Data_Wrangling.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import geopandas as gpd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "section = gpd.read_file('map/section/section.shp')\n",
    "floorPlan = pd.read_excel('Permanent-Halls_FINAL_floors-sections.xls')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "floorPlan = floorPlan.dropna(subset=['Section'])\n",
    "floorPlan.Section = floorPlan.Section.apply(lambda x: str(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df = floorPlan.loc[:,['Date of Source', 'Floor', 'Section', 'Hall Name']]\n",
    "df['Hall Name'] = df['Hall Name'].apply(str)\n",
    "df2 = df.groupby(['Date of Source', 'Floor', 'Section'])['Hall Name'].apply(list)\n",
    "df2.to_csv('df22.csv')\n",
    "df4 = pd.read_csv('df22.csv',header=None)\n",
    "df4.columns=['year', 'floor', 'section', 'hall name']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df4.section = df4.section.replace('CCP', '7a')\n",
    "df4.section = df4.section.replace('SC', '2')\n",
    "df4.section = df4.section.replace('NC', '7a')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df4['hall name'].to_csv('hall.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "js = []\n",
    "for i in xrange(df4.shape[0]):\n",
    "    d = {}\n",
    "    d['year'] = df4.loc[i,'year']\n",
    "    d['floor'] = df4.loc[i, 'floor']\n",
    "    d['section'] = df4.loc[i,'section']\n",
    "    d['hall name'] = df4['hall name'][i]\n",
    "    js.append(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import json\n",
    "with open('data.json', 'w') as outfile:\n",
    "    json.dump(js, outfile)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df4.section = df4.section.replace('12a', '12A')\n",
    "df4.section = df4.section.replace('7a', '7A')\n",
    "df4.section = df4.section.replace('2a', '2A')\n",
    "df4.section = df4.section.replace('13a', '13A')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "section.section = section.section.apply(str)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "section2 = section.loc[:,['geometry', 'section']]\n",
    "merge = pd.merge(right = section2,left= df4, on = 'section', how='inner' )\n",
    "merge.to_csv('merge.csv')\n",
    "merge_geo = gpd.GeoDataFrame(merge)\n",
    "merge_geo.crs = {\"init\": \"epsg:4326\"}\n",
    "merge_geo.to_file('merge')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "merge_geo.to_file('merge')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 359,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x128f49050>"
      ]
     },
     "execution_count": 359,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Au7mvfepYXOcbaa9XO7vjv96eenBPAPb7wnMFt+u572EM/MHlJR07bGflmTNn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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x128c92a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "merge_geo.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>year</th>\n",
       "      <th>floor</th>\n",
       "      <th>section</th>\n",
       "      <th>hall name</th>\n",
       "      <th>geometry</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1904</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>['Ethnology and Archaeology of Northwest Coast...</td>\n",
       "      <td>POLYGON ((-73.97460187538796 40.7807749166183,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1904</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>['Bird Hall', 'Birds']</td>\n",
       "      <td>POLYGON ((-73.97460187538796 40.7807749166183,...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1904</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>['Birds of North America', 'North American Bir...</td>\n",
       "      <td>POLYGON ((-73.97460187538796 40.7807749166183,...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   year floor section                                          hall name  \\\n",
       "0  1904     1       1  ['Ethnology and Archaeology of Northwest Coast...   \n",
       "1  1904     2       1                             ['Bird Hall', 'Birds']   \n",
       "2  1904     3       1  ['Birds of North America', 'North American Bir...   \n",
       "\n",
       "                                            geometry  \n",
       "0  POLYGON ((-73.97460187538796 40.7807749166183,...  \n",
       "1  POLYGON ((-73.97460187538796 40.7807749166183,...  \n",
       "2  POLYGON ((-73.97460187538796 40.7807749166183,...  "
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "merge_geo.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 177,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_ach = pd.read_csv('map/PH_all_floors.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 191,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def change_section(df4):\n",
    "    df4.section = df4.section.replace('CCP', '7a')\n",
    "    df4.section = df4.section.replace('SC', '2')\n",
    "    df4.section = df4.section.replace('NC', '7a')\n",
    "    df4.section = df4.section.replace('12a', '12A')\n",
    "    df4.section = df4.section.replace('7a', '7A')\n",
    "    df4.section = df4.section.replace('2a', '2A')\n",
    "    df4.section = df4.section.replace('13a', '13A')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 193,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "change_section(df_ach)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_ach2 = df_ach[['recordId', 'Names used in AMNH publications', 'useDates', 'floor', 'section']]\n",
    "df_ach2 = df_ach2.dropna()\n",
    "df_ach2 = df_ach2.dropna()\n",
    "df = df.reset_index(drop=True)\n",
    "df_ach2 = df_ach2.reset_index(drop=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "merge_geo.to_csv('merge_geo.csv')\n",
    "df_ach2.to_csv('archive.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 331,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_cut = df_ach.drop_duplicates(subset=['recordId', 'Names used in AMNH publications'])\n",
    "df_cut = df_cut.iloc[:,[0,4,5,6,7]]\n",
    "df_cut = df_cut.dropna(subset=['floor', 'section'])\n",
    "df_cut = df_cut.reset_index(drop=True)\n",
    "df_cut.to_csv('arc.csv')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}