ScenicOrNotEDA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "# local tools                                                                                                                                                                                             \n",
    "import sys\n",
    "import getImage\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "scenicDF = pd.read_csv('data/imagesWithJpg.tsv',sep='\\t') # for debugging locally, update on aws"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 212104 entries, 0 to 212103\n",
      "Data columns (total 9 columns):\n",
      "Unnamed: 0      212104 non-null int64\n",
      "ID              212104 non-null int64\n",
      "Lat             212104 non-null float64\n",
      "Lon             212104 non-null float64\n",
      "Average         212104 non-null float64\n",
      "Variance        212104 non-null float64\n",
      "Votes           212104 non-null object\n",
      "Geograph URI    212104 non-null object\n",
      "Images          212104 non-null object\n",
      "dtypes: float64(4), int64(2), object(3)\n",
      "memory usage: 14.6+ MB\n"
     ]
    }
   ],
   "source": [
    "scenicDF.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = scenicDF.Average.plot.hist(bins=10,range=(1,11))\n",
    "ax.set_xlabel('Scenery Score')\n",
    "ax.set_ylabel('Counts')\n",
    "plt.savefig('sceneryScore.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = scenicDF.Variance.plot.hist()\n",
    "ax.set_xlabel('Scenery Variance')\n",
    "ax.set_ylabel('Counts')\n",
    "plt.savefig('sceneryVariance.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scenicDF[\"Nvotes\"] = scenicDF['Votes'].apply(lambda x: len(x))\n",
    "\n",
    "ax = scenicDF.Nvotes.plot.hist()\n",
    "ax.set_yscale('log')\n",
    "ax.set_xlabel('Number of votes')\n",
    "ax.set_ylabel('Counts')\n",
    "plt.savefig('nVotes.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ax = scenicDF.Nvotes.plot.hist(range=(0,20))\n",
    "ax.set_xlabel('Number of votes')\n",
    "ax.set_ylabel('Counts')\n",
    "plt.savefig('nVotes020.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "bestScore  = scenicDF.Average.idxmax()\n",
    "worstScore = scenicDF.Average.idxmin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "best :  http://www.geograph.org.uk/photo/201726\n",
      "worst:  http://www.geograph.org.uk/photo/830045\n"
     ]
    }
   ],
   "source": [
    "print(\"best : \",scenicDF.iloc[bestScore]['Geograph URI'])\n",
    "print(\"worst: \",scenicDF.iloc[worstScore]['Geograph URI'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "scores for best:  10,10,10,10\n",
      "scores for worst:  1,1,1,1\n"
     ]
    }
   ],
   "source": [
    "print (\"scores for best: \",scenicDF.iloc[bestScore]['Votes'])\n",
    "print (\"scores for worst: \",scenicDF.iloc[worstScore]['Votes'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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