example/example.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from statannot import add_stat_annotation\n",
    "#from pandas.compat import StringIO\n",
    "\n",
    "sns.set(style=\"whitegrid\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot non-hue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Multiple comparisons correction by default"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, multiple comparisons correction (Bonferroni method) is applied."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = sns.load_dataset(\"tips\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value annotation legend:\n",
      "ns: 5.00e-02 < p <= 1.00e+00\n",
      "*: 1.00e-02 < p <= 5.00e-02\n",
      "**: 1.00e-03 < p <= 1.00e-02\n",
      "***: 1.00e-04 < p <= 1.00e-03\n",
      "****: p <= 1.00e-04\n",
      "\n",
      "Thur v.s. Fri: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.000e+00 U_stat=6.305e+02\n",
      "Thur v.s. Sat: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.407e-01 U_stat=2.180e+03\n",
      "Sun v.s. Fri: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=8.041e-02 U_stat=9.605e+02\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYcAAAFKCAYAAAD2aJMUAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAIABJREFUeJzt3Xl0VOXhxvFnSAiIoCSnREEiBxRqgINSypIKBMKWCAmYII5toVLLYll6WFRAkQIKEakR6sKBI13U07A0QOToaCBARRSPQckBwaABIlVDbBYIyJBJ5vcHZSq/C2EymZk7y/fzj5kkc+dhzMwz773vfa/F6XQ6BQDAjzQxOwAAIPBQDgAAA8oBAGBAOQAADCgHAIAB5QAAMKAcAAAGlAMAwCDS7ADAZfv371dWVpbi4uJ07NgxORwOLV68WE6nU5mZmaqrq5MkTZkyRSNGjDA5LRDaKAcElMLCQi1atEjx8fFav369srKyFBERoYkTJ2rkyJE6evSoNmzYQDkAPsZuJQSUdu3aKT4+XpLUtWtXVVVVKSUlRUuWLNGcOXN0+PBhzZ492+SUQOijHBBQmjdv7vraYrHI6XTKarUqNzdX9957r/bu3au0tDTZ7XYTUwKhj3JAwLNarTpy5IjS09O1dOlSnTlzRmVlZWbHAkIaxxwQ8ObOnatly5bpxRdflMVi0fTp09W+fXuzYwEhzcKS3QCA/4+Rg5fl5OTIZrOZHQO4quTkZKWnp5sdA0GAYw5eZrPZVFRUZHYMwKCoqIgPLnAbIwcf6NKli9auXWt2DOAKkydPNjsCgggjBwCAAeUAADCgHAAABhxz8LK0tDSzIwBXxd8mGoLzHAAABuxWAgAYUA4AAAPKAQBgQDkAAAwoBwCAAeUAADCgHAAABpwEB4So/fv3KysrS3FxcTp27JgcDocWL14sp9OpzMxM1dXVSZKmTJmiESNGmJwWgYZyAEJYYWGhFi1apPj4eK1fv15ZWVmKiIjQxIkTNXLkSB09elQbNmygHGDAbiUghLVr107x8fGSpK5du6qqqkopKSlasmSJ5syZo8OHD2v27Nkmp0QgohyAENa8eXPX1xaLRU6nU1arVbm5ubr33nu1d+9epaWlyW63m5gSgYhyAMKM1WrVkSNHlJ6erqVLl+rMmTMqKyszOxYCDMccgDAzd+5cLVu2TC+++KIsFoumT5+u9u3bmx0LAYZVWQEABowcENBycnJks9nMjgFcVXJystLT082O4RMcc0BAs9lsKioqMjsGYFBUVBTSH1wYOSDgdenSRWvXrjU7BnCFyZMnmx3Bpxg5AAAMKAcAgAG7lRDQ0tLSzI4AXFWo/20ylRUAYMBuJQCAAeUAADCgHAAABpQDAMCAcgAAGFAOAAADygEAYEA5AAAMOEMaaKD9+/crKytLcXFxOnbsmBwOhxYvXiyn06nMzEzV1dVJkqZMmaIRI0aYnBbwDOUAeKCwsFCLFi1SfHy81q9fr6ysLEVERGjixIkaOXKkjh49qg0bNlAOCFrsVgI80K5dO8XHx0uSunbtqqqqKqWkpGjJkiWaM2eODh8+rNmzZ5ucEvAc5QB4oHnz5q6vLRaLnE6nrFarcnNzde+992rv3r1KS0uT3W43MSXgOcoB8BKr1aojR44oPT1dS5cu1ZkzZ1RWVmZ2LMAjHHMAvGTu3LlatmyZXnzxRVksFk2fPl3t27c3OxbgEZbsBgAYMHJAQMvJyQnpi7j7W3JystLT082OgSDAMQcENJvNpqKiIrNjhISioiKKFm5j5ICA16VLF61du9bsGEFv8uTJZkdAEGHkAAAwoBwAAAbsVkJAS0tLMztCyOC5REMwlRUAYMBuJQCAAeUAADCgHAAABpQDAMAgaGYr1dXV6dy5c2ratKksFovZcQAgKDidTtXU1OjGG29UkybujweCphzOnTvHMgoA4KEuXbqoVatWbv9+0JRD06ZNJV36B0ZFRZmcBgCCw8WLF1VUVOR6D3VX0JTD5V1JUVFRatasmclpACC4NHR3PAekAQAGlAMAwIByAAAYUA4A4IHy8nLNmzdPFRUVZkfxCcoBADyQnZ2tzz//XNnZ2WZH8QnKAQAaqLy8XDt37pTT6dSOHTtCcvRAOQBAA2VnZ6uurk7SpdUbQnH04NPzHMaPH6/y8nJFRl56mCVLlqikpESvvvqqHA6HfvOb3+hXv/qVLyMAgNft3r1bDodDkuRwOLRr1y49+uijJqfyLp+Vg9Pp1IkTJ7Rr1y5XOZSWlmrWrFnKyclRVFSUrFar+vbtqzvvvNNXMQDA6wYNGqS8vDw5HA5FRkZq8ODBZkfyOp/tViouLpYk/fa3v1VaWpreeOMN7du3T/369VPr1q3VokULjRgxQjabzVcRAMAnrFaraxG7Jk2ayGq1mpzI+3w2cjhz5owSEhK0cOFC1dTUaMKECUpJSVGbNm1cvxMbG6vCwsIGbffQoUPejgoADdajRw8VFBSoR48erg/DocRn5dCzZ0/17NnTdXvs2LFavnz5FfvlnE5ng9f76N69O2srATBdx44dtWLFCs2cOVPR0dFmx7kmu93u0Ydqn5XDJ598opqaGiUkJEi6VAS33XabysrKXL9TVlam2NhYX0UAAJ+JiYlRZmam2TF8xmfHHM6ePasVK1bIbrerurpaW7Zs0fPPP68PP/xQ5eXl+uGHH/Tee+9p4MCBvooAAD5TXFysBx98UMePHzc7ik/4rBwGDx6sxMREjRkzRhkZGcrIyFCvXr00a9YsTZgwQWPGjNGoUaPUo0cPX0UwRaifUg/gkpUrV+r8+fNauXKl2VF8wuJ0Op1mh3DH5f1mgX7M4ZVXXpHNZlNKSkrIzXsGcElxcbH+8Ic/uG6vXr1aHTt2NDHRtXn63skZ0l4UDqfUA5BhtBCKowfKwYvC4ZR6ANLXX399xe2SkhKTkvgO5eBFVzulHkDoiYuLu+L27bffblIS36EcvGjQoEGupUJC9ZR6ANLcuXPrvR0KKAcvCodT6gFInTp1co0ebr/99oA9GN0YlIMXxcTEaMiQIbJYLBo6dGhAnzUJoHHmzp2rFi1ahOSoQfLxkt3hyGq1qqSkhFEDEOI6deqkDRs2mB3DZygHLwv1U+oBhAd2KwEADCgHL2P5DAChgHLwsuzsbH3++eecAAcgqFEOXsTyGQBCBeXgRSyfASBUUA5exPIZAEIF5eBFLJ8BhI9Qn3xCOXgRy2cA4SPUJ59QDl7E8hlAeAiHySeUg5dZrVZ17dqVUQMQwsJh8gnl4GWXl89g1ACErnCYfEI5IKCF+kE/BKdwmHxCOSCghfpBPwSncJh8QjkgYIXDQT8Ep3CYfEI5IGCFw0E/BK9Qn3xCOSBghcNBPwSvUJ98QjkgYIXDQT8gUFEOCFjhcNAPCFSUAwJWOBz0AwIV5YCAlpycrBtuuEHJyclmRwHCCuWAgGaz2fTDDz/IZrOZHQUIK5QDAhbnOQDmoRwQsDjPATAP5YCAxXkO3sU6VWiISF8/wHPPPaeKigplZmbqyJEjevLJJ3Xu3Dn9/Oc/1+LFi13z2ANBfn6+8vLyGrWNyspKSVLr1q0btZ1hw4YpKSmpUdsIdoMGDVJeXp4cDgfnOXjBj9epevTRR82OgwDn05HDhx9+qC1btrhuP/bYY3r66af17rvvyul0auPGjb58eFOUl5ervLzc7BghgfMcvIfjN2gon31sr6ysVFZWlqZOnaqjR4/q3//+ty5cuKB77rlHkpSenq7Vq1frl7/8pa8iNFhSUlKjP63Pnz9fkrR8+XJvRAprl89zsNlsnOfQSFc7fsPoAfXx2cjh6aef1qxZs3TTTTdJkk6fPq02bdq4ft6mTRuVlpb66uERIkJ9cTN/4fgNGsonI4dNmzapbdu2SkhIUE5OjqRLn1YsFovrd5xO5xW33XXo0CGv5fSFs2fPSpIKCgpMThI6HnjgARUXF5sdI6h169ZNn376qWpraxUREaFu3brxN4p6+aQc3n77bZWVlWn06NGqqqrS+fPnZbFYVFZW5vqd77//XrGxsQ3edvfu3dWsWTNvxvWqzZs3S5J69eplchLgfzp27KhJkya5ymHmzJlhvZsunCaf2O12jz5U+6Qc/vKXv7i+zsnJ0ccff6zly5dr1KhRKigoUK9evbRt2zYNHDjQFw8P4P/h+I33XZ540thyCFR+nUe6cuVKPfXUU6qurla3bt00YcIEfz48ENasVqtKSko4fiMmn7jD5+WQnp6u9PR0SdJdd93l2u0CwL8uX5wGcAdnSAMADCgHAIAB5QAAMKAcAAAGlAMAwIByAAAYUA4AAAPKAQBgQDkAAAwoBwCAAeUAADCgHAAABpQDAlp5ebnmzZvHNY8BP6McENCys7P1+eefKzs72+woQFihHBCwysvLtWPHDjmdTuXl5TF6APyIckDAys7OlsPhkCQ5HA5GD4AfUQ4IWLt27ZLT6ZQkOZ1O5efnm5wICB+UAwJWmzZtrrgdGxtrUhIg/FAOCFhlZWX13gbgO5QDAtbgwYNlsVgkSRaLRYMHDzY5ERA+KAcELKvVqsjISElSZGSkrFaryYmA8EE5IGDFxMRowIABkqSBAwcqOjra5ERA+KAcENAuz1YC4F+UAwJWeXm5PvjgA0nS+++/z0lwgB9RDghY2dnZqqurkyTV1dVxEhzgR5QDAtbu3buvOEN6165dJicCwgflgIA1aNCgK2YrMZUV8B/KAQHLarWqSZNLf6JNmjRhKivgR5QDAlZMTIyGDBkii8WioUOHMpUV8KNIswMA9bFarSopKWHUAPhZveXQs2dP1/IFP+Z0OmWxWHTgwAGfBQOkS6OHzMxMs2MAYafecti+fbu/cgAAAki95XD48OF673zbbbd5NQwAIDDUWw6vv/76NX9msVg0fPjweje+atUqvfvuu7JYLBo7dqwmTpyoffv2afny5bLb7UpJSdGsWbM8Sw4A8BmPy+F6Pv74Y3300UfKzc2Vw+HQfffdp4SEBC1YsECvv/662rZtqylTpmjPnj1KTEz0+HEAAN5Xbzk8++yzevLJJzV16tSr/nzNmjXXvG+fPn3097//XZGRkSotLVVtba3OnDmjDh06KC4uTpKUmpoqm81GOQBAgKm3HBISEiRJI0aM8GjjTZs21erVq7V+/XolJyfr9OnTV1z6MTY2VqWlpQ3a5qFDhzzK4i9nz56VJBUUFJicxHyfffaZPv3000Zto7q6WpLUsmVLj7fRs2dP3XPPPY3KAfx/of5ar7cckpKSJEn333+/Kioq9NlnnykyMlJ33323brrpJrceYObMmZo0aZKmTp2qEydOXDE19vKU2Ibo3r27mjVr1qD7+NPmzZslSb169TI5ifmqqqr05ZdfNmobly8N2rZtW4+30bFjR/5/wOuC5bVut9s9+lDt1klwu3fv1hNPPKHOnTurtrZWX3/9tbKystS7d+9r3uerr77SxYsXFR8frxtuuEHDhw+XzWZTRESE63fKysq4aHwIS0pKcn3A8NT8+fMlScuXL/dGJABucqscVq1apTfeeEOdO3eWdGmK68KFC5WTk3PN+5w6dUqrV6/WP/7xD0nSzp07ZbVatWLFCp08eVLt27fX9u3blZGR4YV/BhD68vPzlZeX5/H9KysrJUmtW7duVI5hw4Y1uvQR+NwqB4vF4ioGSerWrdt1r9CVmJiowsJCjRkzRhERERo+fLhGjhypmJgYzZgxQ3a7XYmJiUpOTm7cvwCAW8rLyyU1vhwQHuoth8ufNLp3767XXnvNtUpmTk6O+vXrd92Nz5gxQzNmzLjiewkJCcrNzW1EZCA8NXY3Hbvo0BD1lkO/fv1ksVhco4Tnn3/eddtiseiJJ57wS0gAgH/VWw5Hjx697ga2b9+uUaNGeS0QAMB8jb6ew2uvveaNHACAANLocrjegWkAQPBpdDk09CQ2AEDg4zKhAAADygEAYMAxBwCAQaPLITU11Rs5AAABpN7zHK73xv/WW2/pkUce8WogAID56i2HhQsX+isHACCA1FsOffr0cX1dWVmpH374QU6nU7W1tSopKfF5uIZYt26diouLzY7hynB5HRuzdOrUSZMmTTI1A4Dg5faS3WvXrpUkRUREqKamRnfeeafeeustn4ZriOLiYh36/AtFNDd3xck6x6XrVRwpbtgV7ryp9kKlaY8NIDS4VQ7btm3Trl27lJmZqccff1wfffSR9uzZ4+tsDRbRvLVadBhidgzTnT+50+wIAIKcW7OVYmJiFBsbq06dOuno0aMaM2aMioqKfJ0NAGASt0YOkZGRKikpUadOnfTJJ5+of//+stvtvs4GAFcVCMcYA+X4ouSbY4xulcOUKVO0cOFCvfrqq1q1apW2bt2qQYMGeTUIALiruLhYRYcP6yc/uia9v0XV1UmSyt24tIEvfV9b65PtulUOXbt21d/+9jdJ0tatW3Xy5Ek1acLKGwDM85OICI1uxSVPt531zQSUet/hKysrVVlZqUmTJqmqqkqVlZWy2+36yU9+opkzZ/okEADAfPWOHObMmaMPPvhAktS3b9//3SkyUiNGjPBtMgCAaeoth8tXeZs/fz4XJQeAMOLWMYfly5fr4MGDev/991VTU6P+/furd+/evs4GADCJW0eVt27dqpkzZ6qqqkrnzp3T7NmztXHjRl9nAwCYxK2Rw1//+ldt2rRJsbGxkqRJkybpkUce0bhx43waDgBgDrdGDnV1da5ikKRbbrmFqawAEMLceodv3bq1duzY4bq9Y8cO3XzzzT4LBQAwl1u7lWbMmKEFCxZo6dKlkqSmTZvq5Zdf9mkwAIB56i2HyspLZ94tXbpUmzZt0pdffimLxaLbbrtNDz/8sGw2m19CAsGOtYCuxPVGAp/bJ8ElJCRIkpxOJyfBAQ1UXFysw198roibo0zLUNfk0ho8R7/70rQMklRbddHUx4d7OAkO8JOIm6N088B2ZscwXdW/vjE7Atzg1gFpigEAwgvzUQEABj4th5deekkjR47UyJEjtWLFCknSvn37lJqaquHDhysrK8uXDw8A8JDPymHfvn3au3evtmzZoq1bt+rw4cPavn27FixYoFdeeUVvv/22Dh06FJDXogaAcOfWeQ6eaNOmjebNm6eoqEuzM+644w6dOHFCHTp0UFxcnCQpNTVVNptNiYmJvooBDwXC1EspcKZfMvUS4cZn5dC5c2fX1ydOnNA777yjX//612rTpo3r+7GxsSotLW3Qdg8dOnTV7589e9azoCHq7NmzKigo8Pj+Bw8e1HenSnRrS5/9ibjlBuelSzGe/foL0zJ8V+1o9PPJ3+eVeD69q7HP59X4/JV/7NgxTZkyRY8//rgiIiJ04sQJ18+cTqcsFkuDtte9e3c1a9bM8P1169ap9kKlzp/c2djIQa/2QqUcjij16tXL421s3rxZahmpiT1ivJgsOP2lsFytWrVq/PN5rmEfhEKZN57Pci/mCXb1PZ92u/2aH6rr49MD0gUFBXr44Yc1Z84c3X///br11ltVVlbm+nlZWdkVC/oBAAKDz0YO3377raZNm6asrCzX2dV33323jh8/rpMnT6p9+/bavn27MjIyvPJ40dHR+q7iolp0GOKV7QWz8yd3Kjo62uwYAIKYz8rhtddek91uV2Zmput7VqtVmZmZmjFjhux2uxITE5WcnOyrCAAAD/msHJ566ik99dRTV/1Zbm6urx4WAOAFnCENADAwd54iAHigoqJC3zsc2na20uwopvve4ZClosLr22XkAAAwYOQAIOhER0fLWVqq0a1amx3FdNvOVvpkdiIjBwCAAeUAADCgHAAABpQDAMCAcgAAGDBbCVdVUVGh76sd+ksha19+V+2QwwfzyIFAxsgBAGDAyAFXFR0drcjq01zPQf+9ngOr3CLMUA6AH1RUVMhRaVfVv74xO4rpHJV2VTRjN12gY7cSAMAgpEYOgXCZ0DrHBUlSk8jmpmWovVAp6RbTHh9G0dHRKrX/RzcPbGd2FNNV/esbLkYVBEKmHDp16mR2BElScXGxJKlTJzPfnG8JmOcDQHAKmXKYNGmS2REkSfPnz5ckLV++3OQkAOA5jjkAAAwoBwCAQcjsVgIQXr6vrTX1SnDn6+okSS2amPsZ+/vaWvnibCTKAUDQCYQJF5X/nXzS3uQsMfLN80E5AAg6gTABJdQnn3DMAQBgQDkAAAwoBwCAAccccE3fBcD1HKovXpoR0jLKvM8x31U71Mq0RwfMQTngqgJhNogklf13RkjbOPPytJJ3no/aqoumrspad6FWktSkeYRpGaRLz4NuNTUC3EA54KoCYTaIFDozQgKhbF3rft1qcpZbA+P5QP0oB8APAqFsQ6Vo4R8ckAYAGFAOAAADygEAYEA5AAAMfF4O1dXVGjVqlE6dOiVJ2rdvn1JTUzV8+HBlZWX5+uEBAB7waTkcPHhQDz30kE6cOCFJunDhghYsWKBXXnlFb7/9tg4dOqQ9e/b4MgIAwAM+LYeNGzdq0aJFio2NlSQVFhaqQ4cOiouLU2RkpFJTU2Wz2XwZAQDgAZ+e5/Dss89ecfv06dNq06aN63ZsbKxKS0sbtM1Dhw55JZuvnD17VpJUUFBgcpLQwPPpPTyX3hXqz6dfT4Krq6uTxWJx3XY6nVfcdkf37t3VrFkzb0fzms2bN0uSevXqZXKS0MDz6T08l94VLM+n3W736EO1X2cr3XrrrSorK3PdLisrc+1yAgAEDr+Ww913363jx4/r5MmTqq2t1fbt2zVw4EB/RgAAuMGvu5WaNWumzMxMzZgxQ3a7XYmJiUpOTvZnBACAG/xSDvn5+a6vExISlJub64+HBQB4iDOkAQAGlAMAwIByAAAYcLEf+Ex+fr7y8vIatY3LVy+7fKEaTwwbNkxJSUmNygGEG8rhRwLlzUziDe2ymJgYsyMAYYly8DLezP4nKSmJggOCFOXwI7yZAcAlHJAGABhQDgAAA8oBAGDAMQcgSDR2Nh0z6dAQlAMQJphJh4agHIAgwWw6+BPHHAAABpQDAMCAcgAAGFAOAAADygEAYEA5AAAMKAcAgAHlAAAw4CQ4AGGHC3tdH+UAAB4I9eVIKAcAYYelSK6PYw4AAAPKAQBgQDkAAAwoBwCAAeUAADCgHAAABpQDAMAgaM5zcDqdkqSLFy+anAQAgsfl98zL76HuCppyqKmpkSQVFRWZnAQAgk9NTY2aN2/u9u9bnA2tE5PU1dXp3Llzatq0qSwWi9lxACAoOJ1O1dTU6MYbb1STJu4fSQiacgAA+A8HpAEABpQDAMCAcgAAGFAOAAADygEAYEA5AAAMKAcAgAHlAAAwCJrlMwKJzWbT2rVr5XA45HQ6NXr0aP3ud78zO1ZQWbx4sQ4cOKCamhqVlJTojjvukCSdOXNG6enpmjFjhskJg9+pU6eUnJzsem4vW7Nmjdq2beu6PWnSJD3zzDO65ZZb/B0xKDT09b5x40a1aNFCo0aN8mNK76McGqi0tFTPPfeccnJyFB0drXPnzmn8+PHq2LGjhgwZYna8oLFo0SJJl97AJkyYoG3btkmS/vznP5sZK+TExsa6nttrWbdunZ/SBB9PXu8HDhxQnz59/JzU+9it1EAVFRWqqanRhQsXJEk33nijMjMzdeeddyopKUmnTp2SJO3fv1/jx4+XJI0fP14rVqzQgw8+qGHDhmnPnj2m5Q8GhYWFslqtGjx4sKsscnJyNG/ePNfvjB8/Xvv379f+/fs1duxYpaen64knnjArclCZN2+epk6dqpSUFOXn51/xd4sr1fd6f+eddzRu3DilpaUpOTlZBw4c0L59+5Sfn6/Vq1fr/fffNzl94zByaKC77rpLQ4YM0dChQxUfH6++ffsqNTVVHTp0qPd+NTU12rBhg/Lz87Vq1SolJib6KXHw+c9//qPs7GxVV1crKSlJEydOrPf3T5w4oV27dqlVq1Z+Shg8Tp8+rdGjR7tup6amSpJat26tNWvWSJKeeeYZU7IFg2u93uPi4vT0009rzZo1iomJ0ebNm7V27VqtWbNGSUlJ6tOnjwYMGGB2/EahHDywePFi/f73v9fevXu1d+9ejRs3TitXrqz3Ppf/UDp37qzKykp/xAxaAwYMUFRUlGJiYhQdHa2qqqp6f79jx44UwzVcbbfSvHnz1KNHD5MSBZ9rvd5ffvll5efn6/jx4/r4448btOJpMKAcGmj37t06f/687rvvPmVkZCgjI0MbN27U5s2bJf3vghoOh+OK+zVr1kySWG7cDZGR//uztFgscjqdrv9edvn6HpIatEY9LuE5c8+1Xu9vvvmmXnjhBaWlpal379766U9/qjfffNPsuF4VWlXnB82bN9ef/vQn1z5ap9OpI0eOKD4+XtHR0fryyy8lSTt37jQzZsiJjo7WV199JafTqa+//lpffPGF2ZEQBq71eo+KipLFYtHUqVPVt29f5eXlqba2VpIUERHh+jqYMXJooH79+mn69OmaOnWq69PrgAEDNG3aNP3sZz/T0qVL9dJLL6l///4mJw0tv/jFL/TPf/5TycnJ6tixo3r16mV2JISBa73eX375Zc2bN08pKSmyWCzq37+/CgoKJF36W33hhRfUqlUrJScnmxm/UbjYDwDAgN1KAAADygEAYEA5AAAMKAcAgAHlAAAwoByABrLZbK51s4BQRTkAAAwoB8ANq1at0tChQzV27Fjl5eVJko4fP66JEydq3LhxGjx4sB599FHZ7Xbl5ubKarW67vvNN9+of//+unjxolnxgQajHIDr2LFjh9577z1t3brVtVqsdOmiLmPGjNHGjRv13nvv6dSpU9q9e7eSk5NVUlKiY8eOSZI2bdqk+++/X1FRUWb+M4AGoRyA6/jwww81bNgwtWzZUpGRkcrIyJAkPfbYY4qJidG6dev0xz/+UadPn9b58+cVFRWlBx54QJs2bVJtba22bNmicePGmfyvABqGtZUAN/x4lZmIiAhJ0uzZs1VbW6uUlBQNGjRI3377rev3rFarxo4dqz59+qhz586Ki4szJTfgKUYOwHUMHDhQNptNZ86cUV1dnev6CHv37tW0adN03333SZIOHjzoWo2zbdu2uueee7Rs2TKrcbpkAAAAmUlEQVQ99NBDpmUHPMXIAbiOxMREffHFF8rIyNBNN92ku+66SxUVFZo1a5amTZumFi1aqGXLlurdu7dKSkpc90tPT9fSpUu56h+CEquyAj5QV1enJUuWqF27dpo8ebLZcYAGY7cS4GXV1dXq27evvv32W02YMMHsOIBHGDkAAAwYOQAADCgHAIAB5QAAMKAcAAAGlAMAwOD/AH6hKP1l6kujAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = \"day\"\n",
    "y = \"total_bill\"\n",
    "order = ['Sun', 'Thur', 'Fri', 'Sat']\n",
    "ax = sns.boxplot(data=df, x=x, y=y, order=order)\n",
    "ax, test_results = add_stat_annotation(ax, data=df, x=x, y=y, order=order,\n",
    "                                   box_pairs=[(\"Thur\", \"Fri\"), (\"Thur\", \"Sat\"), (\"Fri\", \"Sun\")],\n",
    "                                   test='Mann-Whitney', text_format='star', loc='outside', verbose=2)\n",
    "plt.savefig('example_non-hue_outside.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Statistical test results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The `add_stat_annotation` function returns the tuple `ax, test_results`, where `test_results` is a list of `StatResult` objects, containing both the original data of the boxes and the statistical test results (p-value, etc)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.000e+00 U_stat=6.305e+02\n",
      "Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.407e-01 U_stat=2.180e+03\n",
      "Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=8.041e-02 U_stat=9.605e+02\n",
      "\n",
      "StatResult attributes: dict_keys(['test_str', 'test_short_name', 'stat_str', 'stat', 'pval', 'box1', 'box2'])\n"
     ]
    }
   ],
   "source": [
    "for res in test_results:\n",
    "    print(res)\n",
    "\n",
    "print(\"\\nStatResult attributes:\", test_results[0].__dict__.keys())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### No multiple comparisons correction"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value annotation legend:\n",
      "ns: 5.00e-02 < p <= 1.00e+00\n",
      "*: 1.00e-02 < p <= 5.00e-02\n",
      "**: 1.00e-03 < p <= 1.00e-02\n",
      "***: 1.00e-04 < p <= 1.00e-03\n",
      "****: p <= 1.00e-04\n",
      "\n",
      "Thur v.s. Fri: Mann-Whitney-Wilcoxon test two-sided, P_val=6.477e-01 U_stat=6.305e+02\n",
      "Thur v.s. Sat: Mann-Whitney-Wilcoxon test two-sided, P_val=4.690e-02 U_stat=2.180e+03\n",
      "Sun v.s. Fri: Mann-Whitney-Wilcoxon test two-sided, P_val=2.680e-02 U_stat=9.605e+02\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = \"day\"\n",
    "y = \"total_bill\"\n",
    "order = ['Sun', 'Thur', 'Fri', 'Sat']\n",
    "ax = sns.boxplot(data=df, x=x, y=y, order=order)\n",
    "test_results = add_stat_annotation(ax, data=df, x=x, y=y, order=order,\n",
    "                                   box_pairs=[(\"Thur\", \"Fri\"), (\"Thur\", \"Sat\"), (\"Fri\", \"Sun\")],\n",
    "                                   test='Mann-Whitney', comparisons_correction=None, \n",
    "                                   text_format='star', loc='outside', verbose=2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Location"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Statistical test annotation can be either drawn inside the plot area (`loc='inside'`), or on top on it (`loc='outside'`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value annotation legend:\n",
      "ns: 5.00e-02 < p <= 1.00e+00\n",
      "*: 1.00e-02 < p <= 5.00e-02\n",
      "**: 1.00e-03 < p <= 1.00e-02\n",
      "***: 1.00e-04 < p <= 1.00e-03\n",
      "****: p <= 1.00e-04\n",
      "\n",
      "Sun v.s. Thur: Custom statistical test, P_val:1.000e-01\n",
      "Sun v.s. Fri: Custom statistical test, P_val:1.000e-03\n",
      "Sun v.s. Sat: Custom statistical test, P_val:1.000e-01\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = \"day\"\n",
    "y = \"total_bill\"\n",
    "order = ['Sun', 'Thur', 'Fri', 'Sat']\n",
    "ax = sns.boxplot(data=df, x=x, y=y, order=order)\n",
    "add_stat_annotation(ax, data=df, x=x, y=y, order=order,\n",
    "                    box_pairs=[(\"Sun\", \"Thur\"), (\"Sun\", \"Sat\"), (\"Fri\", \"Sun\")],\n",
    "                    perform_stat_test=False, pvalues=[0.1, 0.1, 0.001],\n",
    "                    test=None, text_format='star', loc='inside', verbose=2);\n",
    "plt.savefig('example_non-hue_inside.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot with hue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We create boxes with unequal ymax position."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat      cut color clarity  depth  table  price     x     y     z\n",
       "0   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = sns.load_dataset('diamonds')\n",
    "df = df[df['color'].map(lambda x: x in 'EIJ')]\n",
    "# Modifying data to yield unequal boxes in the hue value\n",
    "df.loc[df['cut'] == 'Ideal', 'price'] = df.loc[df['cut'] == 'Ideal', 'price'].map(lambda x: min(x, 5000))\n",
    "df.loc[df['cut'] == 'Premium', 'price'] = df.loc[df['cut'] == 'Premium', 'price'].map(lambda x: min(x, 7500))\n",
    "df.loc[df['cut'] == 'Good', 'price'] = df.loc[df['cut'] == 'Good', 'price'].map(lambda x: min(x, 15000))\n",
    "df.loc[df['cut'] == 'Very Good', 'price'] = df.loc[df['cut'] == 'Very Good', 'price'].map(lambda x: min(x, 3000))\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value annotation legend:\n",
      "ns: 5.00e-02 < p <= 1.00e+00\n",
      "*: 1.00e-02 < p <= 5.00e-02\n",
      "**: 1.00e-03 < p <= 1.00e-02\n",
      "***: 1.00e-04 < p <= 1.00e-03\n",
      "****: p <= 1.00e-04\n",
      "\n",
      "E_Ideal v.s. E_Premium: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.404e-30 U_stat=3.756e+06\n",
      "I_Ideal v.s. I_Premium: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=4.627e-60 U_stat=1.009e+06\n",
      "J_Ideal v.s. J_Premium: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=3.616e-36 U_stat=2.337e+05\n",
      "E_Ideal v.s. E_Good: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=4.681e-18 U_stat=1.480e+06\n",
      "I_Ideal v.s. I_Good: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=4.507e-12 U_stat=4.359e+05\n",
      "J_Ideal v.s. J_Good: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=9.056e-04 U_stat=1.174e+05\n",
      "E_Ideal v.s. E_Very Good: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.562e-01 U_stat=4.850e+06\n",
      "E_Good v.s. I_Ideal: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=1.000e+00 U_stat=9.882e+05\n",
      "I_Premium v.s. J_Ideal: Mann-Whitney-Wilcoxon test two-sided with Bonferroni correction, P_val=4.643e-26 U_stat=8.084e+05\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = \"color\"\n",
    "y = \"price\"\n",
    "hue = \"cut\"\n",
    "hue_order=['Ideal', 'Premium', 'Good', 'Very Good', 'Fair']\n",
    "box_pairs=[\n",
    "    ((\"E\", \"Ideal\"), (\"E\", \"Very Good\")),\n",
    "    ((\"E\", \"Ideal\"), (\"E\", \"Premium\")),\n",
    "    ((\"E\", \"Ideal\"), (\"E\", \"Good\")),\n",
    "    ((\"I\", \"Ideal\"), (\"I\", \"Premium\")),\n",
    "    ((\"I\", \"Ideal\"), (\"I\", \"Good\")),\n",
    "    ((\"J\", \"Ideal\"), (\"J\", \"Premium\")),\n",
    "    ((\"J\", \"Ideal\"), (\"J\", \"Good\")),\n",
    "    ((\"E\", \"Good\"), (\"I\", \"Ideal\")),\n",
    "    ((\"I\", \"Premium\"), (\"J\", \"Ideal\")),\n",
    "    ]\n",
    "ax = sns.boxplot(data=df, x=x, y=y, hue=hue,)\n",
    "add_stat_annotation(ax, data=df, x=x, y=y, hue=hue, box_pairs=box_pairs,\n",
    "                    test='Mann-Whitney', loc='inside', verbose=2)\n",
    "plt.legend(loc='upper left', bbox_to_anchor=(1.03, 1))\n",
    "plt.savefig('example_hue_layout.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Boxplot with bucketed categories"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>total_bill</th>\n",
       "      <th>tip</th>\n",
       "      <th>sex</th>\n",
       "      <th>smoker</th>\n",
       "      <th>day</th>\n",
       "      <th>time</th>\n",
       "      <th>size</th>\n",
       "      <th>tip_bucket</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16.99</td>\n",
       "      <td>1.01</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>(0.991, 4.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.34</td>\n",
       "      <td>1.66</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>(0.991, 4.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>21.01</td>\n",
       "      <td>3.50</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>3</td>\n",
       "      <td>(0.991, 4.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>23.68</td>\n",
       "      <td>3.31</td>\n",
       "      <td>Male</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>2</td>\n",
       "      <td>(0.991, 4.0]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>24.59</td>\n",
       "      <td>3.61</td>\n",
       "      <td>Female</td>\n",
       "      <td>No</td>\n",
       "      <td>Sun</td>\n",
       "      <td>Dinner</td>\n",
       "      <td>4</td>\n",
       "      <td>(0.991, 4.0]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   total_bill   tip     sex smoker  day    time  size    tip_bucket\n",
       "0       16.99  1.01  Female     No  Sun  Dinner     2  (0.991, 4.0]\n",
       "1       10.34  1.66    Male     No  Sun  Dinner     3  (0.991, 4.0]\n",
       "2       21.01  3.50    Male     No  Sun  Dinner     3  (0.991, 4.0]\n",
       "3       23.68  3.31    Male     No  Sun  Dinner     2  (0.991, 4.0]\n",
       "4       24.59  3.61  Female     No  Sun  Dinner     4  (0.991, 4.0]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = sns.load_dataset(\"tips\")\n",
    "df['tip_bucket'] = pd.cut(df['tip'], 3)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[(0.991, 4.0], (4.0, 7.0], (7.0, 10.0]]\n",
       "Categories (3, interval[float64]): [(0.991, 4.0] < (4.0, 7.0] < (7.0, 10.0]]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# In this case we just have to pass the list of categories objects to the add_stat_annotation function.\n",
    "tip_bucket_list = df['tip_bucket'].unique()\n",
    "tip_bucket_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value annotation legend:\n",
      "ns: 5.00e-02 < p <= 1.00e+00\n",
      "*: 1.00e-02 < p <= 5.00e-02\n",
      "**: 1.00e-03 < p <= 1.00e-02\n",
      "***: 1.00e-04 < p <= 1.00e-03\n",
      "****: p <= 1.00e-04\n",
      "\n",
      "Fri_(0.991, 4.0] v.s. Sat_(7.0, 10.0]: t-test independent samples with Bonferroni correction, P_val=6.176e-07 stat=-7.490e+00\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = \"day\"\n",
    "y = \"total_bill\"\n",
    "hue = \"tip_bucket\"\n",
    "data = df\n",
    "ax = sns.boxplot(data=df, x=x, y=y, hue=hue)\n",
    "add_stat_annotation(ax, data=df, x=x, y=y, hue=hue,\n",
    "                    box_pairs=[((\"Sat\", tip_bucket_list[2]), (\"Fri\", tip_bucket_list[0]))],\n",
    "                    test='t-test_ind', loc='inside', verbose=2)\n",
    "plt.legend(loc='upper left', bbox_to_anchor=(1.03, 1))\n",
    "plt.savefig('example_bucketed.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Barplot with hue"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note: Support for barplot has not been fully implemented yet."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value annotation legend:\n",
      "ns: 5.00e-02 < p <= 1.00e+00\n",
      "*: 1.00e-02 < p <= 5.00e-02\n",
      "**: 1.00e-03 < p <= 1.00e-02\n",
      "***: 1.00e-04 < p <= 1.00e-03\n",
      "****: p <= 1.00e-04\n",
      "\n",
      "SI1_I v.s. SI1_J: t-test independent samples with Bonferroni correction, P_val=7.662e-04 stat=-3.937e+00\n",
      "VS1_I v.s. VS1_J: t-test independent samples with Bonferroni correction, P_val=8.226e-04 stat=-3.923e+00\n",
      "VS2_I v.s. VS2_J: t-test independent samples with Bonferroni correction, P_val=6.125e-02 stat=-2.709e+00\n",
      "VVS2_I v.s. VVS2_J: t-test independent samples with Bonferroni correction, P_val=8.168e-13 stat=-7.672e+00\n",
      "VVS1_I v.s. VVS1_J: t-test independent samples with Bonferroni correction, P_val=5.882e-09 stat=-6.321e+00\n",
      "I1_I v.s. I1_J: t-test independent samples with Bonferroni correction, P_val=2.614e-01 stat=-2.206e+00\n",
      "IF_I v.s. IF_J: t-test independent samples with Bonferroni correction, P_val=3.970e-02 stat=-2.881e+00\n",
      "SI2_I v.s. SI2_J: t-test independent samples with Bonferroni correction, P_val=1.000e+00 stat=-8.949e-01\n",
      "SI2_E v.s. SI1_E: t-test independent samples with Bonferroni correction, P_val=3.761e-65 stat=1.749e+01\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = sns.load_dataset('diamonds')\n",
    "df = df[df['color'].map(lambda x: x in 'EIJ')]\n",
    "\n",
    "x = \"clarity\"\n",
    "y = \"carat\"\n",
    "hue = \"color\"\n",
    "# hue_order=['Ideal', 'Premium']\n",
    "box_pairs = [\n",
    "    ((\"SI2\", \"E\"), (\"SI1\", \"E\"))\n",
    "    ]\n",
    "box_pairs = box_pairs + [((clar, 'I'), (clar, 'J')) for clar in df['clarity'].unique()]\n",
    "width = 0.4\n",
    "ax = sns.barplot(data=df, x=x, y=y, hue=hue)\n",
    "add_stat_annotation(ax, plot='barplot', data=df, x=x, y=y, hue=hue,\n",
    "                    box_pairs=box_pairs, test='t-test_ind', loc='inside', verbose=2)\n",
    "plt.legend(loc='upper left', bbox_to_anchor=(1.03, 1), title=hue)\n",
    "plt.savefig('example_barplot_hue.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Tuning y offsets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](example_tuning_y_offsets_w_arguments.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Sat_Yes v.s. Sat_No: t-test independent samples, P_val=4.304e-01 stat=7.922e-01\n",
      "Thur_No v.s. Fri_No: t-test independent samples, P_val=7.425e-01 stat=-3.305e-01\n",
      "Thur_Yes v.s. Sun_No: t-test independent samples, P_val=5.623e-01 stat=-5.822e-01\n"
     ]
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = sns.load_dataset(\"tips\")\n",
    "x = \"day\"\n",
    "y = \"total_bill\"\n",
    "hue = \"smoker\"\n",
    "ax = sns.boxplot(data=df, x=x, y=y, hue=hue)\n",
    "add_stat_annotation(ax, data=df, x=x, y=y, hue=hue,\n",
    "                    box_pairs=[((\"Thur\", \"No\"), (\"Fri\", \"No\")),\n",
    "                                 ((\"Sat\", \"Yes\"), (\"Sat\", \"No\")),\n",
    "                                 ((\"Sun\", \"No\"), (\"Thur\", \"Yes\"))\n",
    "                                ],\n",
    "                    test='t-test_ind', text_format='full', loc='inside', comparisons_correction=None,\n",
    "                    line_offset_to_box=0.2, line_offset=0.1, line_height=0.05, text_offset=8,\n",
    "                    verbose=2)\n",
    "plt.legend(loc='upper left', bbox_to_anchor=(1.03, 1))\n",
    "plt.savefig('example_tuning_y_offsets.svg')\n",
    "plt.savefig('example_tuning_y_offsets.png', dpi=300, bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom p-values as input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Performing Bartlett statistical test for equal variances on pair: ('setosa', 'versicolor') stat=6.89e+00 p-value=8.66e-03\n",
      "Performing Bartlett statistical test for equal variances on pair: ('setosa', 'virginica') stat=1.60e+01 p-value=6.38e-05\n",
      "Performing Bartlett statistical test for equal variances on pair: ('versicolor', 'virginica') stat=2.09e+00 p-value=1.48e-01\n",
      "pvalues: [0.008659557933879902, 6.378941946712463e-05, 0.14778816016231236]\n"
     ]
    }
   ],
   "source": [
    "df = sns.load_dataset(\"iris\")\n",
    "x = \"species\"\n",
    "y = \"sepal_length\"\n",
    "\n",
    "box_pairs = [(\"setosa\", \"versicolor\"), (\"setosa\", \"virginica\"), (\"versicolor\", \"virginica\")]\n",
    "\n",
    "from scipy.stats import bartlett\n",
    "\n",
    "test_short_name = 'Bartlett'\n",
    "pvalues = []\n",
    "for pair in box_pairs:\n",
    "    data1 = df.groupby(x)[y].get_group(pair[0])\n",
    "    data2 = df.groupby(x)[y].get_group(pair[1])\n",
    "    stat, p = bartlett(data1, data2)\n",
    "    print(\"Performing Bartlett statistical test for equal variances on pair:\",\n",
    "          pair, \"stat={:.2e} p-value={:.2e}\".format(stat, p))\n",
    "    pvalues.append(p)\n",
    "print(\"pvalues:\", pvalues)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "p-value annotation legend:\n",
      "ns: 5.00e-02 < p <= 1.00e+00\n",
      "*: 1.00e-02 < p <= 5.00e-02\n",
      "**: 1.00e-03 < p <= 1.00e-02\n",
      "***: 1.00e-04 < p <= 1.00e-03\n",
      "****: p <= 1.00e-04\n",
      "\n",
      "setosa v.s. versicolor: Custom statistical test, P_val:8.660e-03\n",
      "versicolor v.s. virginica: Custom statistical test, P_val:1.478e-01\n",
      "setosa v.s. virginica: Custom statistical test, P_val:6.379e-05\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(<matplotlib.axes._subplots.AxesSubplot at 0x7f1e20d22f98>,\n",
       " [<statannot.StatResult.StatResult at 0x7f1e20ab9240>,\n",
       "  <statannot.StatResult.StatResult at 0x7f1e20ab9e10>,\n",
       "  <statannot.StatResult.StatResult at 0x7f1e20ab99b0>])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(data=df, x=x, y=y)\n",
    "test_results = add_stat_annotation(ax, data=df, x=x, y=y,\n",
    "                                   box_pairs=box_pairs,\n",
    "                                   perform_stat_test=False, pvalues=pvalues, test_short_name=test_short_name,\n",
    "                                   text_format='star', verbose=2)\n",
    "test_results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Custom annotation texts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df = sns.load_dataset(\"tips\")\n",
    "x = \"day\"\n",
    "y = \"total_bill\"\n",
    "order = ['Sun', 'Thur', 'Fri', 'Sat']\n",
    "ax = sns.boxplot(data=df, x=x, y=y, order=order)\n",
    "test_results = add_stat_annotation(ax, data=df, x=x, y=y, order=order,\n",
    "                                   box_pairs=[(\"Thur\", \"Fri\"), (\"Thur\", \"Sat\"), (\"Fri\", \"Sun\")],\n",
    "                                   text_annot_custom=[\"first pair\", \"second pair\", \"third pair\"],\n",
    "                                   perform_stat_test=False, pvalues=[0, 0, 0],\n",
    "                                   loc='outside', verbose=0)\n",
    "plt.savefig('example_custom_text_annot.png', dpi=300, bbox_inches='tight')"
   ]
  }
 ],
 "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": "3.6.9"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}