D-22_Pandas_Profiler.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "bc9304dd",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "df8964ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv('Datasets/wk3_kc_house_test_data.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e882732a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>date</th>\n",
       "      <th>price</th>\n",
       "      <th>bedrooms</th>\n",
       "      <th>bathrooms</th>\n",
       "      <th>sqft_living</th>\n",
       "      <th>sqft_lot</th>\n",
       "      <th>floors</th>\n",
       "      <th>waterfront</th>\n",
       "      <th>view</th>\n",
       "      <th>...</th>\n",
       "      <th>grade</th>\n",
       "      <th>sqft_above</th>\n",
       "      <th>sqft_basement</th>\n",
       "      <th>yr_built</th>\n",
       "      <th>yr_renovated</th>\n",
       "      <th>zipcode</th>\n",
       "      <th>lat</th>\n",
       "      <th>long</th>\n",
       "      <th>sqft_living15</th>\n",
       "      <th>sqft_lot15</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3793500160</td>\n",
       "      <td>20150312T000000</td>\n",
       "      <td>323000.0</td>\n",
       "      <td>3</td>\n",
       "      <td>2.50</td>\n",
       "      <td>1890</td>\n",
       "      <td>6560</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1890</td>\n",
       "      <td>0</td>\n",
       "      <td>2003</td>\n",
       "      <td>0</td>\n",
       "      <td>98038</td>\n",
       "      <td>47.3684</td>\n",
       "      <td>-122.031</td>\n",
       "      <td>2390</td>\n",
       "      <td>7570</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1175000570</td>\n",
       "      <td>20150312T000000</td>\n",
       "      <td>530000.0</td>\n",
       "      <td>5</td>\n",
       "      <td>2.00</td>\n",
       "      <td>1810</td>\n",
       "      <td>4850</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1810</td>\n",
       "      <td>0</td>\n",
       "      <td>1900</td>\n",
       "      <td>0</td>\n",
       "      <td>98107</td>\n",
       "      <td>47.6700</td>\n",
       "      <td>-122.394</td>\n",
       "      <td>1360</td>\n",
       "      <td>4850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16000397</td>\n",
       "      <td>20141205T000000</td>\n",
       "      <td>189000.0</td>\n",
       "      <td>2</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1200</td>\n",
       "      <td>9850</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1200</td>\n",
       "      <td>0</td>\n",
       "      <td>1921</td>\n",
       "      <td>0</td>\n",
       "      <td>98002</td>\n",
       "      <td>47.3089</td>\n",
       "      <td>-122.210</td>\n",
       "      <td>1060</td>\n",
       "      <td>5095</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>461000390</td>\n",
       "      <td>20140624T000000</td>\n",
       "      <td>687500.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.75</td>\n",
       "      <td>2330</td>\n",
       "      <td>5000</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>1510</td>\n",
       "      <td>820</td>\n",
       "      <td>1929</td>\n",
       "      <td>0</td>\n",
       "      <td>98117</td>\n",
       "      <td>47.6823</td>\n",
       "      <td>-122.368</td>\n",
       "      <td>1460</td>\n",
       "      <td>5000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7895500070</td>\n",
       "      <td>20150213T000000</td>\n",
       "      <td>240000.0</td>\n",
       "      <td>4</td>\n",
       "      <td>1.00</td>\n",
       "      <td>1220</td>\n",
       "      <td>8075</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>7</td>\n",
       "      <td>890</td>\n",
       "      <td>330</td>\n",
       "      <td>1969</td>\n",
       "      <td>0</td>\n",
       "      <td>98001</td>\n",
       "      <td>47.3341</td>\n",
       "      <td>-122.282</td>\n",
       "      <td>1290</td>\n",
       "      <td>7800</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 21 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           id             date     price  bedrooms  bathrooms  sqft_living  \\\n",
       "0  3793500160  20150312T000000  323000.0         3       2.50         1890   \n",
       "1  1175000570  20150312T000000  530000.0         5       2.00         1810   \n",
       "2    16000397  20141205T000000  189000.0         2       1.00         1200   \n",
       "3   461000390  20140624T000000  687500.0         4       1.75         2330   \n",
       "4  7895500070  20150213T000000  240000.0         4       1.00         1220   \n",
       "\n",
       "   sqft_lot  floors  waterfront  view  ...  grade  sqft_above  sqft_basement  \\\n",
       "0      6560     2.0           0     0  ...      7        1890              0   \n",
       "1      4850     1.5           0     0  ...      7        1810              0   \n",
       "2      9850     1.0           0     0  ...      7        1200              0   \n",
       "3      5000     1.5           0     0  ...      7        1510            820   \n",
       "4      8075     1.0           0     0  ...      7         890            330   \n",
       "\n",
       "   yr_built  yr_renovated  zipcode      lat     long  sqft_living15  \\\n",
       "0      2003             0    98038  47.3684 -122.031           2390   \n",
       "1      1900             0    98107  47.6700 -122.394           1360   \n",
       "2      1921             0    98002  47.3089 -122.210           1060   \n",
       "3      1929             0    98117  47.6823 -122.368           1460   \n",
       "4      1969             0    98001  47.3341 -122.282           1290   \n",
       "\n",
       "   sqft_lot15  \n",
       "0        7570  \n",
       "1        4850  \n",
       "2        5095  \n",
       "3        5000  \n",
       "4        7800  \n",
       "\n",
       "[5 rows x 21 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "a90cce7f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\Admin\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\ydata_profiling\\profile_report.py:354: UserWarning: Try running command: 'pip install --upgrade Pillow' to avoid ValueError\n",
      "  warnings.warn(\n",
      "Summarize dataset: 100%|██████████████████████████████████████████████████| 319/319 [00:46<00:00,  6.82it/s, Completed]\n",
      "Generate report structure: 100%|█████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.44s/it]\n",
      "Render HTML: 100%|███████████████████████████████████████████████████████████████████████| 1/1 [00:07<00:00,  7.08s/it]\n",
      "Export report to file: 100%|█████████████████████████████████████████████████████████████| 1/1 [00:00<00:00, 26.37it/s]\n"
     ]
    }
   ],
   "source": [
    "prof=ProfileReport(df)\n",
    "prof.to_file(output_file='house_price_profile.html')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "02f4bf2e",
   "metadata": {},
   "outputs": [],
   "source": [
    "prof."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}