LearnTensorflow/OpenCVTensorflowDeeplearning/Chapter3/Practice001.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pylab\n",
    "import scipy.stats as stats\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "data = np.mat([[1,200,105,3,False],[2,165,80,2,False],[3,184.5,120,2,False],[4,116,70.8,1,False],[5,270,150,4,True]])\n",
    "col1 = []\n",
    "for row in data:\n",
    "    col1.append(row[0,1])\n",
    "stats.probplot(col1,plot=pylab)\n",
    "pylab.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0      2\n",
      "1    165\n",
      "2     80\n",
      "Name: 1, dtype: object\n",
      "0        3\n",
      "1    184.5\n",
      "2      120\n",
      "Name: 2, dtype: object\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0       4\n",
      "1     116\n",
      "2    70.8\n",
      "Name: 3, dtype: object\n"
     ]
    },
    {
     "data": {
      "image/png": "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
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "rocksVMines = pd.DataFrame([[1,200,105,3,False],[2,165,80,2,False],[3,184.5,120,2,False],[4,116,70.8,1,False],[5,270,150,4,True]])\n",
    "dataRow1 = rocksVMines.iloc[1,0:3]\n",
    "dataRow2 = rocksVMines.iloc[2,0:3]\n",
    "print(dataRow1)\n",
    "print(dataRow2)\n",
    "plot.scatter(dataRow1,dataRow2)\n",
    "plot.xlabel(\"Attribute1\")\n",
    "plot.ylabel(\"Attribute2\")\n",
    "plot.show()\n",
    "\n",
    "dataRow3 = rocksVMines.iloc[3,0:3]\n",
    "print(dataRow3)\n",
    "plot.scatter(dataRow2,dataRow3)\n",
    "plot.xlabel(\"Attribute2\")\n",
    "plot.ylabel(\"Attribute3\")\n",
    "plot.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}