{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": false
   },
   "source": [
    "# scikit-posthocs\n",
    "\n",
    "## Pairwise Multiple Comparisons Post-hoc Tests\n",
    "\n",
    "in Python 3 (Ubuntu Linux)\n",
    "\n",
    "https://github.com/maximtrp/scikit-posthocs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.        ,  0.00119517,  0.00278329],\n",
       "       [ 0.00119517, -1.        ,  0.18672227],\n",
       "       [ 0.00278329,  0.18672227, -1.        ]])"
      ]
     },
     "execution_count": 1,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scikit_posthocs as sp\n",
    "x = [[1,2,3,5,1], [12,31,54], [10,12,6,74,11]]\n",
    "sp.posthoc_conover(x, p_adjust = 'holm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "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>groups</th>\n",
       "      <th>values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>a</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>a</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>a</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>b</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>b</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>b</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>b</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>b</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>c</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>c</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>c</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>c</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>c</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 2,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import scikit_posthocs as sp\n",
    "import pandas as pd\n",
    "x = pd.DataFrame({\"a\": [1,2,3,5,1], \"b\": [12,31,54,62,12], \"c\": [10,12,6,74,11]})\n",
    "x = x.melt(var_name='groups', value_name='values')\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>a</th>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.000109</td>\n",
       "      <td>0.001853</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.000109</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.121659</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.001853</td>\n",
       "      <td>0.121659</td>\n",
       "      <td>-1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 3,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp.posthoc_conover(x, val_col='values', group_col='groups')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<matplotlib.axes._subplots.AxesSubplot at 0x7f67b595ffd0>,\n",
       " <matplotlib.colorbar.ColorbarBase at 0x7f67b160c128>)"
      ]
     },
     "execution_count": 4,
     "metadata": {
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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"
     },
     "execution_count": 4,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pc = sp.posthoc_conover(x, val_col='values', group_col='groups')\n",
    "heatmap_args = {'linewidths': 0.25, 'linecolor': '0.5', 'clip_on': False, 'square': True, 'cbar_ax_bbox': [0.80, 0.35, 0.04, 0.3]}\n",
    "sp.sign_plot(pc, **heatmap_args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<matplotlib.axes._subplots.AxesSubplot at 0x7f67b1636e10>,\n",
       " <matplotlib.colorbar.ColorbarBase at 0x7f67b15efd68>)"
      ]
     },
     "execution_count": 5,
     "metadata": {
     },
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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"
     },
     "execution_count": 5,
     "metadata": {
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pc = sp.posthoc_conover(x, val_col='values', group_col='groups')\n",
    "# Format: diagonal, non-significant, p<0.001, p<0.01, p<0.05\n",
    "cmap = ['1', '#fb6a4a',  '#08306b',  '#4292c6', '#c6dbef']\n",
    "heatmap_args = {'cmap': cmap, 'linewidths': 0.25, 'linecolor': '0.5', 'clip_on': False, 'square': True, 'cbar_ax_bbox': [0.80, 0.35, 0.04, 0.3]}\n",
    "sp.sign_plot(pc, **heatmap_args)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
   ],
   "source": [
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (Ubuntu Linux)",
   "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": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}