AN_MannKendall.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import pylab as plt\n",
    "import xuleta as xu\n",
    "from scipy import stats\n",
    "from functools import reduce\n",
    "import os\n",
    "month_names = pd.date_range(start='2016-01-01', periods=12, freq='MS').strftime('%b')\n",
    "\n",
    "\n",
    "#for SQL\n",
    "from sqlalchemy import create_engine\n",
    "engine = create_engine('postgresql://denis:_________@localhost:5432/drought')\n",
    "fout = '/Users/denismariano/pcloud/PB_carlos/Document/elsevier_sample/images/'#mac\n",
    "fout = '/home/denis/pcloud/PB_carlos/Document/elsevier_sample/images/'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "https://stats.stackexchange.com/questions/235039/statsmodels-clarification-on-varp-results-attribute-results-forecast-interva"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# PARTIAL CORRELATION ANALYSIS\n",
    "import numpy as np\n",
    "from scipy import stats, linalg\n",
    "\n",
    "def partial_corr(C):\n",
    "    \"\"\"\n",
    "    Returns the sample linear partial correlation coefficients between pairs of variables in C, controlling \n",
    "    for the remaining variables in C.\n",
    "    Parameters\n",
    "    ----------\n",
    "    C : array-like, shape (n, p)\n",
    "        Array with the different variables. Each column of C is taken as a variable\n",
    "    Returns\n",
    "    -------\n",
    "    P : array-like, shape (p, p)\n",
    "        P[i, j] contains the partial correlation of C[:, i] and C[:, j] controlling\n",
    "        for the remaining variables in C.\n",
    "    \"\"\"\n",
    "    \n",
    "    C = np.asarray(C)\n",
    "    p = C.shape[1]\n",
    "    P_corr = np.zeros((p, p), dtype=np.float)\n",
    "    for i in range(p):\n",
    "        P_corr[i, i] = 1\n",
    "        for j in range(i+1, p):\n",
    "            idx = np.ones(p, dtype=np.bool)\n",
    "            idx[i] = False\n",
    "            idx[j] = False\n",
    "            beta_i = linalg.lstsq(C[:, idx], C[:, j])[0]\n",
    "            beta_j = linalg.lstsq(C[:, idx], C[:, i])[0]\n",
    "\n",
    "            res_j = C[:, j] - C[:, idx].dot( beta_i)\n",
    "            res_i = C[:, i] - C[:, idx].dot(beta_j)\n",
    "            \n",
    "            corr = stats.pearsonr(res_i, res_j)[0]\n",
    "            P_corr[i, j] = corr\n",
    "            P_corr[j, i] = corr\n",
    "        \n",
    "    return P_corr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Mann-Kendall test\n",
    "import numpy as np  \n",
    "from scipy.stats import norm, mstats\n",
    "import statsmodels.api as sm  \n",
    "import seaborn as sns  \n",
    "\n",
    "\n",
    "def mk_test(x, alpha = 0.05):  \n",
    "    \"\"\"   \n",
    "    Input:\n",
    "        x:   a vector of data\n",
    "        alpha: significance level (0.05 default)\n",
    "\n",
    "    Output:\n",
    "        trend: tells the trend (increasing, decreasing or no trend)\n",
    "        h: True (if trend is present) or False (if trend is absence)\n",
    "        p: p value of the significance test\n",
    "        z: normalized test statistics \n",
    "\n",
    "    Examples\n",
    "    --------\n",
    "      >>> x = np.random.rand(100)\n",
    "      >>> trend,h,p,z = mk_test(x,0.05) \n",
    "    \"\"\"\n",
    "    n = len(x)\n",
    "\n",
    "    # calculate S \n",
    "    s = 0\n",
    "    for k in range(n-1):\n",
    "        for j in range(k+1,n):\n",
    "            s += np.sign(x[j] - x[k])\n",
    "\n",
    "    # calculate the unique data\n",
    "    unique_x = np.unique(x)\n",
    "    g = len(unique_x)\n",
    "\n",
    "    # calculate the var(s)\n",
    "    if n == g: # there is no tie\n",
    "        var_s = (n*(n-1)*(2*n+5))/18\n",
    "    else: # there are some ties in data\n",
    "        tp = np.zeros(unique_x.shape)\n",
    "        for i in range(len(unique_x)):\n",
    "            tp[i] = sum(unique_x[i] == x)\n",
    "        var_s = (n*(n-1)*(2*n+5) + np.sum(tp*(tp-1)*(2*tp+5)))/18\n",
    "\n",
    "    if s>0:\n",
    "        z = (s - 1)/np.sqrt(var_s)\n",
    "    elif s == 0:\n",
    "            z = 0\n",
    "    elif s<0:\n",
    "        z = (s + 1)/np.sqrt(var_s)\n",
    "\n",
    "    # calculate the p_value\n",
    "    p = 2*(1-norm.cdf(abs(z))) # two tail test\n",
    "    h = abs(z) > norm.ppf(1-alpha/2) \n",
    "\n",
    "    if (z<0) and h:\n",
    "        trend = 'decreasing'\n",
    "    elif (z>0) and h:\n",
    "        trend = 'increasing'\n",
    "    else:\n",
    "        trend = 'no trend'\n",
    "    l = [trend,round(p, 3),round(z,3)]\n",
    "    \n",
    "    return l#trend, h, p, z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#FUNCTION to calculate weighted average\n",
    "def weightaverage(var,lista):\n",
    "    '''\n",
    "        var = dataframe\n",
    "        lista = group of columns\n",
    "        ux = upper extreme value - it can be set to None \n",
    "        lx = lower extreme value - it can be set to None\n",
    "    '''\n",
    "    import pandas as pd\n",
    "    pd.options.mode.chained_assignment = None #error omit\n",
    "    a = aux[aux['geocodig_m'].isin(lista)]\n",
    "    a['w'] = a.area/sum(a.area)\n",
    "    a = a.drop(['sigla','nome_munic','area'],axis=1)\n",
    "    a.T.to_csv('Xaux.csv',header=False,index=None)\n",
    "    aw = pd.read_csv('Xaux.csv')\n",
    "    os.remove('Xaux.csv')\n",
    "    aw = aw.sort_index(axis=1)\n",
    "    l2 =  list(map(float, aw.columns))\n",
    "\n",
    "#weight values and get the weighted average\n",
    "    var_aw = pd.DataFrame(var[l2].values*aw.values, columns=aw.columns,index=var[l2].index)\n",
    "    var_final = var_aw.sum(axis=1,skipna=True)\n",
    "    var_final.index = pd.DatetimeIndex(var_final.index)\n",
    "    #var_final.columns = ['values']\n",
    "    return var_final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def piv_table(var,lista,resample='M'):\n",
    "    region_var = weightaverage(var,lista)\n",
    "    region_var = pd.DataFrame(region_var)\n",
    "    region_var.columns = ['values']\n",
    "    region_var = region_var.resample('M').mean()\n",
    "\n",
    "    region_var['year'] = region_var.index.year\n",
    "    region_var['month'] = region_var.index.month\n",
    "    region_var_piv = region_var.pivot(index='year', columns='month', values='values')\n",
    "    region_var_piv.columns = month_names\n",
    "    return region_var_piv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "cern = [2300705,2304277,2306009,2306702,2306801,2306900,2307601,2308708,2311231,2311504,2312502,2313104,2400505,\n",
    "         2400604,2400901,2401008,2401305,2401453,2402303,2403707,2403905,2404002,2404309,2404903,2405207,2405603,\n",
    "         2405900,2406007,2406908,2407005,2407252,2407302,2407401,2407609,2408003,2408409,2408607,2409308,2409407,\n",
    "         2410009,2410207,2410603,2410702,2410801,2411007,2411908,2413409,2413557,2413607,2413805,2414100,2414308,\n",
    "         2414456,2414506,2414605,2414902,2502003,2502300,2502805,2502904,2504306,2507408,2509370,2510907,2512036,\n",
    "         2512804,2513208,2513653,2513901,2514651,2516904]\n",
    "nd6 = [2300705,2306702,2306801,2306900,2308708] #\n",
    "nd5 = [2301505,2305605,2313302]\n",
    "nd4 = [2601607,2603009,2605707,2607406,2610400,2611002,2612208]\n",
    "nd3 = [2400109,2402402,2403004,2403103,2403400,2408904,2501708,2503407,2503902,2504702,2504074,\n",
    "      2504850,2505006,2506202,2506509,2501105,2510303,2510659,2510808,2511103,2511400,2513406,\n",
    "       2513851,2514107,2514701,2514800,2514909,2515401,2515500,2516508,2517100] #2516755\n",
    "nd2 = [2302800,2306108,2308377,2312205]\n",
    "nd1 = [2201309,2202901,2204402,2206605,2208700,2208858,2209757]\n",
    "pi1 = [2202307,2202505,2204550,2205532,2210953]\n",
    "pi2 = [2202109,2202778,2203859,2204907,2207009,2209104,2209500,2210805,2211506,2211704]\n",
    "ba1 = [2903805,2911907,2912608,2914000,2919009,2919603,2920809,2927200,2901304,2915007,2922854]\n",
    "ba2 = [2915353,2918506,2929255,2930204]\n",
    "al1 = [2600500,2606507,2607505,2700706,2700904,2701209,2701605,2701803,2702355,2702504,2703304,\n",
    "       2703403,2704401,2704609,2705408,2705705,2705804,2706000,2706109,2706208,2706406,2707107,\n",
    "       2707206,2708006,2708402,2708956]\n",
    "\n",
    "lr = [nd1,nd2,nd3,nd4,nd5,nd6,pi1,pi2,ba1,ba2,cern,al1]\n",
    "lrn = ['nd1','nd2','nd3','nd4','nd5','nd6','pi1','pi2','ba1','ba2','cern','al1']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 32.5 s, sys: 1.56 s, total: 34 s\n",
      "Wall time: 50.6 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "#FROM SQL Database\n",
    "# Data ananlysis V2 - resubmission\n",
    "lai = pd.read_sql(\"SELECT * FROM consultas_br.lai_neb\",engine).pivot_table(index=\"acquisition\",\n",
    "                   columns=\"geocodig_m\",\n",
    "                   values=\"mean\").interpolate()*0.01\n",
    "\n",
    "et = pd.read_sql(\"SELECT * FROM consultas_br.et_neb\",engine).pivot_table(index=\"acquisition\",\n",
    "                   columns=\"geocodig_m\",\n",
    "                   values=\"mean\").interpolate()*0.1\n",
    "esi = pd.read_sql(\"SELECT * FROM consultas_br.esi_muni\",engine).pivot_table(index=\"acquisition\",\n",
    "                   columns=\"geocodig_m\",\n",
    "                   values=\"mean\").interpolate()\n",
    "esi.index = pd.DatetimeIndex(esi.index)\n",
    "albedo = pd.read_sql(\"SELECT * FROM consultas_br.albedo_neb\",engine).pivot_table(index=\"acquisition\",\n",
    "                   columns=\"geocodig_m\",\n",
    "                   values=\"mean\").interpolate()*0.001\n",
    "zprec = pd.read_sql(\"SELECT * FROM consultas_br.chirps_zscore_muni\",engine).pivot_table(index=\"acquisition\",\n",
    "                   columns=\"geocodig_m\",\n",
    "                   values=\"mean\").interpolate()\n",
    "#correcting some value for zprec\n",
    "zprec[zprec>5] = np.nan\n",
    "zprec[zprec<-5] = np.nan\n",
    "zprec = zprec.interpolate()\n",
    "aux = pd.read_sql(\"SELECT * FROM consultas_br.aux\",engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 13.8 s, sys: 404 ms, total: 14.2 s\n",
      "Wall time: 18.4 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "datafolder = \"../SQL/\"\n",
    "# Data ananlysis V2 - CSV version - resubmission\n",
    "lai = pd.read_csv(datafolder+\"drought_consultas_br_lai_neb.csv\").pivot_table(index=\"acquisition\",\n",
    "                   columns=\"geocodig_m\",\n",
    "                   values=\"mean\").interpolate()*0.01\n",
    "\n",
    "et = pd.read_csv(datafolder+\"drought_consultas_br_et_neb.csv\").pivot_table(index=\"acquisition\",\n",
    "                   columns=\"geocodig_m\",\n",
    "                   values=\"mean\").interpolate()*0.1\n",
    "esi = pd.read_csv(datafolder+\"drought_consultas_br_esi_muni.csv\").pivot_table(index=\"acquisition\",\n",
    "                   columns=\"geocodig_m\",\n",
    "                   values=\"mean\").interpolate()\n",
    "esi.index = pd.DatetimeIndex(esi.index)\n",
    "albedo = pd.read_csv(datafolder+\"drought_consultas_br_albedo_neb.csv\").pivot_table(index=\"acquisition\",\n",
    "                   columns=\"geocodig_m\",\n",
    "                   values=\"mean\").interpolate()*0.001\n",
    "zprec = pd.read_csv(datafolder+\"drought_consultas_br_chirps_zscore_muni.csv\").pivot_table(index=\"acquisition\",\n",
    "                   columns=\"geocodig_m\",\n",
    "                   values=\"mean\").interpolate()\n",
    "zprec[zprec>5] = np.nan\n",
    "zprec[zprec<-5] = np.nan\n",
    "zprec = zprec.interpolate()\n",
    "aux = pd.read_csv(datafolder+\"drought_consultas_br_aux.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Z-scores"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# MONTHLY\n",
    "def zscore_monthly(df,index=False,datecolumn='acquisition'):\n",
    "\n",
    "    import pandas as pd\n",
    "    import numpy as np\n",
    "\n",
    "    if index == False:\n",
    "        df.index = pd.DatetimeIndex(df[datecolumn])\n",
    "        df = df.drop(datecolumn,axis=1)\n",
    "    else:\n",
    "        df.index = pd.DatetimeIndex(df.index)\n",
    "\n",
    "    # CORE da function\n",
    "    mean=df.groupby(by=[df.index.month]).aggregate(np.nanmean)#[2]\n",
    "    std=df.groupby(by=[df.index.month]).aggregate(np.nanstd)#[2]\n",
    "\n",
    "    df2 = df.copy()\n",
    "    for i in df2.index:\n",
    "        y=i.year\n",
    "        d=i.dayofyear\n",
    "        m=i.month\n",
    "        df2[(df2.index.year==y) & (df2.index.dayofyear==d)] = (df2[(df2.index.year==y) & (df2.index.dayofyear==d)]- mean.loc[m])/std.loc[m]\n",
    "        df2.index.name = 'date'\n",
    "            \n",
    "    return df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CPU times: user 30.1 s, sys: 2.89 ms, total: 30.1 s\n",
      "Wall time: 30.1 s\n"
     ]
    }
   ],
   "source": [
    "%%time\n",
    "zlaim = zscore_monthly(lai,index=True)\n",
    "zalbedom = zscore_monthly(albedo,index=True)\n",
    "zetm = zscore_monthly(et,index=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "### TS decomposition and Mann-Kendall test for trends"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime as datetime  \n",
    "import statsmodels.api as sm  \n",
    "import seaborn as sns  \n",
    "lr = [nd1,nd2,nd3,nd4,nd5,cern,pi1+pi2,ba1+ba2]\n",
    "lrn = ['nd1','nd2','nd3','nd4','nd5','nd6+cern','pi1+pi2','ba1+ba2']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f3659450c18>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# zgpp al1\n",
    "reg = cern\n",
    "zgpp_reg = weightaverage(zgpp,reg)\n",
    "zgpp_reg = zgpp_reg.resample('M').mean()\n",
    "zgpp_reg = zgpp_reg.fillna(method='bfill')\n",
    "reszgpp = sm.tsa.seasonal_decompose(zgpp_reg.loc['2002-10-02':'2016-10-10'])#.loc['2003-02-02':'2016-10-10'])\n",
    "reszgpp.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['increasing', 0.0, 4.5789999999999997]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mk_test(reszgpp.trend.loc['2003-12-02':'2011-10-10'],alpha=0.05)#.loc['2003-01':'2016-07']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Region</th>\n",
       "      <th>Period</th>\n",
       "      <th>Trend</th>\n",
       "      <th>p-value</th>\n",
       "      <th>z stats</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>ND1</td>\n",
       "      <td>2011-2016</td>\n",
       "      <td>no trend</td>\n",
       "      <td>0.197</td>\n",
       "      <td>1.289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>ND2</td>\n",
       "      <td>2011-2016</td>\n",
       "      <td>increasing</td>\n",
       "      <td>0.007</td>\n",
       "      <td>2.711</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>ND3</td>\n",
       "      <td>2011-2016</td>\n",
       "      <td>increasing</td>\n",
       "      <td>0.019</td>\n",
       "      <td>2.355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>ND4</td>\n",
       "      <td>2011-2016</td>\n",
       "      <td>increasing</td>\n",
       "      <td>0.002</td>\n",
       "      <td>3.137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>ND5</td>\n",
       "      <td>2011-2016</td>\n",
       "      <td>increasing</td>\n",
       "      <td>0.034</td>\n",
       "      <td>2.124</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>ND6+CERN</td>\n",
       "      <td>2011-2016</td>\n",
       "      <td>no trend</td>\n",
       "      <td>0.174</td>\n",
       "      <td>-1.360</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>PI1+PI2</td>\n",
       "      <td>2011-2016</td>\n",
       "      <td>increasing</td>\n",
       "      <td>0.001</td>\n",
       "      <td>3.333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>BA1+BA2</td>\n",
       "      <td>2011-2016</td>\n",
       "      <td>increasing</td>\n",
       "      <td>0.000</td>\n",
       "      <td>5.217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Region     Period       Trend  p-value  z stats\n",
       "0       ND1  2011-2016    no trend    0.197    1.289\n",
       "1       ND2  2011-2016  increasing    0.007    2.711\n",
       "2       ND3  2011-2016  increasing    0.019    2.355\n",
       "3       ND4  2011-2016  increasing    0.002    3.137\n",
       "4       ND5  2011-2016  increasing    0.034    2.124\n",
       "5  ND6+CERN  2011-2016    no trend    0.174   -1.360\n",
       "6   PI1+PI2  2011-2016  increasing    0.001    3.333\n",
       "7   BA1+BA2  2011-2016  increasing    0.000    5.217"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(columns = ['Region','Period','Trend','p-value','z stats'])\n",
    "period = ['2011-2016']\n",
    "for reg,regn,i in zip(lr,lrn,range(len(lr))):\n",
    "    var_reg = weightaverage(zetm,reg)\n",
    "    var_reg.index = pd.DatetimeIndex(var_reg.index) #just in case\n",
    "    var_reg = var_reg.resample('M').mean()\n",
    "    var_reg = var_reg.fillna(method='bfill')\n",
    "    resvar = sm.tsa.seasonal_decompose(var_reg.loc['2011-10-02':'2016-10-10'])#.loc['2003-02-02':'2016-10-10'])\n",
    "    #'2011-10-02':'2016-04-20'\n",
    "    #'2003-04-02':'2016-04-20'\n",
    "    teste = mk_test(resvar.trend.loc['2012-04-02':'2016-04-20'],alpha=0.05)\n",
    "    regn = [regn.upper()]\n",
    "    final = regn+period+teste\n",
    "    df.loc[i] = final\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\\begin{tabular}{llllrr}\n",
      "\\toprule\n",
      "{} &    Region &     Period &       Trend &  p-value &  z stats \\\\\n",
      "\\midrule\n",
      "0 &       ND1 &  2011-2016 &    no trend &    0.197 &    1.289 \\\\\n",
      "1 &       ND2 &  2011-2016 &  increasing &    0.007 &    2.711 \\\\\n",
      "2 &       ND3 &  2011-2016 &  increasing &    0.019 &    2.355 \\\\\n",
      "3 &       ND4 &  2011-2016 &  increasing &    0.002 &    3.137 \\\\\n",
      "4 &       ND5 &  2011-2016 &  increasing &    0.034 &    2.124 \\\\\n",
      "5 &  ND6+CERN &  2011-2016 &    no trend &    0.174 &   -1.360 \\\\\n",
      "6 &   PI1+PI2 &  2011-2016 &  increasing &    0.001 &    3.333 \\\\\n",
      "7 &   BA1+BA2 &  2011-2016 &  increasing &    0.000 &    5.217 \\\\\n",
      "\\bottomrule\n",
      "\\end{tabular}\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(df.to_latex())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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