Rapid damage assessment survey data - TC Debbie.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "import seaborn as sns\n",
    "sns.set_context(\"poster\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Longitude</th>\n",
       "      <th>Latitude</th>\n",
       "      <th>OBJECTID</th>\n",
       "      <th>LEVELS</th>\n",
       "      <th>_indices__.x</th>\n",
       "      <th>_indices__.y</th>\n",
       "      <th>_indices__.z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>11088.000000</td>\n",
       "      <td>11088.000000</td>\n",
       "      <td>11088.000000</td>\n",
       "      <td>3298.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>149.948680</td>\n",
       "      <td>-22.553128</td>\n",
       "      <td>13822.813402</td>\n",
       "      <td>2.126743</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.780771</td>\n",
       "      <td>2.902668</td>\n",
       "      <td>5293.582471</td>\n",
       "      <td>24.136121</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>141.079049</td>\n",
       "      <td>-28.321330</td>\n",
       "      <td>109.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>148.598683</td>\n",
       "      <td>-23.390399</td>\n",
       "      <td>10910.750000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>149.160292</td>\n",
       "      <td>-21.157977</td>\n",
       "      <td>13876.500000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>150.520417</td>\n",
       "      <td>-20.402510</td>\n",
       "      <td>19072.250000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>166.033776</td>\n",
       "      <td>-5.497914</td>\n",
       "      <td>22602.000000</td>\n",
       "      <td>999.000000</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Longitude      Latitude      OBJECTID       LEVELS  _indices__.x  \\\n",
       "count  11088.000000  11088.000000  11088.000000  3298.000000           0.0   \n",
       "mean     149.948680    -22.553128  13822.813402     2.126743           NaN   \n",
       "std        1.780771      2.902668   5293.582471    24.136121           NaN   \n",
       "min      141.079049    -28.321330    109.000000     0.000000           NaN   \n",
       "25%      148.598683    -23.390399  10910.750000     1.000000           NaN   \n",
       "50%      149.160292    -21.157977  13876.500000     1.000000           NaN   \n",
       "75%      150.520417    -20.402510  19072.250000     2.000000           NaN   \n",
       "max      166.033776     -5.497914  22602.000000   999.000000           NaN   \n",
       "\n",
       "       _indices__.y  _indices__.z  \n",
       "count           0.0           0.0  \n",
       "mean            NaN           NaN  \n",
       "std             NaN           NaN  \n",
       "min             NaN           NaN  \n",
       "25%             NaN           NaN  \n",
       "50%             NaN           NaN  \n",
       "75%             NaN           NaN  \n",
       "max             NaN           NaN  "
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = \"N:/georisk/HaRIA_B_Wind/projects/impact_forecasting/data/exposure/Qld - RDA - TC Debbie.xlsx\"\n",
    "df = pd.read_excel(filename)\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index([u'Longitude', u'Latitude', u'OBJECTID', u'USERID', u'STREETNUM',\n",
       "       u'STREETNAME', u'LOCALITY', u'LGA', u'SURVEYDATE', u'CONDITION',\n",
       "       u'BUILDTYPE', u'BUSINESS_OPERATION', u'HABITABLE', u'OCCUPIED',\n",
       "       u'WALLTYPE', u'ROOFTYPE', u'WATERHEIGHT', u'HAZARD1', u'HAZARD2',\n",
       "       u'HAZARD3', u'TASK1', u'TASK2', u'TASK3', u'RECOVSUP1', u'RECOVSUP2',\n",
       "       u'VULNPERS1', u'VULNPERS2', u'ANIMWELF', u'COMMENTS', u'ARCHIVE',\n",
       "       u'PHOTOLINK', u'created_user', u'last_edited_user', u'created_date',\n",
       "       u'last_edited_date', u'LEVELS', u'GlobalID', u'_indices__.x',\n",
       "       u'_indices__.y', u'_indices__.z', u'FMETimestamp'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1oAAAJECAYAAAAPCVXBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x248a4270>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "order = ['No Damage', 'Minor', 'Moderate', 'Severe', 'Total']\n",
    "ax = sns.countplot(x=\"CONDITION\", hue=\"HABITABLE\", data=df, order=order)\n",
    "ax.legend(title=\"Habitable\", loc=1)\n",
    "ax.set_yscale(\"log\", nonposy='clip')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "c:\\python27\\lib\\site-packages\\matplotlib\\scale.py:101: RuntimeWarning: invalid value encountered in less_equal\n",
      "  a[a <= 0.0] = 1e-300\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAABA4AAAEECAYAAACofXaBAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nEARBUJEwHARBEARBEARBEARBUJH/BykJiuTJ7rvZAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x27eafb90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "g = sns.factorplot(x=\"CONDITION\", hue=\"ROOFTYPE\", col=\"HABITABLE\", data=df, kind=\"count\")\n",
    "g.axes[0][0].set_yscale(\"log\", nonposy=\"clip\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__class__',\n",
       " '__delattr__',\n",
       " '__dict__',\n",
       " '__doc__',\n",
       " '__format__',\n",
       " '__getattribute__',\n",
       " '__getstate__',\n",
       " '__hash__',\n",
       " '__init__',\n",
       " '__module__',\n",
       " '__new__',\n",
       " '__reduce__',\n",
       " '__reduce_ex__',\n",
       " '__repr__',\n",
       " '__setattr__',\n",
       " '__setstate__',\n",
       " '__sizeof__',\n",
       " '__str__',\n",
       " '__subclasshook__',\n",
       " '__weakref__',\n",
       " '_add_text',\n",
       " '_adjustable',\n",
       " '_agg_filter',\n",
       " '_alpha',\n",
       " '_anchor',\n",
       " '_animated',\n",
       " '_aspect',\n",
       " '_autoscaleXon',\n",
       " '_autoscaleYon',\n",
       " '_axes',\n",
       " u'_axes_class',\n",
       " '_axes_locator',\n",
       " '_axisbelow',\n",
       " '_axisbg',\n",
       " '_cachedRenderer',\n",
       " '_clipon',\n",
       " '_clippath',\n",
       " '_connected',\n",
       " '_contains',\n",
       " '_current_image',\n",
       " '_cursorProps',\n",
       " '_frameon',\n",
       " '_gci',\n",
       " '_gen_axes_patch',\n",
       " '_gen_axes_spines',\n",
       " '_get_axis_list',\n",
       " '_get_legend_handles',\n",
       " '_get_lines',\n",
       " '_get_patches_for_fill',\n",
       " '_get_view',\n",
       " '_gid',\n",
       " '_gridOn',\n",
       " '_hold',\n",
       " '_init_axis',\n",
       " '_label',\n",
       " '_left_title',\n",
       " '_make_twin_axes',\n",
       " '_mouseover',\n",
       " '_navigate',\n",
       " '_navigate_mode',\n",
       " '_oid',\n",
       " '_originalPosition',\n",
       " '_path_effects',\n",
       " '_pcolorargs',\n",
       " '_picker',\n",
       " '_position',\n",
       " '_process_unit_info',\n",
       " '_propobservers',\n",
       " '_rasterization_zorder',\n",
       " '_rasterized',\n",
       " '_remove_method',\n",
       " '_right_title',\n",
       " '_sci',\n",
       " '_set_artist_props',\n",
       " '_set_gc_clip',\n",
       " '_set_lim_and_transforms',\n",
       " '_set_view',\n",
       " '_set_view_from_bbox',\n",
       " '_shared_x_axes',\n",
       " '_shared_y_axes',\n",
       " '_sharex',\n",
       " '_sharey',\n",
       " '_sketch',\n",
       " '_snap',\n",
       " '_stale',\n",
       " '_subplotspec',\n",
       " '_tight',\n",
       " '_transform',\n",
       " '_transformSet',\n",
       " '_update_line_limits',\n",
       " '_update_patch_limits',\n",
       " '_update_transScale',\n",
       " '_url',\n",
       " '_visible',\n",
       " '_xaxis_transform',\n",
       " '_xcid',\n",
       " '_xmargin',\n",
       " '_yaxis_transform',\n",
       " '_ycid',\n",
       " '_ymargin',\n",
       " 'acorr',\n",
       " 'add_artist',\n",
       " 'add_callback',\n",
       " 'add_collection',\n",
       " 'add_container',\n",
       " 'add_image',\n",
       " 'add_line',\n",
       " 'add_patch',\n",
       " 'add_table',\n",
       " 'aname',\n",
       " 'angle_spectrum',\n",
       " 'annotate',\n",
       " 'apply_aspect',\n",
       " 'arrow',\n",
       " 'artists',\n",
       " 'autoscale',\n",
       " 'autoscale_view',\n",
       " 'axes',\n",
       " 'axesPatch',\n",
       " 'axhline',\n",
       " 'axhspan',\n",
       " 'axis',\n",
       " 'axison',\n",
       " 'axvline',\n",
       " 'axvspan',\n",
       " 'bar',\n",
       " 'barbs',\n",
       " 'barh',\n",
       " 'bbox',\n",
       " 'boxplot',\n",
       " 'broken_barh',\n",
       " 'bxp',\n",
       " 'callbacks',\n",
       " 'can_pan',\n",
       " 'can_zoom',\n",
       " 'change_geometry',\n",
       " 'cla',\n",
       " 'clabel',\n",
       " 'clear',\n",
       " 'clipbox',\n",
       " 'cohere',\n",
       " 'colNum',\n",
       " 'collections',\n",
       " 'containers',\n",
       " 'contains',\n",
       " 'contains_point',\n",
       " 'contour',\n",
       " 'contourf',\n",
       " 'convert_xunits',\n",
       " 'convert_yunits',\n",
       " 'csd',\n",
       " 'dataLim',\n",
       " 'drag_pan',\n",
       " 'draw',\n",
       " 'draw_artist',\n",
       " 'end_pan',\n",
       " 'errorbar',\n",
       " 'eventplot',\n",
       " 'eventson',\n",
       " 'figbox',\n",
       " 'figure',\n",
       " 'fill',\n",
       " 'fill_between',\n",
       " 'fill_betweenx',\n",
       " 'findobj',\n",
       " 'fmt_xdata',\n",
       " 'fmt_ydata',\n",
       " 'format_coord',\n",
       " 'format_cursor_data',\n",
       " 'format_xdata',\n",
       " 'format_ydata',\n",
       " 'get_adjustable',\n",
       " 'get_agg_filter',\n",
       " 'get_alpha',\n",
       " 'get_anchor',\n",
       " 'get_animated',\n",
       " 'get_aspect',\n",
       " 'get_autoscale_on',\n",
       " 'get_autoscalex_on',\n",
       " 'get_autoscaley_on',\n",
       " 'get_axes',\n",
       " 'get_axes_locator',\n",
       " 'get_axis_bgcolor',\n",
       " 'get_axisbelow',\n",
       " 'get_children',\n",
       " 'get_clip_box',\n",
       " 'get_clip_on',\n",
       " 'get_clip_path',\n",
       " 'get_contains',\n",
       " 'get_cursor_data',\n",
       " 'get_cursor_props',\n",
       " 'get_data_ratio',\n",
       " 'get_data_ratio_log',\n",
       " 'get_default_bbox_extra_artists',\n",
       " 'get_figure',\n",
       " 'get_frame_on',\n",
       " 'get_geometry',\n",
       " 'get_gid',\n",
       " 'get_images',\n",
       " 'get_label',\n",
       " 'get_legend',\n",
       " 'get_legend_handles_labels',\n",
       " 'get_lines',\n",
       " 'get_navigate',\n",
       " 'get_navigate_mode',\n",
       " 'get_path_effects',\n",
       " 'get_picker',\n",
       " 'get_position',\n",
       " 'get_rasterization_zorder',\n",
       " 'get_rasterized',\n",
       " 'get_renderer_cache',\n",
       " 'get_shared_x_axes',\n",
       " 'get_shared_y_axes',\n",
       " 'get_sketch_params',\n",
       " 'get_snap',\n",
       " 'get_subplotspec',\n",
       " 'get_tightbbox',\n",
       " 'get_title',\n",
       " 'get_transform',\n",
       " 'get_transformed_clip_path_and_affine',\n",
       " 'get_url',\n",
       " 'get_visible',\n",
       " 'get_window_extent',\n",
       " 'get_xaxis',\n",
       " 'get_xaxis_text1_transform',\n",
       " 'get_xaxis_text2_transform',\n",
       " 'get_xaxis_transform',\n",
       " 'get_xbound',\n",
       " 'get_xgridlines',\n",
       " 'get_xlabel',\n",
       " 'get_xlim',\n",
       " 'get_xmajorticklabels',\n",
       " 'get_xminorticklabels',\n",
       " 'get_xscale',\n",
       " 'get_xticklabels',\n",
       " 'get_xticklines',\n",
       " 'get_xticks',\n",
       " 'get_yaxis',\n",
       " 'get_yaxis_text1_transform',\n",
       " 'get_yaxis_text2_transform',\n",
       " 'get_yaxis_transform',\n",
       " 'get_ybound',\n",
       " 'get_ygridlines',\n",
       " 'get_ylabel',\n",
       " 'get_ylim',\n",
       " 'get_ymajorticklabels',\n",
       " 'get_yminorticklabels',\n",
       " 'get_yscale',\n",
       " 'get_yticklabels',\n",
       " 'get_yticklines',\n",
       " 'get_yticks',\n",
       " 'get_zorder',\n",
       " 'grid',\n",
       " 'has_data',\n",
       " 'have_units',\n",
       " 'hexbin',\n",
       " 'hist',\n",
       " 'hist2d',\n",
       " 'hitlist',\n",
       " 'hlines',\n",
       " 'hold',\n",
       " 'ignore_existing_data_limits',\n",
       " 'images',\n",
       " 'imshow',\n",
       " 'in_axes',\n",
       " 'invert_xaxis',\n",
       " 'invert_yaxis',\n",
       " 'is_figure_set',\n",
       " 'is_first_col',\n",
       " 'is_first_row',\n",
       " 'is_last_col',\n",
       " 'is_last_row',\n",
       " 'is_transform_set',\n",
       " 'ishold',\n",
       " 'label_outer',\n",
       " 'legend',\n",
       " 'legend_',\n",
       " 'lines',\n",
       " 'locator_params',\n",
       " 'loglog',\n",
       " 'magnitude_spectrum',\n",
       " 'margins',\n",
       " 'matshow',\n",
       " 'minorticks_off',\n",
       " 'minorticks_on',\n",
       " 'mouseover',\n",
       " 'mouseover_set',\n",
       " 'name',\n",
       " 'numCols',\n",
       " 'numRows',\n",
       " 'patch',\n",
       " 'patches',\n",
       " 'pchanged',\n",
       " 'pcolor',\n",
       " 'pcolorfast',\n",
       " 'pcolormesh',\n",
       " 'phase_spectrum',\n",
       " 'pick',\n",
       " 'pickable',\n",
       " 'pie',\n",
       " 'plot',\n",
       " 'plot_date',\n",
       " 'properties',\n",
       " 'psd',\n",
       " 'quiver',\n",
       " 'quiverkey',\n",
       " 'redraw_in_frame',\n",
       " 'relim',\n",
       " 'remove',\n",
       " 'remove_callback',\n",
       " 'reset_position',\n",
       " 'rowNum',\n",
       " 'scatter',\n",
       " 'semilogx',\n",
       " 'semilogy',\n",
       " 'set',\n",
       " 'set_adjustable',\n",
       " 'set_agg_filter',\n",
       " 'set_alpha',\n",
       " 'set_anchor',\n",
       " 'set_animated',\n",
       " 'set_aspect',\n",
       " 'set_autoscale_on',\n",
       " 'set_autoscalex_on',\n",
       " 'set_autoscaley_on',\n",
       " 'set_axes',\n",
       " 'set_axes_locator',\n",
       " 'set_axis_bgcolor',\n",
       " 'set_axis_off',\n",
       " 'set_axis_on',\n",
       " 'set_axisbelow',\n",
       " 'set_clip_box',\n",
       " 'set_clip_on',\n",
       " 'set_clip_path',\n",
       " 'set_color_cycle',\n",
       " 'set_contains',\n",
       " 'set_cursor_props',\n",
       " 'set_figure',\n",
       " 'set_frame_on',\n",
       " 'set_gid',\n",
       " 'set_label',\n",
       " 'set_navigate',\n",
       " 'set_navigate_mode',\n",
       " 'set_path_effects',\n",
       " 'set_picker',\n",
       " 'set_position',\n",
       " 'set_prop_cycle',\n",
       " 'set_rasterization_zorder',\n",
       " 'set_rasterized',\n",
       " 'set_sketch_params',\n",
       " 'set_snap',\n",
       " 'set_subplotspec',\n",
       " 'set_title',\n",
       " 'set_transform',\n",
       " 'set_url',\n",
       " 'set_visible',\n",
       " 'set_xbound',\n",
       " 'set_xlabel',\n",
       " 'set_xlim',\n",
       " 'set_xmargin',\n",
       " 'set_xscale',\n",
       " 'set_xticklabels',\n",
       " 'set_xticks',\n",
       " 'set_ybound',\n",
       " 'set_ylabel',\n",
       " 'set_ylim',\n",
       " 'set_ymargin',\n",
       " 'set_yscale',\n",
       " 'set_yticklabels',\n",
       " 'set_yticks',\n",
       " 'set_zorder',\n",
       " 'specgram',\n",
       " 'spines',\n",
       " 'spy',\n",
       " 'stackplot',\n",
       " 'stale',\n",
       " 'stale_callback',\n",
       " 'start_pan',\n",
       " 'stem',\n",
       " 'step',\n",
       " 'streamplot',\n",
       " 'table',\n",
       " 'tables',\n",
       " 'text',\n",
       " 'texts',\n",
       " 'tick_params',\n",
       " 'ticklabel_format',\n",
       " 'title',\n",
       " 'titleOffsetTrans',\n",
       " 'transAxes',\n",
       " 'transData',\n",
       " 'transLimits',\n",
       " 'transScale',\n",
       " 'tricontour',\n",
       " 'tricontourf',\n",
       " 'tripcolor',\n",
       " 'triplot',\n",
       " 'twinx',\n",
       " 'twiny',\n",
       " 'update',\n",
       " 'update_datalim',\n",
       " 'update_datalim_bounds',\n",
       " 'update_datalim_numerix',\n",
       " 'update_from',\n",
       " 'update_params',\n",
       " 'viewLim',\n",
       " 'violin',\n",
       " 'violinplot',\n",
       " 'vlines',\n",
       " 'xaxis',\n",
       " 'xaxis_date',\n",
       " 'xaxis_inverted',\n",
       " 'xcorr',\n",
       " 'yaxis',\n",
       " 'yaxis_date',\n",
       " 'yaxis_inverted',\n",
       " 'zorder']"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir(g.axes[0][0])"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "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": "2.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}