├── .gitignore ├── README.md ├── data ├── County_MedianListingPricePerSqft_AllHomes.csv ├── PEP_2017_GCTPEPANNR.US24PR_with_ann.csv ├── PEP_2017_PEPANNRES_with_ann.csv └── finch_beaks_2012.csv ├── ecdf.ipynb ├── lebron_field_goals.ipynb ├── log_plots.ipynb └── swarm_and_jitter.ipynb /.gitignore: -------------------------------------------------------------------------------- 1 | .ipynb_checkpoints -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # dataframed-plot-examples 2 | 3 | Example plots accompanying DataFramed podcast. All code released under and [MIT license](https://opensource.org/licenses/MIT). 4 | 5 | Copyright 2018 Justin Bois 6 | 7 | Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions: 8 | 9 | The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software. 10 | 11 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE. -------------------------------------------------------------------------------- /data/PEP_2017_GCTPEPANNR.US24PR_with_ann.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/justinbois/dataframed-plot-examples/9a2ba3154ce132ad34a11d46e81065247b02e3c3/data/PEP_2017_GCTPEPANNR.US24PR_with_ann.csv -------------------------------------------------------------------------------- /data/PEP_2017_PEPANNRES_with_ann.csv: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/justinbois/dataframed-plot-examples/9a2ba3154ce132ad34a11d46e81065247b02e3c3/data/PEP_2017_PEPANNRES_with_ann.csv -------------------------------------------------------------------------------- /data/finch_beaks_2012.csv: -------------------------------------------------------------------------------- 1 | # Data taken from Fig. 10.3 of 40 years of evolution: Darwin's finches on Daphne Major Island by Grant and Grant. Accessed from the Dryad repository, http://dx.doi.org/10.5061/dryad.g6g3h, and munged into this CSV file. band,species,blength,bdepth 19022,fortis,10,8.5 19028,fortis,12.5,8.9 19032,fortis,9.3,7.5 19041,fortis,10.3,9.6 19044,fortis,11,9.2 19048,fortis,10.1,8.2 19072,fortis,9.6,7.8 19082,fortis,10.9,8.6 19104,fortis,10.3,8.4 19114,fortis,9.8,7.7 19121,fortis,10.1,8 19126,fortis,10.4,8.7 19146,fortis,9.6,8.1 19164,fortis,10.6,8.8 19174,fortis,10.6,9.4 19203,fortis,11.9,10 19210,fortis,11.3,9.6 19217,fortis,11.3,9.6 19224,fortis,9.7,8.1 19226,fortis,9.7,7.5 19252,fortis,10.1,8.4 19263,fortis,10,7.9 19274,fortis,10,8.3 19280,fortis,10,8.9 19288,fortis,11.5,9.1 19328,fortis,9.5,7.7 19349,fortis,11.2,8.3 19362,fortis,10.7,8.6 19372,fortis,10,8.4 19382,fortis,9.8,7.7 19384,fortis,10.9,9.1 19392,fortis,9.2,7.7 19394,fortis,10.2,9 19422,fortis,11.3,10.2 19439,fortis,10.3,8.1 19461,fortis,10.7,8.6 19482,fortis,10,8.4 19502,fortis,9.7,8.1 19511,fortis,9.9,8 19536,fortis,10.7,8.3 19563,fortis,11,10.3 19568,fortis,9.7,8 19602,fortis,10.5,8.8 19604,fortis,11.7,9 19614,fortis,10.8,9.3 19623,fortis,9.1,7.6 19627,fortis,10.9,8.2 19642,fortis,12.2,10 19649,fortis,10.9,8.2 19654,fortis,10.7,8.2 19674,fortis,10.4,8.3 19682,fortis,9.7,7.8 19712,fortis,10.3,8 19734,fortis,9.8,8.4 19746,fortis,10.6,9.3 19749,fortis,10.5,8.9 19774,fortis,12.5,9.7 19782,fortis,9.2,7.6 19815,fortis,10.1,9 19820,fortis,10.6,8.6 19821,fortis,11.5,8.9 19829,fortis,10.8,8.6 19832,fortis,10.5,8.8 19835,fortis,10.1,8.5 19840,fortis,10.7,9.2 19849,fortis,9.6,8 19874,fortis,10.7,9.4 19878,fortis,10.1,7.7 19889,fortis,10,8.4 19914,fortis,10,8.5 19921,fortis,10.4,9 19922,fortis,10.4,9 19928,fortis,11.7,9.3 19932,fortis,10.6,8.7 19942,fortis,11.5,8.1 19946,fortis,10.7,8.7 19947,fortis,10.4,7.9 19952,fortis,10.1,7.7 19974,fortis,10.8,8.4 19993,fortis,11.5,9.25 19994,fortis,11.1,8.1 21049,fortis,9.9,8.3 21052,fortis,10.5,8.5 21080,fortis,12.2,9.9 21082,fortis,10.9,8.4 21087,fortis,12.9,9.9 21088,fortis,9.7,7.2 21089,fortis,9.7,8.2 21090,fortis,10,8.2 21129,fortis,10.1,8.3 21160,fortis,10.2,8.4 21161,fortis,9.6,7.5 21162,fortis,9.9,8.2 21165,fortis,10.6,8.6 21169,fortis,9.8,8.2 21191,fortis,11.1,8.8 21244,fortis,10,8.5 21247,fortis,10.9,8.1 21249,fortis,10,8.3 21258,fortis,10.6,8.5 21259,fortis,10.7,8.1 21261,fortis,9.4,7.3 21262,fortis,10.1,8 21265,fortis,11.8,10.2 21266,fortis,12.2,11.1 21272,fortis,12.9,9.9 21273,fortis,10.1,8.7 21276,fortis,10.1,8.3 21277,fortis,9,7.8 21282,fortis,11.7,9.9 21283,fortis,10.9,10.3 21287,fortis,10.4,8.4 21293,fortis,12.7,8.7 21294,fortis,10.5,9.8 21296,fortis,9.6,8.7 21298,fortis,10.6,9 21299,fortis,10.4,7.8 21341,fortis,10.5,8.5 21343,fortis,10.1,8.2 21349,fortis,10.6,9.2 22000,fortis,10.6,9 19026,scandens,14.3,9.4 19028,scandens,12.5,8.9 19029,scandens,13.7,9.5 19094,scandens,13.8,11 19122,scandens,12,8.7 19125,scandens,13,8.4 19129,scandens,13,9.1 19172,scandens,13.6,8.7 19182,scandens,12.8,10.2 19212,scandens,13.6,9.6 19214,scandens,12.95,8.85 19244,scandens,13.1,8.8 19251,scandens,13.4,9.5 19260,scandens,13.9,9.2 19270,scandens,12.3,9 19278,scandens,14,9.8 19289,scandens,12.5,9.3 19299,scandens,12.3,9 19312,scandens,13.9,10.2 19326,scandens,13.1,7.7 19343,scandens,12.5,9 19374,scandens,13.9,9.5 19401,scandens,13.7,9.4 19406,scandens,12,8 19408,scandens,14.4,8.9 19426,scandens,13.5,9.4 19430,scandens,13.8,9.5 19433,scandens,13,8 19438,scandens,14.9,10 19452,scandens,12.5,8.95 19466,scandens,12.3,8.2 19469,scandens,12.8,8.8 19486,scandens,13.4,9.2 19492,scandens,13.8,9.4 19493,scandens,13.5,9.5 19494,scandens,13.5,8.1 19495,scandens,13.4,9.5 19496,scandens,12.3,8.4 19497,scandens,14.35,9.3 19510,scandens,13.2,9.3 19513,scandens,13.8,9.6 19518,scandens,14.6,9.2 19526,scandens,14.3,10 19527,scandens,13.8,8.9 19528,scandens,13.6,10.5 19543,scandens,12.9,8.9 19553,scandens,13,8.6 19554,scandens,13.5,8.8 19573,scandens,13.2,9.15 19592,scandens,13.7,9.5 19594,scandens,13.1,9.1 19597,scandens,13.2,10.2 19598,scandens,12.6,8.4 19599,scandens,13,10 19619,scandens,13.9,10.2 19622,scandens,13.2,9.3 19652,scandens,15,10.8 19653,scandens,13.37,8.3 19664,scandens,11.4,7.8 19692,scandens,13.8,9.8 19720,scandens,13,7.9 19740,scandens,13,8.9 19747,scandens,13.1,7.7 19766,scandens,12.8,8.9 19783,scandens,13.3,9.4 19844,scandens,13.5,9.4 19848,scandens,12.4,8.5 19852,scandens,13.1,8.5 19854,scandens,14,9.6 19855,scandens,13.5,10.2 19868,scandens,11.8,8.8 19882,scandens,13.7,9.5 19900,scandens,13.2,9.3 19910,scandens,12.2,9 19936,scandens,13,9.2 19940,scandens,13.1,8.7 19941,scandens,14.7,9 19951,scandens,13.7,9.1 19955,scandens,13.5,8.7 19956,scandens,13.3,9.4 21040,scandens,14.1,9.8 21041,scandens,12.5,8.6 21045,scandens,13.7,10.6 21047,scandens,14.6,9 21053,scandens,14.1,9.5 21057,scandens,12.9,8.1 21070,scandens,13.9,9.3 21081,scandens,13.4,9.6 21092,scandens,13,8.5 21093,scandens,12.7,8.2 21106,scandens,12.1,8 21109,scandens,14,9.5 21111,scandens,14.9,9.7 21113,scandens,13.9,9.9 21131,scandens,12.9,9.1 21135,scandens,14.6,9.5 21136,scandens,14,9.8 21159,scandens,13,8.4 21167,scandens,12.7,8.3 21176,scandens,14,9.6 21248,scandens,14.1,9.4 21253,scandens,14.1,10 21255,scandens,13,8.9 21256,scandens,13.5,9.1 21257,scandens,13.4,9.8 21260,scandens,13.9,9.3 21263,scandens,13.1,9.9 21267,scandens,12.9,8.9 21268,scandens,14,8.5 21270,scandens,14,10.6 21271,scandens,14.1,9.3 21278,scandens,14.7,8.9 21279,scandens,13.4,8.9 21280,scandens,13.8,9.7 21281,scandens,13.4,9.8 21285,scandens,13.8,10.5 21286,scandens,12.4,8.4 21288,scandens,14.1,10 21289,scandens,12.9,9 21290,scandens,13.9,8.7 21291,scandens,14.3,8.8 21292,scandens,13.2,8.4 21295,scandens,14.2,9.3 21297,scandens,13,9.8 21340,scandens,14.6,8.9 21342,scandens,13.1,9.8 21347,scandens,15.2,9.1 -------------------------------------------------------------------------------- /ecdf.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Example ECDF plots" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 1, 13 | "metadata": {}, 14 | "outputs": [], 15 | "source": [ 16 | "import pandas as pd\n", 17 | "import numpy as np\n", 18 | "import seaborn as sns\n", 19 | "\n", 20 | "import matplotlib.pyplot as plt\n", 21 | "%matplotlib inline" 22 | ] 23 | }, 24 | { 25 | "cell_type": "markdown", 26 | "metadata": {}, 27 | "source": [ 28 | "
" 29 | ] 30 | }, 31 | { 32 | "cell_type": "markdown", 33 | "metadata": {}, 34 | "source": [ 35 | "### Load the data set" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 2, 41 | "metadata": {}, 42 | "outputs": [], 43 | "source": [ 44 | "df = pd.read_csv('data/finch_beaks_2012.csv', comment='#')\n", 45 | "\n", 46 | "# Delete any birds measured more than once\n", 47 | "df = df.drop_duplicates('band')" 48 | ] 49 | }, 50 | { 51 | "cell_type": "markdown", 52 | "metadata": {}, 53 | "source": [ 54 | "
" 55 | ] 56 | }, 57 | { 58 | "cell_type": "markdown", 59 | "metadata": {}, 60 | "source": [ 61 | "### Function to make x and y values for ECDF plots" 62 | ] 63 | }, 64 | { 65 | "cell_type": "code", 66 | "execution_count": 3, 67 | "metadata": {}, 68 | "outputs": [], 69 | "source": [ 70 | "def ecdf(data, formal=False, x_range=None):\n", 71 | " \"\"\"\n", 72 | " Get x, y, values of an ECDF for plotting.\n", 73 | "\n", 74 | " Parameters\n", 75 | " ----------\n", 76 | " data : ndarray\n", 77 | " One dimensional Numpay array with data.\n", 78 | " formal : bool, default False\n", 79 | " If True, generate x and y values for formal ECDF (staircase). If\n", 80 | " False, generate x and y values for ECDF as dots.\n", 81 | " x_range : 2-tuple, default None\n", 82 | " If not None and `formal` is True, then specifies range of plot\n", 83 | " on x-axis.\n", 84 | "\n", 85 | " Returns\n", 86 | " -------\n", 87 | " x : ndarray\n", 88 | " x-values for plot\n", 89 | " y : ndarray\n", 90 | " y-values for plot\n", 91 | " \"\"\"\n", 92 | " x = np.sort(data)\n", 93 | " y = np.arange(1, len(data)+1) / len(data)\n", 94 | "\n", 95 | " if formal:\n", 96 | " # Set up output arrays\n", 97 | " x_formal = np.empty(2*(len(x) + 1))\n", 98 | " y_formal = np.empty(2*(len(x) + 1))\n", 99 | "\n", 100 | " # y-values for steps\n", 101 | " y_formal[:2] = 0\n", 102 | " y_formal[2::2] = y\n", 103 | " y_formal[3::2] = y\n", 104 | "\n", 105 | " # x- values for steps\n", 106 | " x_formal[0] = x[0]\n", 107 | " x_formal[1] = x[0]\n", 108 | " x_formal[2::2] = x\n", 109 | " x_formal[3:-1:2] = x[1:]\n", 110 | " x_formal[-1] = x[-1]\n", 111 | " \n", 112 | " if x_range is not None:\n", 113 | " if np.all(x >= x_range[0]) and np.all(x <= x_range[1]):\n", 114 | " x_formal = np.concatenate(((x_range[0],), x_formal, (x_range[1],)))\n", 115 | " y_formal = np.concatenate(((0,), y_formal, (1,)))\n", 116 | " else:\n", 117 | " raise RuntimeError('Some data values outside of `x_range`.')\n", 118 | "\n", 119 | " return x_formal, y_formal\n", 120 | " else:\n", 121 | " return x, y" 122 | ] 123 | }, 124 | { 125 | "cell_type": "markdown", 126 | "metadata": {}, 127 | "source": [ 128 | "
" 129 | ] 130 | }, 131 | { 132 | "cell_type": "markdown", 133 | "metadata": {}, 134 | "source": [ 135 | "### Make an ECDF with dots" 136 | ] 137 | }, 138 | { 139 | "cell_type": "code", 140 | "execution_count": 4, 141 | "metadata": {}, 142 | "outputs": [ 143 | { 144 | "data": { 145 | "image/png": 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\n", 146 | "text/plain": [ 147 | "" 148 | ] 149 | }, 150 | "metadata": {}, 151 | "output_type": "display_data" 152 | } 153 | ], 154 | "source": [ 155 | "# Set up axes\n", 156 | "fig, ax = plt.subplots()\n", 157 | "ax.grid(color='#eeeeee')\n", 158 | "ax.set_xlabel('beak length (mm)')\n", 159 | "ax.set_ylabel('ECDF')\n", 160 | "\n", 161 | "# Plot ECDFs\n", 162 | "for species in ['fortis', 'scandens']:\n", 163 | " ax.plot(*ecdf(df.loc[df['species']==species, 'blength']), '.', label=species)" 164 | ] 165 | }, 166 | { 167 | "cell_type": "markdown", 168 | "metadata": {}, 169 | "source": [ 170 | "
" 171 | ] 172 | }, 173 | { 174 | "cell_type": "markdown", 175 | "metadata": {}, 176 | "source": [ 177 | "### Make a formal ECDF plot" 178 | ] 179 | }, 180 | { 181 | "cell_type": "code", 182 | "execution_count": 5, 183 | "metadata": {}, 184 | "outputs": [ 185 | { 186 | "data": { 187 | "image/png": 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\n", 188 | "text/plain": [ 189 | "" 190 | ] 191 | }, 192 | "metadata": {}, 193 | "output_type": "display_data" 194 | } 195 | ], 196 | "source": [ 197 | "# Set up axes\n", 198 | "fig, ax = plt.subplots()\n", 199 | "ax.grid(color='#eeeeee')\n", 200 | "ax.set_xlim(8, 16)\n", 201 | "ax.set_xlabel('beak length (mm)')\n", 202 | "ax.set_ylabel('ECDF')\n", 203 | "\n", 204 | "# Plot ECDFs\n", 205 | "for species in ['fortis', 'scandens']:\n", 206 | " ax.plot(*ecdf(df.loc[df['species']==species, 'blength'], formal=True, x_range=(8, 16)),\n", 207 | " label=species)" 208 | ] 209 | } 210 | ], 211 | "metadata": { 212 | "kernelspec": { 213 | "display_name": "Python 3", 214 | "language": "python", 215 | "name": "python3" 216 | }, 217 | "language_info": { 218 | "codemirror_mode": { 219 | "name": "ipython", 220 | "version": 3 221 | }, 222 | "file_extension": ".py", 223 | "mimetype": "text/x-python", 224 | "name": "python", 225 | "nbconvert_exporter": "python", 226 | "pygments_lexer": "ipython3", 227 | "version": "3.6.4" 228 | } 229 | }, 230 | "nbformat": 4, 231 | "nbformat_minor": 2 232 | } 233 | -------------------------------------------------------------------------------- /lebron_field_goals.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Example Poisson distribution: field goals attempted per game" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 1, 13 | "metadata": {}, 14 | "outputs": [], 15 | "source": [ 16 | "import numpy as np\n", 17 | "import scipy.stats as st\n", 18 | "\n", 19 | "import matplotlib.pyplot as plt\n", 20 | "%matplotlib inline\n", 21 | "%config InlineBackend.figure_format = 'retina'" 22 | ] 23 | }, 24 | { 25 | "cell_type": "markdown", 26 | "metadata": {}, 27 | "source": [ 28 | "The story behind the Poisson distribution is as follows.\n", 29 | ">The number of arrivals of a Poisson processes in a given set time interval is Poisson distributed.\n", 30 | "\n", 31 | "We could model field goal attempts in a basketball game using a Poisson distribution. When a player takes a shot is a largely stochastic process, being influenced by the myriad ebbs and flows of a basketball game. Some players shoot more than others, though, so there is a well-defined *rate* of shooting. Let's consider LeBron James's field goal attempts for the 2017-2018 NBA season. First, the data." 32 | ] 33 | }, 34 | { 35 | "cell_type": "code", 36 | "execution_count": 2, 37 | "metadata": {}, 38 | "outputs": [], 39 | "source": [ 40 | "fga = [19, 16, 15, 20, 20, 11, 15, 22, 34, 17, 20, 24, 14, 14, \n", 41 | " 24, 26, 14, 17, 20, 23, 16, 11, 22, 15, 18, 22, 23, 13, \n", 42 | " 18, 15, 23, 22, 23, 18, 17, 22, 17, 15, 23, 8, 16, 25, \n", 43 | " 18, 16, 17, 23, 17, 15, 20, 21, 10, 17, 22, 20, 20, 23, \n", 44 | " 17, 18, 16, 25, 25, 24, 19, 17, 25, 20, 20, 14, 25, 26, \n", 45 | " 29, 19, 16, 19, 18, 26, 24, 21, 14, 20, 29, 16, 9]" 46 | ] 47 | }, 48 | { 49 | "cell_type": "markdown", 50 | "metadata": {}, 51 | "source": [ 52 | "To show that this random variable is approximately Poisson distributed, we will plot its empirical cumulative distribution function (ECDF) and compare it with the maximum likelihood estimate for the ECDF of the Poisson distribution. First, we'll generate the *x* and _y_ values for the ECDF." 53 | ] 54 | }, 55 | { 56 | "cell_type": "code", 57 | "execution_count": 3, 58 | "metadata": {}, 59 | "outputs": [], 60 | "source": [ 61 | "# Make x and y values for ECDF plot\n", 62 | "x_ecdf = np.sort(fga)\n", 63 | "y_ecdf = np.arange(1, len(x_ecdf)+1) / len(x_ecdf)" 64 | ] 65 | }, 66 | { 67 | "cell_type": "markdown", 68 | "metadata": {}, 69 | "source": [ 70 | "Next, we will draw many samples out of a Poisson distribution to get the theoretical ECDF." 71 | ] 72 | }, 73 | { 74 | "cell_type": "code", 75 | "execution_count": 4, 76 | "metadata": {}, 77 | "outputs": [], 78 | "source": [ 79 | "n_reps = 1000\n", 80 | "x_theor = np.concatenate([np.sort(np.random.poisson(np.mean(fga), size=len(fga))) \n", 81 | " for _ in range(n_reps)])\n", 82 | "y_theor = np.concatenate([y_ecdf]*n_reps)" 83 | ] 84 | }, 85 | { 86 | "cell_type": "markdown", 87 | "metadata": {}, 88 | "source": [ 89 | "Now let's build the plot!" 90 | ] 91 | }, 92 | { 93 | "cell_type": "code", 94 | "execution_count": 5, 95 | "metadata": {}, 96 | "outputs": [ 97 | { 98 | "data": { 99 | "image/png": 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\n", 100 | "text/plain": [ 101 | "
" 102 | ] 103 | }, 104 | "metadata": { 105 | "image/png": { 106 | "height": 263, 107 | "width": 387 108 | }, 109 | "needs_background": "light" 110 | }, 111 | "output_type": "display_data" 112 | } 113 | ], 114 | "source": [ 115 | "# Set up axes\n", 116 | "fig, ax = plt.subplots()\n", 117 | "ax.grid(color='#eeeeee')\n", 118 | "ax.set_xlabel('field goal attempts')\n", 119 | "ax.set_ylabel('ECDF')\n", 120 | "\n", 121 | "ax.plot(x_theor, y_theor, '.', alpha=0.01, color='lightgray');\n", 122 | "ax.plot(x_ecdf, y_ecdf, '.', color='black');" 123 | ] 124 | }, 125 | { 126 | "cell_type": "markdown", 127 | "metadata": {}, 128 | "source": [ 129 | "Indeed, LeBron's field goal attempts per game are Poisson distributed!" 130 | ] 131 | } 132 | ], 133 | "metadata": { 134 | "kernelspec": { 135 | "display_name": "Python 3", 136 | "language": "python", 137 | "name": "python3" 138 | }, 139 | "language_info": { 140 | "codemirror_mode": { 141 | "name": "ipython", 142 | "version": 3 143 | }, 144 | "file_extension": ".py", 145 | "mimetype": "text/x-python", 146 | "name": "python", 147 | "nbconvert_exporter": "python", 148 | "pygments_lexer": "ipython3", 149 | "version": "3.7.0" 150 | } 151 | }, 152 | "nbformat": 4, 153 | "nbformat_minor": 2 154 | } 155 | -------------------------------------------------------------------------------- /swarm_and_jitter.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "metadata": {}, 6 | "source": [ 7 | "# Example swarm and jitter plots" 8 | ] 9 | }, 10 | { 11 | "cell_type": "code", 12 | "execution_count": 1, 13 | "metadata": {}, 14 | "outputs": [], 15 | "source": [ 16 | "import pandas as pd\n", 17 | "import numpy as np\n", 18 | "import seaborn as sns\n", 19 | "\n", 20 | "import matplotlib.pyplot as plt\n", 21 | "%matplotlib inline" 22 | ] 23 | }, 24 | { 25 | "cell_type": "markdown", 26 | "metadata": {}, 27 | "source": [ 28 | "
" 29 | ] 30 | }, 31 | { 32 | "cell_type": "markdown", 33 | "metadata": {}, 34 | "source": [ 35 | "### Load the data set" 36 | ] 37 | }, 38 | { 39 | "cell_type": "code", 40 | "execution_count": 2, 41 | "metadata": {}, 42 | "outputs": [], 43 | "source": [ 44 | "df = pd.read_csv('data/finch_beaks_2012.csv', comment='#')\n", 45 | "\n", 46 | "# Delete any birds measured more than once\n", 47 | "df = df.drop_duplicates('band')" 48 | ] 49 | }, 50 | { 51 | "cell_type": "markdown", 52 | "metadata": {}, 53 | "source": [ 54 | "
" 55 | ] 56 | }, 57 | { 58 | "cell_type": "markdown", 59 | "metadata": {}, 60 | "source": [ 61 | "### Make a swarm plot" 62 | ] 63 | }, 64 | { 65 | "cell_type": "code", 66 | "execution_count": 3, 67 | "metadata": {}, 68 | "outputs": [ 69 | { 70 | "data": { 71 | "image/png": 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\n", 72 | "text/plain": [ 73 | "" 74 | ] 75 | }, 76 | "metadata": {}, 77 | "output_type": "display_data" 78 | } 79 | ], 80 | "source": [ 81 | "ax = sns.swarmplot(data=df, x='species', y='blength')\n", 82 | "ax.set_xlabel('')\n", 83 | "ax.set_ylabel('beak length (mm)')\n", 84 | "ax.grid(axis='y', color='#eeeeee')" 85 | ] 86 | }, 87 | { 88 | "cell_type": "markdown", 89 | "metadata": {}, 90 | "source": [ 91 | "
" 92 | ] 93 | }, 94 | { 95 | "cell_type": "markdown", 96 | "metadata": {}, 97 | "source": [ 98 | "### Make a jitter plot" 99 | ] 100 | }, 101 | { 102 | "cell_type": "code", 103 | "execution_count": 4, 104 | "metadata": {}, 105 | "outputs": [ 106 | { 107 | "data": { 108 | "image/png": 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\n", 109 | "text/plain": [ 110 | "" 111 | ] 112 | }, 113 | "metadata": {}, 114 | "output_type": "display_data" 115 | } 116 | ], 117 | "source": [ 118 | "ax = sns.stripplot(data=df, x='species', y='blength', jitter=0.1, alpha=0.3)\n", 119 | "ax.set_xlabel('')\n", 120 | "ax.set_ylabel('beak length (mm)')\n", 121 | "ax.grid(axis='y', color='#eeeeee')" 122 | ] 123 | } 124 | ], 125 | "metadata": { 126 | "kernelspec": { 127 | "display_name": "Python 3", 128 | "language": "python", 129 | "name": "python3" 130 | }, 131 | "language_info": { 132 | "codemirror_mode": { 133 | "name": "ipython", 134 | "version": 3 135 | }, 136 | "file_extension": ".py", 137 | "mimetype": "text/x-python", 138 | "name": "python", 139 | "nbconvert_exporter": "python", 140 | "pygments_lexer": "ipython3", 141 | "version": "3.6.4" 142 | } 143 | }, 144 | "nbformat": 4, 145 | "nbformat_minor": 2 146 | } 147 | --------------------------------------------------------------------------------