{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "8544ce09",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.formula.api import ols\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "73fa4627",
   "metadata": {},
   "outputs": [],
   "source": [
    "yield_data = pd.read_excel(\"2021 Yield.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6af25c5d",
   "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>Plot Number</th>\n",
       "      <th>Block</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Simp. Treatment</th>\n",
       "      <th>Yield (avg; bu/ac)</th>\n",
       "      <th>Treatment Description</th>\n",
       "      <th>Short Trt Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2S</td>\n",
       "      <td>2.0</td>\n",
       "      <td>75.328418</td>\n",
       "      <td>Corn/Soy, Spring Manure</td>\n",
       "      <td>CS, SM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6S</td>\n",
       "      <td>6.0</td>\n",
       "      <td>77.482016</td>\n",
       "      <td>Corn/Soy, Fall Manure</td>\n",
       "      <td>CS, FM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1C</td>\n",
       "      <td>1.0</td>\n",
       "      <td>232.173281</td>\n",
       "      <td>Corn/Soy, Spring UAN</td>\n",
       "      <td>CS, SUAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>231.645855</td>\n",
       "      <td>Continuous Corn</td>\n",
       "      <td>CC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>208.940519</td>\n",
       "      <td>Continuous Corn + PGC</td>\n",
       "      <td>CC + PGC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Plot Number  Block Treatment  Simp. Treatment  Yield (avg; bu/ac)  \\\n",
       "0            1      2        2S              2.0           75.328418   \n",
       "1            2      2        6S              6.0           77.482016   \n",
       "2            3      2        1C              1.0          232.173281   \n",
       "3            4      2       3.1              3.1          231.645855   \n",
       "4            5      2       4.1              4.1          208.940519   \n",
       "\n",
       "     Treatment Description Short Trt Description  \n",
       "0  Corn/Soy, Spring Manure                CS, SM  \n",
       "1    Corn/Soy, Fall Manure                CS, FM  \n",
       "2     Corn/Soy, Spring UAN              CS, SUAN  \n",
       "3          Continuous Corn                    CC  \n",
       "4    Continuous Corn + PGC              CC + PGC  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yield_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d2ba7c26",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x = \"Treatment\", y = \"Yield (avg; bu/ac)\", data = yield_data)\n",
    "plt.savefig('all_yield_box.png')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3ce43f6d",
   "metadata": {},
   "source": [
    "Including all of the yield data on the same plot doesn't make sense because some treatments are soybeans and some are corn"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "c06d86fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Plot Number                int64\n",
       "Block                      int64\n",
       "Treatment                 object\n",
       "Simp. Treatment          float64\n",
       "Yield (avg; bu/ac)       float64\n",
       "Treatment Description     object\n",
       "Short Trt Description     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yield_data.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "eb07cfcf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Plot Number                int64\n",
       "Block                      int64\n",
       "Treatment                 string\n",
       "Simp. Treatment          float64\n",
       "Yield (avg; bu/ac)       float64\n",
       "Treatment Description     object\n",
       "Short Trt Description     object\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "yield_data['Treatment'] = yield_data['Treatment'].astype('string')\n",
    "yield_data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02180951",
   "metadata": {},
   "source": [
    "Filtering out the soybean treatment data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5bb1dac4",
   "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>Plot Number</th>\n",
       "      <th>Block</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Simp. Treatment</th>\n",
       "      <th>Yield (avg; bu/ac)</th>\n",
       "      <th>Treatment Description</th>\n",
       "      <th>Short Trt Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1C</td>\n",
       "      <td>1.0</td>\n",
       "      <td>232.173281</td>\n",
       "      <td>Corn/Soy, Spring UAN</td>\n",
       "      <td>CS, SUAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>231.645855</td>\n",
       "      <td>Continuous Corn</td>\n",
       "      <td>CC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>208.940519</td>\n",
       "      <td>Continuous Corn + PGC</td>\n",
       "      <td>CC + PGC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>223.988531</td>\n",
       "      <td>Continuous Corn + 30 in ISC</td>\n",
       "      <td>CC + 30ISC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>2C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>237.683989</td>\n",
       "      <td>Corn/Soy, Spring Manure</td>\n",
       "      <td>CS, SM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Plot Number  Block Treatment  Simp. Treatment  Yield (avg; bu/ac)  \\\n",
       "2             3      2        1C              1.0          232.173281   \n",
       "3             4      2       3.1              3.1          231.645855   \n",
       "4             5      2       4.1              4.1          208.940519   \n",
       "5             6      2       3.2              3.2          223.988531   \n",
       "10           11      2        2C              2.0          237.683989   \n",
       "\n",
       "          Treatment Description Short Trt Description  \n",
       "2          Corn/Soy, Spring UAN              CS, SUAN  \n",
       "3               Continuous Corn                    CC  \n",
       "4         Continuous Corn + PGC              CC + PGC  \n",
       "5   Continuous Corn + 30 in ISC            CC + 30ISC  \n",
       "10      Corn/Soy, Spring Manure                CS, SM  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corn_only = yield_data[yield_data['Treatment'].str.contains('1C|2C|3.1|3.2|4.1|4.2|5C|6C')]\n",
    "corn_only.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "8afc2fe2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "24"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(corn_only)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2e61ba81",
   "metadata": {},
   "source": [
    "Making sure all of the treatments were included. There are 8 treatments with corn growing and each treatment has three reps, so 24 is correct. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3fc35598",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x = \"Treatment\", y = \"Yield (avg; bu/ac)\", data = corn_only, hue = 'Short Trt Description', dodge = False)\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.savefig('corn_yield_box.jpg', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "78be2b0b",
   "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>Plot Number</th>\n",
       "      <th>Block</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Simp. Treatment</th>\n",
       "      <th>Yield (avg; bu/ac)</th>\n",
       "      <th>Treatment Description</th>\n",
       "      <th>Short Trt Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2S</td>\n",
       "      <td>2.0</td>\n",
       "      <td>75.328418</td>\n",
       "      <td>Corn/Soy, Spring Manure</td>\n",
       "      <td>CS, SM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6S</td>\n",
       "      <td>6.0</td>\n",
       "      <td>77.482016</td>\n",
       "      <td>Corn/Soy, Fall Manure</td>\n",
       "      <td>CS, FM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2S</td>\n",
       "      <td>2.0</td>\n",
       "      <td>75.667936</td>\n",
       "      <td>Corn/Soy, Spring Manure</td>\n",
       "      <td>CS, SM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>5S</td>\n",
       "      <td>5.0</td>\n",
       "      <td>71.839370</td>\n",
       "      <td>Corn/Soy + Rye</td>\n",
       "      <td>CS + R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>5S</td>\n",
       "      <td>5.0</td>\n",
       "      <td>72.610892</td>\n",
       "      <td>Corn/Soy + Rye</td>\n",
       "      <td>CS + R</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Plot Number  Block Treatment  Simp. Treatment  Yield (avg; bu/ac)  \\\n",
       "0            1      2        2S              2.0           75.328418   \n",
       "1            2      2        6S              6.0           77.482016   \n",
       "6            7      3        2S              2.0           75.667936   \n",
       "7            8      3        5S              5.0           71.839370   \n",
       "8            9      2        5S              5.0           72.610892   \n",
       "\n",
       "     Treatment Description Short Trt Description  \n",
       "0  Corn/Soy, Spring Manure                CS, SM  \n",
       "1    Corn/Soy, Fall Manure                CS, FM  \n",
       "6  Corn/Soy, Spring Manure                CS, SM  \n",
       "7           Corn/Soy + Rye                CS + R  \n",
       "8           Corn/Soy + Rye                CS + R  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soy_only = yield_data[yield_data['Treatment'].str.contains('1S|2S|5S|6S')]\n",
    "soy_only.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "5cfed28a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.boxplot(x = \"Treatment\", y = \"Yield (avg; bu/ac)\", data = soy_only, hue = 'Short Trt Description', dodge = False)\n",
    "plt.legend(bbox_to_anchor=(1.05, 1), loc=2, borderaxespad=0.)\n",
    "plt.savefig('soy_yield_box.jpg', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f04718ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "corn_only = corn_only.rename(columns={\"Yield (avg; bu/ac)\": \"Yield\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "1ae9c5e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "corn_only.to_excel(\"Corn_Yield_2021.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63633870",
   "metadata": {},
   "source": [
    "Saving the corn data as its own spreadsheet in case I want to come back to it "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4ac3f131",
   "metadata": {},
   "source": [
    "### Bar Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "068bc7ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Mean Yield of Corn Treatments'}, xlabel='Treatment'>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corn_only.groupby('Treatment')['Yield'].describe()['mean'].plot(kind='bar', title = 'Mean Yield of Corn Treatments')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b1d825cd",
   "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>Treatment</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1C</td>\n",
       "      <td>224.195358</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2C</td>\n",
       "      <td>238.538890</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.1</td>\n",
       "      <td>228.115141</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.2</td>\n",
       "      <td>223.808229</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.1</td>\n",
       "      <td>215.369255</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Treatment        mean\n",
       "0        1C  224.195358\n",
       "1        2C  238.538890\n",
       "2       3.1  228.115141\n",
       "3       3.2  223.808229\n",
       "4       4.1  215.369255"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corn_means = pd.DataFrame(corn_only.groupby('Treatment')['Yield'].describe()['mean'])\n",
    "corn_means = corn_means.reset_index()\n",
    "corn_means.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f0bae70d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Treatment', 'mean'], dtype='object')\n"
     ]
    }
   ],
   "source": [
    "print(corn_means.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "d0adacf1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(x = 'Treatment', y = 'mean', data = corn_means)\n",
    "ax.set_title('2021 Yield of Corn Treatments')\n",
    "ax.set(xlabel='Treatment', ylabel='Mean Yield (bu/ac)')\n",
    "\n",
    "#for annotating \n",
    "for p in ax.patches:\n",
    "             ax.annotate(\"%.0f\" % p.get_height(), (p.get_x() + p.get_width() / 2., p.get_height()),\n",
    "                 ha='center', va='center', fontsize=10, color='black', xytext=(0, 5),\n",
    "                 textcoords='offset points')\n",
    "        \n",
    "plt.savefig('corn_bar.jpg', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "c3ee477e",
   "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>Block</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>218.087392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>222.997168</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>223.295361</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Block        mean\n",
       "0      1  218.087392\n",
       "1      2  222.997168\n",
       "2      3  223.295361"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corn_block_means = pd.DataFrame(corn_only.groupby('Block')['Yield'].describe()['mean'])\n",
    "corn_block_means = corn_block_means.reset_index()\n",
    "corn_block_means.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "27354de3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = sns.barplot(x = 'Block', y = 'mean', data = corn_block_means)\n",
    "ax.set_title('2021 Corn Yield of Blocks')\n",
    "ax.set(xlabel='Block', ylabel='Mean Yield (bu/ac)')\n",
    "\n",
    "#for annotating \n",
    "for p in ax.patches:\n",
    "             ax.annotate(\"%.1f\" % p.get_height(), (p.get_x() + p.get_width() / 2., p.get_height()),\n",
    "                 ha='center', va='center', fontsize=10, color='black', xytext=(0, 5),\n",
    "                 textcoords='offset points')\n",
    "        \n",
    "plt.savefig('corn_block_bar.jpg', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "634a4586",
   "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>Plot Number</th>\n",
       "      <th>Block</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>Simp. Treatment</th>\n",
       "      <th>Yield (avg; bu/ac)</th>\n",
       "      <th>Treatment Description</th>\n",
       "      <th>Short Trt Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2S</td>\n",
       "      <td>2.0</td>\n",
       "      <td>75.328418</td>\n",
       "      <td>Corn/Soy, Spring Manure</td>\n",
       "      <td>CS, SM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6S</td>\n",
       "      <td>6.0</td>\n",
       "      <td>77.482016</td>\n",
       "      <td>Corn/Soy, Fall Manure</td>\n",
       "      <td>CS, FM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2S</td>\n",
       "      <td>2.0</td>\n",
       "      <td>75.667936</td>\n",
       "      <td>Corn/Soy, Spring Manure</td>\n",
       "      <td>CS, SM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>5S</td>\n",
       "      <td>5.0</td>\n",
       "      <td>71.839370</td>\n",
       "      <td>Corn/Soy + Rye</td>\n",
       "      <td>CS + R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>5S</td>\n",
       "      <td>5.0</td>\n",
       "      <td>72.610892</td>\n",
       "      <td>Corn/Soy + Rye</td>\n",
       "      <td>CS + R</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Plot Number  Block Treatment  Simp. Treatment  Yield (avg; bu/ac)  \\\n",
       "0            1      2        2S              2.0           75.328418   \n",
       "1            2      2        6S              6.0           77.482016   \n",
       "6            7      3        2S              2.0           75.667936   \n",
       "7            8      3        5S              5.0           71.839370   \n",
       "8            9      2        5S              5.0           72.610892   \n",
       "\n",
       "     Treatment Description Short Trt Description  \n",
       "0  Corn/Soy, Spring Manure                CS, SM  \n",
       "1    Corn/Soy, Fall Manure                CS, FM  \n",
       "6  Corn/Soy, Spring Manure                CS, SM  \n",
       "7           Corn/Soy + Rye                CS + R  \n",
       "8           Corn/Soy + Rye                CS + R  "
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soy_only.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "1649b6a5",
   "metadata": {},
   "outputs": [],
   "source": [
    "soy_only = soy_only.rename(columns={\"Yield (avg; bu/ac)\": \"Yield\"})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "035b430d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "soy_means = pd.DataFrame(soy_only.groupby('Treatment')['Yield'].describe()['mean'])\n",
    "soy_means = soy_means.reset_index()\n",
    "\n",
    "ax = sns.barplot(x = 'Treatment', y = 'mean', data = soy_means)\n",
    "ax.set_title('2021 Yield of Soy Treatments')\n",
    "ax.set(xlabel='Treatment', ylabel='Mean Yield (bu/ac)')\n",
    "\n",
    "#for annotating \n",
    "for p in ax.patches:\n",
    "             ax.annotate(\"%.0f\" % p.get_height(), (p.get_x() + p.get_width() / 2., p.get_height()),\n",
    "                 ha='center', va='center', fontsize=10, color='black', xytext=(0, 5),\n",
    "                 textcoords='offset points')\n",
    "        \n",
    "plt.savefig('soy_bar.jpg', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "bb309062",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "soy_block_means = pd.DataFrame(soy_only.groupby('Block')['Yield'].describe()['mean'])\n",
    "soy_block_means = soy_block_means.reset_index()\n",
    "\n",
    "ax = sns.barplot(x = 'Block', y = 'mean', data = soy_block_means)\n",
    "ax.set_title('2021 Soy Yield of Blocks')\n",
    "ax.set(xlabel='Block', ylabel='Mean Yield (bu/ac)')\n",
    "\n",
    "#for annotating \n",
    "for p in ax.patches:\n",
    "             ax.annotate(\"%.1f\" % p.get_height(), (p.get_x() + p.get_width() / 2., p.get_height()),\n",
    "                 ha='center', va='center', fontsize=10, color='black', xytext=(0, 5),\n",
    "                 textcoords='offset points')\n",
    "        \n",
    "plt.savefig('soy_block_bar.jpg', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c0bd1118",
   "metadata": {},
   "source": [
    "## ANOVA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "7f3a933d",
   "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>Plot Number</th>\n",
       "      <th>Block</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>simple_treatment</th>\n",
       "      <th>Yield</th>\n",
       "      <th>Treatment Description</th>\n",
       "      <th>Short Trt Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1C</td>\n",
       "      <td>1.0</td>\n",
       "      <td>232.173281</td>\n",
       "      <td>Corn/Soy, Spring UAN</td>\n",
       "      <td>CS, SUAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>231.645855</td>\n",
       "      <td>Continuous Corn</td>\n",
       "      <td>CC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>208.940519</td>\n",
       "      <td>Continuous Corn + PGC</td>\n",
       "      <td>CC + PGC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>223.988531</td>\n",
       "      <td>Continuous Corn + 30 in ISC</td>\n",
       "      <td>CC + 30ISC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>2C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>237.683989</td>\n",
       "      <td>Corn/Soy, Spring Manure</td>\n",
       "      <td>CS, SM</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Plot Number  Block Treatment  simple_treatment       Yield  \\\n",
       "2             3      2        1C               1.0  232.173281   \n",
       "3             4      2       3.1               3.1  231.645855   \n",
       "4             5      2       4.1               4.1  208.940519   \n",
       "5             6      2       3.2               3.2  223.988531   \n",
       "10           11      2        2C               2.0  237.683989   \n",
       "\n",
       "          Treatment Description Short Trt Description  \n",
       "2          Corn/Soy, Spring UAN              CS, SUAN  \n",
       "3               Continuous Corn                    CC  \n",
       "4         Continuous Corn + PGC              CC + PGC  \n",
       "5   Continuous Corn + 30 in ISC            CC + 30ISC  \n",
       "10      Corn/Soy, Spring Manure                CS, SM  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corn_only = corn_only.rename(columns={\"Simp. Treatment\": \"simple_treatment\"})\n",
    "corn_only.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b4c26b15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\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>Plot Number</th>\n",
       "      <th>Block</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>simple_treatment</th>\n",
       "      <th>Yield</th>\n",
       "      <th>Treatment Description</th>\n",
       "      <th>Short Trt Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2</td>\n",
       "      <td>1C</td>\n",
       "      <td>1.0</td>\n",
       "      <td>232.173281</td>\n",
       "      <td>Corn/Soy, Spring UAN</td>\n",
       "      <td>CS, SUAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>231.645855</td>\n",
       "      <td>Continuous Corn</td>\n",
       "      <td>CC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>208.940519</td>\n",
       "      <td>Continuous Corn + PGC</td>\n",
       "      <td>CC + PGC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>223.988531</td>\n",
       "      <td>Continuous Corn + 30 in ISC</td>\n",
       "      <td>CC + 30ISC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>2C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>237.683989</td>\n",
       "      <td>Corn/Soy, Spring Manure</td>\n",
       "      <td>CS, SM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>2</td>\n",
       "      <td>5C</td>\n",
       "      <td>5.0</td>\n",
       "      <td>229.933270</td>\n",
       "      <td>Corn/Soy + Rye</td>\n",
       "      <td>CS + R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>2</td>\n",
       "      <td>4.2</td>\n",
       "      <td>4.2</td>\n",
       "      <td>179.638775</td>\n",
       "      <td>Continuous Corn + 60in ISC</td>\n",
       "      <td>CC + 60ISC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>2</td>\n",
       "      <td>6C</td>\n",
       "      <td>6.0</td>\n",
       "      <td>239.973125</td>\n",
       "      <td>Corn/Soy, Fall Manure</td>\n",
       "      <td>CS, FM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>1</td>\n",
       "      <td>5C</td>\n",
       "      <td>5.0</td>\n",
       "      <td>214.635798</td>\n",
       "      <td>Corn/Soy + Rye</td>\n",
       "      <td>CS + R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>225.744819</td>\n",
       "      <td>Continuous Corn</td>\n",
       "      <td>CC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>21</td>\n",
       "      <td>1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>212.613434</td>\n",
       "      <td>Continuous Corn + PGC</td>\n",
       "      <td>CC + PGC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>22</td>\n",
       "      <td>1</td>\n",
       "      <td>4.2</td>\n",
       "      <td>4.2</td>\n",
       "      <td>174.955895</td>\n",
       "      <td>Continuous Corn + 60in ISC</td>\n",
       "      <td>CC + 60ISC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>23</td>\n",
       "      <td>3</td>\n",
       "      <td>2C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>235.878142</td>\n",
       "      <td>Corn/Soy, Spring Manure</td>\n",
       "      <td>CS, SM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>24</td>\n",
       "      <td>3</td>\n",
       "      <td>1C</td>\n",
       "      <td>1.0</td>\n",
       "      <td>221.982221</td>\n",
       "      <td>Corn/Soy, Spring UAN</td>\n",
       "      <td>CS, SUAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>25</td>\n",
       "      <td>3</td>\n",
       "      <td>6C</td>\n",
       "      <td>6.0</td>\n",
       "      <td>242.690536</td>\n",
       "      <td>Corn/Soy, Fall Manure</td>\n",
       "      <td>CS, FM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>26</td>\n",
       "      <td>3</td>\n",
       "      <td>4.1</td>\n",
       "      <td>4.1</td>\n",
       "      <td>224.553810</td>\n",
       "      <td>Continuous Corn + PGC</td>\n",
       "      <td>CC + PGC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>27</td>\n",
       "      <td>1</td>\n",
       "      <td>2C</td>\n",
       "      <td>2.0</td>\n",
       "      <td>242.054538</td>\n",
       "      <td>Corn/Soy, Spring Manure</td>\n",
       "      <td>CS, SM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>28</td>\n",
       "      <td>1</td>\n",
       "      <td>1C</td>\n",
       "      <td>1.0</td>\n",
       "      <td>218.430571</td>\n",
       "      <td>Corn/Soy, Spring UAN</td>\n",
       "      <td>CS, SUAN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>31</td>\n",
       "      <td>1</td>\n",
       "      <td>6C</td>\n",
       "      <td>6.0</td>\n",
       "      <td>241.981279</td>\n",
       "      <td>Corn/Soy, Fall Manure</td>\n",
       "      <td>CS, FM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>214.282805</td>\n",
       "      <td>Continuous Corn + 30 in ISC</td>\n",
       "      <td>CC + 30ISC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>33</td>\n",
       "      <td>3</td>\n",
       "      <td>3.1</td>\n",
       "      <td>3.1</td>\n",
       "      <td>226.954749</td>\n",
       "      <td>Continuous Corn</td>\n",
       "      <td>CC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>34</td>\n",
       "      <td>3</td>\n",
       "      <td>5C</td>\n",
       "      <td>5.0</td>\n",
       "      <td>226.563653</td>\n",
       "      <td>Corn/Soy + Rye</td>\n",
       "      <td>CS + R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>35</td>\n",
       "      <td>3</td>\n",
       "      <td>4.2</td>\n",
       "      <td>4.2</td>\n",
       "      <td>174.586423</td>\n",
       "      <td>Continuous Corn + 60in ISC</td>\n",
       "      <td>CC + 60ISC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>36</td>\n",
       "      <td>3</td>\n",
       "      <td>3.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>233.153352</td>\n",
       "      <td>Continuous Corn + 30 in ISC</td>\n",
       "      <td>CC + 30ISC</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Plot Number  Block Treatment  simple_treatment       Yield  \\\n",
       "2             3      2        1C               1.0  232.173281   \n",
       "3             4      2       3.1               3.1  231.645855   \n",
       "4             5      2       4.1               4.1  208.940519   \n",
       "5             6      2       3.2               3.2  223.988531   \n",
       "10           11      2        2C               2.0  237.683989   \n",
       "11           12      2        5C               5.0  229.933270   \n",
       "12           13      2       4.2               4.2  179.638775   \n",
       "13           14      2        6C               6.0  239.973125   \n",
       "16           17      1        5C               5.0  214.635798   \n",
       "17           18      1       3.1               3.1  225.744819   \n",
       "20           21      1       4.1               4.1  212.613434   \n",
       "21           22      1       4.2               4.2  174.955895   \n",
       "22           23      3        2C               2.0  235.878142   \n",
       "23           24      3        1C               1.0  221.982221   \n",
       "24           25      3        6C               6.0  242.690536   \n",
       "25           26      3       4.1               4.1  224.553810   \n",
       "26           27      1        2C               2.0  242.054538   \n",
       "27           28      1        1C               1.0  218.430571   \n",
       "30           31      1        6C               6.0  241.981279   \n",
       "31           32      1       3.2               3.2  214.282805   \n",
       "32           33      3       3.1               3.1  226.954749   \n",
       "33           34      3        5C               5.0  226.563653   \n",
       "34           35      3       4.2               4.2  174.586423   \n",
       "35           36      3       3.2               3.2  233.153352   \n",
       "\n",
       "          Treatment Description Short Trt Description  \n",
       "2          Corn/Soy, Spring UAN              CS, SUAN  \n",
       "3               Continuous Corn                    CC  \n",
       "4         Continuous Corn + PGC              CC + PGC  \n",
       "5   Continuous Corn + 30 in ISC            CC + 30ISC  \n",
       "10      Corn/Soy, Spring Manure                CS, SM  \n",
       "11               Corn/Soy + Rye                CS + R  \n",
       "12   Continuous Corn + 60in ISC            CC + 60ISC  \n",
       "13        Corn/Soy, Fall Manure                CS, FM  \n",
       "16               Corn/Soy + Rye                CS + R  \n",
       "17              Continuous Corn                    CC  \n",
       "20        Continuous Corn + PGC              CC + PGC  \n",
       "21   Continuous Corn + 60in ISC            CC + 60ISC  \n",
       "22      Corn/Soy, Spring Manure                CS, SM  \n",
       "23         Corn/Soy, Spring UAN              CS, SUAN  \n",
       "24        Corn/Soy, Fall Manure                CS, FM  \n",
       "25        Continuous Corn + PGC              CC + PGC  \n",
       "26      Corn/Soy, Spring Manure                CS, SM  \n",
       "27         Corn/Soy, Spring UAN              CS, SUAN  \n",
       "30        Corn/Soy, Fall Manure                CS, FM  \n",
       "31  Continuous Corn + 30 in ISC            CC + 30ISC  \n",
       "32              Continuous Corn                    CC  \n",
       "33               Corn/Soy + Rye                CS + R  \n",
       "34   Continuous Corn + 60in ISC            CC + 60ISC  \n",
       "35  Continuous Corn + 30 in ISC            CC + 30ISC  "
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corn_only"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "3a042920",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Treatment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1C</th>\n",
       "      <td>3.0</td>\n",
       "      <td>224.195358</td>\n",
       "      <td>7.133652</td>\n",
       "      <td>218.430571</td>\n",
       "      <td>220.206396</td>\n",
       "      <td>221.982221</td>\n",
       "      <td>227.077751</td>\n",
       "      <td>232.173281</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2C</th>\n",
       "      <td>3.0</td>\n",
       "      <td>238.538890</td>\n",
       "      <td>3.175706</td>\n",
       "      <td>235.878142</td>\n",
       "      <td>236.781066</td>\n",
       "      <td>237.683989</td>\n",
       "      <td>239.869263</td>\n",
       "      <td>242.054538</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>228.115141</td>\n",
       "      <td>3.116960</td>\n",
       "      <td>225.744819</td>\n",
       "      <td>226.349784</td>\n",
       "      <td>226.954749</td>\n",
       "      <td>229.300302</td>\n",
       "      <td>231.645855</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>223.808229</td>\n",
       "      <td>9.436566</td>\n",
       "      <td>214.282805</td>\n",
       "      <td>219.135668</td>\n",
       "      <td>223.988531</td>\n",
       "      <td>228.570942</td>\n",
       "      <td>233.153352</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.1</th>\n",
       "      <td>3.0</td>\n",
       "      <td>215.369255</td>\n",
       "      <td>8.163310</td>\n",
       "      <td>208.940519</td>\n",
       "      <td>210.776977</td>\n",
       "      <td>212.613434</td>\n",
       "      <td>218.583622</td>\n",
       "      <td>224.553810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>176.393698</td>\n",
       "      <td>2.816385</td>\n",
       "      <td>174.586423</td>\n",
       "      <td>174.771159</td>\n",
       "      <td>174.955895</td>\n",
       "      <td>177.297335</td>\n",
       "      <td>179.638775</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5C</th>\n",
       "      <td>3.0</td>\n",
       "      <td>223.710907</td>\n",
       "      <td>8.037834</td>\n",
       "      <td>214.635798</td>\n",
       "      <td>220.599726</td>\n",
       "      <td>226.563653</td>\n",
       "      <td>228.248461</td>\n",
       "      <td>229.933270</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6C</th>\n",
       "      <td>3.0</td>\n",
       "      <td>241.548313</td>\n",
       "      <td>1.409495</td>\n",
       "      <td>239.973125</td>\n",
       "      <td>240.977202</td>\n",
       "      <td>241.981279</td>\n",
       "      <td>242.335907</td>\n",
       "      <td>242.690536</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count        mean       std         min         25%         50%  \\\n",
       "Treatment                                                                    \n",
       "1C           3.0  224.195358  7.133652  218.430571  220.206396  221.982221   \n",
       "2C           3.0  238.538890  3.175706  235.878142  236.781066  237.683989   \n",
       "3.1          3.0  228.115141  3.116960  225.744819  226.349784  226.954749   \n",
       "3.2          3.0  223.808229  9.436566  214.282805  219.135668  223.988531   \n",
       "4.1          3.0  215.369255  8.163310  208.940519  210.776977  212.613434   \n",
       "4.2          3.0  176.393698  2.816385  174.586423  174.771159  174.955895   \n",
       "5C           3.0  223.710907  8.037834  214.635798  220.599726  226.563653   \n",
       "6C           3.0  241.548313  1.409495  239.973125  240.977202  241.981279   \n",
       "\n",
       "                  75%         max  \n",
       "Treatment                          \n",
       "1C         227.077751  232.173281  \n",
       "2C         239.869263  242.054538  \n",
       "3.1        229.300302  231.645855  \n",
       "3.2        228.570942  233.153352  \n",
       "4.1        218.583622  224.553810  \n",
       "4.2        177.297335  179.638775  \n",
       "5C         228.248461  229.933270  \n",
       "6C         242.335907  242.690536  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corn_only_stats = pd.DataFrame(corn_only.groupby('Treatment')['Yield'].describe())\n",
    "corn_only_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "2f434949",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x='Yield', bins = 'auto', data = corn_only)\n",
    "plt.title(\"Corn Yields 2021\")\n",
    "plt.xlabel('Yield (bu/ac)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.savefig('Corn Histogram.jpg', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c1d9a31",
   "metadata": {},
   "source": [
    "This is not looking very normally distributed. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "0b61b6de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data=corn_only, x=\"Yield\")\n",
    "plt.title('Corn Yield Kernel Density Plot')\n",
    "plt.savefig('corn_yield_kde.jpg', bbox_inches='tight')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "7148eae7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "stats.probplot(corn_only['Yield'], dist=\"norm\", plot=plt)\n",
    "plt.show()\n",
    "plt.savefig('corn_yield_probplot', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ae7e35f4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "unique_treatments = corn_only['Treatment'].unique()\n",
    "for Treatment in unique_treatments:\n",
    "    stats.probplot(corn_only[corn_only['Treatment'] == Treatment]['Yield'], dist=\"norm\", plot=plt)\n",
    "    plt.title(\"Probability Plot - \" +  Treatment)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3de519d",
   "metadata": {},
   "source": [
    "### Bartlett’s Test of Homogeneity of Variances"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6bff9ffb",
   "metadata": {},
   "source": [
    "Following this tutorial: https://www.marsja.se/levenes-bartletts-test-of-equality-homogeneity-of-variance-in-python/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d9538d64",
   "metadata": {},
   "source": [
    "Null Hypothesis: the variances are equal across all samples/groups     \n",
    "Alternative Hypothesis:  the variances are not equal across all samples/groups    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "1308adba",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8.236102945085667 0.3122358533181093\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import bartlett\n",
    "\n",
    "# subsetting the data:\n",
    "trt1 = corn_only.query('Treatment == \"1C\"')['Yield']\n",
    "trt2 = corn_only.query('Treatment == \"2C\"')['Yield']\n",
    "trt31 = corn_only.query('Treatment == \"3.1\"')['Yield']\n",
    "trt32 = corn_only.query('Treatment == \"3.2\"')['Yield']\n",
    "trt41 = corn_only.query('Treatment == \"4.1\"')['Yield']\n",
    "trt42 = corn_only.query('Treatment == \"4.2\"')['Yield']\n",
    "trt5 = corn_only.query('Treatment == \"5C\"')['Yield']\n",
    "trt6 = corn_only.query('Treatment == \"6C\"')['Yield']\n",
    "\n",
    "# Bartlett's test in Python with SciPy:\n",
    "stat, p = bartlett(trt1, trt2, trt31, trt32, trt41, trt42, trt5, trt6)\n",
    "\n",
    "# Get the results:\n",
    "print(stat, p)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7395f4c2",
   "metadata": {},
   "source": [
    "p-value is high, so assume variances are the same. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c477ea8",
   "metadata": {},
   "source": [
    "### Levene’s Test of Equality of Variances"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "f1205773",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.6584854455470804 0.7032800804487055\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import levene\n",
    "\n",
    "# Create three arrays for each sample:\n",
    "trt1 = corn_only.query('Treatment == \"1C\"')['Yield']\n",
    "trt2 = corn_only.query('Treatment == \"2C\"')['Yield']\n",
    "trt31 = corn_only.query('Treatment == \"3.1\"')['Yield']\n",
    "trt32 = corn_only.query('Treatment == \"3.2\"')['Yield']\n",
    "trt41 = corn_only.query('Treatment == \"4.1\"')['Yield']\n",
    "trt42 = corn_only.query('Treatment == \"4.2\"')['Yield']\n",
    "trt5 = corn_only.query('Treatment == \"5C\"')['Yield']\n",
    "trt6 = corn_only.query('Treatment == \"6C\"')['Yield']\n",
    "\n",
    "# Levene's Test in Python with Scipy:\n",
    "stat, p = levene(trt1, trt2, trt31, trt32, trt41, trt42, trt5, trt6)\n",
    "\n",
    "print(stat, p)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2c48c626",
   "metadata": {},
   "source": [
    "Again, p-value is high, so assume variances are the same. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "47834c02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "unique_treatments = corn_only['Treatment'].unique()\n",
    "for Treatment in unique_treatments:\n",
    "    plt.hist(corn_only[corn_only['Treatment'] == Treatment]['Yield'], bins = 'auto')\n",
    "    plt.title(\"Histogram - \" +  Treatment)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1c3c870b",
   "metadata": {},
   "source": [
    "### Corn Analysis of Variance"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7e4330bf",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       df       sum_sq      mean_sq          F        PR(>F)\n",
      "C(Block)              2.0   136.847333    68.423666   2.060247  1.643259e-01\n",
      "C(simple_treatment)   7.0  8476.954203  1210.993458  36.463195  6.190045e-08\n",
      "Residual             14.0   464.959492    33.211392        NaN           NaN\n"
     ]
    }
   ],
   "source": [
    "\n",
    "\n",
    "#corn_formula = 'Yield ~C(Block)+C(Treatment)+C(Block):C(Treatment)'\n",
    "\n",
    "model = ols('Yield ~ C(Block) + C(simple_treatment)', data = corn_only).fit()\n",
    "\n",
    "aov_table = sm.stats.anova_lm(model)\n",
    "\n",
    "print(aov_table)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6615421c",
   "metadata": {},
   "source": [
    "It looks like block does not have a significant effect (p = 0.164), but treatment does (p < 0.05)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "471d1d07",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\gmmyers\\Anaconda3\\lib\\site-packages\\seaborn\\distributions.py:2557: FutureWarning: `distplot` is a deprecated function and will be removed in a future version. Please adapt your code to use either `displot` (a figure-level function with similar flexibility) or `kdeplot` (an axes-level function for kernel density plots).\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5, 0, 'Residuals')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig = plt.figure(figsize = (16, 9))\n",
    "\n",
    "ax = sns.distplot(model.resid, hist = False, kde_kws = {\"shade\" : True, \"lw\": 1}, fit = stats.norm)\n",
    "\n",
    "ax.set_title(\"KDE Plot of Model Residuals (Blue) and Normal Distribution (Black)\")\n",
    "ax.set_xlabel(\"Residuals\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "b8e908be",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                            OLS Regression Results                            \n",
      "==============================================================================\n",
      "Dep. Variable:                  Yield   R-squared:                       0.949\n",
      "Model:                            OLS   Adj. R-squared:                  0.916\n",
      "Method:                 Least Squares   F-statistic:                     28.82\n",
      "Date:                Tue, 16 Nov 2021   Prob (F-statistic):           1.60e-07\n",
      "Time:                        10:31:44   Log-Likelihood:                -69.621\n",
      "No. Observations:                  24   AIC:                             159.2\n",
      "Df Residuals:                      14   BIC:                             171.0\n",
      "Df Model:                           9                                         \n",
      "Covariance Type:            nonrobust                                         \n",
      "==============================================================================================\n",
      "                                 coef    std err          t      P>|t|      [0.025      0.975]\n",
      "----------------------------------------------------------------------------------------------\n",
      "Intercept                    220.8228      3.720     59.362      0.000     212.844     228.801\n",
      "C(Block)[T.2]                  4.9098      2.881      1.704      0.110      -1.270      11.090\n",
      "C(Block)[T.3]                  5.2080      2.881      1.807      0.092      -0.972      11.388\n",
      "C(simple_treatment)[T.2.0]    14.3435      4.705      3.048      0.009       4.251      24.436\n",
      "C(simple_treatment)[T.3.1]     3.9198      4.705      0.833      0.419      -6.172      14.012\n",
      "C(simple_treatment)[T.3.2]    -0.3871      4.705     -0.082      0.936     -10.479       9.705\n",
      "C(simple_treatment)[T.4.1]    -8.8261      4.705     -1.876      0.082     -18.918       1.266\n",
      "C(simple_treatment)[T.4.2]   -47.8017      4.705    -10.159      0.000     -57.894     -37.710\n",
      "C(simple_treatment)[T.5.0]    -0.4845      4.705     -0.103      0.919     -10.577       9.608\n",
      "C(simple_treatment)[T.6.0]    17.3530      4.705      3.688      0.002       7.261      27.445\n",
      "==============================================================================\n",
      "Omnibus:                        1.599   Durbin-Watson:                   1.749\n",
      "Prob(Omnibus):                  0.449   Jarque-Bera (JB):                1.050\n",
      "Skew:                           0.185   Prob(JB):                        0.592\n",
      "Kurtosis:                       2.044   Cond. No.                         9.82\n",
      "==============================================================================\n",
      "\n",
      "Notes:\n",
      "[1] Standard Errors assume that the covariance matrix of the errors is correctly specified.\n"
     ]
    }
   ],
   "source": [
    "print(model.summary())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "21479845",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                                df        sum_sq      mean_sq    F  PR(>F)\n",
      "C(Block)                       2.0  1.368473e+02    68.423666  0.0     NaN\n",
      "C(simple_treatment)            7.0  8.476954e+03  1210.993458  0.0     NaN\n",
      "C(Block):C(simple_treatment)  14.0  4.649595e+02    33.211392  0.0     NaN\n",
      "Residual                       0.0  1.723024e-24          inf  NaN     NaN\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\ProgramData\\Anaconda3.0\\lib\\site-packages\\statsmodels\\stats\\anova.py:138: RuntimeWarning: divide by zero encountered in double_scalars\n",
      "  (model.ssr / model.df_resid))\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model = ols('Yield ~ C(Block) + C(simple_treatment) + C(Block):C(simple_treatment)', data = corn_only).fit()\n",
    "\n",
    "aov_table = sm.stats.anova_lm(model)\n",
    "\n",
    "print(aov_table)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "15b98d9b",
   "metadata": {},
   "source": [
    "## Mean Comparisons Attempt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "1ea48d69",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1C</th>\n",
       "      <th>3.1</th>\n",
       "      <th>4.1</th>\n",
       "      <th>3.2</th>\n",
       "      <th>2C</th>\n",
       "      <th>5C</th>\n",
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       "      <th>6C</th>\n",
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       "      <td>1.000000</td>\n",
       "      <td>0.010440</td>\n",
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       "      <td>0.749633</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.025992</td>\n",
       "      <td>0.484020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2C</th>\n",
       "      <td>0.484020</td>\n",
       "      <td>0.261491</td>\n",
       "      <td>0.193260</td>\n",
       "      <td>0.749633</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.533972</td>\n",
       "      <td>0.000388</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5C</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.533972</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.015651</td>\n",
       "      <td>0.309376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.2</th>\n",
       "      <td>0.010440</td>\n",
       "      <td>0.000743</td>\n",
       "      <td>0.031795</td>\n",
       "      <td>0.025992</td>\n",
       "      <td>0.000388</td>\n",
       "      <td>0.015651</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000101</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6C</th>\n",
       "      <td>0.260172</td>\n",
       "      <td>0.051264</td>\n",
       "      <td>0.108431</td>\n",
       "      <td>0.484020</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.309376</td>\n",
       "      <td>0.000101</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           1C       3.1       4.1       3.2        2C        5C       4.2  \\\n",
       "1C   1.000000  1.000000  1.000000  1.000000  0.484020  1.000000  0.010440   \n",
       "3.1  1.000000  1.000000  0.749633  1.000000  0.261491  1.000000  0.000743   \n",
       "4.1  1.000000  0.749633  1.000000  1.000000  0.193260  1.000000  0.031795   \n",
       "3.2  1.000000  1.000000  1.000000  1.000000  0.749633  1.000000  0.025992   \n",
       "2C   0.484020  0.261491  0.193260  0.749633  1.000000  0.533972  0.000388   \n",
       "5C   1.000000  1.000000  1.000000  1.000000  0.533972  1.000000  0.015651   \n",
       "4.2  0.010440  0.000743  0.031795  0.025992  0.000388  0.015651  1.000000   \n",
       "6C   0.260172  0.051264  0.108431  0.484020  1.000000  0.309376  0.000101   \n",
       "\n",
       "           6C  \n",
       "1C   0.260172  \n",
       "3.1  0.051264  \n",
       "4.1  0.108431  \n",
       "3.2  0.484020  \n",
       "2C   1.000000  \n",
       "5C   0.309376  \n",
       "4.2  0.000101  \n",
       "6C   1.000000  "
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import statsmodels.api as sa\n",
    "import statsmodels.formula.api as sfa\n",
    "import scikit_posthocs as sp\n",
    "\n",
    "sp.posthoc_ttest(corn_only, val_col='Yield', group_col='Treatment', p_adjust='holm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "4d6fdbbb",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>1C</th>\n",
       "      <th>2C</th>\n",
       "      <th>3.1</th>\n",
       "      <th>3.2</th>\n",
       "      <th>4.1</th>\n",
       "      <th>4.2</th>\n",
       "      <th>5C</th>\n",
       "      <th>6C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1C</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.116421</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.116421</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.042947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2C</th>\n",
       "      <td>0.116421</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.463083</td>\n",
       "      <td>0.116421</td>\n",
       "      <td>0.005629</td>\n",
       "      <td>0.000280</td>\n",
       "      <td>0.116421</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.463083</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.320955</td>\n",
       "      <td>0.021985</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.173516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.2</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.116421</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.116421</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.042947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.005629</td>\n",
       "      <td>0.320955</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.001881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.2</th>\n",
       "      <td>0.116421</td>\n",
       "      <td>0.000280</td>\n",
       "      <td>0.021985</td>\n",
       "      <td>0.116421</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.096701</td>\n",
       "      <td>0.000105</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5C</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.116421</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.096701</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.049750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6C</th>\n",
       "      <td>0.042947</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.173516</td>\n",
       "      <td>0.042947</td>\n",
       "      <td>0.001881</td>\n",
       "      <td>0.000105</td>\n",
       "      <td>0.049750</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           1C        2C       3.1       3.2       4.1       4.2        5C  \\\n",
       "1C   1.000000  0.116421  1.000000  1.000000  1.000000  0.116421  1.000000   \n",
       "2C   0.116421  1.000000  0.463083  0.116421  0.005629  0.000280  0.116421   \n",
       "3.1  1.000000  0.463083  1.000000  1.000000  0.320955  0.021985  1.000000   \n",
       "3.2  1.000000  0.116421  1.000000  1.000000  1.000000  0.116421  1.000000   \n",
       "4.1  1.000000  0.005629  0.320955  1.000000  1.000000  1.000000  1.000000   \n",
       "4.2  0.116421  0.000280  0.021985  0.116421  1.000000  1.000000  0.096701   \n",
       "5C   1.000000  0.116421  1.000000  1.000000  1.000000  0.096701  1.000000   \n",
       "6C   0.042947  1.000000  0.173516  0.042947  0.001881  0.000105  0.049750   \n",
       "\n",
       "           6C  \n",
       "1C   0.042947  \n",
       "2C   1.000000  \n",
       "3.1  0.173516  \n",
       "3.2  0.042947  \n",
       "4.1  0.001881  \n",
       "4.2  0.000105  \n",
       "5C   0.049750  \n",
       "6C   1.000000  "
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp.posthoc_conover(corn_only, val_col='Yield', group_col='Treatment', p_adjust='holm')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "822d99d0",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                       df       sum_sq     mean_sq         F    PR(>F)\n",
      "C(Block)              2.0   143.759534   71.879767  1.950678  0.184707\n",
      "C(simple_treatment)   6.0  1513.631179  252.271863  6.846172  0.002433\n",
      "Residual             12.0   442.183244   36.848604       NaN       NaN\n"
     ]
    }
   ],
   "source": [
    "corn_only_no60 = corn_only[corn_only['Treatment'].str.contains('1C|2C|3.1|3.2|4.1|5C|6C')]\n",
    "\n",
    "model = ols('Yield ~ C(Block) + C(simple_treatment)', data = corn_only_no60).fit()\n",
    "\n",
    "aov_table = sm.stats.anova_lm(model)\n",
    "\n",
    "print(aov_table)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "daf7ae70",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>1C</th>\n",
       "      <th>3.1</th>\n",
       "      <th>4.1</th>\n",
       "      <th>3.2</th>\n",
       "      <th>2C</th>\n",
       "      <th>5C</th>\n",
       "      <th>6C</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1C</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.484020</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.260172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.749633</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.261491</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.051264</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4.1</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.749633</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.193260</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.108431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3.2</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.749633</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.484020</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2C</th>\n",
       "      <td>0.484020</td>\n",
       "      <td>0.261491</td>\n",
       "      <td>0.193260</td>\n",
       "      <td>0.749633</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.533972</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5C</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.533972</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.309376</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6C</th>\n",
       "      <td>0.260172</td>\n",
       "      <td>0.051264</td>\n",
       "      <td>0.108431</td>\n",
       "      <td>0.484020</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.309376</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           1C       3.1       4.1       3.2        2C        5C        6C\n",
       "1C   1.000000  1.000000  1.000000  1.000000  0.484020  1.000000  0.260172\n",
       "3.1  1.000000  1.000000  0.749633  1.000000  0.261491  1.000000  0.051264\n",
       "4.1  1.000000  0.749633  1.000000  1.000000  0.193260  1.000000  0.108431\n",
       "3.2  1.000000  1.000000  1.000000  1.000000  0.749633  1.000000  0.484020\n",
       "2C   0.484020  0.261491  0.193260  0.749633  1.000000  0.533972  1.000000\n",
       "5C   1.000000  1.000000  1.000000  1.000000  0.533972  1.000000  0.309376\n",
       "6C   0.260172  0.051264  0.108431  0.484020  1.000000  0.309376  1.000000"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sp.posthoc_ttest(corn_only_no60, val_col='Yield', group_col='Treatment', p_adjust='holm')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b60d0061",
   "metadata": {},
   "source": [
    "### Soy ANOVA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "c888ef0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "        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>Plot Number</th>\n",
       "      <th>Block</th>\n",
       "      <th>Treatment</th>\n",
       "      <th>simple_treatment</th>\n",
       "      <th>Yield</th>\n",
       "      <th>Treatment Description</th>\n",
       "      <th>Short Trt Description</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>2S</td>\n",
       "      <td>2.0</td>\n",
       "      <td>75.328418</td>\n",
       "      <td>Corn/Soy, Spring Manure</td>\n",
       "      <td>CS, SM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>6S</td>\n",
       "      <td>6.0</td>\n",
       "      <td>77.482016</td>\n",
       "      <td>Corn/Soy, Fall Manure</td>\n",
       "      <td>CS, FM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>3</td>\n",
       "      <td>2S</td>\n",
       "      <td>2.0</td>\n",
       "      <td>75.667936</td>\n",
       "      <td>Corn/Soy, Spring Manure</td>\n",
       "      <td>CS, SM</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>3</td>\n",
       "      <td>5S</td>\n",
       "      <td>5.0</td>\n",
       "      <td>71.839370</td>\n",
       "      <td>Corn/Soy + Rye</td>\n",
       "      <td>CS + R</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>2</td>\n",
       "      <td>5S</td>\n",
       "      <td>5.0</td>\n",
       "      <td>72.610892</td>\n",
       "      <td>Corn/Soy + Rye</td>\n",
       "      <td>CS + R</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Plot Number  Block Treatment  simple_treatment      Yield  \\\n",
       "0            1      2        2S               2.0  75.328418   \n",
       "1            2      2        6S               6.0  77.482016   \n",
       "6            7      3        2S               2.0  75.667936   \n",
       "7            8      3        5S               5.0  71.839370   \n",
       "8            9      2        5S               5.0  72.610892   \n",
       "\n",
       "     Treatment Description Short Trt Description  \n",
       "0  Corn/Soy, Spring Manure                CS, SM  \n",
       "1    Corn/Soy, Fall Manure                CS, FM  \n",
       "6  Corn/Soy, Spring Manure                CS, SM  \n",
       "7           Corn/Soy + Rye                CS + R  \n",
       "8           Corn/Soy + Rye                CS + R  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soy_only = soy_only.rename(columns={\"Simp. Treatment\": \"simple_treatment\"})\n",
    "soy_only.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "d1173c42",
   "metadata": {},
   "outputs": [],
   "source": [
    "soy_only.to_excel(\"Soy_Yield_2021.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "291c1d47",
   "metadata": {},
   "outputs": [
    {
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       "\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>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Treatment</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1S</th>\n",
       "      <td>3.0</td>\n",
       "      <td>69.256568</td>\n",
       "      <td>1.013542</td>\n",
       "      <td>68.365015</td>\n",
       "      <td>68.705382</td>\n",
       "      <td>69.045750</td>\n",
       "      <td>69.702345</td>\n",
       "      <td>70.358940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2S</th>\n",
       "      <td>3.0</td>\n",
       "      <td>75.123919</td>\n",
       "      <td>0.670094</td>\n",
       "      <td>74.375402</td>\n",
       "      <td>74.851910</td>\n",
       "      <td>75.328418</td>\n",
       "      <td>75.498177</td>\n",
       "      <td>75.667936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5S</th>\n",
       "      <td>3.0</td>\n",
       "      <td>71.951712</td>\n",
       "      <td>0.610807</td>\n",
       "      <td>71.404874</td>\n",
       "      <td>71.622122</td>\n",
       "      <td>71.839370</td>\n",
       "      <td>72.225131</td>\n",
       "      <td>72.610892</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6S</th>\n",
       "      <td>3.0</td>\n",
       "      <td>75.131992</td>\n",
       "      <td>2.060390</td>\n",
       "      <td>73.635658</td>\n",
       "      <td>73.956980</td>\n",
       "      <td>74.278302</td>\n",
       "      <td>75.880159</td>\n",
       "      <td>77.482016</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           count       mean       std        min        25%        50%  \\\n",
       "Treatment                                                                \n",
       "1S           3.0  69.256568  1.013542  68.365015  68.705382  69.045750   \n",
       "2S           3.0  75.123919  0.670094  74.375402  74.851910  75.328418   \n",
       "5S           3.0  71.951712  0.610807  71.404874  71.622122  71.839370   \n",
       "6S           3.0  75.131992  2.060390  73.635658  73.956980  74.278302   \n",
       "\n",
       "                 75%        max  \n",
       "Treatment                        \n",
       "1S         69.702345  70.358940  \n",
       "2S         75.498177  75.667936  \n",
       "5S         72.225131  72.610892  \n",
       "6S         75.880159  77.482016  "
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "soy_only_stats = pd.DataFrame(soy_only.groupby('Treatment')['Yield'].describe())\n",
    "soy_only_stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "8915d062",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x='Yield', bins = 'auto', data = soy_only)\n",
    "plt.title(\"Soy Yields 2021\")\n",
    "plt.xlabel('Yield (bu/ac)')\n",
    "plt.ylabel('Frequency')\n",
    "plt.savefig('Soy Histogram.jpg', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a0f68cc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.kdeplot(data=soy_only, x=\"Yield\")\n",
    "plt.title('Soy Yield KDE Plot')\n",
    "plt.savefig('soy_yield_kde.jpg', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "8936cdd5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "stats.probplot(soy_only['Yield'], dist=\"norm\", plot=plt)\n",
    "plt.show()\n",
    "\n",
    "plt.savefig('soy_yield_probplot', bbox_inches='tight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3a659962",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import scipy.stats as stats\n",
    "unique_treatments = soy_only['Treatment'].unique()\n",
    "for Treatment in unique_treatments:\n",
    "    stats.probplot(soy_only[soy_only['Treatment'] == Treatment]['Yield'], dist=\"norm\", plot=plt)\n",
    "    plt.title(\"Probability Plot - \" +  Treatment)\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cf95942e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.3076242408562706 0.3465829012906225\n"
     ]
    }
   ],
   "source": [
    "from scipy.stats import bartlett\n",
    "\n",
    "# subsetting the data:\n",
    "trt2 = soy_only.query('Treatment == \"2S\"')['Yield']\n",
    "trt1 = soy_only.query('Treatment == \"1S\"')['Yield']\n",
    "trt5 = soy_only.query('Treatment == \"5S\"')['Yield']\n",
    "trt6 = soy_only.query('Treatment == \"6S\"')['Yield']\n",
    "\n",
    "\n",
    "# Bartlett's test in Python with SciPy:\n",
    "stat, p = bartlett(trt1, trt2, trt5, trt6)\n",
    "\n",
    "# Get the results:\n",
    "print(stat, p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "b5970746",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                      df     sum_sq    mean_sq          F    PR(>F)\n",
      "C(Block)             2.0   4.565992   2.282996   1.796885  0.244617\n",
      "C(simple_treatment)  3.0  72.290509  24.096836  18.965972  0.001830\n",
      "Residual             6.0   7.623180   1.270530        NaN       NaN\n"
     ]
    }
   ],
   "source": [
    "\n",
    "model = ols('Yield ~ C(Block) + C(simple_treatment)', data = soy_only).fit()\n",
    "\n",
    "aov_table = sm.stats.anova_lm(model)\n",
    "\n",
    "print(aov_table)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "940819e7",
   "metadata": {},
   "source": [
    "Again, no effect from block, but treatments do have an effect. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "490cc147",
   "metadata": {},
   "source": [
    "https://medium.com/budding-data-scientist/data-analytics-using-python-part-6-9e740a1dd681"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "af47a3d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from patsy.contrasts import Treatment\n",
    "\n",
    "levels = [1, 2, 3, 4]\n",
    "contrast = Treatment(reference=0).code_without_intercept(levels)\n",
    "print(contrast.matrix)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0746bbf2",
   "metadata": {},
   "source": [
    "https://www.pythonfordatascience.org/mixed-effects-regression-python/"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8b9ef3ae",
   "metadata": {},
   "source": [
    "https://pbgworks.org/sites/pbgworks.org/files/RandomizedCompleteBlockDesignTutorial.pdf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "deb38979",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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