{ "cells": [ { "cell_type": "markdown", "id": "8501b536", "metadata": {}, "source": [ "# Visualizing Distributions\n", "\n", "- `sns.histplot()`\n", "- `sns.kdeplot()`\n", "\n", "If we want to see the shape of a data distribution, the **histogram** can be a good choice. From a histogram we can easily see if a data distribution:\n", "\n", "* is unimodal or multimodel\n", "* has skew, or is symmetrical\n", "* differs between two samples\n", "\n", "In this section we will see how to plot a histogram using Python and what choices we can make to show the data distribution clearly and accurately\n", "\n", "We will also consider some of the limitations of the histogram for small datasets, and explore a related plot, the **Kernel Density Estimate (KDE)** plot, which can mitigate these limitations.\n", "\n", "To summarize the conceptual content of this page, when plotting a histogram we should consider:\n", "\n", "* the width of the bins - narrow bins give more detail but may make it harder to perceive the overall pattern\n", " * the KDE-plot equivalent is **bandwidth** which determines the smoothness of the KDE shape\n", "* the bin boundaries - do we want to place them at round numbers or some other meaningful point?\n", "\n", "When using histograms (and KDE plots) to compare distributions, we should consider:\n", "\n", "* matching the scale on the axes to facilitate comparison\n", "* whether to place the two plots next to each other (horizontally), above one another (vertically) or overlaid (on the same axis), to facilitate comparison\n" ] }, { "cell_type": "markdown", "id": "a77a81dc", "metadata": {}, "source": [ "\n", "\n" ] }, { "cell_type": "markdown", "id": "06a3540a", "metadata": {}, "source": [ "## Example\n", "\n", "We will look at a small sample of height data (these are made-up data designed for the exercise).\n", "\n", "\n", "\n", "### Set up Python libraries\n", "\n", "As usual, run the code cell below to import the relevant Python libraries" ] }, { "cell_type": "code", "execution_count": 1, "id": "7f1d34e0", "metadata": {}, "outputs": [], "source": [ "# Set-up Python libraries - you need to run this but you don't need to change it\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import scipy.stats as stats\n", "import pandas as pd\n", "import seaborn as sns\n", "sns.set_theme(style='white')\n", "import statsmodels.api as sm\n", "import statsmodels.formula.api as smf" ] }, { "cell_type": "markdown", "id": "fb218a2a", "metadata": {}, "source": [ "### Load and inspect the data" ] }, { "cell_type": "markdown", "id": "3bbd70d4", "metadata": {}, "source": [ "Load the file BodyData.csv which contains body measurements for 50 (fictional) people. The code block below will load the data automatically from the internet." ] }, { "cell_type": "code", "execution_count": 3, "id": "5b37c633", "metadata": {}, "outputs": [ { "data": { "text/html": [ "
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IDsexheightweightage
0101708M16164.835
1101946F16568.142
2108449F17576.631
3108796M18081.031
4113449F17980.131
5114688M17274.042
6119187F14854.845
7120679F16064.044
8120735F18888.432
9124269F17274.029
10124713M17576.626
11127076M18081.028
12131626M16265.635
13132218M17072.329
14132609F17274.041
15134660F15963.234
16135195M16971.442
17140073F16870.634
18140114M19595.141
19145185F15761.645
20146279F18081.030
21146519F17274.034
22151451F17173.137
23152597M17274.027
24154672M16769.739
25155594F16568.125
26158165M17576.645
27159457F17677.436
28162323M17374.831
29166948M17475.728
30168411M17576.629
31168574F16366.430
32169209F15963.245
33171236F16467.234
34172289M18181.927
35173925M18989.325
36176598F16971.437
37177002F18081.036
38178659M18181.926
39180992F17778.331
40183304F17677.430
41184706M18383.740
42185138M16971.428
43185223F17072.341
44186041M17576.625
45186887M15459.326
46187016M16164.832
47198157M18081.033
48199112M17274.033
49199614F16467.231
\n", "
" ], "text/plain": [ " ID sex height weight age\n", "0 101708 M 161 64.8 35\n", "1 101946 F 165 68.1 42\n", "2 108449 F 175 76.6 31\n", "3 108796 M 180 81.0 31\n", "4 113449 F 179 80.1 31\n", "5 114688 M 172 74.0 42\n", "6 119187 F 148 54.8 45\n", "7 120679 F 160 64.0 44\n", "8 120735 F 188 88.4 32\n", "9 124269 F 172 74.0 29\n", "10 124713 M 175 76.6 26\n", "11 127076 M 180 81.0 28\n", "12 131626 M 162 65.6 35\n", "13 132218 M 170 72.3 29\n", "14 132609 F 172 74.0 41\n", "15 134660 F 159 63.2 34\n", "16 135195 M 169 71.4 42\n", "17 140073 F 168 70.6 34\n", "18 140114 M 195 95.1 41\n", "19 145185 F 157 61.6 45\n", "20 146279 F 180 81.0 30\n", "21 146519 F 172 74.0 34\n", "22 151451 F 171 73.1 37\n", "23 152597 M 172 74.0 27\n", "24 154672 M 167 69.7 39\n", "25 155594 F 165 68.1 25\n", "26 158165 M 175 76.6 45\n", "27 159457 F 176 77.4 36\n", "28 162323 M 173 74.8 31\n", "29 166948 M 174 75.7 28\n", "30 168411 M 175 76.6 29\n", "31 168574 F 163 66.4 30\n", "32 169209 F 159 63.2 45\n", "33 171236 F 164 67.2 34\n", "34 172289 M 181 81.9 27\n", "35 173925 M 189 89.3 25\n", "36 176598 F 169 71.4 37\n", "37 177002 F 180 81.0 36\n", "38 178659 M 181 81.9 26\n", "39 180992 F 177 78.3 31\n", "40 183304 F 176 77.4 30\n", "41 184706 M 183 83.7 40\n", "42 185138 M 169 71.4 28\n", "43 185223 F 170 72.3 41\n", "44 186041 M 175 76.6 25\n", "45 186887 M 154 59.3 26\n", "46 187016 M 161 64.8 32\n", "47 198157 M 180 81.0 33\n", "48 199112 M 172 74.0 33\n", "49 199614 F 164 67.2 31" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "heightData = pd.read_csv('https://raw.githubusercontent.com/jillxoreilly/StatsCourseBook_2024/main/data/BodyData.csv')\n", "display(heightData)" ] }, { "cell_type": "markdown", "id": "7006abed", "metadata": {}, "source": [ "## Histogram\n", "\n", "Let's start by plotting a histogram of the data to see what the distribution of heights is.\n", "\n", "We use the `Seaborn` function `sns.histplot()`" ] }, { "cell_type": "code", "execution_count": 4, "id": "d1ee3ecd", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.histplot(data = heightData, x=\"height\")\n", "plt.xlabel('height') # set the x axis label\n", "plt.show() # this command asks Python to output the plot created above " ] }, { "cell_type": "markdown", "id": "1185292b", "metadata": {}, "source": [ "If we want to *disaggregate* (separate out) the data, for example by sex, this is super easy using the `hue` property in `Seaborn` functions:" ] }, { "cell_type": "code", "execution_count": 5, "id": "8a6e09ff", "metadata": {}, "outputs": [ { "data": { "image/png": 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asmSJQkJCrnrc6dOn1bJlS/dNCAAA8P9V+4jLsGHDZFmWLMtybbvy5ysfPj4+io+P19NPP+2RYQEAwM9btY+4DB48WIMHD5YkjR8/XkuWLFH79u09NhgAAMBPVTtc/t1rr73m7jkAAACuq1bhcunSJaWmpmrnzp26dOmSKioqKu13OBzavn27WwYEAAC4olbh8uSTT2rTpk3q1auXunbtKh8fbgcDAAA8r1bh8uGHH+rRRx/V9OnT3T0PAADAf1SrQyXl5eWKjY119ywAAADXVKtw6devn/bs2ePuWQAAAK6pVqeKRowYoeTkZOXn5ysuLk5BQUFXPeaXv/xlXWcDAACopFbhMmvWLElSenq60tPTr9rvcDgIFwAA4Ha1CpeMjAx3zwEAAHBdtQqXNm3auHsOAACA66pVuLz00kvXfcx//dd/1eapAQAA/iO3h0tISIjCwsIIFwAA4Ha1CpesrKyrthUXF+vQoUNasmSJnnjiiToPBgAA8FNuu1d/cHCw+vfvr5kzZ2r58uXueloAAAAXt/+SoYiICJ08edLdTwsAAFC7U0VVsSxL2dnZWrduHT91BAAAPKJW4dKlSxc5HI4q91mWxakiAADgEbUKl5kzZ1YZLiEhIRo0aJBuueWWus4FAABwlVqFy8MPP+zuOQAAAK6r1te4lJaW6p133tH+/ftVWFio5s2bKyEhQWPGjFFAQIA7ZwQAAJBUy3ApLCzUhAkTlJWVpRtvvFGtWrXSqVOntHXrVr3xxht688031aRJE3fPCgAAfuZq9ePQK1asUE5Ojl5//XXt2LFDGzZs0I4dO/T6668rLy9PKSkp7p4TAACgduGSkZGhWbNmKSEhodL2hIQEJSUl6cMPP3TLcAAAAP+uVuFy8eJFRUZGVrkvMjJS58+fr8tMAAAAVapVuLRr1047d+6scl9GRoaioqLqNBQAAEBVanVx7tSpUzV79myVlpZq9OjRatmypX744Qdt2bJFGzdu1JIlS9w8JgAAQC3DZcSIETp9+rRSU1O1ceNG13Y/Pz/NnDlT999/v9sGBAAAuKJW4VJcXKwZM2YoMTFR//znP1VQUKDs7Gzdf//9atq0qbtnBAAAkFTDa1y++OIL/fKXv9Srr74qSQoNDdWAAQM0YMAArVy5UmPHjuU3QwMAAI+pdrj83//9nyZNmqSCggJ16NCh0j5/f38tWLBAFy9e1NixY5WTk+P2QQEAAKodLn/605/UvHlz/e1vf9Ndd91VaV9QUJASExO1adMmBQcHKzU11e2DAgAAVDtcMjMzNW3aNDVr1uw/PuaGG27Q5MmTlZmZ6Y7ZAAAAKql2uJw9e7Za92fp1KkTp4oAAIBHVDtcWrRoodzc3Os+Lj8//5pHZQAAAGqr2uFy22236Z133rnu49LT09W1a9c6DQUAAFCVaofL+PHjtX//fj3zzDMqKSm5an9paamWLVumvXv3aty4cW4dEgAAQKrBDehiYmI0f/58PfXUU9q8ebP69u2rm266SU6nU999953279+vc+fO6ZFHHlH//v09OTMAAPiZqtGdc8eNG6cuXbooLS1NGRkZriMvjRs3Vr9+/TRlyhTFxcV5ZFAAAIAa3/K/Z8+e6tmzpyTp3Llz8vHx4Tb/AACgXtTolv8/1bx5c7dGy6lTp9S9e/dqXQQMAAB+fuoULu5UVlam3//+9youLrZ7FAAA4KW8JlxWrVqlxo0b2z0GAADwYl4RLgcOHNCGDRu0bNkyu0cBAABezPZwKSws1OOPP65FixYpIiLC7nEAAIAXsz1clixZovj4eI0ePdruUQCj+QQ3VYVl2T2Gxzl8fO0eAYCNavzj0O6Unp6ugwcPasuWLXaOATQIPgFB8nE4tGff/+pc3jm7x3G7yFtuUo/YtpKP7f/eAmAjW8Nl06ZNysvL06BBgyptT05OVlpamt599117BgMMdi7vnH7Iuf4vRDVNsxbcLwqAzeHy3HPP6fLly5W23XXXXUpKStKIESNsmgoAAHgrW8OldevWVW6/4YYb1KZNm3qeBgAAeDtOFgMAAGPYesSlKl9++aXdIwAAAC/FERcAAGAMwgUAABiDcAEAAMYgXAAAgDEIFwAAYAzCBQAAGINwAQAAxiBcAACAMQgXAABgDMIFAAAYg3ABAADGIFwAAIAxCBcAAGAMwgUAABiDcAEAAMYgXAAAgDEIFwAAYAzCBQAAGINwAQAAxiBcAACAMQgXAABgDMIFAAAYg3ABAADGIFwAAIAxCBcAAGAMwgUAABiDcAEAAMYgXAAAgDEIFwAAYAzCBQAAGINwAQAAxmhk9wBAffMJamL3CB7h4x9k9wgA4HGEC1wsy5LD4bB7DI+yLEtNuva1ewyPcvj42j0Casnh4L8dcD2EC1wcDoeKsj6Rs7jQ7lE8wr91WwW16ahdew7r/Nl8u8dxu8h2N6tn946SD2eAjeXz4z8cyi/k63J2js3DuF+Zs6WktnaPAcMRLqjEWVwoZ9E5u8fwiIrm4ZKkgvNFyss7b+8wHtC8dSu7R4CbWM4yWaWX7R7D7azyUrtHQAPAP80AAIAxCBcAAGAMwgUAABiDcAEAAMYgXAAAgDEIFwAAYAzCBQAAGINwAQAAxiBcAACAMQgXAABgDMIFAAAYg3ABAADGIFwAAIAxCBcAAGAMwgUAABiDcAEAAMYgXAAAgDEIFwAAYAzCBQAAGINwAQAAxiBcAACAMQgXAABgDMIFAAAYg3ABAADGIFwAAIAxCBcAAGAMwgUAABiDcAEAAMYgXAAAgDEIFwAAYAzCBQAAGMP2cDl//rwWL16sAQMGqEePHvrtb3+rgwcP2j0WAADwQraHy+zZs3XkyBE9//zzevvtt9WtWzdNnTpVJ0+etHs0AADgZWwNlzNnzmjfvn1KTk5WQkKC2rVrp4ULF6p169baunWrnaMBAAAvZGu4NG/eXH/605906623urY5HA5ZlqWCggIbJwMAAN6okZ0vHhoaqoEDB1ba9ve//11ff/21+vXrZ9NUAADAW9l+jcu/O3TokBYsWKAhQ4Zo8ODBdo8DAAC8jNeEy/bt2zV16lTFxsbq+eeft3scAADghbwiXF5//XU9/PDDGjBggNatW6fAwEC7RwIAAF7I9nB588039d///d8aN26cVq5cKX9/f7tHAgAAXsrWi3NPnTqlp556SkOHDtWDDz6ovLw8177AwEA1adLExukAAIC3sTVcPvjgA5WVlWnbtm3atm1bpX1jxozRM888Y9NkAADAG9kaLg899JAeeughO0cAAAAGsf0aFwAAgOoiXAAAgDEIFwAAYAzCBQAAGINwAQAAxiBcAACAMQgXAABgDMIFAAAYg3ABAADGIFwAAIAxCBcAAGAMwgUAABiDcAEAAMYgXAAAgDEIFwAAYAzCBQAAGINwAQAAxiBcAACAMQgXAABgDMIFAAAYg3ABAADGIFwAAIAxCBcAAGAMwgUAABiDcAEAAMYgXAAAgDEIFwAAYAzCBQAAGINwAQAAxiBcAACAMQgXAABgjEZ2DwDv4hPUxO4RPMbHP8juEQAAdUS41IBlWXI4HHaP4TGWZalJ1752j+FxDh9fu0cAfpaCg4Ma/P+PVliWfBrw+5Ps/7uQcKkBh8OhoqxP5CwutHsUt/Nv3VZBbTpq157DOn823+5xPCKy3c3q2b2j5MMZUsAO/oH+cjgc2pP5hc6d/cHucdwu8uYI9YjvoI+PfqeCohK7x/GIpiEBuj32RltnIFxqyFlcKGfRObvHcLuK5uGSpILzRcrLO2/vMB7SvHUru0cAIOn8uQL9kJNr9xhu17RpY0lSQVGJzl1omOHiDfinJwAAMAbhAgAAjEG4AAAAYxAuAADAGIQLAAAwBuECAACMQbgAAABjEC4AAMAYhAsAADAG4QIAAIxBuAAAAGMQLgAAwBiECwAAMAbhAgAAjEG4AAAAYxAuAADAGIQLAAAwBuECAACMQbgAAABjEC4AAMAYhAsAADAG4QIAAIxBuAAAAGMQLgAAwBiECwAAMAbhAgAAjEG4AAAAYxAuAADAGIQLAAAwBuECAACMQbgAAABj2B4uFRUVevHFF9W/f3/FxcVpypQpOnPmjN1jAQAAL2R7uKxZs0ZvvfWWli5dqg0bNsjhcOiBBx5QaWmp3aMBAAAvY2u4lJaW6pVXXtHDDz+sgQMHqkuXLnrhhRf0/fffa9u2bXaOBgAAvJCt4ZKVlaWLFy+qT58+rm2hoaGKjo7WgQMHbJwMAAB4o0Z2vnhOTo4kKSIiotL2sLAwZWdn1/j5cnNz5XQ6NWTIELfMVxWrvFSyLI89v218fOXwbaTLl0tVUVFh9zQe0ahRI/n7N9LlkjJVOJ12j+N2vD/zNfT32PDfn6/8/f1UUupURUP8e0KSj8Oh5/x93f682dnZ8vWt3vPaGi6XLl2SJPn7+1faHhAQoIKCgho/X0BAgMevjXE08r/+gwwWGNiw358kBQb4SfKzewyP4f2Zr6G/x4b+/gI88Bd7Q/dj1Fbv7x9bwyUwMFDSj9e6XPlckkpKShQUFFTj5zt48KDbZgMAAN7H1mtcrpwiys3NrbQ9NzdX4eHhdowEAAC8mK3h0qVLF4WEhGj//v2ubYWFhfr888+VkJBg42QAAMAb2XqqyN/fX4mJiXruuefUokULtWnTRs8++6zCw8M1dOhQO0cDAABeyNZwkaSkpCSVl5dr0aJFunz5sm677TalpaVV+yIdAADw8+GwrAb6M1sAAKDBsf2W/wAAANVFuAAAAGMQLgAAwBiECwAAMAbhAgAAjEG4AAAAYxAuAADAGISLh6xZs0bjx4+vtG3+/Pnq3LlzpY8BAwa49ldUVOjFF19U//79FRcXpylTpujMmTP1PXqDUNX65+bmavbs2UpISFDv3r01Z84c5efnu/az/u7z0/UfP378Vd/7Vz7S09Mlsf7uVNX3/7Fjx5SYmKju3btr4MCBWr58uUpLS137WX/3qWr9MzMzdd9996l79+4aNmyYXn/99Ur7Wf8asOB269evtzp37mwlJiZW2j5mzBjr+eeft3Jzc10feXl5rv2rVq2y+vbta+3atcv64osvrClTplhDhw61SkpK6vstGK2q9S8pKbFGjhxp3XvvvdbRo0etw4cPW3fffbc1bdo012NYf/eoav3PnTtX6fs+NzfXmj59unX33XdbFy5csCyL9XeXqtY/Ly/P6tWrl/XEE09Yp0+ftnbv3m316dPHeuaZZ1yPYf3do6r1P3z4sNWlSxdr8eLF1okTJ6yMjAzrjjvusNasWeN6DOtffYSLG+Xk5FhTp0614uPjrbvvvrvSN255ebkVExNjbdu2rcqvLSkpsbp37269+eabrm0FBQVWbGystXXrVo/P3hBca/03bdpkxcfHW2fPnnVt27NnjzVkyBDrwoULrL8bXGv9f2rLli1WdHS0lZWVZVkW3//ucK3137Ztm9WpUydXJFqWZT311FPWqFGjLMti/d3hWus/c+ZM69577630+M2bN1txcXFWSUkJ619DnCpyo88++0xNmzbV//zP/yguLq7SvtOnT6ukpETt27ev8muzsrJ08eJF9enTx7UtNDRU0dHROnDggEfnbiiutf579+5Vnz591LJlS9e2/v37a/v27QoJCWH93eBa6//viouLtXz5ck2cOFGdO3eWxPe/O1xr/Zs1ayZJ+utf/yqn06lvvvlGu3fvdj2O9a+7a63/qVOnlJCQUGlbdHS0Ll26pKNHj7L+NWT7L1lsSAYPHqzBgwdXue/48eNyOBz6y1/+oj179sjHx0cDBw7UrFmz1KRJE+Xk5EiSIiIiKn1dWFiYsrOzPT57Q3Ct9T99+rQSEhK0evVqpaenq7y8XP369dNjjz2m0NBQ1t8NrrX+/+6tt97SxYsX9bvf/c61jfWvu2utf0JCgqZPn66UlBS98MILcjqd6tWrl5544glJrL87XGv9W7VqddU6fvvtt5KkvLw8ORwOSax/dXHEpZ7861//ko+Pj9q0aaPU1FTNnTtXu3fv1owZM1RRUaFLly5J0lW/FTsgIEAlJSV2jNygFBUVKT09XV9++aVWrFihP/7xjzp06JBmzJghy7JY/3ridDr12muvaezYsWrSpIlrO+vvWYWFhTp9+rTGjRunjRs3KiUlRV9//bWWLFkiifX3tF/96lf64IMPlJ6errKyMp05c0YrV66Uw+FQaWkp619DHHGpJw8//LAmTZqk0NBQSVKnTp3UqlUr3X///Tp27JgCAwMlSaWlpa7PJamkpERBQUG2zNyQ+Pn5KTg4WCtWrJCfn58kqWnTprrvvvtY/3r06aef6rvvvtOvf/3rSttZf8967rnnVFhYqFWrVkmSunXrpqZNm2rSpEmaOHEi6+9h99xzj3JycvSHP/xBCxYsUPPmzfXYY49p3rx5atKkiStOWP/q4YhLPXE4HK5ouaJTp06SfjxMe+UQYW5ubqXH5ObmKjw8vH6GbMDCw8PVtm1bV7RIUseOHSVJ33zzDetfT7Zv367Y2FhFRkZW2s76e9ahQ4cUExNTaduV6zBOnTrF+teD6dOn69ChQ9q5c6f27NmjW2+9VZZlKSoqivWvIcKlnsyZM0dTp06ttO3YsWOSpA4dOqhLly4KCQnR/v37XfsLCwv1+eefX3VRF2ouISFBWVlZunz5smvb8ePHJUlRUVGsfz05dOhQpQsQr2D9PSs8PFxffvllpW1Xvv9vueUW1t/D3njjDSUnJ8vHx0etW7eWr6+v3n//fd10001q27Yt619DhEs9GTVqlPbt26e1a9fq66+/1u7du7VgwQKNGjVK7du3l7+/vxITE/Xcc88pIyNDWVlZevTRRxUeHq6hQ4faPb7xfvOb38jX11dz5szR8ePHdejQIS1atEi9e/dWt27dWP964HQ6deLECdeRxn/H+nvW5MmTtXfvXq1cuVJff/21MjMzNW/ePA0cOFBdu3Zl/T2sQ4cO2rhxozZu3Khvv/1WGzZsUGpqqubMmSOJ7/+a4hqXenLnnXcqJSVFqampSk1NVZMmTTR69GjNmjXL9ZikpCSVl5dr0aJFunz5sm677TalpaVddcEWaq5FixZ644039PTTT+vXv/61/P399Ytf/ELz5893PYb196zz58+rrKzM9aO5P8X6e06/fv308ssva/Xq1frLX/6i5s2ba+jQoXrkkUdcj2H9Pad379568sknlZqaqqVLlyoqKkrPPvushg8f7noM6199DsuyLLuHAAAAqA5OFQEAAGMQLgAAwBiECwAAMAbhAgAAjEG4AAAAYxAuAADAGIQLgJ8t7gYBmIdwAeARgwcP1rx58+r0HO+88446d+6sb775xu1fs3btWqWlpdVpPgD1j3AB4LUGDRqkDRs2KCwszO3PvXLlSl26dMntzwvAs7jlPwCv1aJFC7Vo0cLuMQB4EY64APCYsrIyLV++XHfccYfi4+M1ZcoUnTlzxrX/4MGDSkxMVFxcnHr16qW5c+cqPz/ftb+q0z5/+9vfNGLECMXExOiee+5RZmamoqOj9c4771R67SNHjug3v/mNYmJiNGjQoEqnhTp37ixJeumll1yfAzAD4QLAY9577z3961//0jPPPKPFixfr2LFjevTRRyVJBw4c0KRJkxQYGKiVK1dqwYIF+vTTTzVhwgRdvny5yudLT0/XvHnz1KNHD61Zs0bDhg3TjBkz5HQ6r3rskiVLNGrUKL388suKjY3V8uXLtXPnTknShg0bJEn33nuv63MAZuBUEQCPad26tdasWSM/Pz9J0pkzZ5SamqqioiKtWLFCbdu21csvvyxfX19JUlxcnEaOHKlNmzZp3LhxVz1fSkqK7rzzTi1dulSS1L9/f/n5+WnFihVXPXb27Nn67W9/K0mKj4/Xjh079Mknn+jOO+9UfHy8JCk8PNz1OQAzcMQFgMfExsa6okWSIiMjJUmFhYU6cuSIBg4cKMuyVF5ervLyckVGRqp9+/bat2/fVc915swZfffdd7r77rsrbR85cmSVr52QkOD6PDg4WC1btlRhYaE73hYAG3HEBYDHBAcHV/qzj8+P/1bKzs5WRUWF1q1bp3Xr1l31dQEBAVdtu3Ltyw033FBpe6tWrap87aCgoKtem/u2AOYjXADUu5CQEDkcDk2aNKnKIyY/jQ7px9M6kpSXl1dp+0//DKBh41QRgHrXuHFjRUdH66uvvlJMTIzro2PHjnrppZe0f//+q74mPDxcN998s7Zt21Zp+wcffFCrGa4c/QFgFv6XC8AWs2fP1kcffaQ5c+Zo9+7d2rFjh6ZNm6aPP/5Y3bp1u+rxDodDSUlJ2r59u5KTk/XRRx/pz3/+s1JSUiTVPERCQ0N1+PBhHThwgFNIgEEIFwC26Nevn9LS0pSTk6OkpCQ9/vjj8vX11fr16//jT/qMHj1af/zjH5WZmamHHnpI7777rhYuXCjp6utpruehhx7SsWPH9MADDyg7O7uubwdAPXFY/FMDgCG2bt2q6OhotWvXzrVt165devDBB7V582Z16dLFxukA1AfCBYAxpk+frpMnT2rWrFmKiIjQ6dOn9eKLLyoqKkqvvfaa3eMBqAeECwBjnDt3TitWrNCePXuUn5+vli1batiwYUpKSlLjxo3tHg9APSBcAACAMbg4FwAAGINwAQAAxiBcAACAMQgXAABgDMIFAAAYg3ABAADGIFwAAIAxCBcAAGAMwgUAABjj/wHXID66ZJNuwQAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.histplot(data = heightData, x=\"height\", hue=\"sex\")\n", "plt.xlabel('height') # set the x axis label\n", "plt.show() # this command asks Python to output the plot created above " ] }, { "cell_type": "markdown", "id": "5fcca7b0", "metadata": {}, "source": [ "### Choosing the bin boundaries and width\n", "\n", "In a histogram, we group data into *bins*, and count how many data values fall in each bin\n", "\n", "By default, `Seaborn` chooses a set of bins that its algorithm suggests should best display the shape of the data distribution. \n", "\n", "However, we may prefer to set the bin widths to values that are more easily interpretable. \n", "\n", "For example, below I used bins of 5cm to group the heights (in a range from 150 to 200 cm that includes all the data points in my sample). This means I can easily read off from the graph how many men in my sample have a height between, say, 170 and 175cm). \n", "\n", "* Can you find where in the code this is specified?\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "24afb36e", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.histplot(data = heightData, x=\"height\", bins = range(150,200,5))\n", "plt.show() # this command asks Python to output the plot created above" ] }, { "cell_type": "markdown", "id": "8d8d6e29", "metadata": {}, "source": [ "### Histogram is unstable for small $n$\n", "\n", "One problem with using a histogram when you have only a small number of data points is \n", "that the shape of the histogram can depend a lot on where the bin boundaries happen to fall. \n", "\n", "Look at the following plot of brothers' heights, again grouped into 5cm bins but with different bin boundaries: " ] }, { "cell_type": "code", "execution_count": 30, "id": "cc00f97d", "metadata": {}, "outputs": [ { "data": { "image/png": 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tqvnz5+vBBx/06E/2BgAAuBxlDpsff/xRQ4YM0dmzZ9WqVSu3Y76+vpo+fbqys7P14IMP6uTJkx4fFAAA4FLKHDavv/66/P399e677+qOO+5wO1arVi3FxsZq7dq1ql27thITEz0+KAAAwKWUOWxSU1M1YsQIXXPNNb97ToMGDTR06FClpqZ6YjYAAIDLUuawycjIKNPn0wQHB3MrCgAA2KLMYRMQEKD09PRLnnfmzJlSr+oAAABUlDKHTbt27bRu3bpLnpecnKw2bdpc0VAAAADlUeawGTRokHbu3KnZs2crNze32PG8vDy99NJL+uSTTzRw4ECPDgkAAFAWZf6AvrCwME2bNk0vvPCC1q9fr06dOunaa69VYWGhjh8/rp07dyozM1OPP/64unTpUpEzAwAAlOiyPnl44MCBCgkJ0bJly5SSkuK6clOnTh117txZw4YNU0RERIUMCgAAcCmX/SMVbrnlFt1yyy2SpMzMTHl7e/NjFAAAgCOU62dFXeTv7++pOQAAAK7YZf2sKAAAACcjbAAAgDEIGwAAYAzCBgAAGIOwAQAAxiBsAACAMQgbAABgDMIGAAAYg7ABAADGIGwAAIAxCBsAAGAMwgYAABiDsAEAAMYgbAAAgDGqTNgkJyerd+/eCgsLU58+ffThhx/aPRIAAHCYKhE269ev1/Tp0zVgwABt3LhRvXv31sSJE/XFF1/YPRoAAHAQx4eNZVmKj4/X4MGDNXjwYAUFBWnMmDH64x//qM8//9zu8QAAgIP42D3ApXz//fc6duyY+vXr57a+bNkymyYCAABO5fgrNkeOHJEk5eTkaPjw4erUqZPuvfdebd261d7BAACA4zg+bM6dOydJmjJlivr27as333xTt956q0aPHq3U1FSbpzNTUZFl9wgeYcrrAACUneNvRVWvXl2SNHz4cPXv31+S1KZNG+3bt09vvfWWOnXqZOd4RvL29lLS5gPKyMyxe5RyC77OX3d0DKryr0P632sBAFya48MmMDBQkhQcHOy23qpVK3388cc2THR1yMjM0fGfs+0eo9waXVNLUtV/HdL/XgsA4NIcfysqNDRUderU0Z49e9zWDxw4oOuuu86mqQAAgBM5/opNzZo1NWLECC1evFhNmjRReHi43n//fX322Wdavny53eMBAAAHcXzYSNLo0aNVq1Ytvfrqqzp16pRatmyphQsXqkOHDnaPBgAAHKRKhI0kDR06VEOHDrV7DAAA4GCOf48NAABAWRE2AADAGIQNAAAwBmEDAACMQdgAAABjEDYAAMAYhA0AADAGYQMAAIxB2AAAAGMQNgAAwBiEDQAAMAZhAwAAjEHYAAAAYxA2AADAGIQNAAAwBmEDAACMQdgAAABjEDYAAMAYhA0AADAGYQMAAIxB2AAAAGMQNgAAwBiEDQAAMAZhAwAAjEHYAAAAYxA2AADAGIQNAAAwBmEDAACMQdgAAABjEDYAAMAYhA0AADAGYQMAAIxB2AAAAGMQNgAAwBiEDQAAMEaVCpvDhw8rKipK69ats3sUAADgQFUmbPLz8/XEE08oJyfH7lEAAIBDVZmwWbhwoerUqWP3GAAAwMGqRNjs2rVLSUlJeumll+weBQAAOJjjwyYrK0uTJ0/WzJkz1bRpU7vHAQAADub4sImLi1NkZKT69etn9ygAAMDhfOweoDTJycnavXu33nvvPbtHAQAAVYCjr9isXbtWp0+fVvfu3RUVFaWoqChJ0qxZs9SnTx+bpwMAAE7j6Cs2c+fO1YULF9zW7rjjDo0bN069e/e2aSoAAOBUjg6bJk2alLjeoEEDNWvWrJKnAQAATufoW1EAAACXw9FXbEry7bff2j0CAABwKK7YAAAAYxA2AADAGIQNAAAwBmEDAACMQdgAAABjEDYAAMAYhA0AADAGYQMAAIxB2AAAAGMQNgAAwBiEDQAAMAZhAwAAjEHYAAAAYxA2AADAGIQNAAAwBmEDAACMQdgAAABjEDYAAMAYhA0AADAGYQMAAIxB2AAAAGMQNgAAwBiEDQAAMAZhAwAAjEHYAAAAYxA2AADAGIQNAAAwBmEDAACMQdgAAABjEDYAAMAYhA0AADAGYQMAAIxB2AAAAGMQNgAAwBiEDQAAMIbjw+aXX37R008/ra5du+rmm2/WAw88oN27d9s9FgAAcCDHh83EiRO1Z88evfLKK/rnP/+ptm3bavjw4Tp06JDdowEAAIdxdNgcPXpUn332mWbNmqXo6GjdcMMNmjFjhpo0aaKNGzfaPR4AAHAYR4eNv7+/Xn/9dd10002uNS8vL1mWpbNnz9o4GQAAcCIfuwcoTf369dWtWze3tQ8//FA//PCDOnfubNNUAADAqRx9xea30tLSNH36dN1+++2KiYmxexwAgAMUFVl2j+ARprwOuzn6is2vbdmyRU888YQiIiL0yiuv2D0OAMAhvL29lLT5gDIyc+wepdwa+dfWgB7Bdo9hhCoRNitXrtTzzz+vHj16aO7cufL19bV7JACAg2Rk5uj4z9l2jwEHcPytqNWrV+tvf/ubBg4cqPnz5xM1AADgdzn6is3hw4f1wgsvqEePHnrkkUd0+vRp17GaNWuqXr16Nk4HAACcxtFh89FHHyk/P1+bN2/W5s2b3Y71799fs2fPtmkyAADgRI4Om1GjRmnUqFF2jwEAAKoIx7/HBgAAoKwIGwAAYAzCBgAAGIOwAQAAxiBsAACAMQgbAABgDMIGAAAYg7ABAADGIGwAAIAxCBsAAGAMwgYAABiDsAEAAMYgbAAAgDEIGwAAYAzCBgAAGIOwAQAAxiBsAACAMQgbDyoqsuweAQDKpG7t6vw3y0FM2g+7X4ePrc9uGG9vLyVtPqCMzBy7Rym34Ov8dUfHILvHAFDBavn68N8sBzFlPxr519aAHsG2zkDYeFhGZo6O/5xt9xjl1uiaWnaPAKAS8d8sZ6nq++EE3IoCAADGIGwAAIAxCBsAAGAMwgYAABiDsAEAAMYgbAAAgDEIGwAAYAzCBgAAGIOwAQAAxiBsAACAMQgbAABgDMIGAAAYg7ABAADGIGwAAIAxCBsAAGCMKhE2RUVFWrBggbp06aKIiAgNGzZMR48etXssAADgMFUibBISEvTOO+/oueeeU1JSkry8vPTwww8rLy/P7tEAAICDOD5s8vLy9Oabb2rs2LHq1q2bQkJC9Oqrr+rUqVPavHmz3eMBAAAHcXzY7N+/X9nZ2erYsaNrrX79+goNDdWuXbtsnAwAADiNl2VZlt1DlGbTpk0aO3as9uzZo5o1a7rWH3/8cV24cEFLliy5rMcLCwtTYWGhmjZt6ulRJUnZ5/NVWOTof6Slqu7jrVo1fHgdDmLKa+F1OAuvw1lMeR3VvL1Up1b1CnnsEydOqFq1atq7d2+p5/lUyLN70Pnz5yVJvr6+bus1atTQ2bNnL/vxatSoUaHvzamoDa1svA7nMeW18DqchdfhLKa8jorg4+NTrAVKPK8SZrkiF6/S5OXluV2xyc3NVa1atS778Xbv3u2x2QAAgLM4/j02F28Zpaenu62np6crMDDQjpEAAIBDOT5sQkJCVLduXe3cudO1lpWVpX379ik6OtrGyQAAgNM4/laUr6+vYmNjNXfuXAUEBKhZs2Z6+eWXFRgYqB49etg9HgAAcBDHh40kjRs3TgUFBZo5c6YuXLigdu3aadmyZWV6ExEAALh6OP7bvQEAAMrK8e+xAQAAKCvCBgAAGIOwAQAAxiBsAACAMQgbAABgDMIGAAAYg7ABAADGMDpsEhISNGjQILe1adOmqXXr1m6/unbt6jpeVFSkBQsWqEuXLoqIiNCwYcN09OjRyh7dSCXtR3p6uiZOnKjo6Gh16NBBkyZN0pkzZ1zH2Y+K9ds9GTRoULGvj4u/kpOTJbEnFamkr5G9e/cqNjZWUVFR6tatm+bMmaO8vDzXcfaj4pS0H6mpqbr33nsVFRWlnj17auXKlW7H2Q8HsAz11ltvWa1bt7ZiY2Pd1vv372+98sorVnp6uuvX6dOnXccXLlxoderUyfr444+tb775xho2bJjVo0cPKzc3t7JfglFK2o/c3FyrT58+1j333GN99dVX1hdffGH16tXLGjFihOsc9qPilLQnmZmZbl8b6enp1siRI61evXpZ//3vfy3LYk8qSkn7cfr0aat9+/bWU089ZR05csTavn271bFjR2v27Nmuc9iPilHSfnzxxRdWSEiI9fTTT1sHDx60UlJSrFtvvdVKSEhwncN+2M+4sDl58qQ1fPhwKzIy0urVq5fbv5QFBQVWWFiYtXnz5hL/bm5urhUVFWWtXr3atXb27FkrPDzc2rhxY4XPbqLS9mPt2rVWZGSklZGR4VrbsWOHdfvtt1v//e9/2Y8KUtqe/NZ7771nhYaGWvv377csi6+RilDafmzevNkKDg52RaVlWdYLL7xg9e3b17Is9qMilLYfY8aMse655x6389evX29FRERYubm57IdDGHcr6uuvv5afn582bNigiIgIt2NHjhxRbm6uWrZsWeLf3b9/v7Kzs9WxY0fXWv369RUaGqpdu3ZV6NymKm0/PvnkE3Xs2FENGzZ0rXXp0kVbtmxR3bp12Y8KUtqe/FpOTo7mzJmjwYMHq3Xr1pL4GqkIpe3HNddcI0n6+9//rsLCQv3000/avn276zz2w/NK24/Dhw8rOjrabS00NFTnz5/XV199xX44RJX4IZiXIyYmRjExMSUeO3DggLy8vPT2229rx44d8vb2Vrdu3TR+/HjVq1dPJ0+elCQ1bdrU7e81btxYJ06cqPDZTVTafhw5ckTR0dFavHixkpOTVVBQoM6dO+vJJ59U/fr12Y8KUtqe/No777yj7OxsPfroo6419sTzStuP6OhojRw5UvHx8Xr11VdVWFio9u3b66mnnpLEflSE0vajUaNGxf65Hjt2TJJ0+vRpeXl5SWI/7GbcFZvSfPfdd/L29lazZs2UmJioKVOmaPv27Ro9erSKiop0/vx5SSr2U8Nr1Kih3NxcO0Y22rlz55ScnKxvv/1W8+bN07PPPqu0tDSNHj1almWxHzYqLCzUihUr9OCDD6pevXqudfakcmVlZenIkSMaOHCg1qxZo/j4eP3www+Ki4uTxH5Utr/+9a/66KOPlJycrPz8fB09elTz58+Xl5eX8vLy2A+HMO6KTWnGjh2rIUOGqH79+pKk4OBgNWrUSAMGDNDevXtVs2ZNSVJeXp7r95KUm5urWrVq2TKzyapXr67atWtr3rx5ql69uiTJz89P9957L/ths88//1zHjx/Xfffd57bOnlSuuXPnKisrSwsXLpQktW3bVn5+fhoyZIgGDx7MflSyO++8UydPntQzzzyj6dOny9/fX08++aSmTp2qevXqueKF/bDXVXXFxsvLyxU1FwUHB0v6/0u6Fy8fpqenu52Tnp6uwMDAyhnyKhIYGKgWLVq4okaSbrzxRknSTz/9xH7YaMuWLQoPD1fz5s3d1tmTypWWlqawsDC3tYvv+zh8+DD7YYORI0cqLS1N27Zt044dO3TTTTfJsiwFBQWxHw5xVYXNpEmTNHz4cLe1vXv3SpJatWqlkJAQ1a1bVzt37nQdz8rK0r59+4q9YQxXLjo6Wvv379eFCxdcawcOHJAkBQUFsR82SktLc3sD5EXsSeUKDAzUt99+67Z28Wvk+uuvZz8q2apVqzRr1ix5e3urSZMmqlatmv71r3/p2muvVYsWLdgPh7iqwqZv37767LPP9Nprr+mHH37Q9u3bNX36dPXt21ctW7aUr6+vYmNjNXfuXKWkpGj//v2aMGGCAgMD1aNHD7vHN87999+vatWqadKkSTpw4IDS0tI0c+ZMdejQQW3btmU/bFJYWKiDBw+6rmb+GntSuYYOHapPPvlE8+fP1w8//KDU1FRNnTpV3bp1U5s2bdiPStaqVSutWbNGa9as0bFjx5SUlKTExERNmjRJEl8fTnFVvcfmtttuU3x8vBITE5WYmKh69eqpX79+Gj9+vOuccePGqaCgQDNnztSFCxfUrl07LVu2rNibwXDlAgICtGrVKr344ou677775Ovrqz/96U+aNm2a6xz2o/L98ssvys/Pd32r8W+xJ5Wnc+fOWrJkiRYvXqy3335b/v7+6tGjhx5//HHXOexH5enQoYOef/55JSYm6rnnnlNQUJBefvll/fnPf3adw37Yz8uyLMvuIQAAADzhqroVBQAAzEbYAAAAYxA2AADAGIQNAAAwBmEDAACMQdgAAABjEDYA8Dv4NAyg6iFsANgiJiZGU6dOvaLHWLdunVq3bq2ffvrJ43/ntdde07Jly65oPgCVj7ABUGV1795dSUlJaty4sccfe/78+Tp//rzHHxdAxbqqfqQCALMEBAQoICDA7jEAOAhXbADYJj8/X3PmzNGtt96qyMhIDRs2TEePHnUd3717t2JjYxUREaH27dtrypQpOnPmjOt4SbeV3n33XfXu3VthYWG68847lZqaqtDQUK1bt87tuffs2aP7779fYWFh6t69u9ttp9atW0uSFi1a5Po9gKqBsAFgmw8++EDfffedZs+eraefflp79+7VhAkTJEm7du3SkCFDVLNmTc2fP1/Tp0/X559/roceekgXLlwo8fGSk5M1depU3XzzzUpISFDPnj01evRoFRYWFjs3Li5Offv21ZIlSxQeHq45c+Zo27ZtkqSkpCRJ0j333OP6PYCqgVtRAGzTpEkTJSQkqHr16pKko0ePKjExUefOndO8efPUokULLVmyRNWqVZMkRUREqE+fPlq7dq0GDhxY7PHi4+N122236bnnnpMkdenSRdWrV9e8efOKnTtx4kQ98MADkqTIyEht3bpV//nPf3TbbbcpMjJSkhQYGOj6PYCqgSs2AGwTHh7uihpJat68uSQpKytLe/bsUbdu3WRZlgoKClRQUKDmzZurZcuW+uyzz4o91tGjR3X8+HH16tXLbb1Pnz4lPnd0dLTr97Vr11bDhg2VlZXliZcFwEZcsQFgm9q1a7v92dv7//9f68SJEyoqKtLSpUu1dOnSYn+vRo0axdYuvvemQYMGbuuNGjUq8blr1apV7Ln53Bqg6iNsADhO3bp15eXlpSFDhpR4xeW3USL9/20jSTp9+rTb+m//DMBs3IoC4Dh16tRRaGiovv/+e4WFhbl+3XjjjVq0aJF27txZ7O8EBgbquuuu0+bNm93WP/roo3LNcPHqEYCqha9cAI40ceJEffrpp5o0aZK2b9+urVu3asSIEfr3v/+ttm3bFjvfy8tL48aN05YtWzRr1ix9+umneuONNxQfHy/p8kOlfv36+uKLL7Rr1y5uUQFVCGEDwJE6d+6sZcuW6eTJkxo3bpwmT56satWq6a233vrd71Tq16+fnn32WaWmpmrUqFF6//33NWPGDEnF389zKaNGjdLevXv18MMP68SJE1f6cgBUEi+L/xUBYIiNGzcqNDRUN9xwg2vt448/1iOPPKL169crJCTExukAVAbCBoAxRo4cqUOHDmn8+PFq2rSpjhw5ogULFigoKEgrVqywezwAlYCwAWCMzMxMzZs3Tzt27NCZM2fUsGFD9ezZU+PGjVOdOnXsHg9AJSBsAACAMXjzMAAAMAZhAwAAjEHYAAAAYxA2AADAGIQNAAAwBmEDAACMQdgAAABjEDYAAMAYhA0AADDG/wE4BqfRBwoIxAAAAABJRU5ErkJggg==\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.histplot(data = heightData, x=\"height\", bins = range(152,202,5))\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "8fccd808", "metadata": {}, "source": [ "Compare the histogram to the one above with bin boundaries at 150, 155 etc. The shape of the distribution looks quite different! In the top plot, we seem to have quite a broad distribution, whereas in the bottom one, there is a big spike of people with heights betweem 172 and 177 cm.\n", "\n", "Moving the bin boundaries changed how many observations fell in each bin and thus the shape of the histogram. This can happen easily just due to chance when you have a small number of observations in each bin (check the y-axis in the above histogram - you can see that most bins contain fewer than 10 people, which means that moving just one or two observations between bins makes a big difference to the apparent shape of the histogram).\n", "\n", "For this reason, a histogram may not be the best representation of the data for a small sample.\n", "\n", "#### Exercises\n", "* What change in the code moved the bin boundaries?\n", "* What were the old bin boundaries? What are the new bin boundaries?\n", "* Create a new histogram in which the bin boundaries are at 153,158,163 etc" ] }, { "cell_type": "code", "execution_count": 31, "id": "8b6a94b8", "metadata": {}, "outputs": [], "source": [ "# your code here!" ] }, { "cell_type": "markdown", "id": "6c3acdfa", "metadata": {}, "source": [ "### Bin width\n", "\n", "The code above creates histograms of the people's heights. YOu can copy and paste it, then mmodify it, to complete the following exercise:\n", "\n", "* create a histogram with bin widths of 1cm - can you guess how to do this?\n", " * note how much spikier the histogram looks with 1cm bins - it is hard to see the overall shape of the distribution" ] }, { "cell_type": "code", "execution_count": null, "id": "f00bc1d0", "metadata": {}, "outputs": [], "source": [ "# your code here!" ] }, { "cell_type": "markdown", "id": "a0f42c99", "metadata": {}, "source": [ "## KDE plot\n", "\n", "Whist a histogram shows the number of observations in each of a set of discrete bins, the KDE plot estimates a smooth distribution shape that fits the underlying observations. \n", "\n", "You can think of it as the average of all the histograms you would get if you tried all the possible sets of bin boundaries (for a fixed bin width).\n", "\n", "We can add a kde plot to the histogram by adding an extra argument to the function sns.histplot. Here we reproduce the two different histograms of brothers' heights with different bin boundaries, with the KDE plot added.\n", "\n", "\n", "* although the histograms look rather different, the KDE plots look exactly the same as each other\n", "\n", "*note-*\n", "\n", "* I used some additional commands from `Matplotlib` to make sure the x and y axes cover the same range of values for both plots, to make them easier to compare" ] }, { "cell_type": "code", "execution_count": 36, "id": "2dfd2757", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.histplot(data = heightData, x=\"height\", bins = range(150,200,5), kde=\"True\")\n", "plt.xlim(150,200) \n", "plt.ylim(0,16)\n", "plt.show()\n", "# note that without the command plt.show(), Jupyter will put all plots onto the same axes, \n", "# or (if that is impossible, eg when different plot types were used) \n", "# display only the final plot created in the cell\n", "\n", "sns.histplot(data = heightData, x=\"height\", bins = range(152,202,5), kde=\"True\")\n", "plt.xlim(150,200) \n", "plt.ylim(0,16)\n", "plt.show()\n", "\n" ] }, { "cell_type": "markdown", "id": "43bad4a4", "metadata": {}, "source": [ "#### Exercises\n", "\n", "* Can you find the extra argument that adds the KDE plot?\n", "* Try to switch the KDE plot off!" ] }, { "cell_type": "markdown", "id": "3c9735d6", "metadata": {}, "source": [ "## KDE plot (without histogram)\n", "\n", "If you don't want the histogram, you can plot the KDE plot independently (without a histogram), using the `Seaborn` function `sns.kdeplot()`" ] }, { "cell_type": "code", "execution_count": 9, "id": "e79416df", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.kdeplot(data = heightData, x=\"height\", fill=True) # I think KDE plots look nice filled with shading, hence fill=True\n", "plt.xlabel('height') # set the x axis label\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "799595af", "metadata": {}, "source": [ "### Probability density\n", "\n", "When we plot the KDE as a standalone (rather than over a histogram) the x-axis changes to 'Density' rather than 'count'. \n", "\n", "The values of density are such that the area under the curve of the KDE plot is 1. Technically it is a probability density. It means that probabilities could be read off the graph - so the probability of a member of our sample (one of people in the dataframe) having a height between 160 and 170cm is the same as the area under the curve between 160 and 170cm.\n", "\n", "• this is calculated as 10cm (width of the shaded area) x 0.025 (average 'density' in this area) = 0.25 or 25%\n", "\n", "\n", "\n", "One consequence of this is that you cannot tell from the KDE plot how many data points were in the dataset (which we should care about, as laarger datasets are more likely to reliably represent the population!). To counter this you can add a **rugplot**, which shows the individual datapoints - this gives you 'the best of both worlds'" ] }, { "cell_type": "code", "execution_count": 10, "id": "39f25642", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.kdeplot(data = heightData, x=\"height\", fill=True)\n", "sns.rugplot(data = heightData, x=\"height\")\n", "plt.xlabel('height') # set the x axis label\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "765e878e", "metadata": {}, "source": [ "### Bandwidth\n", "\n", "I said you can think of the KDE plot as a kind of average of all the histograms you would get if you tried all the possible locations for bin boundaries (150,155,160, vs 151,156,161 etc)\n", "\n", "This is true but it only averages histograms for one possible bin width, which is chosen by the computer to give (generally) a good result.\n", "\n", "You saw above that changing the bin width from 5cm to 1cm changed the balance between showing to overall shape of the distribution (where is the main peak) vs the details (details more visible with a small bin boundary). Bandwidth does the equivalent adjustment in a KDE plot.\n", "\n", "The code below shows the height KDE with three bandwidths. The argument bw_adjust is a scaling factor for the default bandwidth chosen by the computer:\n", "\n", "* If bw_adjust = 1.0 the default bandwidth is used (grey KDE)\n", "* If bw_adjust = 0.5, a narrower bandwith of half the default is used (red KDE)\n", "* If bw_adjust = 2.0, a wider bandwith of twice the default is used (blue KDE)\n", "\n" ] }, { "cell_type": "code", "execution_count": 11, "id": "e4fc4c6c", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.kdeplot(data = heightData, x=\"height\", fill=True, bw_adjust=1.0, color='k')\n", "sns.kdeplot(data = heightData, x=\"height\", fill=True, bw_adjust=0.5, color='r')\n", "sns.kdeplot(data = heightData, x=\"height\", fill=True, bw_adjust=2.0, color='b')\n", "plt.xlabel('height') # set the x axis label\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "e00e25a3", "metadata": {}, "source": [ "**Note** With its high bandwidth, the blue KDE plot looks very smooth with just one peak. \n", "\n", "In contrast, with low bandwidth, the red KDE plot tracks local peaks in the data distribution, resulting in lots of little bumps in the KDE plot.\n", "\n", "#### Exercise\n", "\n", "Try out some different values for bw_adjust - can you make the KDE plot go even wobblier? or even smoother?" ] }, { "cell_type": "markdown", "id": "2c3c72ca", "metadata": {}, "source": [ "## Considerations when comparing distributions\n", "\n", "Histograms and KDE plots are good for showing the shape of a data distribution, and hence they are also good for comparing the shape of multiple data distributions\n", "\n", "An easy way to compare two groups is to overlay the histograms or KDE plots, using the `hue` property:" ] }, { "cell_type": "code", "execution_count": 12, "id": "318ebe1b", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.histplot(data = heightData, x=\"height\", hue='sex', bins = range(150,200,5))\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "603d106f", "metadata": {}, "source": [ "You might find that too crowded in which case you may want to separate out the plots onto two separate axes:" ] }, { "cell_type": "code", "execution_count": 13, "id": "7078fe08", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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wWC69N1auatWquWsOAACAUlGs98YCAADwNsQOAAAwGrEDAACMRuwAAACj2R47aWlpeu6559S6dWu1aNFCo0aN0g8//GD3WAAAwBC2x87TTz+tn376SStXrtSHH34oPz8/DRkyRFevXrV7NAAAYABbY+fChQuqU6eOXnnlFYWGhqpBgwYaM2aMUlNT9f3339s5GgAAMESJfs5OSVWrVk0LFixw/vmXX35RbGysAgMD1bBhQxsnAwAAprA1dm704osv6oMPPpCvr6+WLVumihUr2j0SAAAwgO337OQaPHiw1q9fr169emns2LH69ttv7R4JAAAYwGNip2HDhmrWrJleeeUV1alTR6tXr7Z7JAAAYABbYyctLU2bNm1STk6O85iPj48aNGiglJQUGycDAACmsDV2UlJSNHHiRO3bt8957Nq1azpy5IgaNGhg42QAAMAUtsZOcHCw2rZtqxkzZighIUHHjh3TpEmTlJ6eriFDhtg5GgAAMIStseNwOLRw4UK1bt1aUVFR6tOnjy5evKg1a9bojjvusHM0AABgCNu/9dzf31/R0dGKjo62exQAAGAgj/luLAAAgNJA7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGi2x86vv/6ql156Se3bt9e9996rJ598UgkJCXaPBQAADGF77EyYMEGHDh3SggUL9OGHH6pp06YaPny4jh8/bvdoAADAALbGzsmTJ7Vnzx5Nnz5dERERuueee/TCCy+oVq1a2rRpk52jAQAAQ9gaO9WqVdOKFSvUrFkz5zGHwyHLsnTx4kUbJwMAAKYoa+cnr1Klijp06JDn2GeffaZTp06pbdu2Nk0FAABMYvs9Ozc6cOCApk6dqocffliRkZF2jwMAAAzgMbGzZcsWDR8+XGFhYVqwYIHd4wAAAEN4ROysXr1a48aNU/v27bVy5Ur5+fnZPRIAADCE7bHz3nvv6ZVXXlH//v21cOFC+fr62j0SAAAwiK03KJ84cUKzZs1Sp06d9NRTTyktLc35mJ+fn/z9/W2cDgAAmMDW2Pn888917do1bd68WZs3b87zWO/evTVnzhybJgMAAKawNXZGjx6t0aNH2zkCAAAwnO337AAAAJQmYgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABiN2AEAAEYjdgAAgNGIHQAAYDRiBwAAGI3YAQAARiN2AACA0YgdAABgNGIHAAAYjdgBAABGI3YAAIDRiB0AAGA0YgcAABjNo2Jn6dKlGjhwoN1jAAAAg3hM7Lz99ttavHix3WMAAADDlLV7gHPnzumFF17QgQMHVL9+fbvHAQAAhrH9ys63336rqlWr6uOPP1Z4eLjd4wAAAMPYfmUnMjJSkZGRdo8BAAAMZfuVHW9iXb9u9wgAADcrW7ky/30vBm/8Wtl+ZcebOHx89NMHHyojNdXuUTyaf6NGCuz8iN1jAECRlPHz47/vReQXEKC6jz9m9xjFRuwUU0ZqqjLOJNs9hkcrX6OG3SMAQLHx33dz8TIWAAAwGrEDAACMRuwAAACjedQ9O3PmzLF7BAAAYBiu7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxA4AADAasQMAAIxG7AAAAKMROwAAwGi2x87169e1ePFitWvXTuHh4Ro2bJhOnjxp91gAAMAQtsfO0qVL9f7772vmzJlau3atHA6HRo4cqaysLLtHAwAABrA1drKysvTWW29p3Lhx6tChg4KDg/X666/r3Llz2rx5s52jAQAAQ9gaO4mJibp8+bJat27tPFalShWFhIRo//79Nk4GAABM4bAsy7Lrk3/xxRcaN26cDh06JD8/P+fxZ555RhkZGVq+fHmxzhcaGqqcnBzVrl3b3aM6ZV++LCsnp9TObwKfcuVUpkIFvlZFwNeq6PhaFR1fq+Lh61V0jjJlVLZSJbefNzk5WWXKlNHhw4fdfm5JKlsqZy2iq1evSpJ8fX3zHC9fvrwuXrxY7POVL1++1O/1KY1NNhVfq6Lja1V0fK2Kjq9V8fD1sk/ZsmXztYBbz19qZy6C3Ks5WVlZea7sZGZmqkKFCsU+X0JCgttmAwAAZrD1np3cl5tSUlLyHE9JSVFgYKAdIwEAAMPYGjvBwcGqXLmy9u7d6zyWnp6uI0eOKCIiwsbJAACAKWx9GcvX11cDBgzQvHnzVL16dd1555167bXXFBgYqE6dOtk5GgAAMIStsSNJ48ePV3Z2tqZNm6aMjAzdf//9io2NLdUblQAAwB+Hrd96DgAAUNpsf7sIAACA0kTsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwAAACjETsAAMBoxM4tsHTpUg0cODDPsSlTpqhx48Z5frVv3975+PXr17V48WK1a9dO4eHhGjZsmE6ePHmrRy8yV9Z4+vTpfI83btxY69atu9XjF6qg9aWkpGjChAmKiIhQq1atNHHiRJ0/f975uDftoSvr86b9k/KvceDAgQXO37hxY8XFxUny7j0syvq8aQ8L+jt6+PBhDRgwQC1atFCHDh00d+5cZWVlOR/3pv2TXFujt+9hfHy8+vTpoxYtWqhLly5avXp1nsfdtocWStWqVausxo0bWwMGDMhzvHfv3taCBQuslJQU56+0tDTn40uWLLHatGljffnll9Z3331nDRs2zOrUqZOVmZl5q5dQKFfXuHXrVis0NNQ6d+5cnudcvXr1Vi/hpgpaX2ZmptW9e3frscces77++mvr4MGDVteuXa0RI0Y4n+Mte+jq+rxl/yyr4DVeuHAhz9wpKSnWqFGjrK5du1q//fabZVnevYdFWZ+37GFB60tLS7Natmxpvfjii1ZSUpK1Y8cOq3Xr1tacOXOcz/GW/bMs19fozXt48OBBKzg42HrppZesH374wdq6dav14IMPWkuXLnU+x117SOyUkrNnz1rDhw+3mjdvbnXt2jXPBmdnZ1uhoaHW5s2bC/zYzMxMq0WLFtZ7773nPHbx4kUrLCzM2rRpU6nPXlQlWaNlWdayZcusXr163YpRXXKz9a1fv95q3ry5lZqa6jy2c+dO6+GHH7Z+++03r9jDkqzPsjx//yzr5mv8X5988okVEhJiJSYmWpblHf8OS7I+y/L8PbzZ+jZv3mwFBQU5/z5almXNmjXL6tGjh2VZ3rF/llWyNVqWd+/h2LFjrcceeyzP8zdu3GiFh4dbmZmZbt1DXsYqJd9++62qVq2qjz/+WOHh4XkeS0pKUmZmpho0aFDgxyYmJury5ctq3bq181iVKlUUEhKi/fv3l+rcxVGSNUrS0aNH1bBhw9Ie02U3W9+uXbvUunVr1ahRw3msXbt22rJliypXruwVe1iS9Umev3/Szdd4oytXrmju3LkaPHiwGjduLMk7/h2WZH2S5+/hzdZ32223SZL+/ve/KycnRz///LN27NjhfJ437J9UsjVK3r2HJ06cUERERJ5jISEhunr1qr7++mu37qHtbwRqqsjISEVGRhb42LFjx+RwOPTOO+9o586d8vHxUYcOHRQVFSV/f3+dPXtWklS7du08H1ezZk0lJyeX+uxFVZI15j4nICBA/fr1U1JSkurVq6cxY8aoXbt2t3IZv+tm60tKSlJERITefPNNxcXFKTs7W23bttVzzz2nKlWqeMUelmR9kufvn3TzNd7o/fff1+XLl/X00087j3n7Ht6ooPVJnr+HN1tfRESERo0apUWLFun1119XTk6OWrZsqRdffFGSd+yfVLI1St69hwEBAfn24vTp05KktLQ0ORwOSe7ZQ67s2OD777+Xj4+P7rzzTsXExGjSpEnasWOHxowZo+vXr+vq1auSlO+d38uXL6/MzEw7Ri62wtaYlZWlpKQkXbp0SVFRUVqxYoVCQ0M1cuRIxcfH2z1+oS5duqS4uDgdPXpU8+fP18svv6wDBw5ozJgxsizL6/ewsPV5+/7dKCcnR++++6769evnDHFJXr+HuX5vfd6+h+np6UpKSlL//v21bt06LVq0SKdOnVJ0dLQkM/avsDV6+x7++c9/1ueff664uDhdu3ZNJ0+e1MKFC+VwOJSVleXWPeTKjg3GjRunIUOGOP8POSgoSAEBAerbt68OHz4sPz8/Sf/9i5z7e0nKzMxUhQoVbJm5uApbY3h4uPbv36+yZcs6/yI3a9ZMx48fV2xsrNq0aWPn+IUqV66cKlasqPnz56tcuXKSpKpVq6pPnz5G7GFh6wsLC/Pq/bvRvn37dObMGT3++ON5jnv7Hub6vfX5+vp69R7OmzdP6enpWrJkiSSpadOmqlq1qoYMGaLBgwcbsX+FrTE4ONir97BXr146e/asZsyYoalTp6patWp67rnnNHnyZPn7+zuDxh17yJUdGzgcDmcE5AoKCpL030uvuZfsUlJS8jwnJSVFgYGBt2bIEipsjZJUsWLFfMUeFBSkc+fO3ZohSyAwMFD169d3hoAkNWrUSJL0888/e/0eFrY+ybv370ZbtmxRWFiY6tatm+e4t+9hrt9bn+Tde3jgwAGFhobmOZZ7T8iJEyeM2L/C1ih59x5K0qhRo3TgwAFt375dO3fuVLNmzWRZlurVq+fWPSR2bDBx4kQNHz48z7HDhw9Lkho2bKjg4GBVrlxZe/fudT6enp6uI0eO5LuZy1MVtsbExES1aNFCCQkJeZ7zzTffePTNdrkiIiKUmJiojIwM57Fjx45JkurVq+f1e1jY+rx9/2504MCBPDdA5vL2Pcz1e+vz9j0MDAzU0aNH8xzL/Tt69913G7F/ha3R2/dwzZo1mj59unx8fFSrVi2VKVNG//znP1WnTh3Vr1/frXtI7NigR48e2rNnj5YtW6ZTp05px44dmjp1qnr06KEGDRrI19dXAwYM0Lx587R161YlJibq2WefVWBgoDp16mT3+EVS2BqDgoLUqFEjzZgxQwkJCTp+/Lhmz56tr776SqNHj7Z7/EI98cQTKlOmjCZOnKhjx47pwIEDmjZtmlq1aqWmTZt6/R4Wtj5v379cOTk5+uGHH5xXHW/k7Xso3Xx93r6HQ4cO1a5du7Rw4UKdOnVK8fHxmjx5sjp06KAmTZoYsX+FrdHb97Bhw4Zat26d1q1bp9OnT2vt2rWKiYnRxIkTJbn33yD37NjgoYce0qJFixQTE6OYmBj5+/urZ8+eioqKcj5n/Pjxys7O1rRp05SRkaH7779fsbGx+S5XeqrC1ujj46OYmBjNmzdPUVFRSk9PV0hIiFatWpXnW2M9VfXq1bVmzRrNnj1bjz/+uHx9ffXII49oypQpzud48x4Wtj5v379cv/76q65du+b8Ft//5c17KN18fd6+h23bttXy5cv15ptv6p133lG1atXUqVMnPfPMM87nePv+FbZGb9/DVq1a6dVXX1VMTIxmzpypevXq6bXXXtOf/vQn53PctYcOy7Isdy8AAADAU/AyFgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAwGrEDAACMRuwA+MPiJ28AfwzEDoBSERkZqcmTJ5foHBs2bFDjxo2d78flzo9ZtmyZYmNjSzQfAO9A7ADwWB07dtTatWtVs2ZNt5974cKFunr1qtvPC8Dz8HYRADxW9erVVb16dbvHAODluLIDoNRcu3ZNc+fO1YMPPqjmzZtr2LBhOnnypPPxhIQEDRgwQOHh4WrZsqUmTZqk8+fPOx8v6CWpjz76SN26dVNoaKh69eql+Ph4hYSEaMOGDXk+96FDh/TEE08oNDRUHTt2zPOSVe77Br3xxhte8R5CAEqG2AFQav7xj3/o+++/15w5c/TSSy/p8OHDevbZZyVJ+/fv15AhQ+Tn56eFCxdq6tSp2rdvnwYNGqSMjIwCzxcXF6fJkyfr3nvv1dKlS9WlSxeNGTNGOTk5+Z4bHR2tHj16aPny5QoLC9PcuXO1fft2SdLatWslSY899pjz9wDMxctYAEpNrVq1tHTpUpUrV06SdPLkScXExOjSpUuaP3++6tevr+XLl6tMmTKSpPDwcHXv3l3r169X//79851v0aJFeuihhzRz5kxJUrt27VSuXDnNnz8/33MnTJigJ598UpLUvHlzbdu2Tf/+97/10EMPqXnz5pKkwMBA5+8BmIsrOwBKTVhYmDN0JKlu3bqSpPT0dB06dEgdOnSQZVnKzs5Wdna26tatqwYNGmjPnj35znXy5EmdOXNGXbt2zXO8e/fuBX7uiIgI5+8rVqyoGjVqKD093R3LAuBluLIDoNRUrFgxz599fP77/1fJycm6fv26Vq5cqZUrV+b7uPLly+c7lnsvz+23357neEBAQIGfu0KFCvk+Nz9XB/hjInYA3HKVK1eWw+HQkCFDCrwy87+hIv33JSdJSktLy3P8f/8MAP+Ll7EA3HKVKlVSSEiIfvzxR4WGhjp/NWrUSG+88Yb27t2b72MCAwN11113afPmzXmOf/755y7NkHuVCYD5+NcOwBYTJkzQ7t27NXHiRO3YsUPbtm3TiBEj9K9//UtNmzbN93yHw6Hx48dry5Ytmj59unbv3q2//e1vWrRokaTix0uVKlV08OBB7d+/n5e3AMMROwBs0bZtW8XGxurs2bMaP368nn/+eZUpU0arVq363e+Q6tmzp15++WXFx8dr9OjR+vTTT/XCCy9Iyn9/UGFGjx6tw4cPa+TIkUpOTi7pcgB4MIfF/9IA8BKbNm1SSEiI7rnnHuexL7/8Uk899ZQ2btyo4OBgG6cD4KmIHQBeY9SoUTp+/LiioqJUu3ZtJSUlafHixapXr57effddu8cD4KGIHQBe48KFC5o/f7527typ8+fPq0aNGurSpYvGjx+vSpUq2T0eAA9F7AAAAKNxgzIAADAasQMAAIxG7AAAAKMROwAAwGjEDgAAMBqxAwAAjEbsAAAAoxE7AADAaMQOAAAw2v8Dh5TWglgnGFcAAAAASUVORK5CYII=\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.histplot(data = heightData.query('sex==\"M\"'), x=\"height\", color='b')\n", "plt.show()\n", "\n", "sns.histplot(data = heightData.query('sex==\"F\"'), x=\"height\", color='r')\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "34e69fc2", "metadata": {}, "source": [ "However, when we try to compare the two plots on separate axes, the matter is confused by non-matching axis ranges\n", "\n", "* It looks like there are more tall women than tall men, because the peak fo the distribution is further right for the women - but check out the numbers on the $x$-axes\n", "* The most common bin for men contains 9 people, but for women, 7 people - this is not immediately apparent as the $y$-axes don't match\n", "* luckily `seaborn` grouped both men and women into 6 bins - but the bin widths are not actually the same in the two plots\n", "\n", "To make the plots more directly comparable, we should fix the range of $x$ and $y$ axes and the bin boundaries on both plots:" ] }, { "cell_type": "code", "execution_count": 15, "id": "6b57b5d5", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "sns.histplot(data = heightData.query('sex==\"M\"'), x=\"height\", color='b', bins=range(150,200,5))\n", "plt.xlim(150,200)\n", "plt.ylim(0,8)\n", "plt.show()\n", "\n", "sns.histplot(data = heightData.query('sex==\"F\"'), x=\"height\", color='r', bins=range(150,200,5))\n", "plt.xlim(150,200)\n", "plt.ylim(0,8)\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "ab3da198", "metadata": {}, "source": [ "## Controlling plot placement with `plt.subplot()`\n", "\n", "If we make one plot after another as above, they appear in a vertical series.\n", "\n", "We might prefer them to sit next to eachother horizontally. Or, if we have multiple plots, to be arranged in a 2x2 or 2x3 grid.\n", "\n", "This can be achieved using the `matplotlib` function `plt.subplot()`, which creates a grid of axes and places each figure within it.\n", "\n", "For example, a 2x1 grid (2 rows, 1 column):" ] }, { "cell_type": "code", "execution_count": 20, "id": "dfa8bcdc", "metadata": {}, "outputs": [ { "data": { "image/png": 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wGmUHAAAYjbIDAACMRtkBAABGs73slJeX680331SXLl0UERGhkSNH6vjx43bHAgAAhrC97KxYsUIff/yx5s2bp5SUFDkcDj377LMqLi62OxoAADCArWWnuLhY7733niZMmKBu3bopNDRUS5cu1Xfffae0tDQ7owEAAEPYWnYOHTqkS5cuqVOnTs41f39/tWnTRrt377YxGQAAMIXDsizLrm/++eefa8KECdq3b59q1KjhXH/++edVWFiod95556aOFxYWprKyMvnXa6iycttO65ZV8XGods1qdsfwiEtXSir1LKpV9VHN6lU5jzuACecgmXEeJpyDZM55mPBvxpkzZ1SlShUdOHDAK8ev6pWj3qArV65Iknx9fV3Wq1evrvz8/Js+XvXq1VVcXFzph24SU2bBedw5TDgHyYzzMOEcJHPOozKrWrXqNV3Ao8f32pFvwNWrOcXFxS5XdoqKilSzZs2bPt6ePXs8lg0AAJjB1nt2mjRpIknKyclxWc/JyVFQUJAdkQAAgGFsLTuhoaHy8/PTrl27nGsFBQU6ePCgoqKibEwGAABMYevLWL6+voqLi9OiRYtUv3593XPPPXrjjTcUFBSkmJgYO6MBAABD2Fp2JGnixIkqLS3V7NmzVVhYqPbt2ys5OdmrNyoBAIC7h62/eg4AAOBttn9cBAAAgDdRdgAAgNEoOwAAwGiUHQAAYDTKDgAAMBplBwAAGI2yAwAAjFbpys6KFSs0dOhQl7UZM2aoVatWLl9du3Z1bi8vL9ebb76pLl26KCIiQiNHjtTx48dvd3TjVDSLnJwcTZkyRVFRUerYsaOmTp2qc+fOObczC+/58TyGDh16zfPi6ldqaqok5uEtFT03Dhw4oLi4OLVr107dunXTwoULVVxc7NzOLLyjollkZGRo4MCBateunXr16qXVq1e7bGcWnnPhwgW9/PLL6tq1qx566CE9/fTTLh/a/c033yguLk6RkZHq3r27kpOTXR7vsVlYlcj7779vtWrVyoqLi3NZj42NtZYsWWLl5OQ4v/Ly8pzbly1bZnXu3Nn68ssvrW+++cYaOXKkFRMTYxUVFd3uUzBGRbMoKiqy+vbtaz355JPW/v37rb1791q9e/e2Ro8e7dyHWXhHRfM4f/68y3MiJyfHGjNmjNW7d2/rhx9+sCyLeXhDRbPIy8uzOnToYM2ZM8fKzs62tm3bZnXq1MlasGCBcx9m4XkVzWLv3r1WaGio9fLLL1tHjx610tPTrUceecRasWKFcx9m4TkjRoywHn/8cWv37t3WsWPHrFdeecUKDw+3jh49ap07d87q2LGjNWvWLOvo0aPWunXrrLCwMGvdunXOx3tqFpWi7Jw9e9YaNWqUFRkZafXu3dvlf7ilpaVWWFiYlZaWVuFji4qKrHbt2llr1qxxruXn51vh4eHWpk2bvJ7dNNebxfr1663IyEgrNzfXubZ9+3arZ8+e1g8//MAsvOB68/ixTz75xGrTpo116NAhy7J4bnja9WaRlpZmhYSEOEumZVnW/PnzrX79+lmWxSw87XqzGD9+vPXkk0+67L9x40YrIiLCKioqYhYelJ2dbYWEhFiZmZnOtfLycismJsZKSEiwkpKSrC5dulglJSXO7YsXL7Z69eplWZZnnxeV4mWsf/zjH6pbt67+/Oc/KyIiwmVbdna2ioqK1KJFiwofe+jQIV26dEmdOnVyrvn7+6tNmzbavXu3V3Ob6Hqz2LFjhzp16qSGDRs617p06aItW7bIz8+PWXjB9ebxny5fvqyFCxdq2LBhatWqlSSeG552vVnUq1dPkvTHP/5RZWVlOnnypLZt2+bcj1l41vVmkZWVpaioKJe1Nm3a6MqVK9q/fz+z8KCAgAC9++67evDBB51rDodDlmUpPz9fe/bsUfv27VW16v/7mM5OnTopKytLeXl5Hp2F7R8EeiN69OihHj16VLjtyJEjcjgc+uCDD7R9+3b5+PioW7dumjRpkurUqaOzZ89Kkpo0aeLyuEaNGunMmTNez26a680iOztbUVFRWr58uVJTU1VaWqro6Gi9+OKL8vf3ZxZecL15/KePP/5Yly5d0m9+8xvnGvPwrOvNIioqSmPGjFFiYqKWLl2qsrIydejQQXPmzJHELDzterMIDAy85md66tQpSVJeXp4cDockZuEJ/v7+6tatm8vaZ599phMnTig6OlpLly5VSEiIy/ZGjRpJkk6fPu3R50WluLJzPf/85z/l4+Oje+65R0lJSZo2bZq2bdumcePGqby8XFeuXJGkaz5FvXr16ioqKrIjsrEuXryo1NRUHT58WIsXL9bvf/97ZWZmaty4cbIsi1nYpKysTB9++KEGDx6sOnXqONeZx+1TUFCg7OxsDRkyRGvXrlViYqJOnDih+Ph4SczidhowYIA2b96s1NRUlZSU6Pjx40pISJDD4VBxcTGz8KLMzEzNnDlTPXv2VI8ePVRYWFjhz1mSioqKPDqLSnFl53omTJig4cOHy9/fX5IUEhKiwMBADRo0SAcOHFCNGjUkScXFxc4/S//+QdasWdOWzKaqVq2aatWqpcWLF6tatWqSpLp162rgwIHMwkZfffWVTp8+raeeesplnXncPosWLVJBQYGWLVsmSWrbtq3q1q2r4cOHa9iwYcziNnr88cd19uxZ/e53v9PMmTMVEBCgF198UdOnT1edOnWc/4gyC8/asmWLXnjhBUVERGjJkiWS/v130H/+RqIk58+/Vq1aHn1eVPorOw6Hw1l0rrp6Wezs2bPOy185OTku++Tk5CgoKOj2hLxLBAUFqVmzZs6iI0kPPPCAJOnkyZPMwiZbtmxReHi4mjZt6rLOPG6fzMxMhYWFuaxdvZckKyuLWdxmY8aMUWZmprZu3art27frwQcflGVZCg4OZhZesHr1ak2YMEFdu3bVypUrncUlKCiowp+zJDVu3Nijs6j0ZWfq1KkaNWqUy9qBAwckSS1btlRoaKj8/Py0a9cu5/aCggIdPHjwmpvUcGuioqJ06NAhFRYWOteOHDkiSQoODmYWNsnMzHS5we8q5nH7BAUF6fDhwy5rV58b999/P7O4jT766CPNnTtXPj4+aty4sapUqaK//OUvuvfee9WsWTNm4WFr1qzRK6+8oiFDhighIcHlJan27dsrMzNTZWVlzrWMjAw1a9ZMDRo08OgsKn3Z6devn3bu3Km3335bJ06c0LZt2zRz5kz169dPLVq0kK+vr+Li4rRo0SKlp6fr0KFDmjx5soKCghQTE2N3fKP8+te/VpUqVTR16lQdOXJEmZmZmj17tjp27Ki2bdsyCxuUlZXp6NGj19wEKIl53EYjRozQjh07lJCQoBMnTigjI0PTp09Xt27d1Lp1a2ZxG7Vs2VJr167V2rVrderUKaWkpCgpKUlTp06VxPPCk7KysjR//nzFxMRo7NixysvLU25urnJzc/XDDz/oV7/6lS5evKhZs2bp6NGj2rBhgz744AONHTtWkmdnUenv2Xn00UeVmJiopKQkJSUlqU6dOurfv78mTZrk3GfixIkqLS3V7NmzVVhYqPbt2ys5Ofmam55wa+rXr6+PPvpIr732mp566in5+vrqF7/4hWbMmOHch1ncXhcuXFBJSYnzV59/jHncHtHR0XrnnXe0fPlyffDBBwoICFBMTIyef/555z7M4vbo2LGjXn31VSUlJWnevHkKDg7WG2+8oV/+8pfOfZiFZ2zevFklJSVKS0tTWlqay7bY2FgtWLBAf/jDH/Tqq68qNjZWgYGBeumllxQbG+vcz1OzcFiWZXnkrAAAAO5Alf5lLAAAgOuh7AAAAKNRdgAAgNFsLzslJSVaunSpunfvrnbt2mnw4MH6+9//bncsAABgCNvLzttvv63169dr3rx5Sk1NVfPmzfXss8/qu+++szsaAAAwgO1lJz09Xf369VN0dLSCg4M1ffp0Xbx4UV9//bXd0QAAgAFsf5+devXqaevWrYqLi1OTJk2UkpIiX19ftW7d+qaPFRUVpeLiYgUGBnohKQAA8Ibc3Fz5+vpqz549Xjm+7WVn1qxZmjx5snr27KkqVarIx8dHiYmJuu+++276WEVFRS5vOw0AAO58paWl8ubb/tledo4dOyZ/f38tX75cjRs31tq1azVt2jStXr1aoaGhN3WsRo0aSfr3S2MAAKBy6Nmzp1ePb2vZOXXqlF588UWtWrXK+aFeYWFhOnr0qJYtW6bly5fbGQ8AABjA1huU9+/fr5KSEoWFhbmsR0REKDs7255QAADAKLaWnSZNmkiSDh8+7LJ+5MgRBQcH2xEJAAAYxtayEx4erqioKE2bNk1/+9vflJ2drYSEBGVkZGjMmDF2RgMAAIaw9Z4dHx8frVixQgkJCZoxY4by8/MVEhKiVatWKTIy0s5oAADAELb/NlbdunU1d+5czZ071+4oAADAQLa/gzIAAIA3UXYAAIDRKDsAAMBolB0AAGA0yg4AADAaZQcAABiNsgMAAIxG2QEAAEaj7AAAAKNRdgAAgNEoOwAAwGiUHQAAYDTKDgAAMBplBwAAGI2yAwAAjEbZAQAARqPsAAAAo1F2AACA0Sg7AADAaJQdAABgNMoOAAAwGmUHAAAYjbIDAACMRtkBAABGo+wAAACjUXYAAIDRKDsAAMBolB0AAGA0yg4AADAaZQcAABjtjig7qamp6tOnj8LCwtS3b1999tlndkcCAACGsL3sbNy4UTNnztSgQYO0adMm9enTR1OmTNHevXvtjgYAAAxga9mxLEuJiYkaNmyYhg0bpuDgYI0fP14///nP9dVXX9kZDQAAGKKqnd/8X//6l06dOqX+/fu7rCcnJ9uUCAAAmMbWKzvZ2dmSpMuXL2vUqFHq3LmzBg4cqC+++MLOWAAAwCC2lp2LFy9KkqZNm6Z+/frpvffe0yOPPKJx48YpIyPDzmgAAMAQtr6MVa1aNUnSqFGjFBsbK0lq3bq1Dh48qPfff1+dO3e2Mx4AADCArVd2goKCJEkhISEu6y1bttTJkyftiAQAAAxja9lp06aNateurX379rmsHzlyRPfdd59NqQAAgElsfRmrRo0aGj16tJYvX67GjRsrPDxcn376qXbu3KlVq1bZGQ0AABjC1rIjSePGjVPNmjW1dOlSfffdd2rRooWWLVumjh072h0NAAAYwPayI0kjRozQiBEj7I4BAAAMZPvHRQAAAHgTZQcAABiNsgMAAIxG2QEAAEaj7AAAAKNRdgAAgNEoOwAAwGiUHQAAYDTKDgAAMBplBwAAGI2yAwAAjEbZAQAARqPsAAAAo1F2gLuEVV5ud4RbZsI5SOacB1BZVHXnQbt371abNm1Uu3bta7YVFBRox44d6tu37y2HA+A5Dh8fffs/61SYm2t3FLfUeeABBT32i0p9DpJUIzBQTZ960u4YwF3FrbLzzDPPKCUlReHh4ddsO3jwoGbMmEHZAe5Ahbm5Kjx9xu4YbqnesKGkyn0OAOxxw2Vn2rRpOnPm33/BWJal+Ph4+fn5XbNfdna2Gv7/fykBAADY7Ybv2enVq5csy5JlWc61q/999cvHx0eRkZF67bXXvBIWAADgZt3wlZ0ePXqoR48ekqShQ4cqPj5eLVq08FowAAAAT3Drnp0PP/zQ0zkAAAC8wq2yc+XKFSUlJWnr1q26cuWKyn/0a5QOh0NbtmzxSEAAAIBb4VbZefXVV7V+/Xp16NBBrVu3lo8Pb9cDAADuTG6Vnc8//1yTJ0/WmDFjPJ0HAADAo9y6JFNaWlrhe+wAAADcadwqO9HR0dq+fbunswAAAHicWy9j9enTR3PnztW5c+cUERGhmjVrXrPPE088cavZAAAAbplbZWfSpEmSpNTUVKWmpl6z3eFwUHYAAMAdwa2yk56e7ukcAAAAXuFW2bnnnns8nQMAAMAr3Co7b7311k/u89vf/tadQwMAAHiUx8uOn5+fGjVq5FbZycrK0oABAzRnzhwNGDDAnWgAAAAu3Co7hw4dumbt8uXLyszMVHx8vObMmXPTxywpKdELL7ygy5cvuxMJAACgQh77nIdatWqpS5cuGj9+vBYuXHjTj1+2bJlq167tqTgAAACSPFh2rmrSpImOHTt2U4/ZvXu3UlJS9Prrr3s6DgAAuMt5rOxYlqXTp09r5cqVN/XbWgUFBXrppZc0e/ZsNWnSxFNxcAewysvtjuARppwHANyt3LpnJzQ0VA6Ho8JtlmXd1MtY8fHxioyMVP/+/d2JgjuYw8dH3/7POhXm5todxW11HnhAQY/9wpjzAIC7kVtlZ/z48RWWHT8/P3Xv3l3333//DR0nNTVVe/bs0SeffOJODFQChbm5Kjx9xu4YbqvesKEkc84DAO5GbpWdCRMmeOSbr1+/Xnl5eerevbvL+ty5c5WcnKxPP/3UI98HAADcvdwqO5JUXFysDRs2aNeuXSooKFBAQICioqIUGxur6tWr39AxFi1apMLCQpe1xx57TBMnTlSfPn3cjQYAAODkVtkpKCjQM888o0OHDulnP/uZAgMDlZWVpU2bNumjjz7SmjVrVKdOnZ88TuPGjStcb9CgAR9JAQAAPMKt38ZavHixzp49q9WrV+uLL75QSkqKvvjiC61evVp5eXlKTEz0dE4AAAC3uFV20tPTNWnSJEVFRbmsR0VFaeLEifr888/dDnT48GE+KgIAAHiMW2Xn0qVLatq0aYXbmjZtqgsXLtxKJgAAAI9xq+w0b95cW7durXBbenq6goODbykUAACAp7h1g/KoUaM0ZcoUFRcXq3///mrYsKG+//57ffLJJ1q7dq3i4+M9HBMAAMA9bpWdPn36KDs7W0lJSVq7dq1zvVq1aho/frwGDRrksYAAAAC3wq2yc/nyZY0bN05xcXH6+uuvlZ+frzNnzmjQoEGqW7eupzMCAAC47abu2fnmm2/0xBNPaNWqVZIkf39/de3aVV27dlVCQoIGDx580594DgAA4E03XHa+/fZbDR8+XPn5+WrZsqXLNl9fX82cOVOXLl3S4MGDdfbsWY8HBQAAcMcNl513331XAQEB+tOf/qTHHnvMZVvNmjUVFxen9evXq1atWkpKSvJ4UAAAAHfccNnJyMjQ6NGjVa9evf+6T4MGDTRixAhlZGR4IhsAAMAtu+Gyk5ube0PvnxMSEsLLWAAA4I5xw2Wnfv36ysnJ+cn9zp07d92rPwAAALfTDZed9u3ba8OGDT+5X2pqqlq3bn1LoQAAADzlhsvO0KFDtWvXLi1YsEBFRUXXbC8uLtbrr7+uHTt2aMiQIR4NCQAA4K4bflPBsLAwzZgxQ/Pnz9fGjRvVuXNn3XvvvSorK9Pp06e1a9cunT9/Xs8//7y6dOnizcx3Bau8XA4ftz66DAAA/IebegflIUOGKDQ0VMnJyUpPT3de4aldu7aio6M1cuRIRUREeCXo3cbh46Nv/2edCnNz7Y7iljoPPKCgx35hdwwAAG7+4yIefvhhPfzww5Kk8+fPy8fHh4+I8JLC3FwVnj5jdwy3VG/Y0O4IAABIcvOzsa4KCAjwVA4AAACv4KYQAABgNMoOAAAwGmUHAAAYjbIDAACMRtkBAABGo+wAAACjUXYAAIDRKDsAAMBolB0AAGA0yg4AADAaZQcAABiNsgMAAIxG2QEAAEazvexcuHBBL7/8srp27aqHHnpITz/9tPbs2WN3LAAAYAjby86UKVO0b98+LVmyROvWrVPbtm01atQoHTt2zO5oAADAALaWnePHj2vnzp2aO3euoqKi1Lx5c82aNUuNGzfWpk2b7IwGAAAMYWvZCQgI0LvvvqsHH3zQueZwOGRZlvLz821MBgAATFHVzm/u7++vbt26uax99tlnOnHihKKjo21KBQDeU9XPT1Z5uRw+tt9FcEtMOAfcPWwtOz+WmZmpmTNnqmfPnurRo4fdcQDA46rUqCGHj4++/Z91KszNtTuOW2oEBqrpU0/aHQO4YXdM2dmyZYteeOEFRUREaMmSJXbHAQCvKszNVeHpM3bHAO4Kd8Q1yNWrV2vChAnq2rWrVq5cqRo1atgdCQAAGML2srNmzRq98sorGjJkiBISEuTr62t3JAAAYBBbX8bKysrS/PnzFRMTo7FjxyovL8+5rUaNGqpTp46N6QAAgAlsLTubN29WSUmJ0tLSlJaW5rItNjZWCxYssCkZAAAwha1l57nnntNzzz1nZwQAAGA42+/ZAQAA8CbKDgAAMBplBwAAGI2yAwAAjEbZAQAARqPsAAAAo1F2AACA0Sg7AADAaJQdAABgNMoOAAAwGmUHAAAYjbIDAACMRtkBAABGo+wAAG5KVT8/WeXldsfwCFPOA9dX1e4AAIDKpUqNGnL4+Ojb/1mnwtxcu+O4rUZgoJo+9aTdMXAbUHYAAG4pzM1V4ekzdscAfhIvYwEAAKNRdgAAgNEoOwAAwGiUHQAAYDTKDgAAMBplBwAAGI2yAwAAjEbZAQAARqPsAAAAo1F2AACA0Sg7AADAaJQdAABgNMoOAAAwGmUHAAAYzfayU15erjfffFNdunRRRESERo4cqePHj9sdCwAAGML2srNixQp9/PHHmjdvnlJSUuRwOPTss8+quLjY7mgAAMAAtpad4uJivffee5owYYK6deum0NBQLV26VN99953S0tLsjAYAAAzhsCzLsuub79+/XwMHDtRf/vIXNWvWzLn+9NNPq1WrVoqPj7+p44WFhamsrExNmjTxcFJ7lF66JKuszO4YbvGpVk1Vatas1OcgcR53EhPOQTLjPEw4B0lyVKmiqrVr2x0Dks6cOaMqVarowIEDXjl+Va8c9QadPXtWkq4pJ40aNdKZM2du+njVq1c36uUvE56EJpyDxHncSUw4B8mM8zDhHHBnqFq1qnx9fb13fK8d+QZcuXJFkq45werVqys/P/+mj7dnzx6P5AIAAOaw9Z6dGjVqSNI1V2OKiopUs2ZNOyIBAADD2Fp2rr58lZOT47Kek5OjoKAgOyIBAADD2Fp2QkND5efnp127djnXCgoKdPDgQUVFRdmYDAAAmMLWe3Z8fX0VFxenRYsWqX79+rrnnnv0xhtvKCgoSDExMXZGAwAAhrC17EjSxIkTVVpaqtmzZ6uwsFDt27dXcnKyV+/KBgAAdw9b32cHAADA22z/uAgAAABvouwAAACjUXYAAIDRKDsAAMBolB0AAGA0yg4AADBapSs7K1as0NChQ13WZsyYoVatWrl8de3a1bm9vLxcb775prp06aKIiAiNHDlSx48fv93RjVPRLHJycjRlyhRFRUWpY8eOmjp1qs6dO+fcziy858fzGDp06DXPi6tfqampkpiHt1T03Dhw4IDi4uLUrl07devWTQsXLnT5XEBm4R0VzSIjI0MDBw5Uu3bt1KtXL61evdplO7PwnAsXLujll19W165d9dBDD+npp592+dDub775RnFxcYqMjFT37t2VnJzs8niPzcKqRN5//32rVatWVlxcnMt6bGystWTJEisnJ8f5lZeX59y+bNkyq3PnztaXX35pffPNN9bIkSOtmJgYq6io6HafgjEqmkVRUZHVt29f68knn7T2799v7d271+rdu7c1evRo5z7Mwjsqmsf58+ddnhM5OTnWmDFjrN69e1s//PCDZVnMwxsqmkVeXp7VoUMHa86cOVZ2dra1bds2q1OnTtaCBQuc+zALz6toFnv37rVCQ0Otl19+2Tp69KiVnp5uPfLII9aKFSuc+zALzxkxYoT1+OOPW7t377aOHTtmvfLKK1Z4eLh19OhR69y5c1bHjh2tWbNmWUePHrXWrVtnhYWFWevWrXM+3lOzqBRl5+zZs9aoUaOsyMhIq3fv3i7/wy0tLbXCwsKstLS0Ch9bVFRktWvXzlqzZo1zLT8/3woPD7c2bdrk9eymud4s1q9fb0VGRlq5ubnOte3bt1s9e/a0fvjhB2bhBdebx4998sknVps2baxDhw5ZlsVzw9OuN4u0tDQrJCTEWTIty7Lmz59v9evXz7IsZuFp15vF+PHjrSeffNJl/40bN1oRERFWUVERs/Cg7OxsKyQkxMrMzHSulZeXWzExMVZCQoKVlJRkdenSxSopKXFuX7x4sdWrVy/Lsjz7vKgUL2P94x//UN26dfXnP/9ZERERLtuys7NVVFSkFi1aVPjYQ4cO6dKlS+rUqZNzzd/fX23atNHu3bu9mttE15vFjh071KlTJzVs2NC51qVLF23ZskV+fn7MwguuN4//dPnyZS1cuFDDhg1Tq1atJPHc8LTrzaJevXqSpD/+8Y8qKyvTyZMntW3bNud+zMKzrjeLrKysaz5ouk2bNrpy5Yr279/PLDwoICBA7777rh588EHnmsPhkGVZys/P1549e9S+fXtVrfr/PrmqU6dOysrKUl5enkdnYftnY92IHj16qEePHhVuO3LkiBwOhz744ANt375dPj4+6tatmyZNmqQ6dero7NmzkqQmTZq4PK5Ro0Y6c+aM17Ob5nqzyM7OVlRUlJYvX67U1FSVlpYqOjpaL774ovz9/ZmFF1xvHv/p448/1qVLl/Sb3/zGucY8POt6s4iKitKYMWOUmJiopUuXqqysTB06dNCcOXMkMQtPu94sAgMDr/mZnjp1SpKUl5cnh8MhiVl4gr+/v7p16+ay9tlnn+nEiROKjo7W0qVLFRIS4rK9UaNGkqTTp0979HlRKa7sXM8///lP+fj46J577lFSUpKmTZumbdu2ady4cSovL9eVK1ck6ZoPFq1evbqKiorsiGysixcvKjU1VYcPH9bixYv1+9//XpmZmRo3bpwsy2IWNikrK9OHH36owYMHq06dOs515nH7FBQUKDs7W0OGDNHatWuVmJioEydOKD4+XhKzuJ0GDBigzZs3KzU1VSUlJTp+/LgSEhLkcDhUXFzMLLwoMzNTM2fOVM+ePdWjRw8VFhZW+HOWpKKiIo/OolJc2bmeCRMmaPjw4fL395ckhYSEKDAwUIMGDdKBAwdUo0YNSVJxcbHzz9K/f5A1a9a0JbOpqlWrplq1amnx4sWqVq2aJKlu3boaOHAgs7DRV199pdOnT+upp55yWWcet8+iRYtUUFCgZcuWSZLatm2runXravjw4Ro2bBizuI0ef/xxnT17Vr/73e80c+ZMBQQE6MUXX9T06dNVp04d5z+izMKztmzZohdeeEERERFasmSJpH//HfSfv5Eoyfnzr1WrlkefF5X+yo7D4XAWnauuXhY7e/as8/JXTk6Oyz45OTkKCgq6PSHvEkFBQWrWrJmz6EjSAw88IEk6efIks7DJli1bFB4erqZNm7qsM4/bJzMzU2FhYS5rV+8lycrKYha32ZgxY5SZmamtW7dq+/btevDBB2VZloKDg5mFF6xevVoTJkxQ165dtXLlSmdxCQoKqvDnLEmNGzf26CwqfdmZOnWqRo0a5bJ24MABSVLLli0VGhoqPz8/7dq1y7m9oKBABw8evOYmNdyaqKgoHTp0SIWFhc61I0eOSJKCg4OZhU0yMzNdbvC7inncPkFBQTp8+LDL2tXnxv33388sbqOPPvpIc+fOlY+Pjxo3bqwqVaroL3/5i+699141a9aMWXjYmjVr9Morr2jIkCFKSEhweUmqffv2yszMVFlZmXMtIyNDzZo1U4MGDTw6i0pfdvr166edO3fq7bff1okTJ7Rt2zbNnDlT/fr1U4sWLeTr66u4uDgtWrRI6enpOnTokCZPnqygoCDFxMTYHd8ov/71r1WlShVNnTpVR44cUWZmpmbPnq2OHTuqbdu2zMIGZWVlOnr06DU3AUpiHrfRiBEjtGPHDiUkJOjEiRPKyMjQ9OnT1a1bN7Vu3ZpZ3EYtW7bU2rVrtXbtWp06dUopKSlKSkrS1KlTJfG88KSsrCzNnz9fMTExGjt2rPLy8pSbm6vc3Fz98MMP+tWvfqWLFy9q1qxZOnr0qDZs2KAPPvhAY8eOleTZWVT6e3YeffRRJSYmKikpSUlJSapTp4769++vSZMmOfeZOHGiSktLNXv2bBUWFqp9+/ZKTk6+5qYn3Jr69evro48+0muvvaannnpKvr6++sUvfqEZM2Y492EWt9eFCxdUUlLi/NXnH2Met0d0dLTeeecdLV++XB988IECAgIUExOj559/3rkPs7g9OnbsqFdffVVJSUmaN2+egoOD9cYbb+iXv/ylcx9m4RmbN29WSUmJ0tLSlJaW5rItNjZWCxYs0B/+8Ae9+uqrio2NVWBgoF566SXFxsY69/PULByWZVkeOSsAAIA7UKV/GQsAAOB6KDsAAMBolB0AAGA0yg4AADAaZQcAABiNsgMAAIxG2QEAAEaj7AC4a/E2Y8DdgbIDwCt69Oih6dOn39IxNmzYoFatWunkyZMef8zbb7+t5OTkW8oHoHKg7AC4Y3Xv3l0pKSlq1KiRx4+dkJCgK1euePy4AO48lf6zsQCYq379+qpfv77dMQBUclzZAeA1JSUlWrhwoR555BFFRkZq5MiROn78uHP7nj17FBcXp4iICHXo0EHTpk3TuXPnnNsreknqT3/6k/r06aOwsDA9/vjjysjIUJs2bbRhwwaX771v3z79+te/VlhYmLp37+7yklWrVq0kSW+99ZbzzwDMRdkB4DX/+7//q3/+859asGCBXn75ZR04cECTJ0+WJO3evVvDhw9XjRo1lJCQoJkzZ+qrr77SM888o8LCwgqPl5qaqunTp+uhhx7SihUr1KtXL40bN05lZWXX7BsfH69+/frpnXfeUXh4uBYuXKitW7dKklJSUiRJTz75pPPPAMzFy1gAvKZx48ZasWKFqlWrJkk6fvy4kpKSdPHiRS1evFjNmjXTO++8oypVqkiSIiIi1LdvX61fv15Dhgy55niJiYl69NFHNW/ePElSly5dVK1aNS1evPiafadMmaKnn35akhQZGakvvvhCf/vb3/Too48qMjJSkhQUFOT8MwBzcWUHgNeEh4c7i44kNW3aVJJUUFCgffv2qVu3brIsS6WlpSotLVXTpk3VokUL7dy585pjHT9+XKdPn1bv3r1d1vv27Vvh946KinL+uVatWmrYsKEKCgo8cVoAKhmu7ADwmlq1arn8t4/Pv///1ZkzZ1ReXq6VK1dq5cqV1zyuevXq16xdvZenQYMGLuuBgYEVfu+aNWte8715Xx3g7kTZAXDb+fn5yeFwaPjw4RVemflxUZH+/ZKTJOXl5bms//i/AeDHeBkLwG1Xu3ZttWnTRv/6178UFhbm/HrggQf01ltvadeuXdc8JigoSPfdd5/S0tJc1jdv3uxWhqtXmQCYj2c7AFtMmTJFf/3rXzV16lRt27ZNX3zxhUaPHq3/+7//U9u2ba/Z3+FwaOLEidqyZYvmzp2rv/71r/rDH/6gxMRESTdfXvz9/bV3717t3r2bl7cAw1F2ANgiOjpaycnJOnv2rCZOnKiXXnpJVapU0fvvv/9ff0Oqf//++v3vf6+MjAw999xz+vTTTzVr1ixJ194f9FOee+45HThwQM8++6zOnDlzq6cD4A7msPi/NAAqiU2bNqlNmzZq3ry5c+3LL7/U2LFjtXHjRoWGhtqYDsCdirIDoNIYM2aMjh07pkmTJqlJkybKzs7Wm2++qeDgYH344Yd2xwNwh6LsAKg0zp8/r8WLF2v79u06d+6cGjZsqF69emnixImqXbu23fEA3KEoOwAAwGjcoAwAAIxG2QEAAEaj7AAAAKNRdgAAgNEoOwAAwGiUHQAAYDTKDgAAMBplBwAAGO3/A8jmwXCFBPF9AAAAAElFTkSuQmCC\n", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.subplot(2,1,1)\n", "sns.histplot(data = heightData.query('sex==\"M\"'), x=\"height\", color='b', bins=range(150,200,5))\n", "plt.xlim(150,200)\n", "plt.ylim(0,8)\n", "\n", "plt.subplot(2,1,2)\n", "sns.histplot(data = heightData.query('sex==\"F\"'), x=\"height\", color='r', bins=range(150,200,5))\n", "plt.xlim(150,200)\n", "plt.ylim(0,8)\n", "\n", "plt.tight_layout() # optimize the white space between the axes - needed when using plt.subplot()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "2b6ba4ce", "metadata": {}, "source": [ "... or a 1x2 grid (one row, 2 columns)" ] }, { "cell_type": "code", "execution_count": 21, "id": "79f6a32c", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.subplot(1,2,1)\n", "sns.histplot(data = heightData.query('sex==\"M\"'), x=\"height\", color='b', bins=range(150,200,5))\n", "plt.xlim(150,200)\n", "plt.ylim(0,8)\n", "\n", "plt.subplot(1,2,2)\n", "sns.histplot(data = heightData.query('sex==\"F\"'), x=\"height\", color='r', bins=range(150,200,5))\n", "plt.xlim(150,200)\n", "plt.ylim(0,8)\n", "\n", "plt.tight_layout() # optimize the white space between the axes - needed when using plt.subplot()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "51e0eb83", "metadata": {}, "source": [ "Perhaps you will agree that the vertical arrangement allows for an easier comparison between men and women in this case - in the vertical arrangement we can easily see that the men are generally taller\n", "\n", "#### Multiple plots in a grid\n", "\n", "Say we now want to plot weights as well as heights - how about a 2x2 grid (2 rows, 2 columns)?" ] }, { "cell_type": "code", "execution_count": 29, "id": "78843037", "metadata": {}, "outputs": [ { "data": { "image/png": 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\n", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.subplot(2,2,1)\n", "sns.histplot(data = heightData.query('sex==\"M\"'), x=\"height\", color='b', bins=range(150,200,5))\n", "plt.xlim(150,200)\n", "plt.ylim(0,8)\n", "\n", "plt.subplot(2,2,2)\n", "sns.histplot(data = heightData.query('sex==\"M\"'), x=\"weight\", color='b', bins=range(50,100,5))\n", "plt.xlim(50,100)\n", "plt.ylim(0,8)\n", "\n", "plt.subplot(2,2,3)\n", "sns.histplot(data = heightData.query('sex==\"F\"'), x=\"height\", color='r', bins=range(150,200,5))\n", "plt.xlim(150,200)\n", "plt.ylim(0,8)\n", "\n", "plt.subplot(2,2,4)\n", "sns.histplot(data = heightData.query('sex==\"F\"'), x=\"weight\", color='r', bins=range(50,100,5))\n", "plt.xlim(50,100)\n", "plt.ylim(0,8)\n", "\n", "plt.tight_layout() # optimize the white space between the axes - needed when using plt.subplot()\n", "plt.show()" ] }, { "cell_type": "markdown", "id": "9895ed83", "metadata": {}, "source": [ "The syntax of `plt.subplot()` is:\n", "* the first number inside brackets is the number of **rows** of axes within your compound figure\n", "* the second number inside brackets is the number of **columns** of axes within your compound figure\n", "* the third number inside brackets is the location in which you want to place the next plot, numbered as if reading from top to bottom and left to right - some examples are shown in the following figures\n", "\n", "**Three rows, two columns:**\n", "\n", "\n", "\n", "**Two rows, four columns:**\n", "\n", "\n" ] }, { "cell_type": "markdown", "id": "c242ad14", "metadata": {}, "source": [ "## Customizing the appearance of your plots\n", "\n", "I told you that `seaborn` produces publication quality figures, but some of the figures above look a bit ugly.\n", "\n", "We can easily change the appearance of our plots using some additional arguments to the functions `sns.histplot()` and `sns.kdeplot()`.\n", "\n", "You can find many examples of how to change the appearance of histograms and KDE plots in the `seaborn` manual pages:\n", "for sns.histplot() and sns.kdeplot() \n", "\n" ] }, { "cell_type": "code", "execution_count": null, "id": "e4237fce", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "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": "3.9.18" } }, "nbformat": 4, "nbformat_minor": 5 }