pandas-profiling.ipynb

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   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "import pandas_profiling"
   ]
  },
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   "source": [
    "df = pd.read_csv(\"Neighborhood_Zhvi_AllHomes.csv\")"
   ]
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    "\n",
    "profile = df.profile_report(pool_size=8, \n",
    "                                      plot={'histogram': \n",
    "                                            {'bins': 50, \n",
    "                                             'bayesian_blocks_bins': False}},\n",
    "                                      correlations={'kendall': False,\n",
    "                                                    'phi_k': False,\n",
    "                                                    'cramers': False,\n",
    "                                                    'recoded': False},\n",
    "                                     missing_diagrams={'bar': False,\n",
    "                                                       'matrix': False,\n",
    "                                                       'heatmap': False,\n",
    "                                                       'dendrogram': False\n",
    "                                                      }\n",
    "                                     )"
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    "profile"
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   "source": [
    "df_sample = df[['RegionID', 'RegionName', 'City', 'State', 'Metro', 'CountyName', '2019-02']]\n",
    "\n",
    "df_sample[\"price_log\"] = np.log10(df_sample.loc[:,'2019-02'])"
   ]
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     "start_time": "2019-08-29T15:56:05.172398Z"
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   "outputs": [],
   "source": [
    "df_sample.to_csv(\"zillow-sample.csv\", index = False)"
   ]
  },
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     "start_time": "2019-08-29T09:05:57.949020Z"
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   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
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   "cell_type": "code",
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    "ExecuteTime": {
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   "outputs": [],
   "source": [
    "plt.plot(df_sample.price_log, df_sample['2019-02'], '.')"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": [
    "#df_sample.head()"
   ]
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   "outputs": [],
   "source": [
    "import multiprocessing"
   ]
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    "df_profile = df_sample.profile_report(pool_size=0, \n",
    "                                      plot={'histogram': \n",
    "                                            {'bins': 50, \n",
    "                                             'bayesian_blocks_bins': False}},\n",
    "                                      correlations={'kendall': False,\n",
    "                                                    'phi_k': False,\n",
    "                                                    'cramers': False,\n",
    "                                                    'recoded': False},\n",
    "                                     missing_diagrams={'bar': False,\n",
    "                                                       'matrix': False,\n",
    "                                                       'heatmap': False,\n",
    "                                                       'dendrogram': False\n",
    "                                                      }\n",
    "                                     )"
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    "df_profile"
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