MANOVA_Test_in_Python_Statsmodels_Example.ipynb

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   "source": [
    "## How to Carry out MANOVA in Python\n",
    "Step one is to import Pandas and MANOVA:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from statsmodels.multivariate.manova import MANOVA"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Step two is to load data and rename columns:"
   ]
  },
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   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sepal_Length</th>\n",
       "      <th>Sepal_Width</th>\n",
       "      <th>Petal_Length</th>\n",
       "      <th>Petal_Width</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>4</td>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      "text/plain": [
       "   Sepal_Length  Sepal_Width  Petal_Length  Petal_Width Species\n",
       "1           5.1          3.5           1.4          0.2  setosa\n",
       "2           4.9          3.0           1.4          0.2  setosa\n",
       "3           4.7          3.2           1.3          0.2  setosa\n",
       "4           4.6          3.1           1.5          0.2  setosa\n",
       "5           5.0          3.6           1.4          0.2  setosa"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "url = 'https://vincentarelbundock.github.io/Rdatasets/csv/datasets/iris.csv'\n",
    "df = pd.read_csv(url, index_col=0)\n",
    "df.columns = df.columns.str.replace(\".\", \"_\") \n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Python MANOVA Example\n",
    "Third step is to carry out the MANOVA using *from_formula*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "maov = MANOVA.from_formula('Sepal_Length + Sepal_Width + \\\n",
    "                            Petal_Length + Petal_Width  ~ Species', data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Final step is to print the results using *mv_test*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                   Multivariate linear model\n",
      "================================================================\n",
      "                                                                \n",
      "----------------------------------------------------------------\n",
      "       Intercept         Value  Num DF  Den DF   F Value  Pr > F\n",
      "----------------------------------------------------------------\n",
      "          Wilks' lambda  0.0170 4.0000 144.0000 2086.7720 0.0000\n",
      "         Pillai's trace  0.9830 4.0000 144.0000 2086.7720 0.0000\n",
      " Hotelling-Lawley trace 57.9659 4.0000 144.0000 2086.7720 0.0000\n",
      "    Roy's greatest root 57.9659 4.0000 144.0000 2086.7720 0.0000\n",
      "----------------------------------------------------------------\n",
      "                                                                \n",
      "----------------------------------------------------------------\n",
      "        Species          Value  Num DF  Den DF   F Value  Pr > F\n",
      "----------------------------------------------------------------\n",
      "          Wilks' lambda  0.0234 8.0000 288.0000  199.1453 0.0000\n",
      "         Pillai's trace  1.1919 8.0000 290.0000   53.4665 0.0000\n",
      " Hotelling-Lawley trace 32.4773 8.0000 203.4024  582.1970 0.0000\n",
      "    Roy's greatest root 32.1919 4.0000 145.0000 1166.9574 0.0000\n",
      "================================================================\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(maov.mv_test())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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