usage-examples.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import scikit_posthocs as sp\n",
    "import numpy as np\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Without p adjustments"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -1.00000000e+00,   3.98391078e-04,   1.39164426e-03],\n",
       "       [  3.98391078e-04,  -1.00000000e+00,   1.86722274e-01],\n",
       "       [  1.39164426e-03,   1.86722274e-01,  -1.00000000e+00]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = [[1,2,3,5,1], [12,31,54], [10,12,6,74,11]]\n",
    "sp.posthoc_conover(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## With Holm p adjustment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.        ,  0.00119517,  0.00278329],\n",
       "       [ 0.00119517, -1.        ,  0.18672227],\n",
       "       [ 0.00278329,  0.18672227, -1.        ]])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp.posthoc_conover(x, p_adjust = 'holm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Exporting to pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>groups</th>\n",
       "      <th>vals</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>a</td>\n",
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       "      <th>2</th>\n",
       "      <td>a</td>\n",
       "      <td>3</td>\n",
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       "    <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>c</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>c</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>c</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>c</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>c</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
      ],
      "text/plain": [
       "   groups  vals\n",
       "0       a     1\n",
       "1       a     2\n",
       "2       a     3\n",
       "3       a     5\n",
       "4       a     1\n",
       "5       b    12\n",
       "6       b    31\n",
       "7       b    54\n",
       "8       c    10\n",
       "9       c    12\n",
       "10      c     6\n",
       "11      c    74\n",
       "12      c    11"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groups = [['a']*5, ['b']*3, ['c']*5]\n",
    "df = pd.DataFrame({'vals': np.concatenate(x), 'groups': np.concatenate(groups)})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
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   "outputs": [
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       "  <thead>\n",
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       "      <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>0.000398</td>\n",
       "      <td>0.001392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>0.000398</td>\n",
       "      <td>-1.000000</td>\n",
       "      <td>0.186722</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>0.001392</td>\n",
       "      <td>0.186722</td>\n",
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      "text/plain": [
       "          a         b         c\n",
       "a -1.000000  0.000398  0.001392\n",
       "b  0.000398 -1.000000  0.186722\n",
       "c  0.001392  0.186722 -1.000000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result = sp.posthoc_conover(df, val_col='vals', group_col='groups')\n",
    "result"
   ]
  }
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