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   "source": [
    "## Exercise 4 - SQLite"
   ]
  },
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   "source": [
    "1 Add the necessary libraries for SQLite3 and make a database named `test.db`."
   ]
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    "2 Create a connection to the database."
   ]
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   "execution_count": null,
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   "outputs": [],
   "source": [
    "#"
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    "3 Make the following table in the database, named `people`:\n",
    "    \n",
    "| name | age | shoe_size |\n",
    "| ------------- |-------------| -----|\n",
    "| Prank | 88 | 47 |\n",
    "| Matt | 34 | 39 |\n",
    "| Ilona | 41 | 36 |\n",
    "| Sonja | 32 | 37 |\n",
    "\n"
   ]
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   "execution_count": 4,
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   "outputs": [],
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    "#"
   ]
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   "source": [
    "4 When the Table has been created, convert the table to the Pandas DataFrame object and add the column `hat_size' to it, then fill that column with the values ​​you choose."
   ]
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   "execution_count": 5,
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   "outputs": [],
   "source": [
    "#"
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   "source": [
    "5 Make a copy of the DataFrame object with the name `person_table_copy` and add a row to it with the values ​you choose. Now save this modified dataframe object in the database as `person_table_copy` (will be created next to the previous table)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#"
   ]
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "6 Finally, save the database changes and close the connection."
   ]
  },
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   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#"
   ]
  }
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