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"
    }
   },
   "cell_type": "markdown",
   "id": "historical-challenge",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "![jsc-logo.jpg](attachment:67258d94-84e6-4a0c-ae8f-c74332ec082e.jpg)\n",
    "Author: [Jens Henrik Göbbert](mailto:j.goebbert@fz-juelich.de)\n",
    "------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "friendly-introduction",
   "metadata": {
    "toc-hr-collapsed": false
   },
   "source": [
    "# JupyterLab Tour\n",
    "\n",
    "This is the first time you are using JupyterLab? Let us have a look at the user interface and some general concepts.\n",
    "\n",
    "This notebook contains material and instructions for the JupyterLab tutorial during Scipy 2019.\n",
    "- https://github.com/jupyterlab/scipy2019-jupyterlab-tutorial\n",
    "\n",
    "-------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "minute-cooling",
   "metadata": {},
   "source": [
    "## JupyterLab Coding\n",
    " \n",
    "**Attention:** be aware of the current `Mode` == Edit or Command\n",
    "- Command -> Edit: Press ENTER\n",
    "- Edit -> Command: Press ESC\n",
    "\n",
    "--------------------------------------------------"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "serial-adventure",
   "metadata": {},
   "source": [
    "#### Exercise 1:\n",
    "1. Run selected cell - `CTRL + Enter` \n",
    "2. Run selected cell and move to next - `SHIFT + Enter`\n",
    "3. Run selected cell and move to a new cell below - `ALT + Enter`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "green-thailand",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "59"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from random import randint\n",
    "randint(1, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "connected-emission",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "cloudy-withdrawal",
   "metadata": {},
   "source": [
    "###  Contextual Help"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bulgarian-emerald",
   "metadata": {},
   "source": [
    "#### Exercise 2:\n",
    "1. Import `pandas`\n",
    "   - !! must be in the namespace before help can work !!\n",
    "2. Get max. help on the pandas library running `pandas?`\n",
    "    - find file path of pandas\n",
    "    - even get the source code shown as help with `pandas??`\n",
    "3. Get help on the signature `pandas.DataFrame()` placing the cursor between the brackets and pressing `SHIFT + Tab`\n",
    "4. Show `Contextual Help` beside the notebook - `CTRL + I` or Menu:Help->Show Contextual Help or Launcher:Show Contextual Help\n",
    "5. Inspect live object `pandas.D<tab>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "legal-movie",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "pointed-orleans",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module 'pandas' from '/p/software/hdfcloud/stages/2020/software/SciPy-Stack/2020-gcccoremkl-9.3.0-2020.2.254-Python-3.8.5/lib/python3.8/site-packages/pandas-1.1.0-py3.8-linux-x86_64.egg/pandas/__init__.py'>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "external-appendix",
   "metadata": {},
   "outputs": [],
   "source": [
    "pandas?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "opposed-springfield",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\u001b[0;31mType:\u001b[0m        module\n",
       "\u001b[0;31mString form:\u001b[0m <module 'pandas' from '/p/software/hdfcloud/stages/2020/software/SciPy-Stack/2020-gcccoremkl-9.3.0-2020.2.254-Python-3.8.5/lib/python3.8/site-packages/pandas-1.1.0-py3.8-linux-x86_64.egg/pandas/__init__.py'>\n",
       "\u001b[0;31mFile:\u001b[0m        /p/software/hdfcloud/stages/2020/software/SciPy-Stack/2020-gcccoremkl-9.3.0-2020.2.254-Python-3.8.5/lib/python3.8/site-packages/pandas-1.1.0-py3.8-linux-x86_64.egg/pandas/__init__.py\n",
       "\u001b[0;31mSource:\u001b[0m     \n",
       "\u001b[0;31m# flake8: noqa\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0m__docformat__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"restructuredtext\"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;31m# Let users know if they're missing any of our hard dependencies\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0mhard_dependencies\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\"numpy\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"pytz\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"dateutil\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0mmissing_dependencies\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;32mfor\u001b[0m \u001b[0mdependency\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mhard_dependencies\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0m__import__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdependency\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mmissing_dependencies\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"{dependency}: {e}\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;32mif\u001b[0m \u001b[0mmissing_dependencies\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mraise\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;34m\"Unable to import required dependencies:\\n\"\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0;34m\"\\n\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjoin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmissing_dependencies\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;32mdel\u001b[0m \u001b[0mhard_dependencies\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdependency\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmissing_dependencies\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;31m# numpy compat\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnumpy\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0m_np_version_under1p16\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0m_np_version_under1p17\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0m_np_version_under1p18\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0m_is_numpy_dev\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_libs\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mhashtable\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0m_hashtable\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlib\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0m_lib\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtslib\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0m_tslib\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;32mexcept\u001b[0m \u001b[0mImportError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# pragma: no cover\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;31m# hack but overkill to use re\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmodule\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreplace\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"cannot import name \"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mraise\u001b[0m \u001b[0mImportError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;34mf\"C extension: {module} not built. If you want to import \"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;34m\"pandas from the source directory, you may need to run \"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;34m\"'python setup.py build_ext --inplace --force' to build the C extensions first.\"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_config\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mget_option\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mset_option\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mreset_option\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdescribe_option\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0moption_context\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0moptions\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;31m# let init-time option registration happen\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mconfig_init\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;31m# dtype\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mInt8Dtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mInt16Dtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mInt32Dtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mInt64Dtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mUInt8Dtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mUInt16Dtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mUInt32Dtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mUInt64Dtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mCategoricalDtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mPeriodDtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mIntervalDtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mDatetimeTZDtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mStringDtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mBooleanDtype\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;31m# missing\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mNA\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0misna\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0misnull\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mnotna\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mnotnull\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;31m# indexes\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mIndex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mCategoricalIndex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mInt64Index\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mUInt64Index\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mRangeIndex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mFloat64Index\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mMultiIndex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mIntervalIndex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mTimedeltaIndex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mDatetimeIndex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mPeriodIndex\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mIndexSlice\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;31m# tseries\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mNaT\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mPeriod\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mperiod_range\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mTimedelta\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mtimedelta_range\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mTimestamp\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdate_range\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mbdate_range\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mInterval\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0minterval_range\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mDateOffset\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;31m# conversion\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mto_numeric\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mto_datetime\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mto_timedelta\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;31m# misc\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mGrouper\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mfactorize\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0munique\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mvalue_counts\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mNamedAgg\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mCategorical\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mset_eng_float_format\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mSeries\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mDataFrame\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msparse\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSparseDtype\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtseries\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0minfer_freq\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtseries\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0moffsets\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcomputation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0meval\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreshape\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mconcat\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mlreshape\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmelt\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mwide_to_long\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmerge\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmerge_asof\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mmerge_ordered\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcrosstab\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mpivot\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mpivot_table\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mget_dummies\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mcut\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mqcut\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_print_versions\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mshow_versions\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mapi\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;31m# excel\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mExcelFile\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mExcelWriter\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mread_excel\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;31m# parsers\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mread_csv\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mread_fwf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mread_table\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;31m# pickle\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mread_pickle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mto_pickle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;31m# pytables\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mHDFStore\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mread_hdf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;31m# sql\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mread_sql\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mread_sql_query\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mread_sql_table\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;31m# misc\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mread_clipboard\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mread_parquet\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mread_orc\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mread_feather\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mread_gbq\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mread_html\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mread_json\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mread_stata\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mread_sas\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mread_spss\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mio\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mjson\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0m_json_normalize\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mjson_normalize\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_tester\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mtest\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtesting\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;31m# use the closest tagged version if possible\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;32mfrom\u001b[0m \u001b[0;34m.\u001b[0m\u001b[0m_version\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mget_versions\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0mv\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mget_versions\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0m__version__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"closest-tag\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"version\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0m__git_version__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"full-revisionid\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;32mdel\u001b[0m \u001b[0mget_versions\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mv\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;31m# GH 27101\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;31m# TODO: remove Panel compat in 1.0\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;32mif\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcompat\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mPY37\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mdef\u001b[0m \u001b[0m__getattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mimport\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"Panel\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m\"The Panel class is removed from pandas. Accessing it \"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m\"from the top-level namespace will also be removed in the next version\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mFutureWarning\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mclass\u001b[0m \u001b[0mPanel\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mreturn\u001b[0m \u001b[0mPanel\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32melif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"datetime\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m\"The pandas.datetime class is deprecated \"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m\"and will be removed from pandas in a future version. \"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m\"Import from datetime module instead.\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mFutureWarning\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mfrom\u001b[0m \u001b[0mdatetime\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdatetime\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdt\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mreturn\u001b[0m \u001b[0mdt\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32melif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"np\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m\"The pandas.np module is deprecated \"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m\"and will be removed from pandas in a future version. \"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m\"Import numpy directly instead\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mFutureWarning\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mreturn\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32melif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"SparseSeries\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"SparseDataFrame\"\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34mf\"The {name} class is removed from pandas. Accessing it from \"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m\"the top-level namespace will also be removed in the next version\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mFutureWarning\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mreturn\u001b[0m \u001b[0mtype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32melif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m\"SparseArray\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m\"The pandas.SparseArray class is deprecated \"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m\"and will be removed from pandas in a future version. \"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m\"Use pandas.arrays.SparseArray instead.\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mFutureWarning\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msparse\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSparseArray\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0m_SparseArray\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mreturn\u001b[0m \u001b[0m_SparseArray\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"module 'pandas' has no attribute '{name}'\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mclass\u001b[0m \u001b[0mPanel\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mclass\u001b[0m \u001b[0mSparseDataFrame\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mclass\u001b[0m \u001b[0mSparseSeries\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mpass\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mclass\u001b[0m \u001b[0m__numpy\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mdef\u001b[0m \u001b[0m__init__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mimport\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarnings\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mdef\u001b[0m \u001b[0m__getattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m\"The pandas.np module is deprecated \"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m\"and will be removed from pandas in a future version. \"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m\"Import numpy directly instead\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mFutureWarning\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf\"module numpy has no attribute {item}\"\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mnp\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m__numpy\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mclass\u001b[0m \u001b[0m__Datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mfrom\u001b[0m \u001b[0mdatetime\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdatetime\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdt\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mdatetime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdt\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mdef\u001b[0m \u001b[0m__getattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0memit_warning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mexcept\u001b[0m \u001b[0mAttributeError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                    \u001b[0;34mf\"module datetime has no attribute {item}\"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mdef\u001b[0m \u001b[0m__instancecheck__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mreturn\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdatetime\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mclass\u001b[0m \u001b[0m__DatetimeSub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetaclass\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m__Datetime\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mdef\u001b[0m \u001b[0memit_warning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdummy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mimport\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m\"The pandas.datetime class is deprecated \"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m\"and will be removed from pandas in a future version. \"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m\"Import from datetime instead.\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mFutureWarning\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mdef\u001b[0m \u001b[0m__new__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0memit_warning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mfrom\u001b[0m \u001b[0mdatetime\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mdatetime\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mdt\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mreturn\u001b[0m \u001b[0mdt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mdatetime\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m__DatetimeSub\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mclass\u001b[0m \u001b[0m__SparseArray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msparse\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSparseArray\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msa\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0mSparseArray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msa\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mdef\u001b[0m \u001b[0m__instancecheck__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mother\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mreturn\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mother\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSparseArray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0;32mclass\u001b[0m \u001b[0m__SparseArraySub\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmetaclass\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0m__SparseArray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mdef\u001b[0m \u001b[0memit_warning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdummy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mimport\u001b[0m \u001b[0mwarnings\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mwarnings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mwarn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m\"The pandas.SparseArray class is deprecated \"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m\"and will be removed from pandas in a future version. \"\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0;34m\"Use pandas.arrays.SparseArray instead.\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mFutureWarning\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m                \u001b[0mstacklevel\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m        \u001b[0;32mdef\u001b[0m \u001b[0m__new__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcls\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0mcls\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0memit_warning\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msparse\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mSparseArray\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0msa\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m            \u001b[0;32mreturn\u001b[0m \u001b[0msa\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m    \u001b[0mSparseArray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m__SparseArraySub\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0;31m# module level doc-string\u001b[0m\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\u001b[0m__doc__\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m\"\"\"\u001b[0m\n",
       "\u001b[0;34mpandas - a powerful data analysis and manipulation library for Python\u001b[0m\n",
       "\u001b[0;34m=====================================================================\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m**pandas** is a Python package providing fast, flexible, and expressive data\u001b[0m\n",
       "\u001b[0;34mstructures designed to make working with \"relational\" or \"labeled\" data both\u001b[0m\n",
       "\u001b[0;34measy and intuitive. It aims to be the fundamental high-level building block for\u001b[0m\n",
       "\u001b[0;34mdoing practical, **real world** data analysis in Python. Additionally, it has\u001b[0m\n",
       "\u001b[0;34mthe broader goal of becoming **the most powerful and flexible open source data\u001b[0m\n",
       "\u001b[0;34manalysis / manipulation tool available in any language**. It is already well on\u001b[0m\n",
       "\u001b[0;34mits way toward this goal.\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34mMain Features\u001b[0m\n",
       "\u001b[0;34m-------------\u001b[0m\n",
       "\u001b[0;34mHere are just a few of the things that pandas does well:\u001b[0m\n",
       "\u001b[0;34m\u001b[0m\n",
       "\u001b[0;34m  - Easy handling of missing data in floating point as well as non-floating\u001b[0m\n",
       "\u001b[0;34m    point data.\u001b[0m\n",
       "\u001b[0;34m  - Size mutability: columns can be inserted and deleted from DataFrame and\u001b[0m\n",
       "\u001b[0;34m    higher dimensional objects\u001b[0m\n",
       "\u001b[0;34m  - Automatic and explicit data alignment: objects can be explicitly aligned\u001b[0m\n",
       "\u001b[0;34m    to a set of labels, or the user can simply ignore the labels and let\u001b[0m\n",
       "\u001b[0;34m    `Series`, `DataFrame`, etc. automatically align the data for you in\u001b[0m\n",
       "\u001b[0;34m    computations.\u001b[0m\n",
       "\u001b[0;34m  - Powerful, flexible group by functionality to perform split-apply-combine\u001b[0m\n",
       "\u001b[0;34m    operations on data sets, for both aggregating and transforming data.\u001b[0m\n",
       "\u001b[0;34m  - Make it easy to convert ragged, differently-indexed data in other Python\u001b[0m\n",
       "\u001b[0;34m    and NumPy data structures into DataFrame objects.\u001b[0m\n",
       "\u001b[0;34m  - Intelligent label-based slicing, fancy indexing, and subsetting of large\u001b[0m\n",
       "\u001b[0;34m    data sets.\u001b[0m\n",
       "\u001b[0;34m  - Intuitive merging and joining data sets.\u001b[0m\n",
       "\u001b[0;34m  - Flexible reshaping and pivoting of data sets.\u001b[0m\n",
       "\u001b[0;34m  - Hierarchical labeling of axes (possible to have multiple labels per tick).\u001b[0m\n",
       "\u001b[0;34m  - Robust IO tools for loading data from flat files (CSV and delimited),\u001b[0m\n",
       "\u001b[0;34m    Excel files, databases, and saving/loading data from the ultrafast HDF5\u001b[0m\n",
       "\u001b[0;34m    format.\u001b[0m\n",
       "\u001b[0;34m  - Time series-specific functionality: date range generation and frequency\u001b[0m\n",
       "\u001b[0;34m    conversion, moving window statistics, date shifting and lagging.\u001b[0m\n",
       "\u001b[0;34m\"\"\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pandas??"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prerequisite-result",
   "metadata": {},
   "source": [
    "6. Enable scrolling from Outputs -> from Context Menu\n",
    "7. Check Property Inspector -> Advance Tools -> Cell Metadata\n",
    "```\n",
    "{\n",
    "    \"scrolled\": true\n",
    "}\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "curious-tuition",
   "metadata": {},
   "outputs": [],
   "source": [
    "pandas.D<tab>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "thousand-vanilla",
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pandas.DataFrame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "breeding-exception",
   "metadata": {},
   "source": [
    "**Extension: JupyterLab Language Server Protocol integration**\n",
    "* Place your cursor on a variable, function, etc -> all the usages will be highlighted\n",
    "* Hover + `CTRL` for tooltip with function/class signature, module documentation, etc.\n",
    "* `.`(dot) automatically triggers completion\n",
    "* Completion suggestions as you type -> Advanced Settings Edior -> Code Completion -> continuousHinting\n",
    "* [more](https://github.com/krassowski/jupyterlab-lsp)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "arbitrary-presence",
   "metadata": {},
   "source": [
    "### Display Output"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ranging-postage",
   "metadata": {},
   "source": [
    "#### Exercise 3:\n",
    "1. Display a pandas dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "worst-spank",
   "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>x</th>\n",
       "      <th>y</th>\n",
       "      <th>color</th>\n",
       "      <th>size</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.451061</td>\n",
       "      <td>0.720827</td>\n",
       "      <td>0.468580</td>\n",
       "      <td>53.753660</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.688694</td>\n",
       "      <td>0.965666</td>\n",
       "      <td>0.963805</td>\n",
       "      <td>74.586615</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.084424</td>\n",
       "      <td>0.829743</td>\n",
       "      <td>0.836154</td>\n",
       "      <td>91.084317</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.176369</td>\n",
       "      <td>0.402909</td>\n",
       "      <td>0.717078</td>\n",
       "      <td>75.296755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.667270</td>\n",
       "      <td>0.741116</td>\n",
       "      <td>0.462383</td>\n",
       "      <td>79.985661</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          x         y     color       size\n",
       "0  0.451061  0.720827  0.468580  53.753660\n",
       "1  0.688694  0.965666  0.963805  74.586615\n",
       "2  0.084424  0.829743  0.836154  91.084317\n",
       "3  0.176369  0.402909  0.717078  75.296755\n",
       "4  0.667270  0.741116  0.462383  79.985661"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from matplotlib import pyplot as plt\n",
    "from matplotlib import style\n",
    "\n",
    "data = {\n",
    "    \"x\": np.random.rand(200),\n",
    "    \"y\": np.random.rand(200),\n",
    "    \"color\": np.random.rand(200),\n",
    "    \"size\": 100.0 * np.random.rand(200),\n",
    "}\n",
    "df = pd.DataFrame(data)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "alive-complexity",
   "metadata": {},
   "source": [
    "#### Exercise 4:\n",
    "1. Inline matplotlib graph"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "excess-draft",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Plotted with Matplotlib')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "style.use('seaborn-whitegrid')\n",
    "plt.scatter('x', 'y', c='color', s='size', data=df, cmap=plt.cm.Blues)\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('y')\n",
    "plt.title('Plotted with Matplotlib')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "dangerous-exchange",
   "metadata": {},
   "source": [
    "#### Exercise 5:\n",
    "1. Collapse output\n",
    "   - click the blue bar\n",
    "   - Menu:View -> Collapse All Output\n",
    "2. Output Cell (Context Menu) -> Create New View For Output\n",
    "   - position Output View by dragging the tab\n",
    "   - update graph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "electrical-finnish",
   "metadata": {},
   "source": [
    "#### Exercise 6:\n",
    "1. Format cell starting with `%matplotlib inline` above\n",
    "    - Context Menu -> Format cell\n",
    "    - Menu:Edit -> Apply * Formatter\n",
    "      - Settings: Advanced Settings Editor -> `JupyterLab Code Formatter`"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "present-maldives",
   "metadata": {},
   "source": [
    "### Misc"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "married-egypt",
   "metadata": {},
   "source": [
    "#### Exercise 6:\n",
    "1. List the content of the current directory using a shell command.\n",
    "2. Run Javascript"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "southeast-mechanics",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "bin   dev  home  lib32\tlibx32\tmnt  p\t   root  sbin  sys  usr\n",
      "boot  etc  lib\t lib64\tmedia\topt  proc  run\t srv   tmp  var\n"
     ]
    }
   ],
   "source": [
    "!ls /"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "unusual-loading",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "alert(\"Hello World\")"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from IPython.display import Javascript\n",
    "Javascript('alert(\"Hello World\")')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "neural-kenya",
   "metadata": {},
   "source": [
    "#### Trust a Notebook\n",
    "Output can become a security issue as it can contain Javascript\n",
    "- a notebook is only trusted, if you run all code inside\n",
    "- if a notebook is not trusted some output cells might not be displayed\n",
    "- check statusbar icon\n",
    "  - Commands Panel -> Trust Notebook\n",
    "  - Run all\n",
    "  - statusbar -> trusted\n",
    "- [more](https://jupyter-notebook.readthedocs.io/en/latest/security.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "advanced-yield",
   "metadata": {},
   "source": [
    "#### Exercise 7:\n",
    "1. Copy cells in your notebook viewing the same notebook side-by.side\n",
    "   - copy = drag cells from one view to the other"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "prospective-compatibility",
   "metadata": {},
   "source": [
    "### Console\n",
    "No out-of-order execution. But all features of a code-cell in a notebook\n",
    "- eg. usefull for developing a notebook and testing stuff in between\n",
    "- [more](https://jupyterlab.readthedocs.io/en/2.2.x/user/code_console.html#code-consoles)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "auburn-filename",
   "metadata": {},
   "source": [
    "#### Exercise 8:\n",
    "1. Try `%matplotlib inline` in a Python console\n",
    "   - Execute code - `SHIFT + Enter`\n",
    "2. Sync with notebook\n",
    "   - Console -> Context Menu -> Show All Kernel Activity "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "resistant-alliance",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f30658725b0>]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "from matplotlib import pyplot as plt\n",
    "plt.plot(range(100))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "organizational-trade",
   "metadata": {},
   "source": [
    "#### Exercise 9:\n",
    "Shared namespace between notebooks and console\n",
    "1. Attach a console to a notebook\n",
    "   - Context Menu -> New Console for Notebook\n",
    "   - Menu:File -> New Console for Notebook\n",
    "   - `%whos`\n",
    "2. Drag notebook cell to the console\n",
    "3. Sync notebook execution with console\n",
    "   - Console -> Context Menu -> Show All Kernel Activity \n",
    "4. Command Panel -> Run Selected Text or Current Line in Console\n",
    "   - shortcut: \"notebook:run-in-console\"\n",
    "4. Attach same console to a different kernel running from another notebook\n",
    "   - Command Panel -> Change Kernel ..."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.8.5"
  },
  "toc-autonumbering": false
 },
 "nbformat": 4,
 "nbformat_minor": 5
}