Distrubtions and Plot Library.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Distrubtions and Plot Library \n",
    "This Module is used for making plots and testing the given column for various type of distributions like normal , welisberg , Exponential , Logistic etc . Find outliers in one column based on Z test "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "//anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning: Matplotlib is building the font cache using fc-list. This may take a moment.\n",
      "  warnings.warn('Matplotlib is building the font cache using fc-list. This may take a moment.')\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Customer</th>\n",
       "      <th>State</th>\n",
       "      <th>Customer Lifetime Value</th>\n",
       "      <th>Response</th>\n",
       "      <th>Coverage</th>\n",
       "      <th>Education</th>\n",
       "      <th>Effective To Date</th>\n",
       "      <th>EmploymentStatus</th>\n",
       "      <th>Gender</th>\n",
       "      <th>Income</th>\n",
       "      <th>...</th>\n",
       "      <th>Months Since Policy Inception</th>\n",
       "      <th>Number of Open Complaints</th>\n",
       "      <th>Number of Policies</th>\n",
       "      <th>Policy Type</th>\n",
       "      <th>Policy</th>\n",
       "      <th>Renew Offer Type</th>\n",
       "      <th>Sales Channel</th>\n",
       "      <th>Total Claim Amount</th>\n",
       "      <th>Vehicle Class</th>\n",
       "      <th>Vehicle Size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>BU79786</td>\n",
       "      <td>Washington</td>\n",
       "      <td>2763.519279</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>2/24/11</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>56274</td>\n",
       "      <td>...</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L3</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Agent</td>\n",
       "      <td>384.811147</td>\n",
       "      <td>Two-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>QZ44356</td>\n",
       "      <td>Arizona</td>\n",
       "      <td>6979.535903</td>\n",
       "      <td>No</td>\n",
       "      <td>Extended</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>1/31/11</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>F</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>42</td>\n",
       "      <td>0</td>\n",
       "      <td>8</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L3</td>\n",
       "      <td>Offer3</td>\n",
       "      <td>Agent</td>\n",
       "      <td>1131.464935</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>AI49188</td>\n",
       "      <td>Nevada</td>\n",
       "      <td>12887.431650</td>\n",
       "      <td>No</td>\n",
       "      <td>Premium</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>2/19/11</td>\n",
       "      <td>Employed</td>\n",
       "      <td>F</td>\n",
       "      <td>48767</td>\n",
       "      <td>...</td>\n",
       "      <td>38</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L3</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Agent</td>\n",
       "      <td>566.472247</td>\n",
       "      <td>Two-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>WW63253</td>\n",
       "      <td>California</td>\n",
       "      <td>7645.861827</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>1/20/11</td>\n",
       "      <td>Unemployed</td>\n",
       "      <td>M</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>65</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>Corporate Auto</td>\n",
       "      <td>Corporate L2</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Call Center</td>\n",
       "      <td>529.881344</td>\n",
       "      <td>SUV</td>\n",
       "      <td>Medsize</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>HB64268</td>\n",
       "      <td>Washington</td>\n",
       "      <td>2813.692575</td>\n",
       "      <td>No</td>\n",
       "      <td>Basic</td>\n",
       "      <td>Bachelor</td>\n",
       "      <td>2/3/11</td>\n",
       "      <td>Employed</td>\n",
       "      <td>M</td>\n",
       "      <td>43836</td>\n",
       "      <td>...</td>\n",
       "      <td>44</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>Personal Auto</td>\n",
       "      <td>Personal L1</td>\n",
       "      <td>Offer1</td>\n",
       "      <td>Agent</td>\n",
       "      <td>138.130879</td>\n",
       "      <td>Four-Door Car</td>\n",
       "      <td>Medsize</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 24 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "  Customer       State  Customer Lifetime Value Response  Coverage Education  \\\n",
       "0  BU79786  Washington              2763.519279       No     Basic  Bachelor   \n",
       "1  QZ44356     Arizona              6979.535903       No  Extended  Bachelor   \n",
       "2  AI49188      Nevada             12887.431650       No   Premium  Bachelor   \n",
       "3  WW63253  California              7645.861827       No     Basic  Bachelor   \n",
       "4  HB64268  Washington              2813.692575       No     Basic  Bachelor   \n",
       "\n",
       "  Effective To Date EmploymentStatus Gender  Income     ...       \\\n",
       "0           2/24/11         Employed      F   56274     ...        \n",
       "1           1/31/11       Unemployed      F       0     ...        \n",
       "2           2/19/11         Employed      F   48767     ...        \n",
       "3           1/20/11       Unemployed      M       0     ...        \n",
       "4            2/3/11         Employed      M   43836     ...        \n",
       "\n",
       "  Months Since Policy Inception Number of Open Complaints  Number of Policies  \\\n",
       "0                             5                         0                   1   \n",
       "1                            42                         0                   8   \n",
       "2                            38                         0                   2   \n",
       "3                            65                         0                   7   \n",
       "4                            44                         0                   1   \n",
       "\n",
       "      Policy Type        Policy  Renew Offer Type  Sales Channel  \\\n",
       "0  Corporate Auto  Corporate L3            Offer1          Agent   \n",
       "1   Personal Auto   Personal L3            Offer3          Agent   \n",
       "2   Personal Auto   Personal L3            Offer1          Agent   \n",
       "3  Corporate Auto  Corporate L2            Offer1    Call Center   \n",
       "4   Personal Auto   Personal L1            Offer1          Agent   \n",
       "\n",
       "  Total Claim Amount  Vehicle Class Vehicle Size  \n",
       "0         384.811147   Two-Door Car      Medsize  \n",
       "1        1131.464935  Four-Door Car      Medsize  \n",
       "2         566.472247   Two-Door Car      Medsize  \n",
       "3         529.881344            SUV      Medsize  \n",
       "4         138.130879  Four-Door Car      Medsize  \n",
       "\n",
       "[5 rows x 24 columns]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import distribution as db\n",
    "a = pd.read_csv('sample data.csv')\n",
    "a.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### hisplot1(dataset,name):\n",
    "It is used to plot a histagram for a given dataset[name] "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Gt7zTkI5DP/rRj3r/e2BjY2fwuc/9ca9zUPcsGpWroY9fi+ee29f7D8pNm57mnHN6nQIA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x116cd6890>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "db.hisplot1(a,'Income')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### lineplot(dataset,name)\n",
    "Used to do autoscaling and plot line for group of data \"\n",
    "Input: Name can be a list of features also \n",
    "Output:Line plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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q3Dm/LsZtJqD7GTAg/Q0+06cDDz1khn/8Y+DSS/1rM0E1za23Lm/P+81vwtc3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\niAqCAZ2IqCAY0ImICoIBnYioIP4f+LE3WhUc8gcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11777e5d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "db.lineplot(a,'Months Since Policy Inception')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### threed(dataset,name):\n",
    "to draw a 3d plot\n",
    "Input:Dataset : DataFrame and name is a list of 3 variables \n",
    "Output : 3d plot "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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118N8882yGsffEGeccQYZGRmMGzeVYPAEYBywHr//B/z+flRUGIFDWK1X0aXL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\ngSlrpXi4MXv2bDZv3kxYWBjPPvtsFsW0U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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1176c1490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "db.threed(a,['Customer Lifetime Value','Income','Number of Open Complaints'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### outliers1(dataset,name):\n",
    "It is used to find the outliers in a numeric data series or a column in a dataframe based on Z test . It displays their Z score . It returns 1 if its an outlier and 0 if its not . \n",
    "Reference : https://onlinecourses.science.psu.edu/stat200/node/135"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
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      "-1.23961684189\n",
      "0.365709777071\n",
      "-1.23961684189\n",
      "0.203389651616\n",
      "0.831009913618\n",
      "0.582410930159\n",
      "-1.23961684189\n",
      "-0.776390558088\n",
      "-0.291174851495\n",
      "-1.23961684189\n",
      "-1.23961684189\n",
      "1.29594794889\n",
      "2.04711058178\n",
      "1.51528256579\n",
      "-0.429859639275\n",
      "-0.415046405356\n",
      "-0.289759364698\n",
      "0.444088243652\n",
      "0.93759936124\n",
      "0.661381926096\n",
      "-0.785706436308\n",
      "-0.776390558088\n",
      "-1.23961684189\n",
      "-0.651366863811\n",
      "1.97126682411\n",
      "-1.23961684189\n",
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      "1.41149117346\n",
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      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db.outliers1(a,'Income') ## print the out"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### typedis(dataset,name,dis):\n",
    "Type any type of ditribution . Dis is used to take in the type of code distribution visit refer http://docs.scipy.org/doc/scipy-0.14.0/reference/stats.html#module-scipy.stats for more reference and throws KS Test Statistic either D,D+,D-  test and p value as a result . we are taking p value as 0.05 to check a hypothesis with 95 % confidence \n",
    "Output : \n",
    "Zscore , Pvalue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It is Not as norm  distribution\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.74633238449748196, 0.0)"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db.typedis(a,'Income','norm')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### norm(dataset,name):\n",
    "Perform KS test for normal distribution .throws KS Test Statistic either D,D+,D-  test and p value as a result . we are taking p value as 0.05 to check a hypothesis with 95 % confidence \n",
    "Output : \n",
    "Zscore , Pvalue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It is Not a normal distribution\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.74633238449748196, 0.0)"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db.norm(a,'Income')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### welisberg(dataset,name):\n",
    "Perform KS test for welisberg distribution .throws KS Test Statistic either D,D+,D-  test and p value as a result . we are taking p value as 0.05 to check a hypothesis with 95 % confidence \n",
    "Output : \n",
    "Zscore , Pvalue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "db.welisberg(a,'Income')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### exponential(dataset,name):\n",
    "Perform KS test for exponential distribution .throws KS Test Statistic either D,D+,D-  test and p value as a result . we are taking p value as 0.05 to check a hypothesis with 95 % confidence \n",
    "Output : \n",
    "Zscore , Pvalue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It is Not a Exponential distribution\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.74633238449748196, 0.0)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db.exponential(a,'Income')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### logistic(dataset,name) \n",
    "Perform KS test for logistic distribution .throws KS Test Statistic either D,D+,D-  test and p value as a result . we are taking p value as 0.05 to check a hypothesis with 95 % confidence \n",
    "Output : \n",
    "Zscore , Pvalue"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false,
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "It is Not a Exponential distribution\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.74633238449748196, 0.0)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "db.logistic(a,'Income')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### distri(dataset,name):\n",
    "It is used to tell whether name of dataset is continous or a discrete distribution\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Customer is  discrete\n",
      "State is  discrete\n",
      "Customer Lifetime Value is  discrete\n",
      "Response is  discrete\n",
      "Coverage is  discrete\n",
      "Education is  discrete\n",
      "Effective To Date is  discrete\n",
      "EmploymentStatus is  discrete\n",
      "Gender is  discrete\n",
      "Income is  discrete\n",
      "Location Code is  discrete\n",
      "Marital Status is  discrete\n",
      "Monthly Premium Auto is  discrete\n",
      "Months Since Last Claim is  discrete\n",
      "Months Since Policy Inception is  discrete\n",
      "Number of Open Complaints is  discrete\n",
      "Number of Policies is  discrete\n",
      "Policy Type is  discrete\n",
      "Policy is  discrete\n",
      "Renew Offer Type is  discrete\n",
      "Sales Channel is  discrete\n",
      "Total Claim Amount is  discrete\n",
      "Vehicle Class is  discrete\n",
      "Vehicle Size is  discrete\n"
     ]
    }
   ],
   "source": [
    "for i in a:\n",
    "    print i,'is ', db.distri(a,i)"
   ]
  }
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