23. Allstats Module.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy as sp\n",
    "from scipy import stats as st\n",
    "import pylab as pl\n",
    "import matplotlib.pyplot as plt\n",
    "class AllStats():\n",
    "    def __init__(self,yourarray):\n",
    "        v=yourarray\n",
    "        self.mini= float(min(v))\n",
    "        self.q1 = float(np.percentile(v, 25))\n",
    "        self.q2 = float(np.percentile(v, 50))\n",
    "        self.q3 = float(np.percentile(v, 75))\n",
    "        self.maxi= float(max(v))\n",
    "        self.mean= float(np.mean(v))\n",
    "        self.median= float(np.median(v))\n",
    "        self.mode= st.mode(v)\n",
    "        self.count= float(v.count())\n",
    "        self.range= float(max(v)-min(v))\n",
    "        self.iqr = float( np.percentile(v, 75)-np.percentile(v, 25) )\n",
    "        self.std= float(np.std(v))\n",
    "        self.var= float(np.var(v))\n",
    "        self.coeffvar= float(((np.std(v))/(np.mean(v)))*100)\n",
    "        \n",
    "    def summarystats(self, v):\n",
    "        mylist= [self.mini, self.q1, self.q2, self.q3, self.maxi, self.mean, self.median, self.mode, self.count,\n",
    "                 self.range, self.iqr, self.std, self.var, self.coeffvar]\n",
    "        d= {'Summary Statistics': mylist}\n",
    "        listnames = ['Min', 'First Quartile: Q1 (25%)', 'Second Quartile: Q2 (50%)', 'Third Quartile: Q3 (75%)', 'Max',\n",
    "                    'Mean', 'Median', 'Mode', 'Count', 'Range', 'IQR', 'StdDev', 'Variance', 'Coeff. of Variation']\n",
    "\n",
    "        return ( pd.DataFrame(data= d, index= listnames) )  \n",
    "    \n",
    "    def outliers_zscore(self,v):\n",
    "        threshold =3.0\n",
    "        self.zscores = [(i- self.mean)/self.std for i in v]\n",
    "        self.outliers = np.where(np.abs(self.zscores) > threshold)\n",
    "        return self\n",
    "    def outliers_modified_zscore(self,v):\n",
    "        threshold = 3.5\n",
    "        median_y = self.median\n",
    "        median_absolute_deviation_y = np.median([np.abs(y - median_y) for y in v])\n",
    "        modified_z_scores = [0.6745 * (y - median_y) / median_absolute_deviation_y\n",
    "                             for y in ys]\n",
    "        outliers= np.where(np.abs(modified_z_scores) > threshold)\n",
    "        return self\n",
    "    def outliers_iqr(self,v):\n",
    "        quartile_1, quartile_3 = np.percentile(v, [25, 75])\n",
    "        iqr = quartile_3 - quartile_1\n",
    "        lower_bound = quartile_1 - (iqr * 1.5)\n",
    "        upper_bound = quartile_3 + (iqr * 1.5)\n",
    "        self.outliers= np.where((v > upper_bound) | (v < lower_bound))\n",
    "        return self\n",
    "\n",
    "    def outliers_std(self,v):\n",
    "        self.outliersmean=[]\n",
    "        self.outliersmedian=[]\n",
    "        for x in v:\n",
    "            if ( (x < (mean - (2 * std))) or (x > (mean + (2 * std))) ) :\n",
    "                self.outliersmean.append(x)\n",
    "            if ( (x < (med - (2 * std))) or (x > (med + (2 * std))) ) : \n",
    "                self.outliersmean.append(x)\n",
    "            else:\n",
    "                continue\n",
    "        #print('outliers_by_mean :', outliers1)   \n",
    "        #print('outliers_by_median :', outliers2)\n",
    "        return self\n",
    "    \n",
    "    def distribution_mean(self, v):\n",
    "        #v = sorted(v)\n",
    "        fit = st.norm.pdf(sorted(v), self.mean, self.std)\n",
    "        pl.plot(sorted(v),fit,'--')\n",
    "        pl.hist(sorted(v),normed=True)\n",
    "        plt.axvline( (self.mean- 2*(self.std) ))\n",
    "        plt.axvline( (self.mean + 2*(self.std) ))\n",
    "        pl.show()      \n",
    "    def distribution_med(self, v):\n",
    "        #v = sorted(v)\n",
    "        fit = st.norm.pdf(sorted(v),self.median, self.std)\n",
    "        pl.plot(sorted(v),fit,'--')\n",
    "        pl.hist(sorted(v),normed=True)\n",
    "        plt.axvline( (self.median- 2*(self.std) ))\n",
    "        plt.axvline( (self.median + 2*(self.std) ))\n",
    "        pl.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import scipy as sp\n",
    "from scipy import stats as st\n",
    "import pylab as pl\n",
    "import matplotlib.pyplot as plt\n",
    "class AllStats():\n",
    "    def __init__(self,yourarray):\n",
    "        v=yourarray\n",
    "        self.mini= float(min(v))\n",
    "        self.q1 = float(np.percentile(v, 25))\n",
    "        self.q2 = float(np.percentile(v, 50))\n",
    "        self.q3 = float(np.percentile(v, 75))\n",
    "        self.maxi= float(max(v))\n",
    "        self.mean= float(np.mean(v))\n",
    "        self.median= float(np.median(v))\n",
    "        self.mode= st.mode(v)\n",
    "        self.count= float(v.count())\n",
    "        self.range= float(max(v)-min(v))\n",
    "        self.iqr = float( np.percentile(v, 75)-np.percentile(v, 25) )\n",
    "        self.std= float(np.std(v))\n",
    "        self.var= float(np.var(v))\n",
    "        self.coeffvar= float(((np.std(v))/(np.mean(v)))*100)\n",
    "        self.skewness = float(st.skew(v))\n",
    "        self.skewtest = st.skewtest(v)\n",
    "        self.kurtosis= float(st.kurtosis(v))\n",
    "        self.kurttest= st.kurtosistest(v)\n",
    "        \n",
    "        \n",
    "    def summarystats(self, v):\n",
    "        mylist= [self.mini, self.q1, self.q2, self.q3, self.maxi, self.mean, self.median, self.mode, self.count,\n",
    "                 self.range, self.iqr, self.std, self.var, self.coeffvar, self.skewness, self.skewtest, \n",
    "                  self.kurtosis, self.kurttest]\n",
    "        d= {'Summary Statistics': mylist}\n",
    "        listnames = ['Min', 'First Quartile: Q1 (25%)', 'Second Quartile: Q2 (50%)', 'Third Quartile: Q3 (75%)', 'Max',\n",
    "                    'Mean', 'Median', 'Mode', 'Count', 'Range', 'IQR', 'StdDev', 'Variance', 'Coeff. of Variation',\n",
    "                    'Skewness', 'SkewnessTest', 'Kurtosis', 'KurtosisTest']\n",
    "\n",
    "        return ( pd.DataFrame(data= d, index= listnames) )  \n",
    "    \n",
    "    def outliers_zscore(self,v):\n",
    "        threshold =3.0\n",
    "        self.zscores = [(i- self.mean)/self.std for i in v]\n",
    "        self.outliers = np.where(np.abs(self.zscores) > threshold)\n",
    "        return self\n",
    "    def outliers_modified_zscore(self,v):\n",
    "        threshold = 3.5\n",
    "        median_y = self.median\n",
    "        median_absolute_deviation_y = np.median([np.abs(y - median_y) for y in v])\n",
    "        modified_z_scores = [0.6745 * (y - median_y) / median_absolute_deviation_y\n",
    "                             for y in ys]\n",
    "        outliers= np.where(np.abs(modified_z_scores) > threshold)\n",
    "        return self\n",
    "    def outliers_iqr(self,v):\n",
    "        quartile_1, quartile_3 = np.percentile(v, [25, 75])\n",
    "        iqr = quartile_3 - quartile_1\n",
    "        lower_bound = quartile_1 - (iqr * 1.5)\n",
    "        upper_bound = quartile_3 + (iqr * 1.5)\n",
    "        self.outliers= np.where((v > upper_bound) | (v < lower_bound))\n",
    "        return self\n",
    "\n",
    "    def outliers_std(self,v):\n",
    "        self.outliersmean=[]\n",
    "        self.outliersmedian=[]\n",
    "        for x in v:\n",
    "            if ( (x < (mean - (2 * std))) or (x > (mean + (2 * std))) ) :\n",
    "                self.outliersmean.append(x)\n",
    "            if ( (x < (med - (2 * std))) or (x > (med + (2 * std))) ) : \n",
    "                self.outliersmedian.append(x)\n",
    "            else:\n",
    "                continue\n",
    "        #print('outliers_by_mean :', outliers1)   \n",
    "        #print('outliers_by_median :', outliers2)\n",
    "        return self\n",
    "    \n",
    "    def distribution_mean(self, v):\n",
    "        #v = sorted(v)\n",
    "        fit = st.norm.pdf(sorted(v), self.mean, self.std)\n",
    "        pl.plot(sorted(v),fit,'--')\n",
    "        pl.hist(sorted(v),normed=True)\n",
    "        plt.axvline( (self.mean- 2*(self.std) ))\n",
    "        plt.axvline( (self.mean + 2*(self.std) ))\n",
    "        pl.show()      \n",
    "    def distribution_med(self, v):\n",
    "        #v = sorted(v)\n",
    "        fit = st.norm.pdf(sorted(v),self.median, self.std)\n",
    "        pl.plot(sorted(v),fit,'--')\n",
    "        pl.hist(sorted(v),normed=True)\n",
    "        plt.axvline( (self.median- 2*(self.std) ))\n",
    "        plt.axvline( (self.median + 2*(self.std) ))\n",
    "        pl.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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