td><td>Water</td><td>Drink</td><td>0.3</td><td>1</td></tr>\n",
       "  </tbody>\n",
       "  </table>\n",
       "  <div class='footer'>\n",
       "    <div class='frame_dimensions'>20 rows &times; 7 columns</div>\n",
       "  </div>\n",
       "</div>\n"
      ],
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       "<Frame#7f2949b0b540 20x7>"
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     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_url = dt.fread(url='https://raw.githubusercontent.com/vopani/datatableton/main/data/datatableton_sample.csv')\n",
    "data_url"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8d807e06",
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    },
    "tags": []
   },
   "source": [
    "**Exercise 14: Read `users.csv` from `datatableton_sample.zip` and assign it to `data_zip`**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
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    {
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       "  <thead>\n",
       "    <tr class='colnames'><td class='row_index'></td><th>Person</th><th>Category ID</th><th>Return ID</th><th>Region</th></tr>\n",
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       "  <div class='footer'>\n",
       "    <div class='frame_dimensions'>14 rows &times; 4 columns</div>\n",
       "  </div>\n",
       "</div>\n"
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       "<Frame#7f294a419720 14x4>"
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     },
     "execution_count": 8,
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   ],
   "source": [
    "data_zip = dt.fread('datatableton_sample.zip/users.csv')\n",
    "data_zip"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f0d181aa",
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   },
   "source": [
    "**Exercise 15: Create a dt.Frame `data_pd` from the pd.DataFrame `dframe` and create a pd.DataFrame `pd_data` from the dt.Frame `data_pd`**"
   ]
  },
  {
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   "outputs": [
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    "data_pd = dt.Frame(dframe)\n",
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       "    v1 v2\n",
       "0    0  N\n",
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       "3    3  E\n",
       "4    4  R\n",
       "5    5  G\n",
       "6    6  I\n",
       "7    7  V\n",
       "8    8  E\n",
       "9    9  U\n",
       "10  10  P"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd_data = data_pd.to_pandas()\n",
    "pd_data"
   ]
  },
  {
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   "id": "31d6ebf4",
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   },
   "source": [
    "**Exercise 16: Create a dt.Frame `data_np` from the np.array `nparray` and create a np.array `np_data` from the dt.Frame `data_np`**"
   ]
  },
  {
   "cell_type": "code",
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     "execution_count": 11,
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   ],
   "source": [
    "data_np = dt.Frame(nparray)\n",
    "data_np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "985cd5bf",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([['0', 'C'],\n",
       "       ['1', 'O'],\n",
       "       ['2', 'D'],\n",
       "       ['3', 'E']], dtype=object)"
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     "execution_count": 12,
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   "source": [
    "np_data = data_np.to_numpy()\n",
    "np_data"
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    "tags": []
   },
   "source": [
    "**Exercise 17: Create a dt.Frame `data_ls` from the list `dlist` and create a list `ls_data` from the dt.Frame `data_ls`**"
   ]
  },
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    "data_ls = dt.Frame(dlist)\n",
    "data_ls"
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    {
     "data": {
      "text/plain": [
       "[[0, 1, 2, 3], ['D', 'A', 'T', 'A']]"
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     "execution_count": 14,
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    "ls_data = data_ls.to_list()\n",
    "ls_data"
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   "source": [
    "**Exercise 18: Create a dt.Frame `data_dc` from the dictionary `ddict` and create a dictionary `dc_data` from the dt.Frame `data_dc`**"
   ]
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    "data_dc = dt.Frame(ddict)\n",
    "data_dc"
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    {
     "data": {
      "text/plain": [
       "{'x': [0, 1, 2, 3, 4, 5], 'y': ['P', 'Y', 'T', 'H', 'O', 'N']}"
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    "dc_data = data_dc.to_dict()\n",
    "dc_data"
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  {
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   },
   "source": [
    "**Exercise 19: Create a dt.Frame `data_tp` from the tuples `dtup` and create tuples `tp_data` from the dt.Frame `data_tp`**"
   ]
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    "data_tp = dt.Frame(dtup)\n",
    "data_tp"
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   "execution_count": 18,
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    {
     "data": {
      "text/plain": [
       "[(0, 'L'), (1, 'E'), (2, 'A'), (3, 'R'), (4, 'N')]"
      ]
     },
     "execution_count": 18,
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   ],
   "source": [
    "tp_data = data_tp.to_tuples()\n",
    "tp_data"
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  },
  {
   "cell_type": "markdown",
   "id": "13bf297a",
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   },
   "source": [
    "**Exercise 20: Create a dt.Frame `data_pa` from the pyarrow.Table `patable` and create a pyarrow.Table `pa_data` from the dt.Frame `data_pa`**"
   ]
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   "cell_type": "code",
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   "source": [
    "data_pa = dt.Frame(patable)\n",
    "data_pa"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "34a99013",
   "metadata": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "pyarrow.Table\n",
       "v1: int64\n",
       "v2: string"
      ]
     },
     "execution_count": 20,
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    "pa_data = data_pa.to_arrow()\n",
    "pa_data"
   ]
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  {
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   "source": [
    "✅ This completes Set 2: Files and Formats (Exercises 11-20) of **DatatableTon**: *💯 datatable exercises*\n",
    "\n",
    "#### Set 03 • Data Selection • Beginner • Exercises 21-30\n",
    "\n",
    "| Style | Colab | Kaggle | Binder | GitHub |\n",
    "| ----- | ----- | ------ | ------ | ------ |\n",
    "| Exercises | [![Open in Colab](https://img.shields.io/static/v1?label=&message=Open%20in%20Colab&labelColor=grey&color=blue&logo=google-colab)](https://colab.research.google.com/github/vopani/datatableton/blob/main/notebooks/03_data_selection_exercises.ipynb) | [![Open in Kaggle](https://img.shields.io/static/v1?label=&message=Open%20in%20Kaggle&labelColor=grey&color=blue&logo=kaggle)](https://www.kaggle.com/rohanrao/datatableton-data-selection-exercises) | [![Open in Binder](https://img.shields.io/static/v1?label=&message=Open%20in%20Binder&labelColor=grey&color=blue&logo=data:image/png;base64,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)](https://mybinder.org/v2/gh/vopani/datatableton/main?filepath=notebooks%2F03_data_selection_exercises.ipynb) | [![Open in GitHub](https://img.shields.io/static/v1?label=&message=Open%20in%20GitHub&labelColor=grey&color=blue&logo=github)](https://github.com/vopani/datatableton/blob/main/notebooks/03_data_selection_exercises.ipynb) |\n",
    "| Solutions | [![Open in Colab](https://img.shields.io/static/v1?label=&message=Open%20in%20Colab&labelColor=grey&color=blue&logo=google-colab)](https://colab.research.google.com/github/vopani/datatableton/blob/main/notebooks/03_data_selection_solutions.ipynb) | [![Open in Kaggle](https://img.shields.io/static/v1?label=&message=Open%20in%20Kaggle&labelColor=grey&color=blue&logo=kaggle)](https://www.kaggle.com/rohanrao/datatableton-data-selection-solutions) | [![Open in Binder](https://img.shields.io/static/v1?label=&message=Open%20in%20Binder&labelColor=grey&color=blue&logo=data:image/png;base64,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)](https://mybinder.org/v2/gh/vopani/datatableton/main?filepath=notebooks%2F03_data_selection_solutions.ipynb) | [![Open in GitHub](https://img.shields.io/static/v1?label=&message=Open%20in%20GitHub&labelColor=grey&color=blue&logo=github)](https://github.com/vopani/datatableton/blob/main/notebooks/03_data_selection_solutions.ipynb) |\n",
    "\n",
    "You can find all the exercises and solutions on [GitHub](https://github.com/vopani/datatableton#exercises-)"
   ]
  }
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