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" ], "text/plain": [ " cut15 numage\n", "0 1 37\n", "1 1 55\n", "3 1 37\n", "4 1 42\n", "6 1 69\n", "7 1 20\n", "8 1 38\n", "11 1 32\n", "14 1 58\n", "15 1 46\n", "16 1 51\n", "18 1 57\n", "22 1 87\n", "26 1 28\n", "27 1 38\n", "30 1 82\n", "31 1 65\n", "32 1 75\n", "34 1 63\n", "40 1 68\n", "43 1 53\n", "45 1 64\n", "48 1 59\n", "49 1 41\n", "50 1 76\n", "51 1 60\n", "53 1 22\n", "54 1 52\n", "61 1 22\n", "65 1 26\n", "... ... ...\n", "1701 1 30\n", "1702 1 33\n", "1703 1 28\n", "1706 1 24\n", "1708 1 18\n", "1709 1 20\n", "1712 1 40\n", "1715 1 19\n", "1716 1 36\n", "1717 1 33\n", "1719 1 69\n", "1720 1 47\n", "1723 1 46\n", "1727 1 47\n", "1728 1 65\n", "1729 1 40\n", "1733 1 79\n", "1736 1 37\n", "1737 1 43\n", "1738 1 66\n", "1741 1 35\n", "1743 1 39\n", "1744 1 44\n", "1746 1 40\n", "1749 1 72\n", "1750 1 70\n", "1753 1 67\n", "1761 1 37\n", "1764 1 67\n", "1766 1 39\n", "\n", "[774 rows x 2 columns]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "remainers = filtered.loc[filtered['cut15'] == 1]\n", "remainers" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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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": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "leavers = filtered.loc[filtered['cut15'] == 2]\n", "leavers\n" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [], "source": [ "import statistics" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "51.715341959334566" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "statistics.mean(leavers['numage'])" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [], "source": [ "leave_ages = leavers['numage']" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2 71\n", "9 60\n", "17 74\n", "19 61\n", "20 47\n", "21 56\n", "23 76\n", "25 35\n", "28 44\n", "29 38\n", "33 24\n", "36 53\n", "39 79\n", "41 64\n", "42 51\n", "47 31\n", "55 65\n", "56 28\n", "58 31\n", "60 21\n", "62 36\n", "63 49\n", "64 64\n", "66 49\n", "68 56\n", "69 69\n", "72 50\n", "75 51\n", "76 51\n", "79 60\n", " ..\n", "1604 34\n", "1611 43\n", "1626 45\n", "1636 70\n", "1637 44\n", "1639 44\n", "1650 38\n", "1655 54\n", "1659 55\n", "1661 46\n", "1667 74\n", "1668 35\n", "1670 21\n", "1683 26\n", "1688 41\n", "1696 57\n", "1710 56\n", "1711 40\n", "1713 51\n", "1714 62\n", "1721 52\n", "1726 35\n", "1730 57\n", "1731 60\n", "1739 28\n", "1754 54\n", "1755 18\n", "1758 20\n", "1765 40\n", "1768 31\n", "Name: numage, Length: 541, dtype: int64" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "leave_ages" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "51.715341959334566" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sum(leave_ages) / len(leave_ages)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "51.715341959334566" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "leave_ages.mean()" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "51.715341959334566" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "leaver_mean = leavers['numage'].mean()\n", "leaver_mean" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "48.01550387596899" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "remain_mean = remainers['numage'].mean()\n", "remain_mean" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3.6998380833655773" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "observed_diff = leaver_mean - remain_mean\n", "observed_diff" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "%matplotlib inline" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "image/png": 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kFw/eZzS3/BAz9F5X1beAx5K8omvaAHyNGarxEG/mp9M2MFt17gUuSnJi93N+cF/OzDEJkOT0bvlLwO8w2p/D78ehPriY0ochv87oT7evAA90t9czml/exui36zbglAFr/BXg/q7Gh4A/69pfDnwZ2M3oT+cXDr0/u7peC9w1azV2tTzY3R4G/qRrn5n3uqvnPGCxe7//Hjh51mrs6jwR+E/gpcvaZqpO4H3A17ufm78BXjhLx2RX4z8x+gX0ILBhVvajZ8ZKUuOamrqRJD2TQS9JjTPoJalxBr0kNc6gl6TGGfSS1DiDXpIaZ9BLUuP+H3UPG7gcerVZAAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(remainers['numage']);\n", "plt.hist(leavers['numage']);" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "image/png": 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"text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.hist(leavers['numage']);" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [], "source": [ "import random" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [], "source": [ "my_list = [5, 1, 2, 5, 6]" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[6, 2, 1, 5, 5]" ] }, "execution_count": 31, "metadata": {}, "output_type": "execute_result" } ], "source": [ "random.shuffle(my_list)\n", "my_list" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "pandas.core.series.Series" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "type(leavers['numage'])" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "541" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "leave_list = list(leavers['numage'])\n", "len(leave_list)" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "774" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "remain_list = list(remainers['numage'])\n", "len(remain_list)" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[37,\n", " 55,\n", " 37,\n", " 42,\n", " 69,\n", " 20,\n", " 38,\n", " 32,\n", " 58,\n", " 46,\n", " 51,\n", " 57,\n", " 87,\n", " 28,\n", " 38,\n", " 82,\n", " 65,\n", " 75,\n", " 63,\n", " 68,\n", " 53,\n", " 64,\n", " 59,\n", " 41,\n", " 76,\n", " 60,\n", " 22,\n", " 52,\n", " 22,\n", " 26,\n", " 26,\n", " 28,\n", " 30,\n", " 23,\n", " 73,\n", " 46,\n", " 37,\n", " 69,\n", " 76,\n", " 66,\n", " 55,\n", " 49,\n", " 58,\n", " 57,\n", " 76,\n", " 21,\n", " 38,\n", " 62,\n", " 68,\n", " 20,\n", " 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40,\n", " 19,\n", " 36,\n", " 33,\n", " 69,\n", " 47,\n", " 46,\n", " 47,\n", " 65,\n", " 40,\n", " 79,\n", " 37,\n", " 43,\n", " 66,\n", " 35,\n", " 39,\n", " 44,\n", " 40,\n", " 72,\n", " 70,\n", " 67,\n", " 37,\n", " 67,\n", " 39]" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "remain_list" ] }, { "cell_type": "code", "execution_count": 39, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 3, 4, 5]" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "list1 = [1, 2, 3]\n", "list2 = [3, 4, 5]\n", "list1.extend(list2)\n", "list1" ] }, { "cell_type": "code", "execution_count": 41, "metadata": {}, "outputs": [], "source": [ "list1 = [1, 2, 3]\n", "list2 = [3, 4, 5, 6]" ] }, { "cell_type": "code", "execution_count": 43, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[1, 2, 3], [3, 4, 5, 6]]" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[list1, list2]" ] }, { "cell_type": "code", "execution_count": 44, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 2]" ] }, "execution_count": 44, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[1, 2]" ] }, { "cell_type": "code", "execution_count": 45, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[[1, 2, 3], [3, 4, 5, 6]]" ] }, "execution_count": 45, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[list1, list2]" ] }, { "cell_type": "code", "execution_count": 47, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1315" ] }, "execution_count": 47, "metadata": {}, "output_type": "execute_result" } ], "source": [ "pooled = remain_list\n", "pooled.extend(leave_list)\n", "len(pooled)" ] }, { "cell_type": "code", "execution_count": 48, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2 71\n", "9 60\n", "17 74\n", "19 61\n", "20 47\n", "21 56\n", "23 76\n", "25 35\n", "28 44\n", "29 38\n", "33 24\n", "36 53\n", "39 79\n", "41 64\n", "42 51\n", "47 31\n", "55 65\n", "56 28\n", "58 31\n", "60 21\n", "62 36\n", "63 49\n", "64 64\n", "66 49\n", "68 56\n", "69 69\n", "72 50\n", "75 51\n", "76 51\n", "79 60\n", " ..\n", "1604 34\n", "1611 43\n", "1626 45\n", "1636 70\n", "1637 44\n", "1639 44\n", "1650 38\n", "1655 54\n", "1659 55\n", "1661 46\n", "1667 74\n", "1668 35\n", "1670 21\n", "1683 26\n", "1688 41\n", "1696 57\n", "1710 56\n", "1711 40\n", "1713 51\n", "1714 62\n", "1721 52\n", "1726 35\n", "1730 57\n", "1731 60\n", "1739 28\n", "1754 54\n", "1755 18\n", "1758 20\n", "1765 40\n", "1768 31\n", "Name: numage, Length: 541, dtype: int64" ] }, "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ "leavers['numage']" ] }, { "cell_type": "code", "execution_count": 49, "metadata": {}, "outputs": [], "source": [ "random.shuffle(pooled)" ] }, { "cell_type": "code", "execution_count": 50, "metadata": {}, "outputs": [], "source": [ "fake_remain = pooled[:774]\n", "fake_leave = pooled[774:]" ] }, { "cell_type": "code", "execution_count": 51, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "function" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "source": [ "mean = statistics.mean\n", "type(mean)" ] }, { "cell_type": "code", "execution_count": 52, "metadata": {}, "outputs": [], "source": [ "fake_difference = mean(fake_remain) - mean(fake_leave)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "0.37642512907955705" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "source": [ "fake_difference" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fake_differences = []\n", "for i in range(10000):\n", " # shuffle the pooled list\n", " # put the first 774 into fake remain\n", " # put the rest into fake leave\n", " # calculate the mean for fake remain\n", " # calculate the mean for fake leave\n", " # calculate the difference\n", " # put that into the \"fake_differences\" list" ] } ], "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 }