Pandas/Pandas-Data-Report.ipynb

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   "source": [
    "# Daily-Dose-of-Data-Science\n",
    "\n",
    "[Daily Dose of Data Science](https://avichawla.substack.com) is a publication on Substack that brings together intriguing frameworks, libraries, technologies, and tips that make the life cycle of a Data Science project effortless. \n",
    "\n",
    "Author: Avi Chawla\n",
    "\n",
    "[Medium](https://medium.com/@avi_chawla) | [LinkedIn](https://www.linkedin.com/in/avi-chawla/)\n",
    "\n",
    "# A No-code Tool To Understand Your Data Quickly\n",
    "\n",
    "Post Link: [Substack](https://avichawla.substack.com/p/a-no-code-tool-to-understand-your)\n",
    "\n",
    "LinkedIn Post: [LinkedIn](https://www.linkedin.com/posts/avi-chawla_python-datascience-dataanalysis-activity-7005485867913908228-bsT3?utm_source=share&utm_medium=member_desktop)"
   ]
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     "text": [
      "Requirement already satisfied: pandas-profiling in /Users/avi/anaconda3/lib/python3.9/site-packages (3.3.0)\n",
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     ]
    }
   ],
   "source": [
    "!pip install pandas-profiling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "66e8a0a1",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-05T11:05:08.795416Z",
     "start_time": "2022-12-05T11:05:07.098138Z"
    }
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "from sklearn.datasets import load_iris"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "e1b948ab",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-05T11:05:08.808413Z",
     "start_time": "2022-12-05T11:05:08.796462Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import numpy as np'); }\n",
       "    "
      ],
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       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "iris = load_iris()\n",
    "iris_data = pd.DataFrame(data= np.c_[iris['data'], iris['target']],\n",
    "                     columns= iris['feature_names'] + ['target'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a73d0786",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-05T11:05:20.761354Z",
     "start_time": "2022-12-05T11:05:08.811679Z"
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   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "5e7c5ee3341445ca842e6e903eca945b",
       "version_major": 2,
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      "text/plain": [
       "Summarize dataset:   0%|          | 0/5 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
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    {
     "data": {
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       "version_minor": 0
      },
      "text/plain": [
       "Generate report structure:   0%|          | 0/1 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "",
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      "text/plain": [
       "Render widgets:   0%|          | 0/1 [00:00<?, ?it/s]"
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     "metadata": {},
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      "text/plain": [
       "VBox(children=(Tab(children=(Tab(children=(GridBox(children=(VBox(children=(GridspecLayout(children=(HTML(valu…"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from pandas_profiling import ProfileReport\n",
    "\n",
    "profile = ProfileReport(iris_data, title=\"Pandas Profiling Report\")\n",
    "\n",
    "profile.to_widgets()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3f9f9656",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2022-12-05T11:08:23.262690Z",
     "start_time": "2022-12-05T11:08:23.233173Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<video src=\"../_Extras/pandas profiling.mp4\" controls  >\n",
       "      Your browser does not support the <code>video</code> element.\n",
       "    </video>"
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      "text/plain": [
       "<IPython.core.display.Video object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Video\n",
    "Video(\"../_Extras/pandas profiling.mp4\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "62b120df",
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