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'region2'],\n", " dtype='object', length=370)" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "audit_data.columns" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "brexit_age = audit_data[['cut15', 'numage']]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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1771 rows × 2 columns

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" ], "text/plain": [ " cut15 numage\n", "0 1 37\n", "1 1 55\n", "2 2 71\n", "3 1 37\n", "4 1 42\n", "5 1 0\n", "6 1 69\n", "7 1 20\n", "8 1 38\n", "9 2 60\n", "10 1 0\n", "11 1 32\n", "12 3 79\n", "13 2 0\n", "14 1 58\n", "15 1 46\n", "16 1 51\n", "17 2 74\n", "18 1 57\n", "19 2 61\n", "20 2 47\n", "21 2 56\n", "22 1 87\n", "23 2 76\n", "24 3 20\n", "25 2 35\n", "26 1 28\n", "27 1 38\n", "28 2 44\n", "29 2 38\n", "... ... ...\n", "1741 1 35\n", "1742 3 39\n", "1743 1 39\n", "1744 1 44\n", "1745 4 18\n", "1746 1 40\n", "1747 3 60\n", "1748 3 36\n", "1749 1 72\n", "1750 1 70\n", "1751 3 31\n", "1752 3 20\n", "1753 1 67\n", "1754 2 54\n", "1755 2 18\n", "1756 4 18\n", "1757 3 24\n", "1758 2 20\n", "1759 3 36\n", "1760 3 42\n", "1761 1 37\n", "1762 5 19\n", "1763 6 36\n", "1764 1 67\n", "1765 2 40\n", "1766 1 39\n", "1767 3 20\n", "1768 2 31\n", "1769 3 47\n", "1770 3 25\n", "\n", "[1771 rows x 2 columns]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "brexit_age" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.frame.DataFrame" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(brexit_age)" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 37\n", "1 55\n", "2 71\n", "3 37\n", "4 42\n", "5 0\n", "6 69\n", "7 20\n", "8 38\n", "9 60\n", "10 0\n", "11 32\n", "12 79\n", "13 0\n", "14 58\n", "15 46\n", "16 51\n", "17 74\n", "18 57\n", "19 61\n", "20 47\n", "21 56\n", "22 87\n", "23 76\n", "24 20\n", "25 35\n", "26 28\n", "27 38\n", "28 44\n", "29 38\n", " ..\n", "1741 35\n", "1742 39\n", "1743 39\n", "1744 44\n", "1745 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cut15numage
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1757 rows × 2 columns

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" ], "text/plain": [ " cut15 numage\n", "0 1 37\n", "1 1 55\n", "2 2 71\n", "3 1 37\n", "4 1 42\n", "6 1 69\n", "7 1 20\n", "8 1 38\n", "9 2 60\n", "11 1 32\n", "12 3 79\n", "14 1 58\n", "15 1 46\n", "16 1 51\n", "17 2 74\n", "18 1 57\n", "19 2 61\n", "20 2 47\n", "21 2 56\n", "22 1 87\n", "23 2 76\n", "24 3 20\n", "25 2 35\n", "26 1 28\n", "27 1 38\n", "28 2 44\n", "29 2 38\n", "30 1 82\n", "31 1 65\n", "32 1 75\n", "... ... ...\n", "1741 1 35\n", "1742 3 39\n", "1743 1 39\n", "1744 1 44\n", "1745 4 18\n", "1746 1 40\n", "1747 3 60\n", "1748 3 36\n", "1749 1 72\n", "1750 1 70\n", "1751 3 31\n", "1752 3 20\n", "1753 1 67\n", "1754 2 54\n", "1755 2 18\n", "1756 4 18\n", "1757 3 24\n", "1758 2 20\n", "1759 3 36\n", "1760 3 42\n", "1761 1 37\n", "1762 5 19\n", "1763 6 36\n", "1764 1 67\n", "1765 2 40\n", "1766 1 39\n", "1767 3 20\n", "1768 2 31\n", "1769 3 47\n", "1770 3 25\n", "\n", "[1757 rows x 2 columns]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "filtered_brexit = brexit_age.loc[not_zero]\n", "filtered_brexit" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.frame.DataFrame" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(filtered_brexit)" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 1\n", "1 1\n", "2 2\n", "3 1\n", "4 1\n", "6 1\n", "7 1\n", "8 1\n", "9 2\n", "11 1\n", "12 3\n", "14 1\n", "15 1\n", "16 1\n", "17 2\n", "18 1\n", "19 2\n", "20 2\n", "21 2\n", "22 1\n", "23 2\n", "24 3\n", "25 2\n", "26 1\n", "27 1\n", "28 2\n", "29 2\n", "30 1\n", "31 1\n", "32 1\n", " ..\n", "1741 1\n", "1742 3\n", "1743 1\n", "1744 1\n", "1745 4\n", "1746 1\n", "1747 3\n", "1748 3\n", "1749 1\n", "1750 1\n", "1751 3\n", "1752 3\n", "1753 1\n", "1754 2\n", "1755 2\n", "1756 4\n", "1757 3\n", "1758 2\n", "1759 3\n", "1760 3\n", "1761 1\n", "1762 5\n", "1763 6\n", "1764 1\n", "1765 2\n", "1766 1\n", "1767 3\n", "1768 2\n", "1769 3\n", "1770 3\n", "Name: cut15, Length: 1757, dtype: int64" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "brexit_column = filtered_brexit['cut15']\n", "brexit_column" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0 True\n", "1 True\n", "2 False\n", "3 True\n", "4 True\n", "6 True\n", "7 True\n", "8 True\n", "9 False\n", "11 True\n", "12 False\n", "14 True\n", "15 True\n", "16 True\n", "17 False\n", "18 True\n", "19 False\n", "20 False\n", "21 False\n", "22 True\n", "23 False\n", "24 False\n", "25 False\n", "26 True\n", "27 True\n", "28 False\n", "29 False\n", "30 True\n", "31 True\n", "32 True\n", " ... \n", "1741 True\n", "1742 False\n", "1743 True\n", "1744 True\n", "1745 False\n", "1746 True\n", "1747 False\n", "1748 False\n", "1749 True\n", "1750 True\n", "1751 False\n", "1752 False\n", "1753 True\n", "1754 False\n", "1755 False\n", "1756 False\n", "1757 False\n", "1758 False\n", "1759 False\n", "1760 False\n", "1761 True\n", "1762 False\n", "1763 False\n", "1764 True\n", "1765 False\n", "1766 True\n", "1767 False\n", "1768 False\n", "1769 False\n", "1770 False\n", "Name: cut15, Length: 1757, dtype: bool" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "is_remain = brexit_column == 1\n", "is_remain" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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541 rows × 2 columns

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" ], "text/plain": [ " cut15 numage\n", "2 2 71\n", "9 2 60\n", "17 2 74\n", "19 2 61\n", "20 2 47\n", "21 2 56\n", "23 2 76\n", "25 2 35\n", "28 2 44\n", "29 2 38\n", "33 2 24\n", "36 2 53\n", "39 2 79\n", "41 2 64\n", "42 2 51\n", "47 2 31\n", "55 2 65\n", "56 2 28\n", "58 2 31\n", "60 2 21\n", "62 2 36\n", "63 2 49\n", "64 2 64\n", "66 2 49\n", "68 2 56\n", "69 2 69\n", "72 2 50\n", "75 2 51\n", "76 2 51\n", "79 2 60\n", "... ... ...\n", "1604 2 34\n", "1611 2 43\n", "1626 2 45\n", "1636 2 70\n", "1637 2 44\n", "1639 2 44\n", "1650 2 38\n", "1655 2 54\n", "1659 2 55\n", "1661 2 46\n", "1667 2 74\n", "1668 2 35\n", "1670 2 21\n", "1683 2 26\n", "1688 2 41\n", "1696 2 57\n", "1710 2 56\n", "1711 2 40\n", "1713 2 51\n", "1714 2 62\n", "1721 2 52\n", "1726 2 35\n", "1730 2 57\n", "1731 2 60\n", "1739 2 28\n", "1754 2 54\n", "1755 2 18\n", "1758 2 20\n", "1765 2 40\n", "1768 2 31\n", "\n", "[541 rows x 2 columns]" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "leavers = filtered_brexit.loc[is_leave]\n", "leavers" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1315" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "all_voters = len(remainers) + len(leavers)\n", "all_voters" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.4114068441064639" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(leavers) / all_voters" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.6.5" } }, "nbformat": 4, "nbformat_minor": 2 }