06 Analysis of optical water types and its limnological parameters.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "toc": true
   },
   "source": [
    "<h1>Table of Contents<span class=\"tocSkip\"></span></h1>\n",
    "<div class=\"toc\"><ul class=\"toc-item\"><li><span><a href=\"#1-Introduction\" data-toc-modified-id=\"1-Introduction-1\">1 Introduction</a></span></li><li><span><a href=\"#2-Data-access\" data-toc-modified-id=\"2-Data-access-2\">2 Data access</a></span></li><li><span><a href=\"#3-PCA-Analyses\" data-toc-modified-id=\"3-PCA-Analyses-3\">3 PCA Analyses</a></span><ul class=\"toc-item\"><li><span><a href=\"#3.1-Feature-scaling\" data-toc-modified-id=\"3.1-Feature-scaling-3.1\">3.1 Feature scaling</a></span></li><li><span><a href=\"#3.2-Compute-PCA\" data-toc-modified-id=\"3.2-Compute-PCA-3.2\">3.2 Compute PCA</a></span></li><li><span><a href=\"#3.3-PCA-figure\" data-toc-modified-id=\"3.3-PCA-figure-3.3\">3.3 PCA figure</a></span></li></ul></li><li><span><a href=\"#4-Boxplot-Analysis\" data-toc-modified-id=\"4-Boxplot-Analysis-4\">4 Boxplot Analysis</a></span></li><li><span><a href=\"#5-Statistical-tests\" data-toc-modified-id=\"5-Statistical-tests-5\">5 Statistical tests</a></span><ul class=\"toc-item\"><li><span><a href=\"#5.1-Shapiro-test\" data-toc-modified-id=\"5.1-Shapiro-test-5.1\">5.1 Shapiro test</a></span></li><li><span><a href=\"#5.2-Kurskal-test\" data-toc-modified-id=\"5.2-Kurskal-test-5.2\">5.2 Kurskal test</a></span></li><li><span><a href=\"#5.3-Mann-Whitney-U-test\" data-toc-modified-id=\"5.3-Mann-Whitney-U-test-5.3\">5.3 Mann-Whitney U test</a></span></li></ul></li></ul></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1 Introduction\n",
    "<hr>\n",
    "This notebook analyses the association of Optical Water Types (OWTs) with limnological parameters: Total Suspended Matter (TSM) concentration, Chlorophyll-a (Chl-a) concentration, Colored Dissolved Organic Matter (CDOM) absorption coefficient at 440 nm, and Secchi Depth (Zsd).\n",
    "\n",
    "#### The analyses is performed using 3 methods:\n",
    "1 - Principal component analysis (PCA).\n",
    "<br>\n",
    "The PCA analyses shows that each OWT is related with a limnological parameter (e.g. OWT and high Chl-a concentration).\n",
    "<br>\n",
    "<br>\n",
    "2 - Boxplots\n",
    "<br>\n",
    "The boxplots are a graphical method for comparing the limnological concentration among all OWTs.\n",
    "<br>\n",
    "<br>\n",
    "3 - Statistical tests\n",
    "<br>\n",
    "The tests were done for supporting that an OWT x have higher/lower limnological concentration than OWT Y."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 Data access\n",
    "<br>\n",
    "Obs.: Notice that the raw dataset is not available because it is not an open source."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "hide_input": true
   },
   "outputs": [],
   "source": [
    "# library used\n",
    "import psycopg2\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# access data\n",
    "con = psycopg2.connect(host=\"localhost\",\n",
    "                       database=\"browts400_800\",\n",
    "                       user=\"postgres\",\n",
    "                       password=\"postgres123\")\n",
    "\n",
    "cluster = pd.read_sql_query('SELECT * FROM owt_named_2',\n",
    "                            con,\n",
    "                            index_col='ids')\n",
    "\n",
    "si = pd.read_sql_query('SELECT * FROM si',\n",
    "                       con,\n",
    "                       index_col='ids')\n",
    "\n",
    "si2 = pd.read_sql_query('SELECT * FROM si_int',\n",
    "                        con,\n",
    "                        index_col='ids')\n",
    "lim = pd.read_sql_query('SELECT * FROM limnologia',\n",
    "                        con,\n",
    "                        index_col='id')\n",
    "\n",
    "con.close()\n",
    "\n",
    "# remove samples with silhouette  lower than 0\n",
    "newid = si[si['OWTs'] != 'OWT 6'] # it is an exception due to a wrong measurement\n",
    "newid = newid[newid['s']<0].dropna().index\n",
    "cluster = cluster.drop(newid)\n",
    "newid = si2[si2['OWTs'] != 'OWT 6'] # it is an exception due to wrong a measurement\n",
    "newid = newid[newid['s']<0].dropna().index\n",
    "cluster = cluster.drop(newid)\n",
    "\n",
    "# concatenate lim with cluster\n",
    "lim = cluster.join(lim.loc[:, ['clorofila_a_µg_l_',\n",
    "                               'mstotal_mg_l',\n",
    "                               'cdom_440_m_1',\n",
    "                               'secchi_m']])\n",
    "\n",
    "# remove known outliers\n",
    "lim = lim.replace(0.0, np.nan) \n",
    "lim = lim.replace(-0.875000, np.nan)\n",
    "lim = lim.drop('BNDES_Março2016_TAP7')\n",
    "lim = lim.replace('Artifact', 'Artefact')\n",
    "\n",
    "# remove artifact OWT and samples without all representative values\n",
    "lim1 = lim[lim['OWTs'] != 'Artefact'].dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A secondary dataset is created without OWT 5 and 8 because they represent extreme conditions with high Chl-a and TSM, respectively. Thus, the PCA analysis will be performed with and without those extreme conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "hide_input": true
   },
   "outputs": [],
   "source": [
    "# create a dataset without  OWT 5 and 8\n",
    "lim2 = lim1[lim1['OWTs'] != 'OWT 5']\n",
    "lim2 = lim2[lim2['OWTs'] != 'OWT 8']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3 PCA Analyses"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.1 Feature scaling\n",
    "<br>\n",
    "Scaling is necessary for the PCA analysis because values of CDOM and Zsd are at a very lower scale than those of Chl-a and TSM. Here, the robust method is used because there is no normal distribution and there are many outliers needed for the analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# library used\n",
    "from sklearn.preprocessing import RobustScaler\n",
    "\n",
    "# dataset 1 scaling\n",
    "scaled_lim = lim1.iloc[:,1:].applymap(lambda x: x)\n",
    "scaled_lim = RobustScaler().fit(scaled_lim).transform(scaled_lim)\n",
    "scaled_lim = pd.DataFrame(scaled_lim,\n",
    "                          index=lim1.index,\n",
    "                          columns=lim1.columns[1:]).join(lim1.loc[:,'OWTs'])\n",
    "\n",
    "# dataset 2 scaling\n",
    "scaled_lim2 = lim2.iloc[:,1:].applymap(lambda x: x)\n",
    "scaled_lim2 = RobustScaler().fit(scaled_lim2).transform(scaled_lim2)\n",
    "scaled_lim2 = pd.DataFrame(scaled_lim2,\n",
    "                           index=lim2.index,\n",
    "                           columns=lim2.columns[1:]).join(lim2.loc[:,'OWTs'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scaling changes in the dataset 1\n"
     ]
    },
    {
     "data": {
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99NFcccUVfOYzn6GtrS23v/N0sm3bNtsxR2zPfLE9JUlSoxl1gh4RBwD/BnwwpfRsRFwNfBJI2e1ngeVAjPD09JIVKa0CVgEsXLgw5XWa8OLFi1mwYAGdnZ1D5yt/9rOf9XzlnHCKe77Ynvlie0qSpEYzqgQ9IvahnJxfl1L6d4CU0taKx/8J+Fp2dzNwRMXTDwe2TEq0DcrzlSVJkiRJe7PXc9AjIoAuoC+ldHnF+kMqNnsz0JstrwXOiIhZEXE0cAzwvckLWZIkSZKk/BnNCPobgHcA90bEPdm6jwLFiDiO8vT1h4BzAVJK90XEjcD9lCvAv2+6VnCXJEmSJGm09pqgp5Q2MvJ55bfs4TmdQOcE4pIkSZIkaVrZ6xR3SZIkSZJUfSbokiRJkiTVARN0aRxKpRKtra0sWbKE1tZWSqVSrUOSJEmS1OBGfR10SWWlUomOjg66uroYGBhg5syZtLW1AXh9e0mSJEnj5gi6NEadnZ10dXVRKBRoamqiUCjQ1dVFZ6d1ESVJkiSNnwm6NEZ9fX0sWrRo2LpFixbR19dXo4gkSZIk5YEJujRGLS0tbNy4cdi6jRs30tLSUqOIJEnS3lg/RlIj8Bx0aYw6Ojpoa2sbOge9u7ubtrY2p7hLklSnrB8jqVGYoEtjNNiRt7e309fXR0tLC52dnXbwkiTVqcr6MT09PSxevJiuri7a29vtvyXVFRN0aRyKxSLFYnGok5ckSfXL+jGSGoXnoEuSJCnXrB8jqVGYoEuSJCnXBuvHdHd309/fP1Q/pqOjo9ahSdIwTnGXJElSrlk/RlKjMEGXJElS7lk/RlIjcIq7JEmSJEl1wARdkiRJkqQ6YIIuSZIkSVIdMEGXJEmSJKkOmKBLkiRJklQHTNAlSZIkSaoDJuiSJEmSJNUBE3RJkiRJkuqACbokSZIkSXXABF2SpGkkIq6JiCcjordi3Sci4rGIuCf7OaWWMUrVUCqVaG1tZcmSJbS2tlIqlWodkiS9RFOtA5AkSVPqWuBK4Iu7rL8ipfT3Ux+OVH2lUomOjg66uroYGBhg5syZtLW1AVAsFmscnST9liPokiRNIymlbwFP1TqORuCIa350dnbS1dVFoVCgqamJQqFAV1cXnZ2dtQ5NkoZxBF2SJAG8PyLeCdwFnJ9SenqkjSLiHOAcgHnz5tHT0zN1EU6hDRs20NXVxYc//GGOPvpoNm3axPnnn8/999/PkiVLah2exqivr4+BgQF6enrYtm0bPT09DAwM0NfXl9v/4elisD2VD7anCbokSYKrgU8CKbv9LLB8pA1TSquAVQALFy5MixcvnqIQp9b73/9+rrvuOgqFAj09PXzoQx/iuOOOo729nU9+8pO1Dk9j1NLSwsyZM1m8eDE9PT0sXryY7u5uWlpayOv/8HQx2J7KB9tzFFPcI+KIiOiOiL6IuC8izsvWz42I2yLip9ntyyue85GIeDAiHoiIk6v5C0iSpIlJKW1NKQ2klHYC/wQcX+uYaq2vr49FixYNW7do0SL6+vpqFJEmoqOjg7a2Nrq7u+nv76e7u5u2tjY6OjpqHZokDTOaEfR+ylPdfhARBwJ3R8RtwLuADSmlSyLiQuBCYEVELADOAI4FDgVuj4hXpZQGqvMrSJKkiYiIQ1JKj2d33wz07mn76aClpYWNGzdSKBSG1m3cuJGWlpYaRqXxGiwE197eTl9fHy0tLXR2dlogTlLd2WuCnnXYj2fLz0VEH3AYcBqwONtsNdADrMjWfymltAPYFBEPUv4m/juTHbwkSRqbiChR7r9fERGbgY8DiyPiOMpT3B8Czq1ZgHVicMR1sOr34IirRcUaV7FYpFgsOoVWUl0b0znoETEfeC3wXWDe4LftKaXHI+KV2WaHAXdWPG1ztm7X15oWRWYqWfQgf2zTfLE988X2HFlKaaQhw64pD6TOFYtF7rjjDpYuXcqOHTuYNWsWZ599tiOukqSqGnWCHhEHAP8GfDCl9GxE7HbTEdall6yYJkVmKvmNbf7Ypvlie+aL7amJKJVK3Hzzzaxbt27YdbNPOOEEk3RJUtWM6jroEbEP5eT8upTSv2ert0bEIdnjhwBPZus3A0dUPP1wYMvkhCtJklR9XjdbklQLo6niHpSnvvWllC6veGgtsCxbXgZ8tWL9GRExKyKOBo4Bvjd5IUuSJFWXVdwlSbUwmhH0NwDvAN4UEfdkP6cAlwAnRsRPgROz+6SU7gNuBO4Hvg68zwrukiSpkQxWca9kFffGViqVaG1tZcmSJbS2tlIqlWodkiS9xGiquG9k5PPKAZbs5jmdgHPAJElSQ7KKe76USiU6OjqG2nOwpgBgTQFJdWVMVdwlSZKmA6+bnS+VNQUGC0h2dXXR3t5um0qqKybokiRJI/C62flhTQFJjWJUVdwlSZKkRmVNAUmNwgRdkiRJuTZYU6C7u5v+/v6hmgIdHR21Dk2ShnGKuyRJknLNmgKSGoUj6FPAy3pIkiTVVrFYpLe3lw0bNtDb22tyLqkuOYJeZV7WQ5IkSZI0Go6gV1nlZT2ampooFAp0dXV5HVVJkiRJ0jAm6FXmZT0kSZIkSaNhgl5lXtZDkiRJkjQaJuhV5mU9JEmSJEmjYZG4KisWi9xxxx0sXbqUHTt2MGvWLM4++2wLxEmSJEmShjFBr7JSqcTNN9/MunXrhlVxP+GEE0zSJUmSJElDnOJeZVZxlyRJkiSNhgl6lVnFXZIkSZI0GiboVWYVd0mSJEnSaJigV5lV3CVJkiRJo2GRuCobLATX3t5OX18fLS0tdHZ2WiBOkiRJkjSMCfoUKBaLFItFenp6WLx4ca3DkSRJkiTVIae4S5IkSZJUB0zQJUmSJEmqAybokiRJkiTVARN0SZIkSZLqgAm6JEmSJEl1wARdkiRJkqQ6YIIuSZIkSVIdMEGXJEmSJKkO7DVBj4hrIuLJiOitWPeJiHgsIu7Jfk6peOwjEfFgRDwQESdXK3BJkiRJkvJkNCPo1wJ/McL6K1JKx2U/twBExALgDODY7DlXRcTMyQpWkiRJkqS82muCnlL6FvDUKF/vNOBLKaUdKaVNwIPA8ROIT5IkSZKkaaFpAs99f0S8E7gLOD+l9DRwGHBnxTabs3UvERHnAOcAzJs3j56engmE0hi2bds2LX7P6cQ2zRfbM19sT0mS1GjGm6BfDXwSSNntZ4HlQIywbRrpBVJKq4BVAAsXLkyLFy8eZyiNo6enh+nwe04ntmm+2J75YntKkqRGM64q7imlrSmlgZTSTuCf+O009s3AERWbHg5smViIkiRpsuym+OvciLgtIn6a3b68ljFKkjRdjStBj4hDKu6+GRjs5NcCZ0TErIg4GjgG+N7EQpQkSZPoWl5a/PVCYENK6RhgQ3ZfkiRNsb1OcY+IErAYeEVEbAY+DiyOiOMoT19/CDgXIKV0X0TcCNwP9APvSykNVCd0SZI0Vimlb0XE/F1Wn0a5rwdYDfQAK6YsKEmSBIwiQU8pFUdY3bWH7TuBzokEJUmSptS8lNLjACmlxyPilbvbcDoVed2wYQP/+q//yiOPPMKRRx7J//yf/5MlS5bUOixNkAUk88X2zBfbc2JV3CVJ0jQzXYq8lkolrrvuOq655hoGBgaYOXMmbW1tLFiwgGJxpLELNQoLSOaL7Zkvtuc4z0GXJEm5snWwvkx2+2SN46m5zs5Ourq6KBQKNDU1USgU6OrqorPTSYKSpOoxQZckSWuBZdnyMuCrNYylLvT19bFo0aJh6xYtWkRfX1+NIpIkTQcm6JIkTSNZ8dfvAH8QEZsjog24BDgxIn4KnJjdn9ZaWlrYuHHjsHUbN26kpaWlRhFJkqYDz0GXJGka2U3xVwCrn1Xo6Oigra2Nrq4uBgYG6O7upq2tzSnukqSqMkGXxqFUKtHZ2UlfXx8tLS10dHRYNEiScmTwPb29vX3ovb6zs9P3eklSVZmgS2NUKpXo6OgYGlUZrOwL+MFNknKkWCxSLBatKixJmjKegy6NkZV9JUmSJFWDCbo0Rlb2lSRJklQNJujSGFnZV5IkSVI1mKBLYzRY2be7u5v+/v6hyr4dHR21Dk2SJElSA7NInDRGVvaVJEmSVA0m6NI4WNlXkiRJ0mRzivsUKJVKtLa2smTJElpbWymVSrUOSZIkSZJUZxxBrzKvmS1JkiRJGg1H0KvMa2ZLkiRJkkbDBL3KvGa2JEmSJGk0TNCrzGtmS5IkSZJGwwS9yrxmtiRJkiRpNCwSV2VeM1uSJEmSNBqOoE+BYrFIb28vGzZsoLe31+RckiRpinnZW0mNwBF0SZIk5ZqXvZXUKBxBlyRJUq552VtJjcIEXZIkaQROic4PL3srqVE4xV2SJGkXTonOl8HL3hYKhaF1XvZWUj1yBF2SJGkXTonOFy97K6lROIIujUOpVKKzs3Po0nkdHR2OqEhSjjglOl+87K2kRmGCLo2R0x4lKf+cEp0/xWKRYrFIT08PixcvrnU4kjSivU5xj4hrIuLJiOitWDc3Im6LiJ9mty+veOwjEfFgRDwQESdXK3CpVpz2KEn555RoSVItjGYE/VrgSuCLFesuBDaklC6JiAuz+ysiYgFwBnAscChwe0S8KqU0MLlhS7XjtEdJyj+nREuSamGvI+gppW8BT+2y+jRgdba8Gji9Yv2XUko7UkqbgAeB4ycpVqkutLS08La3vY3Zs2dTKBSYPXs2b3vb25z2KEk5UywW6e3tZcOGDfT29pqcS5KqbrznoM9LKT0OkFJ6PCJema0/DLizYrvN2bqXiIhzgHMA5s2bR09PzzhDaRzbtm2bFr9n3u23336sWbOGv/qrv6JYLFIqlVizZg0LFy60fRucx2i+2J6SJKnRTHaRuBhhXRppw5TSKmAVwMKFC9N0KNZhUZJ8uPfeeznrrLO45557OOuss2hpaeGss87iy1/+su3b4DxG88X2lCRJjWa810HfGhGHAGS3T2brNwNHVGx3OLBl/OFJ9WfHjh0sWbJk2LolS5awY8eOGkUkSZIkKQ/Gm6CvBZZly8uAr1asPyMiZkXE0cAxwPcmFqJUX5qammhvb2f79u2klNi+fTvt7e00NXnVQkmSJEnjN5rLrJWA7wB/EBGbI6INuAQ4MSJ+CpyY3SeldB9wI3A/8HXgfVZwL183u7W1lSVLltDa2kqpVKp1SJqAWbNmsX37dpYuXcratWtZunQp27dvZ9asWbUOTZIkSVID2+uQX0ppdyVLl4y0MqXUCXhB6EypVKKjo4Ouri4GBgaYOXMmbW1tAFaDbVDbt2/nda97HZ///Oe5+uqriQhe97rX8YMf/KDWoUmSpN0olUp0dnYOXTavo6PDz2KS6o5zcquss7OTrq4uCoXCUMGirq4u2tvb7RQa2JYtW9iwYcPQly5nnnlmrUOSJEm74YCJpEYx3nPQNUp9fX0sWrRo2LpFixbR19dXo4g0UU1NTbzwwgvD1r3wwguegy5JUp2qHDBpamqiUCjQ1dVFZ6eTPiXVFzOKKmtpaWHjxo0UCoWhdRs3bqSlpaWGUWkiBgYGmDFjBsuXL+eRRx7hyCOPZMaMGQwMTPtyC5Ik1aW+vj5uuukmli5dyo4dO5g1axbLly93wERS3XEEvco6Ojpoa2uju7ub/v5+uru7aWtro6Ojo9ahaZwWLFjAueeeS3NzMwDNzc2ce+65LFiwoMaRSZKkkcyZM4dVq1bx6U9/mnXr1vHpT3+aVatWMWfOnFqHJknDOIJeZYPnNbW3tw8VJens7PR8pwbW0dEx4nlsTpOTpHyxqFh+PPvss8yePZuVK1fy8MMPc9RRRzF79myeffbZWoemcfL4VF6ZoE+BYrFIsVgcKhKnxuaXLpLyKiIeAp4DBoD+lNLC2kZUOxYVy5f+/n5mzZrFY489RkqJxx57jH333Zf+/v5ah6Zx8PhUnjnFXZIkVSqklI6bzsk5WFQsjyKC9evXc9ttt7F+/XoiotYhaZw8PpVnjqBLY+S3tpKUf319fWzevJnW1tah2VIrVqywqFgD2759O8Vika1btzJv3jy2b99e65A0Th6fyjMTdGmMvLa9pBxLwK0RkYAvpJRW7bpBRJwDnAMwb948enp6pjbCKTJ37lw++MEP8rGPffb5aHEAAB05SURBVIyjjz6aTZs28cEPfpC5c+fm9nfOu3333Zdf/vKXAPzyl79k3333ZceOHbZnA/L4zK9t27ZN+zY0QZfGyGvbS8qxN6SUtkTEK4HbIuInKaVvVW6QJe2rABYuXJjyWltl9uzZ9Pf3c9xxxzEwMMBxxx3HvvvuS1NTk/VkGlBTUxPNzc18+ctfHpr99pa3vIWBgQHbswF5fOaXNbtM0KUxa2lp4aKLLmLNmjVD06pOP/10r20vqeGllLZkt09GxFeA44Fv7flZ+bRlyxauvfbaYQVBL7vsMt71rnfVOjSNw8DAADNmzGD58uU88sgjHHnkkcyYMYOBgYFah6Zx2LJlC+eee+5Lrmv/hS98odahSRNmgi6NUaFQ4NJLL+XSSy9lwYIF3H///axYsYJ3v/vdtQ5NksYtIpqBGSml57Llk4C/q3FYNdPS0sIDDzwwbN0DDzzgl7ENasGCBZx++umsWbMGgObmZs4666yh+2oshx56KGvWrGHdunVDMyLOOussDj300FqHJk2YCbo0Rt3d3axYsYJrrrlmWGESO3lJDW4e8JWssnUTcH1K6eu1Dal2CoUCF198MQcffDApJX7xi19w8cUX8973vrfWoWkcOjo6OO+882hubialxPbt21m1ahWf+9znah2aximltMf7UqMyQZfGqK+vjx/+8Id86lOfGjpP5sUXX+Tiiy+udWiSNG4ppZ8Bf1TrOOrFmjVrOPDAA9lvv/0A2G+//TjwwANZs2YNK1eurHF0mggvr9b4PAVFeeZ10KUxamlpYePGjcPWbdy40WmPkpQjmzdv5qabbmLTpk184xvfYNOmTdx0001s3ry51qFpHDo7O7nhhhvYtGkTGzZsYNOmTdxwww1eN7tBeQqK8swEfQqUSiVaW1tZsmQJra2tlEqlWoekCejo6KCtrY3u7m76+/vp7u6mra2Njo6OWocmSZpEV155JbNnz6ZQKDB79myuvPLKWoekcaq8bvbg57HNmzd7BZYGNVgPaPny5dx8880sX76cSy+9lEKhUOvQpAlzinuVlUolOjo66OrqGipi0dbWBuA1sxvUYLtVTqvq7Oy0PSUpR5qbm1m7di3vec97OOWUU7jlllu4+uqraW5urnVoGodDDz2UFStWcN1111lULAesB6Q8cwS9yjo7O+nq6qJQKNDU1EShUKCrq8spVQ2uWCzS29vLhg0b6O3tNTmXpJzZsWMHzc3NrFu3jtNOO41169bR3NzMjh07ah2axsmiYvnR19fHxz/+8WGfxT7+8Y87I0K5YIJeZU6pkiSp8fT397Ny5cqhEfPm5mZWrlxJf39/jSPTeGzZsoXLLruM9vZ2Tj75ZNrb27nsssvYsmVLrUPTOFgPSHnmFPcqc0qVJEmNZ9asWTz99NP09vYOXbHj8ssvZ9asWbUOTePQ0tLC4YcfPqw9u7u7Tega1GA9oMFTSAfrATlDVXngCPoUcEpV/lj4T5Ly7eyzz2bFihVcfvnlPP/881x++eWsWLGCs88+u9ahaRws8JovxWKRzs7OYTMirAfU2Pxs/VuOoFfZli1bOPfcc1m6dCk7duxg1qxZLF++nC984Qu1Dk3jZOE/Scq/wWudf/SjHx3qv9/97nd7DfQGZYFXqX6VSiXOO+88mpubSSmxfft2zjvvPGB6fraOehjNXbhwYbrrrrtqHUZVHHHEEWzbto05c+bw8MMPc9RRR/HMM89wwAEH8Oijj9Y6PI1Da2srK1eupFAoDJsm197eTm9vb63D0wQMtqfyYbq0Z0TcnVJaWIt957n/rjRd/pemC9uz8e1usMQvXRrTEUccQX9/P9dff/1Qe5555pk0NTXlOl/aXf/tFPcq+/Wvf82zzz5Le3s7t9xyC+3t7Tz77LP8+te/rnVoGqe+vj4WLVo0bN2iRYss/CdJOeOUy3yxPfPDqyTly+bNm/niF784rD2/+MUvsnnz5lqHVhNOca+yp556igsvvHDYdRovuOACLrnkklqHpnEarBxaKBSG1lk5VJLyxSmX+eLpaflSeZWkyuugO1jSuL7xjW/woQ99aKg9//Iv/7LWIdWMCfoUuP/++3nwwQfZuXMnDz74IPfff3+tQ9IEWDlUkvLvggsu4IUXXhi6zBrACy+8wAUXXGBC14AqR1wHp7h3dXXR3t5uezYgr5KUL3PnzuUzn/kMl112GQsWLOD+++/nggsuYO7cubUOrSZM0KusubmZtWvX8p73vIdTTjmFW265hauvvnpYh6/GYqEZScq/zZs3M3v2bB577DFSSjz22GPMnDmTX/3qV7UOTePg6Wn541WS8mP//fdnYGCAlStX8sgjj3DkkUdywAEHsP/++9c6tJrwHPQq27FjB83Nzaxbt47TTjuNdevW0dzczI4dO2odmiagWCzS29vLhg0b6O3tNTmXpBx6/vnnh0Zw5s6dy/PPP1/jiDReg6enVfL0tMa1ZcuWoXoCJ5544lBdgS1bttQ6NI3Dli1bOPPMM3n88cfZuXMnjz/+OGeeeea0bc8JJegR8VBE3BsR90TEXdm6uRFxW0T8NLt9+eSE2pj6+/tZuXLl0Ih5c3MzK1eupL+/v8aRSZKkvdm6deuwWzUmr4OeL3PmzOH2228fGjVPKXH77bczZ86cGkem8Tj00EO5/vrrOeSQQ5gxYwaHHHII119//bQ9ZWEyRtALKaXjKkrEXwhsSCkdA2zI7k9bs2bN4umnnx422vr0008za9asWoemCbASrCRJjaNYLHLqqaeydOlSTjzxRJYuXcqpp57qDLgG9fTTTwNwwAEHDLsdXK/GMnjVq8FZSs8///y0vupVNc5BPw1YnC2vBnqAFVXYT0M4++yzWbGi/OsvWLCAyy+/nBUrVvDud7+7xpFpvKzsK0lSYymVStx8882sW7duWBX3E044wb67AQ2OnG/btm3YreehN6annnqKOXPmMHv2bFJKzJ49m4MOOoinnnqq1qHVREzkHzkiNgFPAwn4QkppVUQ8k1KaU7HN0ymll0xzj4hzgHMA5s2b98df+tKXxh1Hvfvc5z7HzTffzIsvvsg+++zDqaeeOpTQqfG87W1vY2BggI997GMcffTRbNq0iU996lPMnDmTG2+8sdbhaQK2bds29C28Gt90ac9CoXB3xSy2KbVw4cJ011131WLXVRcRu33MJKDxtLa2cvrpp7NmzZqhAq+D93t7e2sdnsbI4zNfIoLLLruMD3/4w0NXWfjMZz7DBRdckOv2jIgR+++JJuiHppS2RMQrgduAdmDtaBL0Snnu4CsN/sOpsUUEt956KyeeeOJQm952222cdNJJuX4TmQ48RvNlurTn7jr4qZDn/nswAWhqaqK/v3/oFkwAGtGMGTM46qijuOaaa4ZG0JcvX87DDz/Mzp07ax2exmjw+DzhhBP40Ic+xBVXXMEdd9wBeHw2oohg3rx5lEqloeOzWCyydevWXLfn7vrvCZ2DnlLakt0+CXwFOB7YGhGHZDs9BHhyIvvIg/b2dmbPnk2hUGD27Nm0t7fXOiRJkjQK++23HzNmzGC//fardSiagH333Zf29nYKhQJNTU0UCgXa29vZd999ax2aJuDOO+/krW99K3feeWetQ9EIImJUP1AuxPmmN72JE088kTe96U1DhTlH+xp7mlXRaMadoEdEc0QcOLgMnAT0AmuBZdlmy4CvTjTIRtbe3s5VV101VFVyzpw5XHXVVSbpDezwww9n2bJlwyrBLlu2jMMPP7zWoUmSJtlzzz3Hzp07ee6552odiibghRde4MorrxzWd1955ZW88MILtQ5NEzA4+8FZEPUppTSqn+uvv56DDz6Y+fPnA8H8+fM5+OCDuf7660f9GnkaaZ9Ikbh5wFeybyuagOtTSl+PiO8DN0ZEG/AI8NaJh9m4Pv/5z9PU1DRU5OCpp56iqamJz3/+86xcubLG0Wk8LrvsMs477zyWL1/OI488wpFHHkl/fz+f/exnax2aJGkvJmOUZbSvkacPjI1uwYIFnH766bS3tw+dg37mmWeyZs2aWoemCh6f09NgocbOzk6IoLm5mU9/+tPTtoDjuBP0lNLPgD8aYf0vgSUTCSpPBs9XmzdvHlu3bmXu3LleS7XBDXsTgWn/JiJJjWS0H8pLpRLLli3jxRdfHFq3zz77sHr1at/v68hYErr77rtv2PLgfRO6+jHav/HJJ5/Mrbfe+pL1J510EuvXr5/ssDQFisUixWKR+RfeTO8lp9Y6nJqajOugaxQGk3KT8/o1lnNczjzzTO677z527tzJfffdx5lnnjktz5GRpLwqFousXr2aY489FmIGxx57rMl5HRrL9Nfrr79+WHtO1+mzebB+/XpOOumkoc9UEWFyrtwwQZcyY+mkB3+OWvG1cT1PklT/isUivb29HHXBWnp7e03OG5ztmS/r169n586dHLXia+zcudPkXLlhgi5JkiRJUh0wQZckSZIkqQ6YoE+RpqamYbeSJEmSJFUyQZ8i++23HzNmzGC//fardSiSJEmSpDrkcO4Uee6554bdSpIkSZJUyQR9nCbjUlljeQ0rf0uSJElSvjnFfZzGcs3NffbZZ9hz99lnH6+9KUmSJEkaxgS9yorFIqtXr+bYY4+FmMGxxx7L6tWrvfamJEmSJGkYp7hPgWKxSLFYZP6FN9N7yam1DkeSJKnu/NFFt/Kr37w4Jfuaf+HNVd/HQfvtw48+flLV9yNNhbwdn1C/x6gJuiRJakh5+8BYrx8Wp8qvfvMiD03BQEZPTw+LFy+u+n6mKsmoVx6f+ZK34xPq9xg1QZckSQ0pbx8Y6/XDojQeHp/S+HgOuiRJAiAi/iIiHoiIByPiwlrHI0nSdOMIunJnKqdUgdOqJOVDRMwE/hE4EdgMfD8i1qaU7q9tZJIkTR8m6MqdqZpSBU6rkpQrxwMPppR+BhARXwJOA0zQJUmaIk5xlyRJAIcBj1bc35ytkyRJU8QRdEmSBBAjrEsv2SjiHOAcgHnz5tHT01PlsHbvwJYLefXqKTpVfnX1d3FgC/T0NFd/R3XK9swX2zNf8taeUL9taoIuSZKgPGJ+RMX9w4Etu26UUloFrAJYuHBhmqrL4YzkuQsvyV2V6MXLqr+femV75ovtmS95a0+o3zY1QSd/12kEi4pJksbs+8AxEXE08BhwBnBmbUOSJNWLKauJ9PWpy5fqkQk6+btOI1hUTJI0Niml/oh4P7AemAlck1K6r8Zh7VWePjDW64fFqWR7SvVpqgowz7/w5inbV70yQZckSQCklG4Bbql1HKPlB8Z8sT3zxy9cpLEzQZckSVJDihiptuEonnfp2J+T0ktqJmoP/MJFU3l8Qn6OURN05c6UVpmEKascCnY+1fKa17yGe++9d+j+q1/9an784x/XMCJJ1WJCly/j+RtP5SmH0nTm8Tk+Jujk97IB0zWhe65vaqpMwtRWDlV17JqcA9x777285jWvMUlvUCeffDK33XYbKSUighNPPJH169fXOizVCT8wSpLqmQk6U5fQWSROqj+7Jud7W6/6dvLJJ3PrrbcO3U8pceutt3LyySebpEuSpLpngq5cmtIvKCxMkgvz5s3jkksu4cILL2Tr1q21DkfjNJicv+c97+GUU07hlltu4eqrrx6WtEuSJNWrqiXoEfEXwOcoX6rln1NKl1RrX5MhT1UmYXondFNZKMTCJPkwc+ZMnnjiCXp6enjiiSdoampiYGCg1mFpnJYvX85VV11FT08PV111FTt27OCaa66pdViSJEl7VZUEPSJmAv8InAhsBr4fEWtTSvdXY38TZZVJKX/GUghqYGBgxO1H+xoWgqq+sbTnNddcM2JCbntKkqR6V60R9OOBB1NKPwOIiC8BpwF1maBLqm+vXv3qMT+n9drWKkQysvHEd++y6XuO+x9ddCu/+s2LY3rOUSu+NqrtHr70vwFwwHFLmfOny3jmW6vZds+6Mb3GWGdUHbTfPvzo4yeN6TmSJEkjqVaCfhjwaMX9zcCfVG4QEecA50D53M+enp4qhVIdhUJhXM8b73X9uru7x/dEjdpUtqntOTbP9Y39DJnBRG0qjDbxG9S8Dw33njeZds4/nwOr9Nq//WLmUeBTzHkdwOC66lytYyfQ07OyKq8tSdOJl0GUqpegj3R0DTsKUkqrgFUACxcuTI12+RIv05I/tmn9emjxOJ50yejbc/bs2ezYsWPo/qxZs3j++efHsVONxr1Ud/aAl1mTpMbkZzEJZlTpdTcDR1TcPxzYUqV9SdKEPP/886SU6O7uJqVkct7g1q9fz86dO+nu7mbnzp0m55IkqWFUK0H/PnBMRBwdEfsCZwBrq7QvSZIkSZIaXlWmuKeU+iPi/cB6ypdZuyaldF819iVJkiRJUh5U7TroKaVbgFuq9fqSJEmSJOVJtaa4S5IkSZKkMTBBlyRJkiSpDpigS5IkSZJUB0zQJUmSJEmqAybokiRJkiTVgUgp1ToGIuLnwMO1jmMKvAL4Ra2D0KSyTfPF9syX6dKeR6WUDq7Fju2/1aBsz3yxPfNlOrXniP13XSTo00VE3JVSWljrODR5bNN8sT3zxfbUZPF/KV9sz3yxPfPF9nSKuyRJkiRJdcEEXZIkSZKkOmCCPrVW1ToATTrbNF9sz3yxPTVZ/F/KF9szX2zPfJn27ek56JIkSZIk1QFH0CVJkiRJqgMm6JIkSZIk1QETdEmSJEmS6kDuE/SI+ERE/M0kvdbBEfHdiPhhRLwxIm6JiDnZY9smYx/VFhHzI6K31nHUUvY3OHMU2x0XEaeMYrvFEfG1yYludCLiXRFx5VTuc6pNxTEVEW+JiBQRC3dZ/7KIeKzybxwRR2fH/08j4oaI2LdKMb0/Ih7M4npFNfYxXUREz65tm61fGBH/qxYxaXTsu4ez7y6z/65/9t323RNl3z0NEvSxioimPTy8BPhJSum1KaX/k1I6JaX0zFTFpkkzH9hrBw8cB+y1g1djiogDgQ8A3x3h4U8C39xl3aXAFSmlY4CngbYqhfZt4M+Bh6v0+tNeSumulNIHah2HJo9997QxH/vvac2+e/qaTn137hL0iHhnRPw4In4UEf97l8eOi4g7s8e/EhEvz9b3RMSnI+KbwHkRcVREbMi22xARR0bEccBlwCkRcU9E7BcRD+36LVlEHJA95wcRcW9EnLaXeNdExN0RcV9EnLOXbbdVLL8lIq7Nln8v+72+HxF/1ygjAhOVfZP+k4j454jojYjrIuLPI+Lb2Telx0fEn2XtdU82enIgcAnwxmzdhyJidkT8S9ZeP4yIQvYN698Bb8+2e3v2endk29wREX8wyjg/ERGrI+LW7H/mv0fEZdn+vh4R+2TbnZL9Phsj4n9N9bf6U23XYzX7lvs72f/xJyu2i4j4TNbG90bE27P1iyPimxFxY0T8R0RcEhFnRcT3su1+by8hfJLyMf38LnH9MTAPuLUyBuBNwJezVauB0/fwu42qzUeSUvphSumhvcSeKxHRHBE3Z/8Lvdnx9sdZ+94dEesj4pBs29+PiNuzbX8w2M4RcUH29/1RRFxS8fJvzf4n/iMi3phtu8dRs4m0n8Zn1/eDXR6z786ZsP9uWLseq2HfDdh3h3335Eop5eYHOBZ4AHhFdn8u8Angb7L7Pwb+LFv+O+AfsuUe4KqK1/n/gGXZ8nJgTbb8LuDKiu0eqtjXtuy2CXhZtvwK4EGyy9ntJua52e1+QC/wO3vYdlvF8luAa7PlrwHFbPndlduN8Brzgd5at9Uktfd8oB94NeUvm+4GrgECOA1Yk7XlG7LtD8jaZzHwtYrXOR/4l2z5D4FHgNkjtPfLgKZs+c+Bf8uWh73eCHF+AtgI7AP8EfBrYGn22FcodxazgUeBo7P1pb285rDYGu1nN8fqWuCd2f33VRxT/wO4DZhJufN9BDgk+7s/ky3PAh4DLsqecx7Z8b2b/b+2ov16gIXZ8ozs/hGVf+PBY7ni+Ufs6TgaTZuP4m/00ODfJ+8/WRv/U8X9g4A7gIOz+28HrsmWvwu8OVueDewPLM2233/w/6mibT+bLZ8C3J4tT/iYrfXfLE8/u3k/+AT23ZWvMX9P7zmN9oP9d0P237s5Vu27h7/GQ9h323dP8CdvI+hvAr6cUvoFQErpqcEHIuIgYE5KaXDqy2rgTyuee0PF8uuB67Pl/w0sGkMMAXw6In4M3A4cRvmNaXc+EBE/Au6k/MZxzBj2Nej1wE3Z8vV72jCHNqWU7k0p7QTuAzak8pF4L+UPAN8GLo+ID1Bu//4RXmMR5XYmpfQTytOTXjXCdgcBN0X5PMArKHdUo7UupfRiFtdM4OvZ+sE4/xD4WUppU7a+NIbXbkQjHatv4Le/d+UI2iKglFIaSCltpTx97b9kj30/pfR4SmkH8J/89pvzwb/rS0TEDMrtd/4ID78XuCWl9OiuTxth27T7Xw/Ye5vrt+4F/jwiLs2+KT8CaAVui4h7gI8Bh2cjaIellL4CkFJ6PqX0a8ofuP8lWx723g/8e3Z7N2P7u9t+U8e+e/r13WD/3Yjsu1XJvrtK9nTOViMK9n7g7c72PTw2ltc8CzgY+OOU0osR8RDlb4peIiIWU/7nfH1K6dcR0bO7bQefUrHcONM0qmtHxfLOivs7KX9bfklE3Ez5G7g7I+LPR3iNkd7AR/JJoDul9OaImE/5G74xxZlS2hkRL2YfQobiHEMMebG7Y3WkdXv62+yx/XfznAMpdyA95dlv/C6wNiL+ivIH5jdGxHspj9jsG+Vppx8B5kREU/Yh8XBgyx7iGoptD22uTErpP7LpiacAF1MedbkvpfT6yu0i4mW7eYk9vfcP/k8MMLa/u+03dey7pyf778Zj360h9t3Vk7cR9A3A2yLidwAiYu7gAymlXwFPD57HALyDlxaSGHQHcEa2fBbl6RKjdRDwZNbBF4Cj9rLt01kH/4fAf93La+8fEQuy5cWUvxmC8jf4/yNbPmPXJ01nEfF72Tf0lwJ3Uf6m+znKb/SDvkW5nYmIVwFHUp7Ctet2B1GeigXlKVST6SfA/5V9cIDytKA8G+lY/TbDj7tB36J8LuHMiDiY8ujZ98a745TSr1JKr0gpzU8pzad8/PxVKhcfOSuldGS2/m+AL6aULsze4LspT08FWAZ8dbwxaLiIOBT4dUrpX4G/B/4EODgiXp89vk9EHJtSehbYHBGnZ+tnRcT+lEdflmfLw9771RDsu+27X8L+uy7Zd2uIfXf15CpBTyndB3QC38ymnl2+yybLgM9kU9iOo3wu20g+APx1tt07KJ8TM1rXAQsj4i7Kb1Q/2cO2Xweasv18kvKbzZ78BvjbiLgb+BVQiIgTgA8C/09EfI/yOT2/GkO8effBKBeu+BHlv986yucz9mcFKT4EXAXMjIh7KU+XfFc27aobWBBZkRnKRUkujohv89sPWJMipfQbylO0vh4RG4Gt5Lgdd3Osnge8LyK+T/nD1KCvUG6zHwHfAC5IKT0xxSEDrKB8nD0I/A7QVY2dRMQHImIz5W/6fxwR/1yN/dSZVwPfy6bEdQB/S/kD1aXZ/8c9wAnZtu+gPL34x5QTst9NKX2d8nmQd2WvMSmX59LUsO+2794N++86Y9+9e/bd9t2TKX478q96FxHbUkoHjLB+f+A3KaUUEWdQLjqzxwq0qj8RcUBKaVuU5279I/DTlNIVtY5LkjR+9t35Z/8taTI1zFx87dEfA1dmHcMzlKvXqvGcHRHLgH2BHwJfqHE8kqTqse/OD/tvSZPGEfQpkJ2rs2GEh5aklH45wvbfpXzpiUrvSCndO4Z9vprh1TQBdqSU/iR7/GTg0l0e35RSevNo96HhIuKveemUym+nlN5XT685nUREB/DWXVbflFLqnKTXH3f7RMRXgKN3Wb0ipbR+MmLT3nl8aU/su6cP++/6Yt+tPZkOx5YJuiRJkiRJdSBXReIkSZIkSWpUJuiSJEmSJNUBE3RJkiRJkuqACbokSZIkSXXg/weFYSa3Vkkh1gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scaling changes in the dataset 1\n",
    "print('Scaling changes in the dataset 1')\n",
    "\n",
    "# library used\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# plot\n",
    "plt.figure(figsize=(14, 4))\n",
    "i = 1\n",
    "for x, y in zip([lim1, scaled_lim],\n",
    "                ['Raw data', 'Scaled Data']):\n",
    "    \n",
    "    ax = plt.subplot(1, 2, i)\n",
    "    g = x.boxplot(ax=ax)\n",
    "    g.set_title(y)\n",
    "    \n",
    "    \n",
    "    \n",
    "    i = i + 1\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Scaling changes in the dataset 2\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1008x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scaling changes in the dataset 2\n",
    "print('Scaling changes in the dataset 2')\n",
    "\n",
    "# library used\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# plot\n",
    "plt.figure(figsize=(14, 4))\n",
    "i = 1\n",
    "for x, y in zip([lim2, scaled_lim2],\n",
    "                ['Raw data', 'Scaled Data']):\n",
    "    \n",
    "    ax = plt.subplot(1, 2, i)\n",
    "    g = x.boxplot(ax=ax)\n",
    "    g.set_title(y)\n",
    "    \n",
    "    \n",
    "    \n",
    "    i = i + 1\n",
    "\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3.2 Compute PCA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# library used\n",
    "from sklearn.decomposition import PCA\n",
    "\n",
    "# dataset 1\n",
    "# performs the PCA using standardized data\n",
    "scaled_pc = PCA(n_components=2).fit(scaled_lim.loc[:, ['clorofila_a_µg_l_',\n",
    "                                               'mstotal_mg_l',\n",
    "                                               'cdom_440_m_1',\n",
    "                                               'secchi_m']])\n",
    "\n",
    "# saves the variance ratio of each PC\n",
    "scaled_pc_vr = scaled_pc.explained_variance_ratio_\n",
    "\n",
    "# saves the components\n",
    "scaled_components = scaled_pc.components_\n",
    "\n",
    "# transform the value to the PC dimension\n",
    "scaled_pc = scaled_pc.transform(scaled_lim.loc[:, ['clorofila_a_µg_l_',\n",
    "                                       'mstotal_mg_l',\n",
    "                                       'cdom_440_m_1',\n",
    "                                       'secchi_m']])\n",
    "\n",
    "# saves the values in a pandas DataFrame\n",
    "scaled_pc = pd.DataFrame(scaled_pc, columns=['PC 1', 'PC 2'])\n",
    "scaled_pc.index = scaled_lim.index\n",
    "scaled_pc = scaled_pc.join(scaled_lim.loc[:, 'OWTs'])\n",
    "\n",
    "\n",
    "# dataset 2\n",
    "# performs the PCA using standardized data\n",
    "scaled_pc2 = PCA(n_components=2).fit(scaled_lim2.loc[:, ['clorofila_a_µg_l_',\n",
    "                                                 'mstotal_mg_l',\n",
    "                                                 'cdom_440_m_1',\n",
    "                                                 'secchi_m']])\n",
    "\n",
    "# compute the variance ratio of each PC\n",
    "scaled_pc_vr2 = scaled_pc2.explained_variance_ratio_\n",
    "\n",
    "# compute the components\n",
    "scaled_components2 = scaled_pc2.components_\n",
    "\n",
    "# transform the value to the PC dimension\n",
    "scaled_pc2 = scaled_pc2.transform(scaled_lim2.loc[:, ['clorofila_a_µg_l_',\n",
    "                                          'mstotal_mg_l',\n",
    "                                          'cdom_440_m_1',\n",
    "                                          'secchi_m']])\n",
    "\n",
    "# saves the values in a pandas DataFrame\n",
    "scaled_pc2 = pd.DataFrame(scaled_pc2, columns=['PC 1', 'PC 2'])\n",
    "scaled_pc2.index = scaled_lim2.index\n",
    "scaled_pc2 = scaled_pc2.join(scaled_lim2.loc[:, 'OWTs'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hide_input": true
   },
   "source": [
    "## 3.3 PCA figure"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 518.4x734.4 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# dataset 1\n",
    "# library used\n",
    "import seaborn as sns\n",
    "\n",
    "# compute figure size\n",
    "fig = plt.figure(figsize=(7.2, 10.2))\n",
    "\n",
    "# create subplot a\n",
    "ax = plt.subplot(2, 1, 1)\n",
    "\n",
    "# plot\n",
    "g = sns.scatterplot(x='PC 1',\n",
    "                  y='PC 2',\n",
    "                  hue='OWTs',\n",
    "                  hue_order=['OWT 1', 'OWT 2', 'OWT 3', 'OWT 4', 'OWT 5', 'OWT 6', 'OWT 7', 'OWT 8'],\n",
    "                  palette=['#02b7d7', '#0057ed', '#7cb600', '#00f625', '#188400', '#000000', '#fffb00', '#ff0004'],\n",
    "                  data=scaled_pc,\n",
    "                  s=15,\n",
    "                  ax=ax)\n",
    "\n",
    "# change axis limits and its labels\n",
    "g.set_xlim(-10, 23)\n",
    "g.set_ylim(-5, 20)\n",
    "g.set_xlabel('PC 1 ('+str(scaled_pc_vr[0])[2:4]+'%)')\n",
    "g.set_ylabel('PC 2 ('+str(scaled_pc_vr[1])[2:4]+'%)')\n",
    "\n",
    "# add the subplot name\n",
    "ax.text(-8, 18,\n",
    "         'a)',\n",
    "        fontsize=14,\n",
    "       horizontalalignment='center',\n",
    "       verticalalignment='bottom')\n",
    "\n",
    "# draw the PC lines\n",
    "ax.arrow(0,0,0,20,\n",
    "        color='grey',\n",
    "        linewidth=0.3)\n",
    "\n",
    "ax.arrow(0,0,0,-20,\n",
    "        color='grey',\n",
    "        linewidth=0.3)\n",
    "\n",
    "ax.arrow(0,0,30,0,\n",
    "        color='grey',\n",
    "        linewidth=0.3)\n",
    "\n",
    "ax.arrow(0,0,-30,0,\n",
    "        color='grey',\n",
    "        linewidth=0.3)\n",
    "\n",
    "# draw the parameters eigenvectors\n",
    "for x, y in zip([0, 1, 2, 3],\n",
    "               ['Chl-a', 'TSM', 'CDOM', 'Z$_{sd}$']):\n",
    "\n",
    "    ax.arrow(0, 0,\n",
    "              scaled_components[0][x]*max(scaled_pc['PC 1']),\n",
    "              scaled_components[1][x]*max(scaled_pc['PC 2']),\n",
    "              color='black',\n",
    "              width=0.0005,\n",
    "              head_width=0.5)\n",
    "    \n",
    "    ax.text(x=scaled_components[0][x]*max(scaled_pc['PC 1']),\n",
    "         y=scaled_components[1][x]*max(scaled_pc['PC 2']),\n",
    "         s=y,\n",
    "         color='black',\n",
    "        fontsize=12,\n",
    "       horizontalalignment='center',\n",
    "       verticalalignment='bottom');\n",
    "\n",
    "#dataset 2\n",
    "# create subplot b\n",
    "ax2 = plt.subplot(2, 1, 2)\n",
    "\n",
    "# plot\n",
    "g = sns.scatterplot(x='PC 1',\n",
    "                  y='PC 2',\n",
    "                  hue='OWTs',\n",
    "                  hue_order=['OWT 1', 'OWT 2', 'OWT 3', 'OWT 4', 'OWT 6', 'OWT 7'],\n",
    "                  palette=['#02b7d7', '#0057ed', '#7cb600', '#00f625', '#000000', '#fffb00'],\n",
    "                  data=scaled_pc2,\n",
    "                  s=15,\n",
    "                    legend=False,\n",
    "                  ax=ax2)\n",
    "\n",
    "\n",
    "# change axis labels\n",
    "g.set_xlabel('PC 1 ('+str(scaled_pc_vr2[0])[2:4]+'%)')\n",
    "g.set_ylabel('PC 2 ('+str(scaled_pc_vr2[1])[2:4]+'%)')\n",
    "\n",
    "# add the subplot name\n",
    "ax2.text(-2.5, 3.75,\n",
    "         'b)',\n",
    "        fontsize=14,\n",
    "       horizontalalignment='center',\n",
    "       verticalalignment='bottom')\n",
    "\n",
    "# draw the pc lines\n",
    "ax2.arrow(0,0,0,20,\n",
    "        color='grey',\n",
    "        linewidth=0.3)\n",
    "\n",
    "ax2.arrow(0,0,0,-20,\n",
    "        color='grey',\n",
    "        linewidth=0.3)\n",
    "\n",
    "ax2.arrow(0,0,30,0,\n",
    "        color='grey',\n",
    "        linewidth=0.3)\n",
    "\n",
    "ax2.arrow(0,0,-30,0,\n",
    "        color='grey',\n",
    "        linewidth=0.3)\n",
    "\n",
    "# draw the parameters eigenvectors\n",
    "for x, y in zip([0, 1, 2, 3],\n",
    "               ['Chl-a', 'TSM', 'CDOM', 'Z$_{sd}$']):\n",
    "\n",
    "    ax2.arrow(0, 0,\n",
    "              scaled_components2[0][x]*max(scaled_pc2['PC 1']),\n",
    "              scaled_components2[1][x]*max(scaled_pc2['PC 2']),\n",
    "              color='black',\n",
    "              width=0.0005,\n",
    "              head_width=0.1)\n",
    "    \n",
    "    ax2.text(x=scaled_components2[0][x]*max(scaled_pc2['PC 1']),\n",
    "         y=scaled_components2[1][x]*max(scaled_pc2['PC 2']),\n",
    "         s=y,\n",
    "         color='black',\n",
    "        fontsize=12,\n",
    "       horizontalalignment='center',\n",
    "       verticalalignment='bottom');\n",
    "    \n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 4 Boxplot Analysis"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 662.4x662.4 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# order to plot along x axis in the boxplots\n",
    "order = ['OWT 1',\n",
    "         'OWT 2',\n",
    "         'OWT 3',\n",
    "         'OWT 4',\n",
    "         'OWT 5',\n",
    "         'OWT 6',\n",
    "         'OWT 7',\n",
    "         'OWT 8',\n",
    "        'Artefact']\n",
    "\n",
    "# create the figure and subplots\n",
    "f, ax = plt.subplots(nrows=2, ncols=2, figsize=(9.2, 9.2))\n",
    "ax1 = ax[0][0]\n",
    "ax2 = ax[0][1]\n",
    "ax4 = ax[1][0]\n",
    "ax3 = ax[1][1]\n",
    "\n",
    "# set the Chl-a and TSM subplots to log scale (due to the outliers)\n",
    "ax1.set_yscale(\"log\")\n",
    "ax2.set_yscale(\"log\")\n",
    "\n",
    "# Chl-a subplot\n",
    "sns.boxplot(x=\"OWTs\",\n",
    "            y=\"clorofila_a_µg_l_\",\n",
    "            data=lim,\n",
    "            whis=1.5,\n",
    "            showfliers=True,\n",
    "            order=order,\n",
    "            ax=ax1,\n",
    "            fliersize=1,\n",
    "            color='grey')\n",
    "\n",
    "# change tick params\n",
    "ax1.tick_params(axis='both',\n",
    "                which='major',\n",
    "                labelsize=10,\n",
    "                pad=2,\n",
    "                rotation=90)\n",
    "\n",
    "# add grid lines\n",
    "ax1.grid(axis='y')\n",
    "\n",
    "# change y label\n",
    "ax1.set_ylabel('Chl-a (mg m$^{-3}$)', fontsize=10)\n",
    "\n",
    "# remove x label\n",
    "ax1.set_xlabel('', fontsize=0)\n",
    "\n",
    "# add subplot name\n",
    "ax1.text(0, 125, 'a)', fontsize=10)\n",
    "\n",
    "# set the gridlines backwards\n",
    "ax1.set_axisbelow(True)\n",
    "\n",
    "# TSM\n",
    "sns.boxplot(x=\"OWTs\",\n",
    "            y=\"mstotal_mg_l\",\n",
    "            data=lim,\n",
    "            fliersize=1,\n",
    "           color='grey',\n",
    "            whis=1.5,\n",
    "            showfliers=True,\n",
    "            order=order,\n",
    "            ax=ax2)\n",
    "\n",
    "# change tick params\n",
    "ax2.tick_params(axis='both',\n",
    "                which='major',\n",
    "                labelsize=10,\n",
    "                pad=0.6,\n",
    "                rotation=90)\n",
    "\n",
    "# add grid lines\n",
    "ax2.grid(axis='y')\n",
    "\n",
    "# change y label\n",
    "ax2.set_ylabel('TSM (g m$^{-3}$)', fontsize=10)\n",
    "\n",
    "# remove x label\n",
    "ax2.set_xlabel('', fontsize=0)\n",
    "\n",
    "# add subplot name\n",
    "ax2.text(0, 125, 'b)', fontsize=10)\n",
    "\n",
    "# set the gridlines backwards\n",
    "ax2.set_axisbelow(True)\n",
    "\n",
    "# Zsd\n",
    "sns.boxplot(x=\"OWTs\",\n",
    "            y=\"secchi_m\",\n",
    "            data=lim,\n",
    "            fliersize=1,\n",
    "            color='grey',\n",
    "            whis=1.5,\n",
    "            showfliers=True,\n",
    "            order=order,\n",
    "            ax=ax3)\n",
    "\n",
    "# change tick params\n",
    "ax3.tick_params(axis='both',\n",
    "                which='major',\n",
    "                labelsize=10,\n",
    "                pad=0.6,\n",
    "                rotation=90)\n",
    "\n",
    "# add grid lines\n",
    "ax3.grid(axis='y')\n",
    "\n",
    "# change y label\n",
    "ax3.set_ylabel('Z$_{sd}$ (m)', fontsize=10)\n",
    "\n",
    "# remove x label\n",
    "ax3.set_xlabel('', fontsize=0)\n",
    "\n",
    "# add subplot name\n",
    "ax3.text(0.5, 4.4,'d)', fontsize=10)\n",
    "\n",
    "# set the gridlines backwards\n",
    "ax3.set_axisbelow(True)\n",
    "\n",
    "# CDOM\n",
    "sns.boxplot(x=\"OWTs\",\n",
    "            y=\"cdom_440_m_1\",\n",
    "            data=lim,\n",
    "            fliersize=1,\n",
    "            color='grey',\n",
    "            whis=1.5,\n",
    "            showfliers=True,\n",
    "            order=order,\n",
    "            ax=ax4)\n",
    "\n",
    "# change tick params\n",
    "ax4.tick_params(axis='both',\n",
    "                which='major',\n",
    "                labelsize=10,\n",
    "                pad=0.6,\n",
    "                rotation=90)\n",
    "\n",
    "# add grid lines\n",
    "ax4.grid(axis='y')\n",
    "\n",
    "# change y label\n",
    "ax4.set_ylabel('CDOM (m$^{-1}$)', fontsize=10)\n",
    "\n",
    "# remove x label\n",
    "ax4.set_xlabel('', fontsize=0)\n",
    "\n",
    "# add subplot name\n",
    "ax4.text(0, 6, 'c)', fontsize=10)\n",
    "\n",
    "# set the gridlines backwards\n",
    "ax4.set_axisbelow(True)\n",
    "\n",
    "# remove or add spaces among subplots\n",
    "plt.tight_layout(pad=0.1, h_pad=0.01)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 5 Statistical tests"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.1 Shapiro test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shapiro p-values\n"
     ]
    },
    {
     "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>clorofila_a_µg_l_</th>\n",
       "      <th>mstotal_mg_l</th>\n",
       "      <th>cdom_440_m_1</th>\n",
       "      <th>secchi_m</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OWT 1</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWT 2</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWT 3</th>\n",
       "      <td>0.46</td>\n",
       "      <td>0.14</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWT 4</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.52</td>\n",
       "      <td>0.05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWT 5</th>\n",
       "      <td>0.35</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.55</td>\n",
       "      <td>0.36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWT 6</th>\n",
       "      <td>0.85</td>\n",
       "      <td>0.21</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWT 7</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.10</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWT 8</th>\n",
       "      <td>0.06</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.57</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       clorofila_a_µg_l_  mstotal_mg_l  cdom_440_m_1  secchi_m\n",
       "OWT 1               0.00          0.00          0.00      0.03\n",
       "OWT 2               0.00          0.00          0.00      0.13\n",
       "OWT 3               0.46          0.14          0.40      0.02\n",
       "OWT 4               0.00          0.30          0.52      0.05\n",
       "OWT 5               0.35          0.00          0.55      0.36\n",
       "OWT 6               0.85          0.21          0.04      0.66\n",
       "OWT 7               0.00          0.00          0.10      0.00\n",
       "OWT 8               0.06          0.00          0.01      0.57"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# library used\n",
    "from scipy.stats import shapiro\n",
    "\n",
    "# dictionary for saving the output\n",
    "shap_table = {}\n",
    "\n",
    "for x in ['OWT 1', 'OWT 2', 'OWT 3', 'OWT 4', 'OWT 5', 'OWT 6', 'OWT 7', 'OWT 8'] :\n",
    "    \n",
    "    subset = lim[lim.loc[:, 'OWTs'] == x]    \n",
    "    shap_list = []\n",
    "    \n",
    "    for y in ['clorofila_a_µg_l_', 'mstotal_mg_l', 'cdom_440_m_1', 'secchi_m']:\n",
    "        \n",
    "        shap_res = np.round(shapiro(subset.loc[:,y].dropna())[1], 2)\n",
    "        shap_list.append(shap_res)\n",
    "        \n",
    "    shap_table[x] = shap_list\n",
    "\n",
    "print('Shapiro p-values')\n",
    "pd.DataFrame(shap_table.values(),\n",
    "             index=shap_table.keys(),\n",
    "            columns = ['clorofila_a_µg_l_', 'mstotal_mg_l', 'cdom_440_m_1', 'secchi_m'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.2 Kurskal test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Kruskal p-values\n"
     ]
    },
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Kruskal p-value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>clorofila_a_µg_l_</th>\n",
       "      <td>5.972417e-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mstotal_mg_l</th>\n",
       "      <td>7.208428e-40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cdom_440_m_1</th>\n",
       "      <td>1.405944e-33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>secchi_m</th>\n",
       "      <td>3.983034e-50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   Kruskal p-value\n",
       "clorofila_a_µg_l_     5.972417e-25\n",
       "mstotal_mg_l          7.208428e-40\n",
       "cdom_440_m_1          1.405944e-33\n",
       "secchi_m              3.983034e-50"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# library used\n",
    "from scipy.stats import kruskal\n",
    "\n",
    "# dictionary for saving the results\n",
    "kruskal_table = {}\n",
    "\n",
    "for z in ['clorofila_a_µg_l_', 'mstotal_mg_l', 'cdom_440_m_1', 'secchi_m']:\n",
    "    \n",
    "    subset = lim.loc[:, ['OWTs', z]]\n",
    "    owt_table = {}\n",
    "    i = 1\n",
    "    for x in ['OWT 1', 'OWT 2', 'OWT 3', 'OWT 4', 'OWT 5', 'OWT 6', 'OWT 7', 'OWT 8']:\n",
    "    \n",
    "        subset2 = subset[subset.loc[:, 'OWTs'] == x].drop('OWTs', axis=1)\n",
    "        owt_table[i] = subset2.dropna().values\n",
    "        i = i + 1\n",
    "    \n",
    "    kruskal_res = kruskal(owt_table[1],\n",
    "                         owt_table[2],\n",
    "                         owt_table[3],\n",
    "                         owt_table[4],\n",
    "                         owt_table[5],\n",
    "                         owt_table[6],\n",
    "                         owt_table[7],\n",
    "                         owt_table[8])[1]\n",
    "    kruskal_table[z] = kruskal_res\n",
    "\n",
    "print('Kruskal p-values')\n",
    "pd.DataFrame(kruskal_table.values(),\n",
    "            index=kruskal_table.keys(),\n",
    "            columns=['Kruskal p-value'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5.3 Mann-Whitney U test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mann-Whitney p-values \n",
      "\n",
      "clorofila_a_µg_l_\n"
     ]
    },
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       "      <th></th>\n",
       "      <th>OWT 1</th>\n",
       "      <th>OWT 2</th>\n",
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       "      <th>OWT 4</th>\n",
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       "      <td>0.07</td>\n",
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       "      <td>0.51</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWT 4</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.51</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWT 5</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWT 6</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWT 7</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWT 8</th>\n",
       "      <td>0.11</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.48</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       OWT 1  OWT 2  OWT 3  OWT 4  OWT 5  OWT 6  OWT 7  OWT 8\n",
       "OWT 1   1.00   0.07   0.00   0.00   0.00   0.00   0.01   0.11\n",
       "OWT 2   0.07   1.00   0.00   0.00   0.00   0.00   0.00   0.02\n",
       "OWT 3   0.00   0.00   0.99   0.51   0.00   0.00   0.00   0.00\n",
       "OWT 4   0.00   0.00   0.51   0.98   0.00   0.00   0.00   0.00\n",
       "OWT 5   0.00   0.00   0.00   0.00   0.95   0.00   0.00   0.00\n",
       "OWT 6   0.00   0.00   0.00   0.00   0.00   0.98   0.00   0.28\n",
       "OWT 7   0.01   0.00   0.00   0.00   0.00   0.00   1.00   0.48\n",
       "OWT 8   0.11   0.02   0.00   0.00   0.00   0.28   0.48   0.98"
      ]
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     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "mstotal_mg_l\n"
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       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>OWT 5</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWT 6</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>OWT 7</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.02</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.11</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWT 8</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       OWT 1  OWT 2  OWT 3  OWT 4  OWT 5  OWT 6  OWT 7  OWT 8\n",
       "OWT 1   1.00   0.00   0.00   0.01   0.00   0.00   0.00   0.00\n",
       "OWT 2   0.00   1.00   0.00   0.57   0.00   0.00   0.00   0.00\n",
       "OWT 3   0.00   0.00   0.99   0.00   0.01   0.00   0.02   0.00\n",
       "OWT 4   0.01   0.57   0.00   0.98   0.00   0.00   0.00   0.00\n",
       "OWT 5   0.00   0.00   0.01   0.00   0.95   0.00   0.11   0.00\n",
       "OWT 6   0.00   0.00   0.00   0.00   0.00   0.98   0.00   0.00\n",
       "OWT 7   0.00   0.00   0.02   0.00   0.11   0.00   1.00   0.00\n",
       "OWT 8   0.00   0.00   0.00   0.00   0.00   0.00   0.00   0.98"
      ]
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    },
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     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "cdom_440_m_1\n"
     ]
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       "      <td>0.58</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWT 4</th>\n",
       "      <td>0.09</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWT 5</th>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWT 6</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWT 7</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.78</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWT 8</th>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       OWT 1  OWT 2  OWT 3  OWT 4  OWT 5  OWT 6  OWT 7  OWT 8\n",
       "OWT 1   1.00   0.00   0.00   0.09   0.01   0.00   0.00   0.00\n",
       "OWT 2   0.00   1.00   0.58   0.00   0.00   0.00   0.70   0.00\n",
       "OWT 3   0.00   0.58   0.99   0.00   0.00   0.00   0.78   0.00\n",
       "OWT 4   0.09   0.00   0.00   0.98   0.01   0.00   0.00   0.00\n",
       "OWT 5   0.01   0.00   0.00   0.01   0.95   0.00   0.00   0.13\n",
       "OWT 6   0.00   0.00   0.00   0.00   0.00   0.98   0.00   0.00\n",
       "OWT 7   0.00   0.70   0.78   0.00   0.00   0.00   1.00   0.00\n",
       "OWT 8   0.00   0.00   0.00   0.00   0.13   0.00   0.00   0.98"
      ]
     },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "\n",
      "secchi_m\n"
     ]
    },
    {
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       "      <th></th>\n",
       "      <th>OWT 1</th>\n",
       "      <th>OWT 2</th>\n",
       "      <th>OWT 3</th>\n",
       "      <th>OWT 4</th>\n",
       "      <th>OWT 5</th>\n",
       "      <th>OWT 6</th>\n",
       "      <th>OWT 7</th>\n",
       "      <th>OWT 8</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>OWT 1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
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       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>OWT 2</th>\n",
       "      <td>0.0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
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       "    <tr>\n",
       "      <th>OWT 3</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWT 4</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWT 5</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWT 6</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWT 7</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.13</td>\n",
       "      <td>0.00</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>OWT 8</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.98</td>\n",
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      "text/plain": [
       "       OWT 1  OWT 2  OWT 3  OWT 4  OWT 5  OWT 6  OWT 7  OWT 8\n",
       "OWT 1    1.0   0.00   0.00   0.00   0.00   0.00   0.00   0.00\n",
       "OWT 2    0.0   1.00   0.00   0.00   0.00   0.01   0.00   0.00\n",
       "OWT 3    0.0   0.00   0.99   0.00   0.91   0.00   0.00   0.00\n",
       "OWT 4    0.0   0.00   0.00   0.98   0.00   0.00   0.00   0.00\n",
       "OWT 5    0.0   0.00   0.91   0.00   0.94   0.01   0.13   0.00\n",
       "OWT 6    0.0   0.01   0.00   0.00   0.01   0.98   0.00   0.00\n",
       "OWT 7    0.0   0.00   0.00   0.00   0.13   0.00   1.00   0.00\n",
       "OWT 8    0.0   0.00   0.00   0.00   0.00   0.00   0.00   0.98"
      ]
     },
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    },
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     "output_type": "stream",
     "text": [
      "\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# library used\n",
    "from scipy.stats import mannwhitneyu\n",
    "from IPython.core import display as ICD\n",
    "\n",
    "# dictionary for saving results\n",
    "utest_res = {}\n",
    "\n",
    "for z in ['clorofila_a_µg_l_', 'mstotal_mg_l', 'cdom_440_m_1', 'secchi_m']:\n",
    "    \n",
    "    utest_table = {}\n",
    "    for x in ['OWT 1', 'OWT 2', 'OWT 3', 'OWT 4', 'OWT 5', 'OWT 6', 'OWT 7', 'OWT 8']:\n",
    "\n",
    "        subset1 = lim[lim.loc[:, 'OWTs'] == x].loc[:, z].dropna().values        \n",
    "        utest_list = []\n",
    "        \n",
    "        for y in ['OWT 1', 'OWT 2', 'OWT 3', 'OWT 4', 'OWT 5', 'OWT 6', 'OWT 7', 'OWT 8']:\n",
    "\n",
    "            subset2 = lim[lim.loc[:, 'OWTs'] == y].loc[:, z].dropna().values\n",
    "            u_res = np.round(mannwhitneyu(subset1, subset2, alternative='two-sided')[1], 2)\n",
    "            utest_list.append(u_res)\n",
    "            \n",
    "        utest_table[x] = utest_list\n",
    "        \n",
    "    utest_res[z] = pd.DataFrame(utest_table, index=utest_table.keys())\n",
    "\n",
    "print('Mann-Whitney p-values \\n')\n",
    "for x in utest_res.keys():\n",
    "    print(x)\n",
    "    ICD.display(utest_res[x])\n",
    "    print('\\n')"
   ]
  }
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