DAwPy_S1_(Numpy_Arrays)/daily_schedule.ipynb

{
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  {
   "cell_type": "markdown",
   "id": "a1a10f50-ac18-4421-bfd6-393f24abc397",
   "metadata": {},
   "source": [
    "<a href=\"https://colab.research.google.com/github/4dsolutions/clarusway_data_analysis/blob/main/DAwPy_S1_(Numpy_Arrays)/daily_schedule.ipynb\"><img align=\"left\" src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open in Colab\" title=\"Open and Execute in Google Colaboratory\"></a><br/>\n",
    "[![nbviewer](https://raw.githubusercontent.com/jupyter/design/master/logos/Badges/nbviewer_badge.svg)](https://nbviewer.org/github/4dsolutions/clarusway_data_analysis/blob/main/DAwPy_S1_%28Numpy_Arrays%29/daily_schedule.ipynb)\n",
    "\n",
    "___"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3d4fcd75-5688-490f-991c-f5e8b7c7e71c",
   "metadata": {
    "tags": []
   },
   "source": [
    "# Data Analysis with Python\n",
    "\n",
    "<a data-flickr-embed=\"true\" href=\"https://www.flickr.com/photos/kirbyurner/52136642608/in/dateposted-public/\" title=\"ds_paths\"><img src=\"https://live.staticflickr.com/65535/52136642608_bd45cb00a9_c.jpg\" width=\"800\" height=\"261\" alt=\"ds_paths\"></a><script async src=\"//embedr.flickr.com/assets/client-code.js\" charset=\"utf-8\"></script>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9fbb76c5-cb37-41bf-8de9-3452cbb2144a",
   "metadata": {},
   "source": [
    "## Sessions on Github\n",
    "\n",
    "* [Subfolder on Github](https://github.com/4dsolutions/clarusway_data_analysis)\n",
    "* [This Notebook on Github](https://github.com/4dsolutions/clarusway_data_analysis/blob/main/DAwPy_S1_(Numpy_Arrays)/daily_schedule.ipynb)\n",
    "\n",
    "These curriculum materials are under active development so you may want to 'git clone' the repo locally, and occassionally `git pull` the updates.  \n",
    "\n",
    "You may also grab the repository as a single `zip` file, through the Github GUI."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ce811caa-3c50-444c-b268-ca627b292846",
   "metadata": {},
   "source": [
    "## Other Relevant Resources\n",
    "\n",
    "LMS:\n",
    "* [Clarusway LMS](https://lms.clarusway.com/)\n",
    "<br />*Log in and find your course home page.*\n",
    "\n",
    "Instructor Albums:\n",
    "* [Clarusway Album](https://flic.kr/s/aHBqjzCs82)\n",
    "* [Intro to Python](https://flic.kr/s/aHsknZHG75)\n",
    "* [Python for Data Science](https://flic.kr/s/aHsmfpMPL3)\n",
    "\n",
    "In Class Tools:\n",
    "\n",
    "* [Slack](https://slack.com/)\n",
    "* [PearDeck](https://joinpd.com)\n",
    "\n",
    "Sandboxes:\n",
    "* [Regex 101](https://regex101.com/)\n",
    "* [Codepen.io](https://codepen.io/)\n",
    "* [Replit](https://replit.com/~)\n",
    "* [Flask Demo](http://thekirbster.pythonanywhere.com/)\n",
    "\n",
    "Downloads:\n",
    "* [Anaconda](https://www.anaconda.com/download) -- add more with conda install\n",
    "* [PyPI](https://pypi.org/) -- add more with pip install\n",
    "* [Python Home Page](https://python.org)\n",
    "\n",
    "Tip:  Keep curating your own bookmarks, in your browser, in SQL, in Notebooks.  Good practice.  Valuable content.  See **Bookmarks Project** below."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50efa356-891a-4612-a39d-23255e78e8d1",
   "metadata": {},
   "source": [
    "## Our Daily Schedule\n",
    "\n",
    "Substitute the start time in your timezone. [World Clock](https://www.timeanddate.com/worldclock/).\n",
    "\n",
    "<a data-flickr-embed=\"true\" href=\"https://www.flickr.com/photos/kirbyurner/52748129764/in/datetaken/\" title=\"dymaxion_timezones\"><img src=\"https://live.staticflickr.com/65535/52748129764_80f772ef03.jpg\" width=\"500\" height=\"333\" alt=\"dymaxion_timezones\"/></a><script async src=\"//embedr.flickr.com/assets/client-code.js\" charset=\"utf-8\"></script><br />\n",
    "World Map showing Timezones\n",
    "\n",
    "Make a pandas DataFrame using the np.datetime type?  Think about it.\n",
    "\n",
    "\n",
    "* [Türkiye Time](https://www.timeanddate.com/worldclock/turkey/istanbul) UTC + 3\n",
    "* [Central European Time](https://www.timeanddate.com/time/zones/cet) UTC + 1\n",
    "* [Eastern Daylight Time](https://www.timeanddate.com/time/zones/pdt) UTC -4\n",
    "* [Pacific Daylight Time](https://www.timeanddate.com/time/zones/pdt) UTC -7 \n",
    "\n",
    "Weekday session segments (Eastern Daylight Time):\n",
    "\n",
    "* The first segment is between => 19:00 - 19:45  \n",
    "* 15-minute break between => 19:45 - 20:00\n",
    "* The second segment is between => 20:00 - 20:45 \n",
    "* 15-minute break between => 20:45 - 21:00 \n",
    "* The third segment is between => 21:00 - 21:45 \n",
    "\n",
    "Saturday session segments (Eastern Daylight Time):\n",
    "\n",
    "* The first segment is between => 10:00 - 10:45  \n",
    "* 15-minute break between => 10:45 - 11:00\n",
    "* The second segment is between => 11:00 - 11:45 \n",
    "* 15-minute break between => 11:45 - 12:00 \n",
    "* The third segment is between => 12:00 - 12:45 \n",
    "\n",
    "<a data-flickr-embed=\"true\" href=\"https://www.flickr.com/photos/kirbyurner/52867813382/in/datetaken/\" title=\"Screen Shot 2023-05-03 at 3.16.44 PM\"><img src=\"https://live.staticflickr.com/65535/52867813382_b92cbda7af_c.jpg\" width=\"800\" height=\"466\" alt=\"Screen Shot 2023-05-03 at 3.16.44 PM\"/></a><script async src=\"//embedr.flickr.com/assets/client-code.js\" charset=\"utf-8\"></script>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f06609d2-d454-41c3-bb2e-3db2550b4383",
   "metadata": {},
   "source": [
    "## Python Warmup Notebooks\n",
    "\n",
    "How familiar are you with Python?  \n",
    "\n",
    "We have some additional Notebooks to help bring you up to speed on this popular and important language.\n",
    "\n",
    "*  [Introduction to Python](https://github.com/4dsolutions/clarusway_data_analysis/blob/main/python_warm_up/warmup_python_intro.ipynb)\n",
    "*  [3rd Party Libraries](https://github.com/4dsolutions/clarusway_data_analysis/blob/main/python_warm_up/warmup_3rd_party_datascience.ipynb)\n",
    "*  [Object Types](https://github.com/4dsolutions/clarusway_data_analysis/blob/main/python_warm_up/warmup_data_structures.ipynb)\n",
    "*  [Object Oriented Paradigm](https://github.com/4dsolutions/clarusway_data_analysis/blob/main/python_warm_up/warmup_object_oriented.ipynb)\n",
    "*  [Calling Callables and Type Checking](warmup_callables.ipynb)\n",
    "*  [Class and Static Methods, Properties](https://github.com/4dsolutions/clarusway_data_analysis/blob/main/python_warm_up/warmup_object_oriented2.ipynb)\n",
    "*  [SQLite3 and Context Managers](https://github.com/4dsolutions/clarusway_data_analysis/blob/main/python_warm_up/warmup_object_sql.ipynb)\n",
    "*  [Iterators and Generators](https://github.com/4dsolutions/clarusway_data_analysis/blob/main/python_warm_up/warmup_generators.ipynb)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "263e64e4-1278-4bfc-8340-46ac366163d5",
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.display import YouTubeVideo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "0154f4d5-fcb2-4f59-909a-cbc955805c8a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Copyright (c) 2001-2023 Python Software Foundation.\n",
       "All Rights Reserved.\n",
       "\n",
       "Copyright (c) 2000 BeOpen.com.\n",
       "All Rights Reserved.\n",
       "\n",
       "Copyright (c) 1995-2001 Corporation for National Research Initiatives.\n",
       "All Rights Reserved.\n",
       "\n",
       "Copyright (c) 1991-1995 Stichting Mathematisch Centrum, Amsterdam.\n",
       "All Rights Reserved."
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "copyright"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "38190af2-3b64-4c97-85c7-a3675d6534ae",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "e24cc5f3-7b23-439f-aa0a-56744f0c8a12",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_array = numpy.array([1,2,3,4,5])\n",
    "my_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "94cfb73b-c59e-4aa9-b2b4-e3cbeb28c0ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 301,  602,  903, 1204, 1505])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_array * 301"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7b7ec2c-a3bb-42c0-8648-beadaf747cfc",
   "metadata": {
    "tags": []
   },
   "source": [
    "## Brief Overview\n",
    "\n",
    "A session-by-session overview of the core content.\n",
    "\n",
    "####  [ Session 1 ] numpy arrays\n",
    "\n",
    "The numpy package depends on Python, and introduces a new star of the show:  the numpy array, or nd-array.\n",
    "\n",
    "Array based computing removes a lot of the looping syntax, making concise expressions the new reality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "e5edabfe-c300-46e8-a995-2a36c1b9111e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [7, 8, 9]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "new_star = np.array(list(\"123456789\"), dtype=int).reshape(3,3)\n",
    "new_star"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7047ac00-7e48-493e-a666-22b5d5cfe870",
   "metadata": {},
   "source": [
    "####  [ Session 2 ] numpy Selection and Indexing \n",
    "\n",
    "Array based computing removes a lot of the looping syntax, making concise expressions the new reality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "a179c946-1e6f-4864-96d6-283aa8eddeb3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 3, 5, 7, 9])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a = np.arange(1, 10, 2)\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "6738629a-5535-4660-ae1c-b955b9634df7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 3., 5., 7., 9.])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b = np.linspace(1, 9, 5)\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "14abb5eb-4868-48eb-8cbb-cbca8c8cda33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.,  6., 10., 14., 18.])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c = a + b\n",
    "c"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6337c628-4043-40c2-a27c-be59a11c730d",
   "metadata": {},
   "source": [
    "####  [ Session 3 ]  pandas.Series\n",
    "\n",
    "The pd.Series \"wraps\" or \"includes\" a single dimensional numpy array, which one may extract using the .to_list method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "9bd9d158-90be-44c2-a997-c1c974695f57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     2.0\n",
       "1     6.0\n",
       "2    10.0\n",
       "3    14.0\n",
       "4    18.0\n",
       "dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "c_p = pd.Series(c)\n",
    "c_p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5d3fd9c3-32c7-4e3e-b589-89d6f474cf0f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 2.,  6., 10., 14., 18.])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c_p.to_numpy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5b0f8b39-c36f-4681-99ca-72ef57b66b7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a  1    0.436798\n",
       "   2    0.142265\n",
       "   3    0.502065\n",
       "b  1    0.711117\n",
       "   3    0.754188\n",
       "c  1    0.672215\n",
       "   2    0.999270\n",
       "d  2    0.405367\n",
       "   3    0.938490\n",
       "dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MultiIndex\n",
    "\n",
    "data = pd.Series(np.random.uniform(size=9),\n",
    "                 index=[[\"a\", \"a\", \"a\", \"b\", \"b\", \"c\", \"c\", \"d\", \"d\"],\n",
    "                        [1, 2, 3, 1, 3, 1, 2, 2, 3]])\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "0ca3afdf-734b-4b9c-a3f6-e858a3d267b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1  a    0.436798\n",
       "2  a    0.142265\n",
       "3  a    0.502065\n",
       "1  b    0.711117\n",
       "3  b    0.754188\n",
       "1  c    0.672215\n",
       "2  c    0.999270\n",
       "   d    0.405367\n",
       "3  d    0.938490\n",
       "dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.swaplevel(0, 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3660a111-14e2-48f0-afd7-9a7e0506a52d",
   "metadata": {},
   "source": [
    "####  [ Session 4 ]  pandas.DataFrame:  Selection and Indexing\n",
    "\n",
    "We have many ways to initialize a pandas DataFrame, getting us a \"cityscape\" of side-by-side Series (columnar objects, each with its own data type)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "9fb2a7d8-68eb-4542-8456-4070b3d293de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Col_A</th>\n",
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       "      <th>Col_C</th>\n",
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       "      <td>0.55</td>\n",
       "      <td>29</td>\n",
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       "      <th>mars</th>\n",
       "      <td>3</td>\n",
       "      <td>0.72</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>earth</th>\n",
       "      <td>5</td>\n",
       "      <td>0.60</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>neptune</th>\n",
       "      <td>7</td>\n",
       "      <td>0.54</td>\n",
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       "    <tr>\n",
       "      <th>saturn</th>\n",
       "      <td>9</td>\n",
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       "         Col_A  Col_B  Col_C\n",
       "mercury      1   0.55     29\n",
       "mars         3   0.72     15\n",
       "earth        5   0.60     17\n",
       "neptune      7   0.54     23\n",
       "saturn       9   0.42     12"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "rng     = np.random.RandomState(0)\n",
    "planets = pd.DataFrame({\"Col_A\": a, \n",
    "                   \"Col_B\": np.round(rng.uniform(0, 1, 5), 2) ,\n",
    "                   \"Col_C\": rng.randint(11, 30, 5)},\n",
    "                   index = ['mercury', 'mars', 'earth', \n",
    "                            'neptune', 'saturn'])\n",
    "planets"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2d5278d1-2034-4ba4-b74b-ccd29d971a2f",
   "metadata": {},
   "source": [
    "#### [ Session 5]  pandas.DataFrame:  GroupBy and Useful Operations (Part 1)\n",
    "\n",
    "With groupby, we're able to:  split, chunk, combine (aggregate)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "6a7270d3-2b4d-4066-bffa-9dd30e3d3d50",
   "metadata": {},
   "outputs": [
    {
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       "      <th>Colorado</th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">a</th>\n",
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       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">b</th>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Ohio     Colorado\n",
       "    Green Red    Green\n",
       "a 1     0   1        2\n",
       "  2     3   4        5\n",
       "b 1     6   7        8\n",
       "  2     9  10       11"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# MultiIndex\n",
    "\n",
    "frame = pd.DataFrame(np.arange(12).reshape((4, 3)),\n",
    "                    index=[[\"a\", \"a\", \"b\", \"b\"], [1, 2, 1, 2]],\n",
    "                    columns=[[\"Ohio\", \"Ohio\", \"Colorado\"],\n",
    "                             [\"Green\", \"Red\", \"Green\"]])\n",
    "frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "4b354498-e9f8-4647-bc4a-22c93df2aa26",
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th colspan=\"2\" halign=\"left\">Ohio</th>\n",
       "      <th>Colorado</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>Green</th>\n",
       "      <th>Red</th>\n",
       "      <th>Green</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
       "      <th>a</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>6</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
       "      <th>a</th>\n",
       "      <td>3</td>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>b</th>\n",
       "      <td>9</td>\n",
       "      <td>10</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Ohio     Colorado\n",
       "    Green Red    Green\n",
       "1 a     0   1        2\n",
       "  b     6   7        8\n",
       "2 a     3   4        5\n",
       "  b     9  10       11"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "frame.swaplevel(0,1).sort_index(level=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5f5f3e8a-e8a4-4b08-9f7b-5d679a075576",
   "metadata": {},
   "source": [
    "####  [ Session 6]  pandas.DataFrame:  GroupBy and Useful Operations (Part 2)\n",
    "\n",
    "With hierarchical indexing, we have the freedom swap columns for rows, potentially bringing about new insights into the data.\n",
    "\n",
    "[Check It Out!](https://nbviewer.org/github/4dsolutions/clarusway_data_analysis/blob/main/DAwPy_S5_6_%28Groupby_and_Useful_Operations%29/Advanced.ipynb) (what our class work really looks like)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "05d1e749-4bec-42de-beba-ff8dddceca46",
   "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>Col_A</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>far</th>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>near</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Col_A\n",
       "far       2\n",
       "near      3"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "system = planets.groupby([\"near\", \"near\", \"near\", \"far\", \"far\"])\n",
    "system[['Col_A']].agg('count')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d0a9e208-f879-4cb9-937f-d35ce0fa2fc8",
   "metadata": {},
   "source": [
    "####  [ Session 7 ]  Handling Missing Values\n",
    "\n",
    "These may be fine as is, but oft times we see them as problematic in the data.  We have cleaning tools."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "0f8fc2f1-0aa0-4e53-a68c-b036761456e9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(np.NaN) == type(None)  # nope!"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1aaf80ca-3661-41b3-9add-b448d012e3fb",
   "metadata": {},
   "source": [
    "####  [ Session 8 ]  Handling Outliers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "c661a660-1a46-4ab1-bc8d-14e7ca1203de",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 700x300 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "df = sns.load_dataset('diamonds')\n",
    "plt.figure(figsize = (7, 3))\n",
    "sns.boxplot(x = df['table']);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88b2c4e7-c881-4e4d-bc1d-0de7aa9d715c",
   "metadata": {},
   "source": [
    "####  [ Session 9 ] Combining DataFrames (Part 1)\n",
    "\n",
    "* df.join(other, on='key')\n",
    "\n",
    "    Connect rows with an index or key column on the left and the other's index column. Other may   be a list of indexed objects, in which case left index (not another column) must be used. [docs](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.join.html#)\n",
    "\n",
    "* pandas.merge(left, right, key='key') \n",
    "\n",
    "    Connect rows with any key or index on both the left and right. [docs](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.merge.html)\n",
    "\n",
    "* pandas.concat([df1, df2, ...], axis=1)\n",
    "\n",
    "    Concatenate or “stack” objects together along an axis. [docs](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.concat.html)\n",
    "\n",
    "* combine_first\n",
    "\n",
    "    Splice together overlapping data to fill in missing values in one object with values from another. [docs](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.combine_first.html#)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b272274f-80b6-4128-87ef-363f2d95cfff",
   "metadata": {},
   "outputs": [],
   "source": [
    "df1 = pd.DataFrame(dict(A=[10,11,12], B=[4,5,6]))\n",
    "df2 = pd.DataFrame(dict(C=[1,2,3],    D=[4,5,6]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dfdc3eb7-0dbc-440d-b6ea-465871ff1bed",
   "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>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A  B\n",
       "0  10  4\n",
       "1  11  5\n",
       "2  12  6"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "a3b7bbfe-c309-4628-b633-a397fc2e4543",
   "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>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   C  D\n",
       "0  1  4\n",
       "1  2  5\n",
       "2  3  6"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6e97ecc1-24cf-4aab-8ede-effff0b02cf5",
   "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",
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       "    }\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>E</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   E   F\n",
       "0  4  20\n",
       "1  5  21\n",
       "2  6  22"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3 = pd.DataFrame({'E': [4,5,6],\n",
    "                    'F': [20,21,22]})\n",
    "df3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "b6a12dfb-02cb-4bfb-b182-6714809eec10",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   C1    C2    C3    \n",
       "    A  B  C  D  E   F\n",
       "0  10  4  1  4  4  20\n",
       "1  11  5  2  5  5  21\n",
       "2  12  6  3  6  6  22"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([df1, df2, df3], axis=1, keys=['C1','C2','C3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "64f37cee-7228-476f-a64e-e832c945820e",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th rowspan=\"3\" valign=\"top\">C1</th>\n",
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       "      <td>NaN</td>\n",
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       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
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       "      <td>4.0</td>\n",
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       "      <th>1</th>\n",
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       "      <td>2.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">C3</th>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.0</td>\n",
       "      <td>20.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.0</td>\n",
       "      <td>21.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.0</td>\n",
       "      <td>22.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         A    B    C    D    E     F\n",
       "C1 0  10.0  4.0  NaN  NaN  NaN   NaN\n",
       "   1  11.0  5.0  NaN  NaN  NaN   NaN\n",
       "   2  12.0  6.0  NaN  NaN  NaN   NaN\n",
       "C2 0   NaN  NaN  1.0  4.0  NaN   NaN\n",
       "   1   NaN  NaN  2.0  5.0  NaN   NaN\n",
       "   2   NaN  NaN  3.0  6.0  NaN   NaN\n",
       "C3 0   NaN  NaN  NaN  NaN  4.0  20.0\n",
       "   1   NaN  NaN  NaN  NaN  5.0  21.0\n",
       "   2   NaN  NaN  NaN  NaN  6.0  22.0"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat({\"C1\":df1, \"C2\":df2, \"C3\":df3}, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "93f2a1f1-860f-438b-b326-eafa2f511de7",
   "metadata": {},
   "source": [
    "####  [ Session 10 ]  Combining DataFrames (Part 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dab8d902-7ec1-485d-be96-ade118721e18",
   "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>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>5</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>12</td>\n",
       "      <td>6</td>\n",
       "      <td>3</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A  B  C  D\n",
       "0  10  4  1  4\n",
       "1  11  5  2  5\n",
       "2  12  6  3  6"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1, df2, left_on='B', right_on='D', suffixes = ['_df1', '_df2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "b7298be0-e21a-44a6-ad1c-1ce6b0ef2c3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ? df1.join"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "112d8437-0215-40ef-97fc-3f4bedcfdbd3",
   "metadata": {},
   "source": [
    "Given `join` has no way to disambiguate the column names, name collisions in the output are  not OK.  Column names must be unique."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "4cf039fb-0fad-4cec-ae08-640ab7ed6c5b",
   "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>A</th>\n",
       "      <th>C</th>\n",
       "      <th>F</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>11</td>\n",
       "      <td>2</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>12</td>\n",
       "      <td>3</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A  C   F\n",
       "B           \n",
       "4  10  1  20\n",
       "5  11  2  21\n",
       "6  12  3  22"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.set_index('B').join([df2.set_index('D'), df3.set_index('E')])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0dad44fe-4210-49f7-a806-98cd0368a649",
   "metadata": {},
   "source": [
    "#### [ Session 11 ]  Working with Text and Time Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3185c40b-fa2b-4ea1-93bf-92cf00b50085",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.datetime64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.datetime64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "6f393fc6-23a9-4ca6-b3d4-7c8c9222d7dc",
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "from datetime import datetime"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7eb9e84d-9816-4910-a80f-568a6082d2dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65.0054794520548"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "today = datetime.now()\n",
    "then  = datetime.strptime(\"1958, 5, 17\", \"%Y, %m, %d\")\n",
    "(today - then).days/365"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4656b0cc-7dd0-4ec4-9aaa-9c4310a7bd25",
   "metadata": {},
   "source": [
    "####  [ Session 12 ]  Working with Text and Time Data / Regex & Data I/O"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "e36050ec-0b0c-4198-a89d-4304089f2aa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.datetime64('1970-01-01')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.datetime64(0, 'D')  # the start of an era"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "80b2246e-4d7b-4e93-a2bf-c471e86d7f5e",
   "metadata": {},
   "source": [
    "####   [ Session 13 ]  Regex & Data I/O"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ef36fc0d-48d1-451b-af4a-333426b72158",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "535390bc-673b-44fd-baac-b82c8a221356",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/jpeg": 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Tl+dKB707AN0+1e1om/SvE1mcP60mNby9vom8I5vFrQ42hE9wf+7hMS5+Ulwh+6r8+lbZzyQ2ojhlkiSWSWVo0L8siPRHqVctg8yTxYwNO/UVoX0IuFZnu58f0VvDEDt9qeSaR/ceCGHb0IPnV3y95VuvEjw1wyyYjCS7GMyyIJBE3mjlSNPUMSBsSM8mpZ3u979/Q6cLpqyvZe/qUz9F3gMzXxudDrBDDKhkZWVXklMQEa6gNWArM2OmFz1FYX06LtWn4fCMao4LqVh5gSyWSR/iYpP7p9Ku3vf7yV4UkbPbSz87UsZRo1QSKNWiRmbUupcsCFbZW9K4u7f9pZr+5mu7llDv5L9iKKMHREmd9KDJJ/WZmP61EFbTxY5Nzea3RGhSWPUnYep2H8a2Pu2t4jODzIshWCjWuoudAwBnfKs2MdcH0rqbuD7kra3t0v8AiUUclw6c1Y5wDDZxY1DUj+AzacM8jjwHwrjSWbeNfAOMa7XTZ3RUHwcsJIoGxeF9Kvtt9ZC222+9aaeKlCalp0TM1XDxnFx16tcDme4gIrDnjyCPUEVtXbvse/C7s2TO0tvJGZ7GWQ5k5aFVlt5D+s8BZCHxujr6YEILavVYbFRrU7+a+x5TE4SVGpbxTKy47EQSCNx1rUpoGZgqqSxOABuST5Vcl6tnMdLTQl+nhmQN8tm3+W9e1l2ZihOpE8RH2jktg+hPQH2xmuXDCJytGSt6nVnjMsLuLv6EZ2X4VyIUj/W+05Hm7bn7hsoPoBUnXrJHXmVruwtFJI4E80pOTG0UUlSIhRQ7hQWZgqqMszEBVA6kk7Ae5r14bPFKMxyxv+46t/gaqnWjF2bLoUZSV0jyxSGs6e2xWFItNTuJwsMpM0GimRIjtXHmP5MP8GqseNw9atHtG3gA9T/gD+dVxx5a8/tF/wA59yPQ7OX8ld7NSmFeRHtWRcCvA1jNh5tTTXoR70yosaEpppwpMUhobTaeRTTSGKKcKaKcKAFAoooFMQ4U5aaKeBTQmKKWkWlAqQCinAUgpwPvTFc6d+iQNVpOFX6xGuF1EeosW0g58xIDt6ffWxdoOyTXAUsGSSNjomjcxyBTjUhYfaU4HUjp1qH+gzfx8u9iJPMSaOXG2OXLGsefX7cWCegygO7CrouGCyyKfX+BrfSk8iOVWiu1ZQdz3brGDo1IDo1aX+2IwAisFXSyrgABiRsNthVp903DDaxuQuFKHTuT4fF4t9+vmfSvfjFvzSIovE7MAFUepA3PkB5k9AD6Vs3BrCXlsMZGGUZ/YQsp6Y8waOJFNsrXjvYvnyLKp8S4wHijmQkFSGKSA5IIGN8b+uMRPF+7ea4vDdTmF2dg8sZVlikcCz3eMhiyk20BMauqnRjADOGtzh0gikaFhur6N+mTkj8R0raoLBSRkD/Ki6tuuTUddGU/3Zd0cNpKbhvrJWJwBkQwqSWCxxnyUk6c9AcDFWv2xhD2kiMNQZCuk+erCgewyQPap/4dQNgNq17j10CcHGhPG3p4d1H446082Yg4WKi7PcGhtILy2aGGJpIY7jnFj9dDAySksSG08sh/s7YJ2zvXJck/MkaTGOY7SY9NbM2PuzXUnfldNFwu4vLk6bi6QcPtYMjEUUzFpiSuCzmASEnoDgYxXLfD0361lqO7ub6EbKxbH0euGczivDVx/wDNrL/4EdxL/AqK7V7w+7Hh/Ehm6t1MoGFuI/qrhfTEqYYgfsPqX2rlr6IHD9fFoD15NvdTfLUIYB/+Q/xq/PpTcXkhsoBFJJFJJeRgPGxRwqR3EjaWXcfZH41ypy/c2daMW3GKKX7yfoy3sGp7F1vohvy30Q3QHsTiGbzOcxk+SmuduO8OaKRopI3hmT7UMyNFKvzRwGx74wa6b7I/SJvbYhLtFvohtrGmG6UfP+hlx6ERk+bGor6Vfedw7itlYraMDOl5rlSWLl3MMa291lSGGdDOybxsyMVG5xVlKo0/syNSnff5o5hlT26U0D2rOuYaxmX3rpJ3Oe9Bn3UaPalC+9Cn3qREBt5VtXZbsrPLhtIRD0Z9s/uqAWPzwAfWpvsT2SCgSzDLHdIz0T0Zh5t5gH7Pz6bzC+DmujRwGaOafl+TmV9o5ZZYeZff0PeDcmwuX6mW/uFDYxlLUR2g2ydtcMhxk/aqnO13EDJd3nEQ4xHxB5FG+THZ3CRIwPusGcV6diO2vEuGpy7OeKa31SOLS7jAWNpZJZZDDcQhZEy7lgkiyDOd99tes7UrYm3ZtT/DOjtudUjI+ps9Tl2Jz51x1s2bclOO5O3fwOv/ABKCyuMt7SfdxOk/pW2PM4RI4GTBNbSj2BlSFj/4cr/dXMPdJ2eF3xOygdco9wHkHkY4FkuGU+z8oIf366l7SXI4h2alkjIcz8J5y4IP1othKAcdGEgAI6giqh7kGhHFuHyqFX4iK5C46F3txKuB0BKJJ0xmsKpuSlJdPqbe0UWovr9DoTvW7LSX9o9tHOIC7KWYoXDqp1ctgGUhWYLkgnYEYIJql+C93/ErS6sA1qjiG7ib4u2kDKsTSETB0YLKqmJmB201O/SO4vfW95aPazzQB4JVXQQUeVHRmVo3BjkbQwIUgkhWx51rPAPpE3kB0XtqlwB1eA8iYD1MUmY3b2DRiqXZye/lf1LopqKtZ8behtv0qoQTwo/ri6nUHz0NaXJcfLUsZ+4VpfdL3bpxWaWS61GwtZOUIQxUXdyoVpOaVwxt4NSroBw8mrOyYMN3u958XFLq0e2EyRW0MmRKnLbn3DIGUjJVtEUQ8Skr9aRnrVwdykrDs8GtvFPyr91xuWujNfN95MvT2xWhV2oOC3N379FoZ3Q+ZVHvSsumr1PSWDsw8v6NMPDDKG5YhFvEAJBty1lEYQSg7aVfWG261r/aD6Oirk8OvpbVc5W3mQXVuv8AVTWVlRT++xFVbwnlXEVvZ2x1TyvCkajeRJA0bSTt1ZTGA8rucdD61fX0ie01xawWgtJzBcTXYQMEST6pIbqSXUjghkGlc9Nyu4qEXZ3i9dP26b+BOcdLSXPfru4nNHFra5t7i4tJkS5uIbiG2QWQcieWaOOQIgl04dVdS2+B4h+rvhyX+mRYZoLq1mbIWO6t5YSxUMSFYgodgTs2+KtLuH4A0/FxLK3Na2W4v55MaQ13etLFF4ckKBGbjSuTpCpjoMSH0l+J8/iNvbA+Cxt2nceXxF3qijB/rJBHKcek4rqUMbWU4wv0d/Xy3eBzK+CouEp26q3pp1+5UywE9Ka0Vbt3Ud2sHF7u4W7WRrWzhQAJLJETd3B1htUbK2YYIxsdv9oG1a5247LxWPE7i1tZ7ma3ggh5i3Mom5dzLqk5cbaAwVIOWxDEkmbc7CuhHaOar2aXS5zpbOy0u0b62I3hKW0l9w+3u5oYbZ7gT3DTuiRtFa6ZRCS5CnnT8lNJ6rqrp667o+z96C62VkwbfmWhWLOfPXasmfnXNVvaJJhXRHHo6qw/BgavT6J3ZmOOC5vUjVBdTGOEKMD4e2LRhgPLmTc1tuoEZrmbTg+0zOzv6W9+p09mTXZWV01639+hWvfx3aWXDJLEWUt2s085ZoHuWkg+FgXXOzIw5h1M0UQ+sH9IT1FaRc9a3LvW498ZxK7nBzFAfgLf00wMTcOPLx3BZdQ6iJPatMk3NdDZUMtPM+P0OftWeapl5fU8TSYrLitiaWa2wCSOldOVVLec2NJvcat2il3x6D+J6/5VX/HW61u/Gx1rQONNvXmKlXtJuT4npqdLs4KPIgLivBlrLkjpnJNRuSsYhFNxWU0NebR0gszwxQaVhSUhjSKQ0402kMUU4U0U4UwYGlFIKUUCHCn4poxTlqREUUtJSimMWnLTRTwBTIs376PnEzDxiwbUUV5xC+CRqWYNGqNv4lMhjOk7ZCnyFdX95PEWhePSMtIXXUThV0mUlnOegVc++w28uGuH3bRuksZxJG6SIfR42V0P3MAa7uv+Ix8SsYLuEBlkVZFAwSrMAssf70bhkYdQQwrVQ1Tj4mHFqzUvAg+C3l1bScxGCSMpXxLnKsVPQ5HVRg/65sSy4lPoB0KupTsD+2S7YwMaSxb3GcY86rLtT2bntYIzHNJJiQ5ByREuEAjTbaINqILBsA4GkKFaSt+N3yKFIVhgYb6mTf0HgjPTzPy8jWnsmV0oOaurG0X8s0jK8+gMD9qNcZAxpznzAH8+UrHxZhtnJxkepHqPXyrWzNfONjEDpyCwBXPlnRgY+TfKsrhFnIAGnkDupJGlNCgsCMKuSdODnBLdBUZQsRd4smk485B29sn2qoe/XvZPDJLVFRZnmJkmQnB+HXIwD5M76SCQRiOQeeRakoGN/wCf9a4b79OONdcVvHbpHKYEH7KQNyiB83Dt82NUVW4x0LqUc8tSS72e8ubi8ySOgighBSCAHVoDaNUjttrlk0rkgAAKqgbFm1zhq79Kh7AbVN8Lxkb1Q23G5tgknZHVf0IrDN1eS4/orWCLP/GlmkI/CJTXQnb3svZcQVba6wXGZIgspjmT9UyIFIJA6HIZfUVU30I7HFpfTft3YhB9Rbwwj8A0jitS+lXf6uJgAkfDW0IDKSrJI73EhYMpBVtOjBBrmN6d7Oko3no7WQ3vI+jlexapLGRbxBkiGQrDcgeivtBKfduV99c9cZs5YpWinikhljOHimQo6Z3HhYfZYbhlyrDcEirg7I/SA4nZYV3W/hXbl3JKzAeiXSAsT7zJKfcVWPef2s/SPEbq+0PEtw8WiNyrMiRQW8QBKkr9pGOx887Zq2kmnYrqPmbN2H7mbviFgLyykiuXR5Ibi1fEE8csbdI5MmGVXjZJVWTlHEijUcb1xxjg8kTvHJG8ckbtHJG4AdHXYqwBIyPUEgjBBIIroL6GfankXz2rN9VfR+EHoLmAOy/IyQGQE+ZhjFZ30tuxixX4uwMJeRjWQP8A5iAJGennJCYsDqTG1ONVwer0E6Snp5HLjWxHlWzd33AuY5lceCMjA8mk6j7l2PzK+9XZ2U+jJeXNqZ5Z0s5nwYLaWHmeDc5uGVlkikfYhY86APECSVXUHt/gHns7nlRvZBOdJDMLiEmUnHjVFdZCesLoHAZeua6eBr05VFn0XX34nMx1Gcabyavp78DLpK9rTRIoeN1kQ9HRg6/3lJGfakkhNeljVjLVM8xKlKLs0eYenxy15EUmak0mRTaG2lnySzWks1k7ghzayGOOTIIIlgOYJAQd8pn3rK4Wjwx24t30zWZhe2d9xzIAFUSYxlJF1RuB1V2rGzT45MVilgKTTst+83Qx9VNXe7cdJ9ne2nC+PW/wl0qR3GxlsZ30TxyqP6S3fKtIoz4Z4T0bfGStax247hbeOGaYcSu44YY5JnEy29yESNWdtLyRq4AUHqx+dUrf28UyhZo0kA6a1BI/dPVfuIpr2GpGi+JvhC4KvAL665LoRhkaNpSpRhsVO2K4VTZVWL+Vr6PxO5S2rSkvmuvJrwHdgOyfxUnDYJGZHvHjWfBA0oIZbiYKf1XKRlAR0LZ8sV073g9uIuC/BW8dpm2ZXGIyqCNIhGAkSthXky+rSWXIB3JORWvcT2Ne+nF4ZDDbWNxiERheZcXMSkSksQdNvGHMJUDLnmDKhRmz+0PeTwaR3tLqSN0VyjNNAzWpkUkMnNZDFqQ7E5AByM5BrnYnKpPK7blfu3nRoZnFZlfe2u/cQJ76OCRF50ikFw48YjsXW4k89LSaAjHP7UmPeqL7dd4E3Ebz4mReUiKYrWANq5MbFS7Ow2aaUqpbT4QERRnBY3V2x7kLS4hM3C5RE7LrjTmGexm9iCWMYboJIWGnJOD0rnDi9got5pgHiliWZXiYhuXPCZI3jJ6+GRSM56DNRpU5ST6a/wCSc5wi1a+umv0On/otcN02Mt4/2r2d5ATgYgg+oi/snRJKD6S5qkLrtAtzPdXbEj4q4kmBPlAumK3H3W8cZ6dSa6I7wbQ2PAZobcH6ixW3QrklU0RwvJtvlELSE/1Sa537vuxc3E3EMCH4bKpPcHKxRxZAdEbH1kzJlQiZwSCxUUo1pU5prf8AkHRjVg76L8F+dwditlwgXU31ZuBLxG4JGCqOuqMNnpy7VIlIPQqa5t+MebmXMgIlvJZLqQHqvObUkf8A3cXLjHsldE/Sc4oIuHpZp4TfSpbYX9W2Qc242/ZMMZh/71a58uRlq6mzKd5ub4fV7/fU5e0p/Iorj9Fu99DyeOQqI4v6ad0t4P8AizssaH5Lq1n2U11F24v04LwZhB1treO2tQerTsEggyPMmRldvbUaqHuB4B8RxRZCMx8PhMx9Dc3IeKEeh0Qid/YtGfSpf6T/ABzm3drZKfBaob2f05snMhtVPuq8+Qj3jNU4uTrV8q7l9375FuEgqNG7739l75lPLAIo0iBzoUAk9WbqzE+ZZiWJ9TScO4bJcz21nCSs15OkKuMExRjMtzPpZSGEVukmBt42j6jYumyTWHLbxmdNMt0LhEdRHZNNzzHIULBltwZQraVzuoPnmutiZdlQyxaXDfY5eGj2tfNJN8d1zomx+jVwxRiSfiM7ftSXsik/2YRGo+QFYvGvo0WhB+Fvb+2fy1TC4iz/AFo5V1MP7YqmoeySjxvwvif/ABXt7wn5li2r8a6C+jMJ/hrjmNO0AuCtqLjUZFRUj1ga/HoDkgA9CrV5uT1tdPub/wAHo1FqN9V3o5P7yuztzY3ElndhDKiiRJYxiOeFiVWQA/ZOoFSvkQR0AJrv9GPNIkcaF5JZEijRerySMqIozgZLEbnYdfKugvpbcWWfirKhBFrbR27H1ld3ndffQrRg+hJrK+h92GE9699IuY7IaY89DdSqRn35UJJ9mlQ+VKM2tESlBP5mVB2l7mOKWcbzXVi8UUeNcomtpEGSqj7EpbdiANupFRnZ/u7vLoarazup0/bigdo//EICZ9gxNfQjvLSw5CvxFoxbRSLIVlP1cki6uWGj3MxByyxYbLAHSSoxrHYnvs4feXSWUCXCs4YQu8SpE/LRnKqA5dfApI1Io2xttQ5NOzkJK8bqJwb2p7FXNoQLq2nti32efEyK/XIVz4WPyzt1IxvqV9b48q+l30jbSF+C8S5ygqlrLImQDpljUtCy56MJAuMeuPOvm3xdx/AZ+eBU4N3sQkk43IKUV5CvWY15VoMwhpDTjTTQMUUtIKWgTFFKKaKUUID0UU6mCnCpkRaUUlKKENjxThTBTlpkR4NdJfQx7RyEXdm51QxhLiNT1QyMY5Qu/wBg+BsYwGLH9Y55tFXV9DebHE513w/D5sjyJWewIz8gWwfLNX0HaaKMRG9NnVHaCxLKQDkadQIPljyPyFRvD+zbEA83PQ4IBxtnzzvSXPFJIwVYZUAqG3J0n9Vvceu4o4T2pVRvkgfreWPc7/wrdNJnPpNonbGxZcA5OOp9fkBtRxADb1/y/wBahrntjHnCnJ9B0H3+Xrua9OGzl9zux8vL7x99V2JNmZw+31vlvsLjb1OQR18v8favnbx+/wCbcTy9ebNLID68yV5M/wAa+k9nFgH+d6+aUli0cjRuMNEzI49GRmVh9xBrPiVovH7GnCb2+77mdZDCipCzmwawLfGB/PrXsjVUloab2Z0v9HXv5ThluLO4tWkhMssvxEDgzAzOWPMhfSrBc41I+cKPCTV8yWvAO0ILo8Uk+kZeJjb3sYAwNaELKQvQCRWT0zXz6trnHnUtZ8V3ByQynKMCVdSPNXXDKR6gisk8O+G7qbIVlx0fM6Q7xfo03sWp7GVLxOohl0wXON9lf/4eU/vcmudOQ2ogjBVmVlOMqysyMpwSMhgRsSPTIIq0+xn0huK2Y089byIDAjvAWkUY20XKYlz55lEv3VoXdvw6Ce4ht7u+jsYSuZbuUMdRz4kRipijkfc8ydlQdfEQFMYuUN5J2mbZ3ScGup7qJLJC9xFJHMG6Rw6HVlknfoibEY6sCQAScV3pfcMjlMTSxo7QuJY9QDcuUK661yOoDMAceedjitN4SeE8D4eJImiitAA3NVhI9zIRsdYJa4mfGwGdhthRtC9zPe3+k7q5haMQgIstshOZDEraJeac6eYGaNtK7APjfGTS382vEm7uOm5Gud7/AHtvK72PDJNJGRc343EQ6GK18nmPQzfZTfGW3WsuxnZyNrvhlqi+F7+OZ8nUzi2El3I8jHd2ZoV1Meuame8HgAs+JXsIGFmKXkPpypjJrUeX1dwJRjyV4/apzuCsOZxYP+raWUj/ACkupYo0+/lxS/jW+EFHDOf9T09d3kYpTbxCgv2pX9N/mbn3o9zK3d1BPZi3sWxN8XcpCplm1CERqYhpSU/0jcyQ+EhMBug0ztd3IX9uhltrleIBQS1vLCkE7AdeRLGeUz+kciKDjGsZrYu/Ptfcm7NhBO1vFHBFLcPD4Z3aZpwsSy9YlCRhyyAMdQGQKzPo9ccmM11aSTSzxpDDcRGZzLJGZHuY5I+Y2XZDy1ZQxJGW3wRjPTlUprPFtK/Pj3eBdONObyySbty4d/ic/Rssiq6Z0t6gqwIJDKyndXVgVZTuCCPKk5BreO8rhKwcT4ki4EZljuseSm4gjabH70qSSH3c1IcL7mc8GivoZLxeIy2xuuSriWKeafMsULwTBkjADxxFojGQq5J2Jrtra2SnFyV273t0OK9k5pySdkrWv11K0aOvMirBvO6rjSpzPgrZ9smGO9HxA26DXCsBb25oHv51omQ2rwujIzRyRyKUlikXZo5EO6uvp8iMgg1uw+0KdZ2i9fIw4jZ9SirtadNTwDV72z7158ulCEVrk0zJFNF4fRe7ZwRrJwuZ1jn+InuLXWQouormR52EZOA00UrujRDxaQjDIJwzivdNxC3JS0NvdW2W0LNI0EyKzFtDkRvHKBk+Pwk+Yqk7iNJF0SoHXIOD5EdGUjdWHkykEetbBwXtdxK3AW34pdKg6RziG8UD0VrhGlAHkDIcV5rEbOqxk3T3evdroemobRpSjaej9O/TU6J7seBDhFhM15LFGvMlu5tDH4e2UrGCiFgpKgJqJ0jLu+BuK5fu5hdLdO6lBfTXc5QjDIl1LM6KR5OsbKSD+tmsntLxq6uyPjrye7VWDLC4jitww3VjBAiJIyncGXXg7jB3qMluMmrsDs6UdZ8rW795RjdoxekHxvfuLu7Ad/scUEcHE4bhZ4kCG4gge4guNI0iQcoM8buBlo3UAEnBI6TvZjvztLi+W1AWztxC0glvWS2edtSokcELMCAcuxL4bwY0DIaudY7sim3kiyDTIiOvo6q4/BgRSqbIf9MvMcNrRf7o+T+xYXf12pS44nKQ4MHDrcQhgcrzZljublwc4IEQtlyOhDivPsp3T8Xmtre8VrIi4iSf4aUTwSxLIA6IZBzFZwhXOpFw2R5VWj8NiML24QRxOrKVjATGv7RG2Mn3B+8VvXZ3vG4xbgBOIidQABHeW0UgwOg5kHJk/HNV1cLXpQUYeNufiWUsVRqybnbpfl4HQPch2RksLR/iFVbmeaSecIdYXokSKwHiCwonQdS3rXMnavtQjXFzPdF7ea6nZ9FzHLCViXEdvHmVFUlYUTIB6lverQ4b3/3qf/EcPgnHm1pdGM/dFcIB93MqI73e+mPiVi9jDaXsEty8ccxuIUEaW2rXOVmjkeNi6LygAQ31ufLbBTVSnNNJ36p72bpunODTat0a3I0zs7wGS9ureyhfQ1xqaSZQCYLaMKZZVztrOpY0yCNcik7A10ldy8P4BaxxwW5HMbRFDEA09zKAWZ5JHILkAFnmlbYfNQaS7ie0EFrxdOeyxpc2TW0EjEBBMs8cnKLHZTIgGM4yUA6kVffed2ATiHJbnyW81uZOXIgVxplEYdXjcYYHQvmCMVLGVJSqO/AjhIQjBW3PXxNQs++qXXGknDZF5siRRmO6hlYvIwVRp0puT7kD1qx+3naNLG0uLuT7MMbPp6a36Rxj3eQqg92rT+wHY3h1vdFBOby/iTWzSsrtbqx0grHGBFbFtwMgOwD4JAbGt/SCEvEbiLhNu6qIYxf3btnQDqaOzhbSCcu/NlxjpCp9KwVq8aNOVSbslz4eRsjBTkoxXvxOW+L3Mkjs75kmldpJMbmSaVssAPMs7aQB5YFdv90/ZhOF8NihcqrRxtNdSE7c1gZJmJ/ZT7IP7KLXP/cr3ZSfprROY5E4ekd1KYyzJzpNXwsbFkUhxhp8Y2CJ+1VjfS67UyRWa2Fusklxfa9axIzutnFoM7aUBIDs0cO4wVkffao0qkHDtE9LaP7k6qblk8zmbvd7xZeIXUlw7Ny9TC2jY7QwZwgC9Fd1AZ26knGcACo/up7yxwy8W7NsLopG6Ihn5GhpMK0meVJqbl5QDYYdqr3j8zK5RwyMOqMCrAe6kAj8KhWnNaYUU1cqnXtpwOjO/n6SB4rY/BRWr2oeVGuGaZJQ8cZLrEmkK3ilCMSRjCkb6q5xv7ksSTvmvKWWseQ1fGnbUzynfQa1MNONJUmQQlIaUUhoBAKU0gpTQAoFOAptKMU0Jj6dTBT6YgpyiminCmMeopyUtrAznCgsfPHQD1Y9FHuxAqbtuzbAkSEqdDPpTxOulQ+ZBsFGDjrnPXSN6tp0pT/aiqU4x3sho0JwACSdgACSSegAG5J9BXVH0Vew3wiz3NwNNxNFykXbMURKsRn9tyELY28KjyOYXuy7C28SJNEOYzgHmtgv7hfJADsVX0GScVaPB0eIYGcD1zjc9Pxx/Ct1HDZdZbzFXxGZZYmw8N8RKtvnbf8Agfb/AArDv+yi6sgYB9Nj5+eak+CRYYH88ZrcUtVYD/LFTnoVxRodj2Lj6kt/Gtn4bw9IxgfifzqZXhgA2P3f+1eN3AB/rjf/ANqpRZZHjDj+fy/npXHn0le6yWG6mvrZC8UxMkyIMtG5+3IABlkc5ZiMkMWPQ7de83BrX+0EQcYOD/Pn8qc4KcbMhGbhK6Pn5at4RXtprpHvM7qLV0kuP6FgCzOg+039dMaXJOBkYYnAyaoLjPBGhzqz4ZDC50SKqzgIzQlnRRrVWUnVgHxaSwGaojRlu3muNaMiPUGnoxpJISuNQYBhlcggMOmVPRlz5jIpox70NW0ZO5krKa94Lkg1hDFPBFGVDTaJa1uguMAAAkhQTpVjgMwTOgMQACwGSAN9q3juw7dPY3ltdgM4hk+sRMapIHUxzKoYhS2htagkAuibiqzUj3rL4buyrv4mCj7yB/jVMsPFqyLViGtWdcd83bzhvEIrW8tbhRcWkmia2mVoLn4S6McchEMoV3WOUQSl01qFSQ53Nbp9F/h+3EbnH9Jcx2yn1S1iXOPbmzSj7qoRVjIHNRHWPxDWobTp3yNQOCMda6R7mZRZcAhuZNv9lm4hLn/6/OuyD8lcL91QxdCVC0G7rf6W+4sLXjXTmlZ7vN3+xUfeJxBE4jxBrlxA73HgWb6rVBDFDDE8ZfAdG0M4KE/a9as36NPBXCXN86lRdGKO3DAqzW1uJdMuk4IEsssrLnqgQ+YqP4d3427on6QsJkYBW1RRLeQ5IByoX69D7GPb9o9aje3/AH6NPG0HDYZ4mkBVr25j5IhU5BMEDnnSTYzpLqiKcElsaTnXaSiqdt2q0d/epe1CMnO+/e7q2n/BpveNc/GXnEOWf/iryLh8JG/QW1izDywJTM2fQV0D3ndqxwy0Ro4xJI7pbW0RbQmso5BdtyI4442Y6QSQuBjORQvc5w5ZOJcPt0/o7dZ7th1ysEYiQsfMme5R8+ZBNbV9J3i2q9s7cEYt7ea6cf15mFvAf7iXVTdF9oqfHReer+pFVV2bqcNX5aL6Eh3T94F/NxGO3uZ450nhnk0JAsXIaHkkGMglmjOvQRKWOShBG4OqfSB4fGnGJGXC86wtppugGtJb6ISt7mJFQk+US+lVvcnU6uHljkTPLlhlkhlTVp1BZImVsHSuVOQcDI2rY+yPB5eI39vDc3Et0bkos8k3L1/A2QmmMJ5aJqWSSURMxGoic5OwrVVws8NPtF+1delreLMtLEwxMeze99Ot7+CJDsH3a3/EUE0JhtLVt4p7mN5ZJ1/7SK3V49MJ/VklkBYbhcEMc3tb3O8UtUMiG34jGoy6QRvbXQAG5jieWWObG50CRGPRQxIFXV3w2fEHt44uHqQuo8/kyrBPylA0xwM2FTUerBkYBQF+1kab3G8QvI72S0mW+ELW7y6L0SOYpY5LdRyp5C2uORZDlVkcAoCMZOcjxtbNfM/fTca1gqWSyivv57/sUCjq6q6HKMMg9PUEEHcMCCCp3BBB3FCoa3bvX4THb8U4iq4WNmhuyOio08WZ8egaSJpj7ysfOp3u87mLi9hS5uLl7GKVQ8MEMUbXPLYZR55J1dI2dcNyVjyoI1NnKjtR2lGNJSlvfBdDjS2a3UajuXF9SrHjNY7Crt439H6cLqs+JCU9RHewRsrewntViZPny5PlVN8StZop57a4iEVxbOqSosizJ40SVGSRcZDRsrYZVYahkCrsNtCFV5dUyjE7PnSWbRroYdFZMVsTT3siPI1tc0YlTZh09ZcUSpivMCndMWqMgXBpplNelhZyyyxwW8L3FxNq5cKaQSFwXd3chI4kyNUjkAEqBksAc7tb2dvbCSKO+tRD8RqEEsUy3ELuil2iZgkbRyhQWAZcMAcHwmsc8VSjPI3qbIYWrKGe2hHTBJFKSKroeqsAR89+hHqNxS26BV0LNdrGB/RC9uxEB6aBNjHtWPLWJfzYX57VHEUKTi5ySdiWHr1VJQi2rmTwHj1zZSOeHXMtq05QNHFHBIkjLqEY0SxMScsf1ty7b710l2E7PyW/PluZzdXl3Ist1cFFiDFI44o44408KRRIuFGTuznbVgUD3GcF+J4gjMMx2w57Z6awcQj58whx/wAM10/Xxv4y2g4zjhYPS15d73Lw3+KPe7MpZk6ku5Fc9guIcY4X8VnhcF+11dzXMs8PEEhlfWQsaiK4hVQsUKpGED/qn1NTHZG3uZ7u64newm3mnWO2trYyJK1tZw+LDPGSmuednlYKx2EY2IIG3E02uDiviPE18P8Ap5KKjpuTTsuG+3obKeBhCefW5Gdp+ztteIY7qCOdD5SKCV90b7SN/WQgj1rk3v77k24fm5tS0lmSAwbeS2LHChm/XiJIUSHxA4DZzqPYtY/EbNJUeKRQ8cisjowyGRgQykehBxVGyNuV9n1E4tuH9UeDXTk+T89CWKwUK8dd/BnzXmhIrFZasLvO7JGyu57Y5IikIRj1aNgHiY+5jZc++a0S6jxX2mlVjVgpx1TSa7nqjyM4uLae8xWNNpzCkNSYkJSGlFIaBgKWkFLQAopwpopRQhMeacKYtXF9HXuKuONuZGY29hG2mW405aRhjMVuD4WcD7TnKpkZDHw1JuwIqnhljJNIsUMck0rnCRRI0kjnc4REBZjgZwAavPsX9GfiWBcX8KRQqGPw7SFppTpbQrchsRrqwT9YH2xgZ27U7tO7bh/CozHY26RagBJKfHPLjzlmbLsMkkJkIuTpVQcVtU8QYYPSlCqlJNrQhVi3FqL1OQu3fZcWEaLZQIiq0uoLGfEiLDIhkZSJCSs6Eyag2VcZ0nSK17fcFeKa7itwoiSa5LxwpgxpFJyCZCqACJtGsRBmjUNnC5Ndfd5HZ3U0WAcakBxsCA0IwfIjCLt7D0qhO1XZ7mXUpAB5ssrFC5QTanZ+VqXcGQ+Ee5HngV6bA0nXp3XBNnn5t052ZrPdN2n5Mniy0Nw4DoWi1x3JCamjiBVlglZtIIDHIQHLHJ6F4WiSJqUqQc4I9iQd/Ygg/KudTwuOxkaOSGeeWYqORylVVgLRODLBPEZpZOYjFYDylYRxs5ZJdNe/BeN3AnR4JXecpGRHgSpJG/xBEN5JAYlS4iiVdMYESxMmgHSW0xnTZbGV9VuOmrWyGkY+/wBPKpjhk2NvSqN4J3vaVXnoQrBFM4aJY0mcO3JKvJqL4UsDHrDDcZG9bla94VsHKNKiupIZWYIwICFshsEYDqT+8vqKpnSk+BfFlnSTYG1YF1PnatTPa1NGdakHOhgwIYA4ODnBx0OOnSsaXtUikBiBkDAJAJzsMDqcnpjOaoVJoncnrkjGev4/xqC4tdrGpeQhVG+SR1/ny+VaP2m72FVuTbx82YyNEFzpCyIHLK7HZdGnxKAzjYBSSAas4n2tu5wrXNwI/iMxwJAkjJHGziOYXSyR/VnDJpkCoxDakkANNx6ELNmxdvO1EtyuYGHILvDDIgkkBmVLppndVglLchIJiEVJEcBipJClaRueJTG5WGyBblxi3iUcqXWIo5Q7q4RUm2M3LnKh+Ty8YIGN5l4/NbS8mFI4pptKW8M00vwbWig6ZYWa4jSASvbFOVJGJlkfXzYnQBda7P2VhLDcSLzLZG5Ec/xEMtzFaRSyNq5EsEEzSO+kCL4iO3eNlQc6XLa641ZwkpR0fcWwiknf8/QlrfjKXHwkysrQROtncpechNSCBTBJIzExF3t4ZIImkJXXZqxC850qe7U92tvJLEIXEInVgmnLRiRAcYDtqMcqpK8YBBKQSSE6ZY1Sr+xDpzZLZ30w3kbW/MkIRUlDLJZzSYdlQJcxxByWYJHJMNRGSbK4JxgRW1pb3qzxSwu3M5aapLQW5hVJ7pFVnSJ7R4IVIjk1Q27ABhcl11fq+0t2kU/uRnGUH8jNG7XdjrmzP1qho+glTxIfTPmh/e28gTWvEV2BBwgTxqQRLG6ghlKlHBGMgqWRlI32LD3PWqo74e59reJry2Q8pBqnjB2Vd8yIPJVGNS77bjHSsU0s3yp2LqdW61KYSpfspHm4hH9cH+7lv8qhwB+dbB2DUG5j9tZ/5JKdJXml1RKs7Qk+jLPuoRJHJGSQJI3jJGzAOrKSPcZyPKtw493mcRm4dJw2SK1dJY4rcXMJkt3SAPEJA1u3MRyYAy5SVdyPDtWn5p2uunicDTru8jjYbG1KCtEkLm+JNYzXJrGJpM1pVNJGd1JMm+yfaW6sbj4q0aDmNEIJI7mNnjeMOZPA0bpJC5J3YawdMfh8PiZ2j7ST3tzcXlxHHFJNyY1ijkMqpDBGFUB2RCdUrzyY0jAkA3xmocNQWrL+hpqr2nE1frqnZdnwHht6sn6PVwq8Xg1Y+ssr2JP+JzOGS6R7mKKRvkjelViDWZazsCjxyGKaGRZYJVGTFKmdLYOzKQWRkOzIzqdjUMfQdWk4x3/gns+sqdVOW78l697vaW+tOIkR3TwRywxtbq6RyW8rLzBMmlwDzFOhiI3Rirqd8beHDO/K6iwLuyWcDrJZSBX+fw1yyj+7O3yrxse+qzuIfh+M2R3xqeOA3tnIRkB1RVe4hbz0vGdOdnbrWid7DdnDbj9FS/7dJPbrHHFdXq8tOajzyNavKECrAsgwyYDMm1eXjBKWWSd+/Xyd0eolK8cytbu081Yge3nFXv34lcBGVrtpBDFJhXEMcSQQowGdJkWMuRk4MpFdQ3Zh4zwoi1m0R3UKhWAzoZShaGVAQcBlMUkRwcF1rmjsb2VvOITXC2bRA2kMUrpMGCzvO8wSESrnktoikfXpcZKAjBJErwbid1wyZpAk9hcEgy29xE5trsgdCY9UMrnGkT276x0Ofs1fi404/Knu0d9L9xThXOXzNb9Vbh3+mpi39ndcMnEaGXhd3uycltVndBcZKRt9ROuMExsqyqCPs9a1q4nctcXN1IrSyyS3VxIqlEG2cKpLEJFEiooJJ0oMk11D3x20V3wO5muYzAy2TXiB/wCltbiOEzRkHAIkjfwkDGoalOzEVy9xWzM0MkR8LSwsh/qs6EdPQE9KvwDXzNL5ktH/AI5lGOu8qb0b18OvIubu+7nLU2i33GGY6ouebczPBbWkRXUBLymQzShD42kZkDZCqAMt6WnYnstetyrSUW852UW91PazE+qwzMFl++NxW89hO1Npxqxa3lwsxhEV5aFtMsTEAFk6ExlvFHMuxGOhBUUx3w9301kha5Vb2wzj4kr9bbgnCm5QdFGw+JiwM7sqZzWDM5Su34vn15G5JRjZLwXLpzNR7wOzrWHEJbH4n4tI4IZea8SxyxtM0+mGUoeXI4jQPrVI9mTIJJNRnDLWWWR44La5uWjRZJfhoxKYkdnVC661c6ij4EYdvAdq81tAhc65JGchmklkaWRtKRxoDI5LFUjREUE7KorbO6ntldcLa6kiitZ47hkll5rSwzIkMQRUWVRJGY1AdwGjGGkc53rtTnWo0FbV8eNkcWEKVbEO+i4cLsd3ZduoOE8QM91HOqTWvw7BoJI5oGWUy6uVMqMySZAbRk5jTY1nd9feGOLSW3IjeOztXeZXlCrJcTtG8SlUBJjhjjkk3fDOzDwqF8XSNnxqObh63d3CsUbW3xMsUmJRHHy+YQxZQCQm5yowciuNIhkNJy1ia4kkuWiRVRYuexdIgqgACKMpHgDqhPnXPwkXVr5pd50cXJUqNo9xjSmorihP4VOlMFBpZ3kbRFFGrSSyv+zHGoLOcbnA2GScDetz4X3F8XuF1GG1tQRnTc3DNNv6pbxSop9uYf8AKt+0cUklTXiYNnYVtuo/A2v6OfZ/k2RmYYe6cv78pMpEPkfHIPaQVZZrVJBxexjUTcMhuLeJAueGXLSypGgAGLa5iiaTCj7MblvY1NdmePQXkKz28gkjYkdCrKy7MjowDJIp2KMAR94r4Rt7B4tYidevFrM9HvXRXXJaHvcHUp5FCD3EhSVj3d/FGVV5I0Z86A7qpbGM6QxBbGR0rJAyM9Qeh8j8q4TpytexsutwhpKcVphqFiSOa/pZ8LAuoJR/vYCp9zE7b/3ZFH3CucuKx4rqn6V6ZNl+7c/42dcycbh3NfaPhmo5bNpN8mvKTS9DyO0Y2xEre9Ea05pmayJoq8jHXduYrHmKQ0/TTDQAoopKWgBRSiminChCZtndP2Ll4pfW9jEcGZ/HJjIihUFpZT5eFAcA4BcouRqr6h9k+z8Flbw2ttGIoIEEcaDyA6knqzscsznJZixJJJNcwf8A/PfsKEhueKyLhpybW2J/7GMq07rvuJJgsfQEG3byausg1Qk7sLrcLQTRWvdvuN/DW7SYyennt4WJbbyUDNOnBzkoriQqVFCLkzE7a8dhiQll1ld/3N1wx26AnP4VQHa7jtpzGjazxhZGOrY6VjaT7JxuVGwOOo3869e8LtrqSQLIvhJDnW3+1ePogxjCYwSCepP2SSKt7Y8ZQZ0sZW0yRi41uRMCYlGiOQBo44ohJFnfLbeWF9Ng6bw6+Xf4+/fecb9ZUlLRL+1P6oneCcWt7q6hhtuHhGBALxsFZU1YkmlckDCq+CTgacLjfBwY+1FtDJJy7JyodVkgS6kSa/kSS4CTxqqMzosgaQ4DIukgsWIJir68glsrWW5tzBHErQ/EQ6Eu791M6CG3BXlfDxRSAz3MsbkuNGWZ98A6ESK4inka0JFsbqfmTXXDAq3YFqUhYBIp43PLaMaG1yjAZCF0PGT3acvb335cVw1Lo4qo+C/tj+DZOHcftVTRb2kU76ZJNNuyx2yoIEZ5ZHlaLEyAkMGGyjOG0lVh+OcQ4c5ZYoFlQEMeQsGuQYj1OYkuQdCvgHYHAVmACjRiQQCYGyMPJCqjLZmKYSMDEha7LKGiUqApzI3NLEAaSCrrp5TyHkRGUQy5mWGKG3gtY4VBkQo4na5IG7DXIoIaEONcLxjjakXmVvJP6lqxEun9q/BOcK/RsE9vz7KaUW0xF1auEYR6gXURxJM6u2yytFqxMqsF5hSVEsb6Qna7hN8tmiKl5IOVO08LAfD2ziQyKZGIUSShQY7d8aeW7ymJELjnlbJYZHlRZNRgfRHCY9EkrKrx6l8StAwVpDHCWDaBJA4CMlv7XFwJRPCQ6gzSgPBykDKJLQ5nLYDNNEJSZicsYo2kPKhkIyVsRUnUjVb1W7RJceC0/wAabh522np5L8E5PeWFrKQ8EgSSVdKPlZ5kBV45YTEJssOYukyCPVJkDQ8b8v3ve0FpcBneEOirrmEbm1vVVNETQFZQsNxKkbxs3JLFwFGFaUusZacZ0r8PE0ZMTAiHTcQywqfsTR3EKrIgiLJmOWZHIMcZCzyTl829uTEjLC8813HyyEV3a5luZNckbgGJ3u+WsbmYRumnmFvC8aBR4+rJ3dv7Y/ZE3N8te5fgxPj4z9a8ECrdKlzdPkNYSiNleO3LRwNyrsujqY3xlv1m5oWsYcBjuJ5zwtEntlje5kZmeBbW3Ek5SO9+ICxhlCftSKQA5YDJC2ckD67+cxzWNnpMgiE8MfFL6YvNb2XIlwqiNi7ysiN9UJG35qacJ+0jRSstxb2yxLgJd8Hgt4JLVpoftW9zAqpc6opCr2907FgTpeJgHEP4jVTusv8AZH8Bmlw+3vl70Ivh9vBO0TJAEhaMySsea6QcttEpl0apBHqMbk9US4jJ2GoySzxsiarea2EqAhg91LyeGBUJY6MrNbGRySo0mMr9khgEiOJcDltoebG6XdgzMoubYsqo0gRWinBAktZnRUDW1ypjcAaeaAHHrwZopOaFzDz2LS8hEWCK1hieR9CSSmdZwVk1wBpEkhaXTqKLSWPqO37f7Y/gk2mjrPuF4kJrJWaGOMB5BEY1KRSQhjokjQsSituShwVORpXoLSsUjdCjRgq4KsOoKkYII6bjbFcu/Rf4mEmuI5EEEt3HFdwwxoqWzWygRCWLDsVZnzqQsd1b7BQhumeES5qqc3PeZW2tDgXvG7PGxvbq0PSGZlTOSTE2HhJJG5MTJk+uqvTsAf8AaE+T/wDkerd+mpwAJdWt4oOJ4WhkPlrhIZD82SRhn0jHpVM9i5NNxEf6xH95WX/E1ClpUXei6o81J9zLVpM0lFd889YKKTNFK47BmkozTXbG9RlJJXZKMbuyElkxWDPeEV5Xc9Wf3fdzDzKs18zxId1t02lIPTmsQeXkfqKNW+5QjFeR2xtyjhoZ60rR4Li/Dj9EemwGzXuiry4vkVe3FyPOvNuPE/rfxrq3g/YLh8AHLs4Mj9Z4xK//AIkup/41I3PZ61cYa2t2Ho0MbD8CteHn8dYdS+WlK3O6T8tfqd5bJqNayXkct9kO2V1aSma0uWgd9IkUgSwTBAQvNhfYkA4DxmN8basVcvAPpH3IGLmwhkP/AGltcmMMf+DOhKf+K9SfH+6Hhs+cQfDt+1bNy8f93gw/8lV/x/uLuEybW5SUeSTKYmx6a01qx+aoK34b4qwGIfzScH/+lp5q687FcsDWpr9qa6e0ZfeX3pXPE05BSO1s9Ss8SSmaa40EMiyvoRI4gwDGJAxfABcLqVsTur7BHi8t7i6e2NmkCwshDj4mXmSuZ4CQJYhEIV0kr/SPpZSMiveO9leIWu81rMFH66Dmx49S8RZVH7xFRnDeJgOsqM8cy7LPDI0U6j9kSxkPp/qElT5g16eniKdSj/JafVO9/I5rpy7W8/BNbi8rzu04rC6n4UySxn6m7sLiJGU+RxcPFJGD+tGeYnUEsKvHjnETBwmSXiXLLJZMbwAZR35REiKOjcxjoCgeIsABuBXMvBe+ri8ShRexzAdDdWiSOB+/A9vq+bAn3qO7VduLm/KfG3RmVGDpAkYgtlkX7LmMF2kdTuplkcKd1Cneq4U5VJavXrZf8l1ScacenS/tEPwmzdYYEfd0hjV/PxKiht/PcHetl7McAN1PbWeDi6mVJPa3jBmuT8miQxfOVa8O7n9DyXk/6ZdViWCJLTmfExxGSRne4mFzEFSJ0CwxAmRW/pcbHJ6J7qew/C4Zfi7C5a5BiaJP9rF3HGjtEzGN8s4LaEBLOdhitmJxfyukl0vfzMeHwqzds++1uJH/AEoOLhLKOyXY30ojcDbFrDiW4+QcBIP+/Fc8Xko8TuQFUFmPkFGST9wq+u+/sBxG7uhc2wtpY0gEKQyTPDIuXLysp5TxkyEINyu0a+9c8LIJnSCVdAa/hs511K64F5FBOA6ko6faXUNjvVNDExoxk/6uBdWw0q0or+lbzo/6PXYVLS3/AEjdKFu7mLmEvj/ZLQjWkCk/Y8GJJW2LPkHIRcHD+1t/xINPazJYWZYrbFrdZ7i4VTjnuJWCRRMQdMYXXgZLDpWw/SGkZeEXmnIUrGkpXOVt3mgS4YY3wsDSE46DJ8qwr3iNtbW/OLxx2saDS4I5YjAAQR42bIwFVck7AZryHxJtOvhlGFG+aT37/DvZ18DQhO7luW5Ej3U9p7id7y1u+W1xZPEDNCpRJop4zJE/LLNokADBlBIyBitPueGrb8fu0iGmO8sIr2VBsouUnkgMmOgaVMFiPtFcnNbb3K8HlSKe7uEMc/EJviDG32oYFVI7WF/R0hVWYeTu48qrHtHxd7mS9urc+PiM0PB+GMM7xRmVZrlSP93ra4lDdMRofPNXbSjOps+NKr/qSyr/ALnb6b/AMPZV3KO5E53Xdi7Xikl7xK9toLqKeQWtilxEkqraWjOplQOp0me4Mr5HkqV62PcckV1czQXJ4ZZyBEW2sAkZZUAzJJLKrrGzuW2hVSF0jWDkVvnaO/h4PwtmRcx2duscMfnI4CxwptuWkkKgkb5YmtAn7OrKhueLyfFyLGZZEl2s7YBdTrDbA8vCDI5smuRsZ1eVSx2Kw2z8PCjOObclG172FRjUrTc07dSaue5SAjVDxHi8UnUSLxGWYE9ctHPzInX20j7utax2T4ldw3txwq/dJp4YUube6RBH8Vau3L1SRDwpKj+Fgux3x9nU25/Rv4c0fDUcqY0uZprqGHfEFvM2YY1B6DlhXx6ua1S0lF5xq/vV3itYU4XE3k8iOZ7oj2SRljz54NZPiHC4Z4F1JQSlpbRXT5aFuCqVFWy3uisPpMeKeBP2IGc+2tyP/wDOqPt+zk10T8NBPc4OCbaCadQfQtEjIPvNdVdi+xUHGeJXt1dKJbKxmFnDbn+jnuIVQzPMP1443YhYzlWJycgYNnd6nbqLhFvERBr5jcqGJCsUY0qWOp9JVFA6BVJPkNiR09jU3QwNOL0tG/nr9WZ8VadZ2V22cD8Q7rOIoMtw6/Axkk2VwQOvpGfLB3x19q1DiPCjG2hxy3/ZkBjb+7IFI+/pXaUH0odJxNw8hfWG5Dtj92SGME/2hWV20+kJwS44fcs0ayzpCxisb62BMsreFF3EkLqHILaHJChjjaulGpfcymVNrfE4Nu7fFYLipO7bbyz1OAAMnc4A2A9hsPKoyQ1pi2ZppXGClpop1TIMBWVw2yeWSOKMapJXSKNfV5GVEH3sQKxRVt/RJ4ELjjELsAUs45bxgRnJjCxxY9CJpY3B/qfeHFXdkVzlli5Pgdo8K0cPtbaxh+xbQpECOrFFw7kDqXbLk+rGtn4bxk6dz5VVM18ZJi2dgf8AX1/GtrtLrCD8Pb0rrujBLKcuEnvZuUvGyP5/1qqe/wD7QOLVFV8a5G1gMA8kQVtaJkEFsNqx6K2M/ZOwve46/Leql+kDfhvhYMlZJDI1vIMDlzx8po/ESNJcgqDnqR++jo04xkmkOrdwZVnaHjabHxyKQstuvMV1hjeSU6bgITiRkQNywzEK6hicbYMOmAJeX0fOaUZtLRyUE4GALmcLhlsU+yka6eeRpUrGjMcuwsTaxyX3EAJRDK9pZWbsXS4uYSyyNIoOBZW7L4guBIwEQIBOdXu+Pc25iurhZb25mMrTxTDTHIXBjtRDyxkqAVYKoCZVUVdIOdMql9CmFLyM3iXEpLh/i7zRNgxolrrNvLJGwZYFtIo4yFtkbyjAGzAeJ9RnOyUlyShR472RoIYJIDJD8C3DxHcMba8bCNHcKUUowJZGdPEHUofHsyif0ou+bJCsLNfo96stsjRSRJaxJJb6GLs3KVgSfEQIyFJEhwOKFFbKyRWeUHwys4uZHNviWfMgOiGbwtoDGNlghJ3VJDBq+hLdwMnh/Zq2eJzFdBI55QkTXdu4njlltiEtP0ok3w7xmElFfBicNjMXNJGHxK1EYNteoikypBHCkE6rCBGqgyPI8nKBl0qBq1KSGGI9ovK1co1vzlgeciMouLf4O2RIZpdQZQrfEeADlMS2tlUMHjQH1tOOmaOJJ7gJIi5tOIsNUaLqC8i9CIvM4cz/AFcd5GizWLkRyKgwtVuTiTSbfv0NXismt5BG5L25V5I5CSCgRXnKFl+yH5ROxA1KJYykkRKJa8TuJOeuCXhcwggEAOsd+cvqOgEzRwKU8CAsi6dMkiS7be28qLHO6xqspZUMdxBcWskkepmEdzbNLEGXTqK/0gXXqjZUiDYs/ElA2Lkg4QOdkKgjLAsFwqg5Ak8bCVdUKLdMlbd1oWpmB2dsHtkYRp8RcnEjeIDVKZFHiZtwUV2Kg75IMgQ3McbbZwnhyxwNeul48aCT4a0iup5JowJIhdtDKDHcci15pM8sRjMkkixF8FtEPdWEHIie7uZLeCaQpoiiaa8vghYTR2wGkRqxPJa6kVRrdo1VVM0Rie0faEc2OaaLS3ht7W2tSrG2hjLRpBbMyurJbFm1S6f9pvC67pBLrplJXsFnL37/AMExfPGHiVGWKCzeeO0S0RWhhlbxzm6tLl7m6uJZYsq7SMcJGF0KDldW+A0OQsbNEjqHi5lzcWfE7kLnAeGNYoZQrFwzZjDJKVfQgU7HZJMHXLOLoZEE4dI3iiJKyLOIk5VzI0bIGc/7wA6vrVJw726MYmdeWqiCRo00xy2t5LbvIkvxMRnkEUgjEZBYIXCHKKQAknTshRfv3749TVLMT2MkrwSmCa3hUX1vcctdWt9EkHIZmS8g3VjHIDsy7FsVIcb7Ohy8ttbm3uIow99wzdgImVWNzZZLPJaFSGeHU0tsSGDMg1xTPFezyTSmDVy7WHTi30W8NzBLO94oMWiLXPCscDTmOVxJy2MhDsp5mh9mriQCG4ty0FzbEutxzGIlkBhMEMSlConVNY5WoiWNQNOx1021Ld+q3+/fT62R3NcV1cQF0F5EZcfESllIleVeTDCBHFEqLJOwlfCuGl5JbRs1db9n7rOP44rlmPtFZ6rVJoZMTI3FYIo5AsUF/MH0W+gISsEk8Uzoqjwi5tgEIDs9390nH+ci5/xz1++rk0ZpXb3GD9MKw5nCxJje3uIpPkHJgP3fWjrtt7VyTw+cqysOqlW/ukH/ACruLvs4fzuGXqAaibaQqP60al1/5lG/zrhWNum1KejuaKOqaZdMcgIBG4IBHyO4pa1/sRf64tB+1Ht/Z/V/D7P3D1qezXbp1M8VJHCq08knFi5pKKTNSI2FrFvpPKsiovicnXYn5edYdoVMtO3M37Pp5ql+RaX0fex4mka9mGUhbTApGzSjBMhHmI8gL5ayT1QVflQvYngwtbWC3AGY41DY85D4pG/tSFj99TBNfnPbm0pY7FyqN/KnaPSK3ee99WfScHh1SppcePeBNJmikzXINQUlFJmkSFzWv9oextldZM9tE7H9fTok/wDFTTJ/zVPGkzVlKvUpSzU5NPmm0/QUoRkrSVyo+O9xFs2TbzzQHyV8Txj7jok+8ua0Ljnc1xKLJj5Vyo6ct9D490l0qPkHaumaTNegwvxZtCho5qS5SV/VWfqY6mzKE+Fu73Y444hDdWp/2iCaHyzJGyqfk5GhvuJrEi4lFq1hVVx0kT6uQfKSMq4PuDXaDelR03ArZjlreBj6mGMn8Sua7dP44i1/No69JfZr7mX+EyX7J+aOaez3eRxZGEdnf3znyhbTfZHTYTxyyj+ywxWxdhO5q5liC3TtaxhcLuHuS48SyHB0xtzMSEklic7LnUOgLaBUGlFVB6KAo/AACvTNYcb8ZVqkctCCh1bzPw0SXkzRR2You83f0Ivgfek1ugg41BJEyroN9DC9xYXK4xzGMSu9szj7UU6KoOcMw6R/DePdkYZFnhk4ZzVOqMQgSyK3XMVvGGZX/wCGgatlElNDem1XUfjSajapSTlzTt6Wf1Iy2Ur/ACyaRC9pO0lzxUGCCOey4e+09zMphu7qM9YbaA/WW8bjZp5gr6SQib6wzu2sUuOKO0ahbXg0K2kCqPALudEaXT/wLblx48jK3vU5rrTOFdlbyzMx4fxOW3WaeW5eCe2trqEyzNrkOdEVxgn/AOsSAAN8VHA/EVOti+2xjsor5Ek2k3vfO9h1sDKFLJSW/eT3ere/F8Ts7AbxWSjiV36GTLR2MR99fMnKnqI1qI7yUN3JacKQnN/Lm4I6pY2+mS5OR9nmeCEHzLkVk9jOBSQNdT3Ey3F3eTCaeVI+UmEjSKGKNC7lY4kXYFmOWc+dQXZ7j72PFb+8vLC+mV44bWzmtIo7qOO1QcyTUkcvxCvLOxZgIjgRrVsMXR2jtVTlJdnD9t3a9uV+rv3Ii6UsPhrJfM+WpbPe72m/R3D5ZYlHNwtvaRADxXEpEUCKOmzEHH7KmtH7N8PThtikbNqEETPM/nJKdUkz56kySFsZ9QKieMdpV4xxK2aJJxYcNja4Jnt57fm8Ql1RxIEnjRm+GiDyah0Z065qPtOAQ8c4tdWtw7Gz4bbqhiSZonkvrnS/NARwxFtEqgEggO56+IHo7Xi9pYyGDg/lis0mvfu5ThbYei6st70Rqvc53nvwuW4WaNprO7na5cxby288mOYwQkcyJsDKggrp2z0PSHCuNcO4vAyxvBdwsBriYAsvpricCSNh5FlBB6VSfaD6ON1Hn4O9jnT9VL1SkoHpz4FKufdol+dQ3Bu5PjCTI6CK2dSCLlLnJTcZI0IJG2/UIAPQ16dOUFlS0MTUJ65rM2rvI+jVFIGfh8xhfci3nJkhP9VZd5YvmeYPYVyF234PNbTSwTo0U0TaZI2xlTjIIIOGVlwwYbEEEbEV9Ow+hMyOPAuXkbCjwjxOfJRsSfIV88/pK9s4b/id1cQHMWI7eFgNpUhBDTZ9GfZTg5TB2wAbcqi1YrU3JO5TF5WE1Zd49YbVqRlkIKcKYKcKkRFrpr6HfDeXY8UvTjxvFap6/Vo00oz782HYfs1zKa7D7mrX4fs3a+HDXMtxcMMDJ1TPGjHp1jjQ774xWjCq9RPlqZMY7U7c2kbH2bbYk+Zz/n6VulpJ4R/PrVf8Bm2H8+9b3wz7I8sfKt+YzZTInTO34+dc6d+PapoLmccuOXRDHFCJUR1juGZHWbS3jzyVnUacrqZNQbbHR5Tb+feuSPpSQBOIBv2413yMbbf4KKSqZdSzIpaMxO89ke7uJAebY2Yt4tUJQARyo0iIpBILSXLzO+GZiWnYE4JERwq4mhvQ89wqvFBLG0quSUZrWSIwQvEjIbpBNsFyOZqLEtqBj+zomM9nKtsFSKWKMskXhk0ynW0hlbQZWDmPUzIuVUDSU22Ti3GUiiG85iUAQTQvG4lmMMcfxFwoaPn6HR0Us2lgrLIgYESSc76kVDL8vh79/Qn2TDC2WNre3m5M0aRq+Ip0VNMs8EjZ5czW28jlShikjlYAi7otXkSV2YoJE0tPJM91JLyYYpUWKJUYc2OTS+WkQvzDIzrzI2SKHhnMqkq7uJkcMUkdWnjaSaSXS7lmSZJbmQskpZQZjHLrjkjnY4LcNGjxzPrFvHzLO5UFJVQLdAxvG3iAQ2rRvby508lkyypbSRLtNSGTT37995MWcqTxcyBFlhbAkhIy0ZIIKsoJxgYwF+0v2TsoTWLmwktsy255lszamiLgPbuy45iyMCoUxkDnEMjxnTMroNTeHFuJxpLrUcoSGCSXl5KiZf0iCwXxFVVjpbTqIwzAOxw0j8YdA5bFtMao07MsheYZZ2V12VMMoBPMOXYSB3dYpnJqa1HFOO7cZMd/NY6reJbQ82UNcWsoV4rphHasLdiJmSGWHWjrEjhkkd+TOWxAmSvHuGhGmaSZEj2fhUmpbszDAFul2IwvwuU8d02iaJYlHKado5xrPF7SJ1KuOWGfXmILtIRoJMeQmH04ZAVBZDhxypWkjxLNH9trd5I1Atp2WaWViSoWO3ZEIaZQQUS8UNENLJyzoqiWZbixRUu/375mwXPaWWSRvjZigBileGGMBOHwoUtYo7eIEcmRIp9IiVhyowSddwymHJ4RwYxyyTu/MkGI7YiPlJFGFCq4ikKmPCDQkb6Ai5LkOfq9U4ZEsW2oNNJ/SPs6oCUcKuchyW0lpNwxwAeXk3U9Y3gUtJzGjMbLKZQTlHCkIwwTqkYOEGSxYEDOTG6OMeLFLkjN4lxcNCwtSZLiVhGJC2AI2AWR0ZtLARsyx63WPRrdwEAGMu4mSBWuJOWwtuWp0IsLXd0oKINKgMER4dZOFAeBiAz2TrJrDXEs/LnDwsuZS0eTzBNqWVjKgVdOt1jdRESojXA1yc3mZVvdgIRP9YiOJysgVlf4bkNKXz4fE0bWuvOmSe/uVXmYYs5NvUSjwJvhl2rhuZbupn0TuolkMqSSNw5olt2iWNlJWKORLbBCNLw+MOplAXKtgiQXEishuOXJd3OLi4kYkRyT25uJSzo95BfoAJC66kEhUKbhah+IzOkU2l8yqk7c3xgySnJmnByVLSQw8UnDZHhmtPOODlv4Hay29u9vCqtPLGoXBdJEuGltY5GikjZWVkee1O2YnS0hlGoSEx1d5Kxp3D+IySzwOJHWSPSyszkhHiaSdeQAv1SM3jSNQFV5GxpU7dL9yb6NONgB0GB5VSPePc5vNQQeJ8c7RJGx5cUMbI2pQCSCCGBZTGY9IUZL2V3X8QwRk7f+1R/qsEtVc6S4rEJIHB6OhU/eCMD5g4r56SW5jYoT4kLIfmhKn+Ir6CcDuxJFj1ArhnvT4fyOI3sWNxcO3/ikSjHtiTb/ADq6e4VL9zMfs/xExuHB+YP6wPUfz54qy7K7WRQynI/iD6H3qnoJMVsHBeKFDldv8D8x51ZQxLpOz3EcRhVV1W8saioix44G+0Me46fhUlFcKehH+f4V0oYinPczlVMPUhvR6OcV793nDfiL+0i6jnCR/wByLMrA+zBNP9oVg3r4X51YP0aOG67m4uCNoYljX01TNk49wsRHyf3rzPxXjf0+FqTXCLt3y0XrY7ew6GeS6v0RfxNJmjNJX57PoNgzRSE0hNIdhc03NFFIlYKKTNITQMCaTNFJmkOwUlFJmkOwtITSE0lIlYM0UUhNAwpCaM00mlcdhaM0hNJTuOwrN61yl3i8XiuruW50gHViGVcpKqIAqMsiYkUlRq2PVjV7d9XHvh7KQKcST/Up6gMDzG9dowwz5FlrlTi9zjNfRvgnZ/yTxMuPyx7lq352XgzgbYxNpKmuGr+xuXDe+XitpgRcRuCg20T8u6GPTVOjy4/t1MP9Kbi4GOZaH3Nqc/wn0/w+6qC4pdnNRElwa952JxO26Fpd4XfFxHiClLq7kkiP+5ULDCfTVHEqiTHUc0vg71Wt7fFvOsJpa880400iLqNiu1eZNKaaasIAKcKYKcKQCsa7Uvsw8K4VBuCtja6h0wzQozDG2+pjXG/A+GPcTQ28f255Y4U/eldUXpv1au1u9rSHjjXGmJRGoB/VQAAbbdB/CteGVlKXT6mHFvWMfEiuzIyR/Pp/rVp8Lh8I/n51W3ZKHcfz/Jqx7eXC/wA/z91XQehF7wv7oKDv+P8APrXJH0i+Jcy7Qg74K/rZJIcLjSC2dWMY88V0T2740I4mJOMbD+Ncg94t7zp/M5JxjOQQrYOBk7HGwqFWdlYnTV5GdwWVVEiSFijQ8oHCp4Z8lpszSnRLI55JA0oQyO3gWlu2mQQxXDBtDz2yAgCExrFwwRqwGgESBMAvpcMnieN15kOCeUCQYhIkh0IFeJVJB5cXMZEXHhJYsAcls58GDmXQJijVxrxLCSrnOdFjIQrYJyNetiM4Jdj+sc2Ld75j4++R58GhKpcxxs2kuVUN4SG+C4jA6sGC+JZLuGEsVUtkHSmdInL9PDJKWQIXnXfmP4xc8ZEizxRqXWFoLtxIyujxjDrqIVX1WQloJM5baXJOWJVH4GhY9TjCbk7CspbsM0R+ICXSqjJcqzIpbxBIriQ4xIqaQLnGBq5cuArSRidiMtdSWMmgaiBlWR/rOXJoOoNGZf8AczI5IKXY+pm1ZPLd3EsdxGadGkukfnK+OcH3ZQBpUTooQ6QCFW4QRlcgHkO/Lr1W4OoRMhguI3IMIATxSY1fDA/VpzdWXsnHw8wY8vRrRBjySFGDRnQ6uQoXUq6gGDKoc64XwWV7Sf1cRvofRUnqhLQ9o7qOSMsuoKpVWRgDpZllKrrAVGVhGcAhcqjDEUceJIw3JUsMkAgqRvjcElSMjIyfssCp1FmXBC15fENoZOY2hJSwh1ERpIQVeQQHCq22nKooXGDjwqcV7nOxPnsdth6Y+e+fMkkgschOYZdSRhuNOSQTgk+p+Yzklt985Az0YsFfJ4zZO0phZo44kHNLq6SJpOwmJRzzJJM/Vx6tWHBzh3laENxuPTAwM7ee5GcZwT+JznJzkW4Go4yAianxjVkEBtC/rHfr5DLHZCxV76DtYno3+ysausSHQiq31jM+GCKxAAuJQNbykYiXEmAI7aKPIbiJiCMrsZHZRDHbhRziuqIaAySH4GJS0EMZUtcSvJISdOBEh9n21KibojE6o31MQhHi+H1LquLhSWc4QFFOsCXDrGboHVNKTG0q4xaLjQqhV3ikkjGmM4VEhGmLU+oW7Ye/fv1JteMOlxGukF21l9J0xyS/7KsdtC32WhV7KK0aTxBybrxFSpqd7EW8cU64JYKr2ceytGsIc3UM5I8StIYnn1EnJuE0hVAzo3Z0E69Ll5IFLWkOW0NJnxSxo4wTGo5wg0hpGCbEIyNsfZO80NayqxPNFqssejUvJje0sw/M8jLyZV5Y8gDvtTgrshJ2RB9p20SQoTIZ1Qm4LjCtKzHLq2rxgnJB0jCsoreu7/iPiXf03qt+3Nw3xsqsd43dD7Yklxj+zp/hUx2T4iQy49qyzlaoyzL8iZ2R2G4j4B6Afl/P4Vz39Ljgwjv4rhdluYd9v14Tgkn1Kugx/VqyewHFvCNz0G/4VhfSZ4X8Rw0SjdrWVJNv2GzG/wBwDaz+79x0XurFUdJJnLyN71kwTY86xQfalU1UaictL/HnUra8S961/gXDZLiQRxJqbz9APVj5Cr27Adgo7cBnCvKceNh4VzjOM9MH7z+NSjSciupWUO81jhPZ28nACRPg9NeV6+eD4se+KvTunsW4ZamOeKQzSytK3LCuMYRFUEsp2VQxGOrnrXrZSpGoCOgbzdiC3vpX396kL/jVuYwJfrMdG21lsD7LDdDt1XGBnpVG0dj0MdS7Kte109HbduXcQw+PqUpZo2XgN4j3oW8YP1U5OSAMRAZG255pIHvg1otr3l3V04bUsMOc6I9mwSNOpyS7YzuV0g+grzveFyzFmRtSatS85cunTYyKTrG2csNXmxYkmorsv2ft1YNJcrGoHiVY5H04J2ViAuPQnoPWuNR+Gtm4B9oqTl3qVTyST87G57Sr1/llO3lH8HRHCmUwIdRZzuSfTbGc7+v4fOvStU4F2tsAoSOfZdsusgP3syBSfkfwxU5Z8ZgkOlJonb9lZEZv7oOf4V8w+IaVWrip1oYeVOHBZHHRcXpa7PT7PqU401F1FJ/9SfgZ2aQmkzSV5s6YuaTNJmigYUGkJpM0rjsBNJmiikSsFJmgmmk0DsKaQmkzSUh2FzSUUmaQ7C0maTNaN3v9sfg4NCN/tEwIjx1RejSn0x0X1b1CtWrB4Spiq0aNNat/8t9FvZCtVjSg5y3Iqrv57SCe60I2Y7cGMehkJzKw+8Kn/d586pbjtz71OcWuMCtK4vPmvumAwcMLQhRhuird/N+L1PD16zqzc3vZF3kmT1rDanymvE1sKANFGKQ0hgaaaU000hgDSg00UooAuX6InARNxM3DD6uxgkuCcZXmMOTEp2O/jeQdN4852wbd49xDnTk+hP47Z8+tav8ARjtuRwe+usENcXIhB9Ut4lIx7a5nHXqp9KleBLlsnc5/n+f9K2ReWklz1OfL56zfLQ33sxHgD7s1sFxdAKd8fftUJwptK+X8586h+1HFNKnfGP5/y6VOLSRJo0Lvu48VXQD1zXOnEbol9X7Jz7+fT+fSt771eNcyUjOwqvENZZyvIvpRsrkzM5MYYMxUMAuc7LjC48Rxnl6sHGN8bAEznFw5tojnxs5O2MqEt5IsE+p0Ag+59jWucHxnS6hlyABjLAEvq+zhyNs4GRtsATtORsTDI+lwFkyNbFiqqIbgrrPUOMqjH7QMYzuM6abuiMo6nhA5zE6EwrE8hLbSGPSllzGGcCUyNJgRHwnWEJ0kkY80DSzNy7fQJixii8SqikCQFWYqulIzrLt9WqEthVxj2gXUJEUZLKGRTgPplUcvKhiMvJBaYGT/AEoG+qsu0speQVVm0LHqnkGZCIiIp2hhQNgxorpLJ4kDuR+rEXMrXINWEuQJVjiDF4bY6DPpJkleQjFvbAjVo2bkwMB1llcIHKR+V/JnJz4SugANqDoj7IJOssaOMNcn7bBY4sKtSU0aAaQCqJqjWIt4hqCGSJnUbTuul7ucYMaFLddPhjqO4hOIxrkIkdgOXGVGjSvhVnUbLCgBVIeh6fYDGaV9CKRHyT5YlsmQhNJ/qhQoVQOg06VAwfCFAPk/kINieg8umD65wdsbHBzsQc+Jdctw2aZRLrkfNwo5sechwCxRpgQRqGTpGCyhm2IYo+DLESduh/167nHmdyfMkthmeO9DPBLc9RjG+xPvjbqfv36YBLEKM6e0cCBnAjV1fks7Ipbltu2otqiXUcIz6Ezqw3hdqxETSRnp1G43G4yCDjcAgEHScEZ0qxrNvLJHChgFY7CQDpuBiRcAsuWA1AAjIwB4ISluHbU9oyUJVspIDnBJiw+wzrwPhpj5Tgct/CJFHU5drbuGDwALMQ0TRGMLHc53e3e3I0x3BA3tD4JdOYSHVFeMtS41RTGMCFMo7uAwUBtKRnDGaNt1VQpClsZVWeOXPgmQjSSrKVCYJypTBcRSEEkw4BKSgl7chSCU+zLNf379+INe/fv0MW+s8x6oImQmRCynXzrd2wsShm8RgZ9XLlGDqbRLqZIXO92WgzqqnJMiGPO4lKyfpMBSARpTnrqOejD1GYW1ZsoEVF5OoRoFWNzyzbXIEzBipZnsrpHlLacxk4A0is6OdFWdkQj4W4lUbkHFtEbaAMGIZWeBxFpwMtEWZslUqyDsyuauir+K3nMnlfJOpzgkAEqvhXIHnpA/161LcCuNLCtYt233+Z+dTFi+K5jd3c2OOli/ewnFRgb74q0YWW5glt33E0TxEezKRnfbzFc/dkLzGN/KrU4BxvSVPy8+m9aqcjJKN1Y5turYxs0bbOjMjfvISrfxBp1hatI6om7McAe/v6ADcn0rc+/bhPKv3dR4LgLMv7xAVwP7Qz5/ap3YbhpgzNIviIwBj7AOOvuen8PM1NR+axKVVKNyx+7fs/HaJgYeVsF3O2T6L5hRW4NfnYAY+Ryf4/5iq8tO0qA759qcva9Ax66j5HpjyFallS3mD5pO5YbcT0KSSi+pxqf0/VHmfWoqbia5GTqOM5JAJ9Nt8/LYDrVd8Q7Slzy13J+yFGo7eeBliM+de8XZjidztDaznYAyNG0S4P7Ek+iM++kkj22NZq+Mo0VepJJdWl9S6lQnN/Km+5Nlsx8ds0tZRPc8uVo25QA1EucgKqpliAcZIAA/aUjIrVb1HXzAP2hliT1zkkn8M/6bH2Z7gLokPcXMUZP2lRWnfy6uxRQ3ltrHnk9KsCz7lrJdJMl0+Dk5lRQ3seXErAfukH3rzeJ+NNm0nbO5f9Kb9dF5M6tPYuJqa5bLq7emrNbsOCcPNjNJJPHbzckmFndh9ac6FbVkbtjKKCxB2GcVOfR5lJjV57WUjBzqUBM46DmY1/4DzrfeE9m7WAgxW8KMBgOEXmY/4hBkP3salCa8zjf/AJDk7rD0vGT+0bf+R1aHw9b/AFJeS+7/AAe95KhI0LoGkeE9Qd+uCRt02PlXOHbPt7cre3DQXEiKshjVMho8RgR55bgp4ipbOM+KuiKjeNcDt7gYnhil9C6KSPk2NS/cRXltnbao0sVUr4mkpZ96VrK7u7Rd0/PxOvXwUnSjTpyat73opfhHffOm08Mco9UJif5n7aE+wC1u/Be+CwlwHZ4G9JUJX+/HrUD3bTUf2g7krKTJhea3bfADc2P71ky/3CQVX3HO5K/i3heG5UeQbkyH+zJ9X/8Adrv5Ph7H7m6UvGP1vAxXx9DhmXn+GdDcM4nFMNUMscq+sbq4+/STj76y6434nwy8s21TQT25XpIVZV/syr4D/Zap3gPerfw4xcGRR+rMBLn+231n4PVFf4IlJZ8LWjJdfyrp+hOG2UnapBp++DsdVE0mapHgnfsdhcWwPq0D4/COTP8A+St34N3p8Pmx9dymP6sylMfN94v+evPYr4d2hh/3Um1zj8y9L+p0aW0MPU3SXjp9TdSaM14WV4ki6o3SRf2kYOv4qSK9s1xZRcXZ7zarPcFJmgmm5qJKwpNJmitN7d94VvZgqCJZ/KJD9k//AFW3CD23Y+mNxpwuErYqoqdGLlJ8vq+S6shVqwpRzTdkS/bPtNFZQmWQ5O4jjB8Uj/sj0HmW6KPuB5c7X9oJLiV5pWy7ny6Ko+yijyVRsB8yckkn17X9ppbqQyzNqboANlRfJUX9VR95PUknJrSeK39fXPh74fhs6GaWtR73yX+1dOb4nk9oY94iVlpFbl92YvGb2tVvJsmsq/uc1Fua9LY5bGs1MNKaSkMM00mlNNoGgzTaU0hpAFKKaKWgZ1N3ffV9nbEdObJdSHyzm5mjBPrlVGD6VJdm136fj99RkjCPhXCoxt/skLkDYfWAyE+W5L5P3+pJyuB3QBHy6/h/Oa2T0yrojn0tXJ9X9TejcAL6D+PT/P5VXHb6/wA5xW3Xt34D8vT8Kq/tnfLg+38+tKb0LLFO9p7gs5yerH/GooVmcfbx/ef86xAKzI0LcZUYOgsCcANnc9dLAdM42c46DJ86n7ILNyo1ZVXMySKMFZAzycvEavuEZgdSr9XzNaqVQ4huBqGLI32TgnpgeJAGbJHhBIzjJI8jXlwyAxyEkAGGUByXAQAGRWTOGOHI0BxkDO/XNXRdu4i9e8mLe7ZpjpxlInaRwoJ1ty2jwCM5WfkKseFzJq2TWwEnJoUqwZFCPGEkONMbSc6KPSTsWSH4eYN002gfIMik+UXFVYouC31TPpDxplmyGATTHqkMjSM0JY43aMr9mmxRQ8wSMxlSJmPLKrplkkMZjfzVzISpZSoAAt0YaM6bUVs9ZtanZNGAiqu40AiMiM4OSFkF2XcHLPFK7aWVCjLOJ5FjldImaFVc6FhjKpJoWJpQMNPgprPLVuWWOrU7Oq5nEjNKsaq4HxBZp7gNqCrEzKVGGLhUOdnwzalzgzyGSRseBpLMywA4ydUhAJUHSNCkAAY0dAMk68+EMktqXkVOSRAC3ZidjkkA5GDksE31YBOpsbnq32tJL1i/DyvJyYkbGCzu8bnmKrDUQCgdodWlQmNUrFQQMpFHfnZ7s5FFpVvDqPiJ8z6seuw2A6AY2xtWy3PJiB0hAAMajjbbHtvUnC63laqvkUp2L7AStITcsFhk8Qh0CN8tpYnlr4LcrtmNNStgAggLXRndz2A4R8P9fBAy4ZGMi8yTw6gBrbVIG0uHUhs5bbGTVY8Z4ojsTHl2UEkKQPLfAzuSBsBknYDqMQXFe1dwodITypSi6piCwjLhWVtCAgDxFGlUvy2wTHoZXpwypNeopXk9TWO8vs3Gl3cW1qGmQPIAZJQ2BmPlqBp1AxB9Ik1F9n1eB9B1zgHw+frFYmJ3WaNgVcRaplDAAE6FR2R10lvG36yRLLOWEsrljcRzQzId5gpEMrLkeKQAxq5ycMNSPlsDDGOSU4jYtIgZA3NbwsNZhWQqFCSMVjdm5eM6dY3EfimWNNcXC+qLFO3ys8uBcDnQkysksXNxG4PjuIbhmbQY4Q7sDdSRYjQOCZLtF5iygPFdvp0MMDRkSc486WdEMaTzeMcxkbSdT/Wk+AeIEamCA1sfAJYokEM0glGt1jijX6xxJhXgQKTpSQ6cxAHfSBlVRV8u9S9s5rW3FpaxrLIY3DQBlYxILyGPVEEJkUGOREcnOCDjS0WYtJR0Gr3uymeIRaJCB0OGX5MAR+X3V72zVsV/2JvZYhKlrOwjBD4Q6go3DaPt4AONhtitThlxWCcWmaou6LD4RdhQDnG1Tcfac7acnpv+VaVw6JmwW6DoPz9a2ThNlkjPSrYJy0RmnJRNr4nxA3QhaSNRyQwjYjLeLHU9ANhtjy+WMmBwV09BjGf5/nao93woA3rIgwPu/n863XymF/MYycJSRiMgMDv+PoPP7q0XtVcjmtGpwIyVJBOSw2OTk9OmPXV7VvMl6IOdcY3RDoz+0fCg+9iB99VWWJyTuScknqSdyfvNZ5NNGqjHW51T9C26X4O8jz41uw59QkkFuq++C0L4+Rq+Sa4s+jl22FhfDmtpt7kCGYnohzmKU7dEclScgBJHY/ZrtLNfGPjHBzo4+U3+2dmn4JNeD9Gj2+yaqnQS4rR/YKTNFJmvKHUsLSE0hNJRclYUmkopCaQxabmsHjvFY7eJ5pW0ogyT1JJwAqjzZjgAepqm+K99c+r6mCFUzsJS7uR6ko6KpPoA2PU11tm7ExePTdCOi4t2V+XeZsRjKVDSb15F4mtU4/3ecPuMmS1i1Hq8YMLk+paIoW/tZrC7te8SO+1RleVOo1FNWpXUEAtG2AdiRlSMjI+0N63as9WGL2dWcG5QmuTt43T1RZB0sRDNo0U1xvuDhbJtrqWI+Syqsy/IFeW4HuSx+dUDd3AR3UOHCuyh1+y4VmAZc76WAyPY1193n8ZNtY3MqnD8spGfSSTEaEfultXyU1xffxafur6X8H4zF4ujOeInmimlG6V72u9bXe9bzzm16NKlNRpqz3v7fcluH8aZG1I7Rt+0jFW/FSDW68D727+LH+0GUD9WZVkz82IEn/PVNzXJFCcRIr1GIwNDEK1WEZd6TOZTrTp/tbXczpax7+XwOZaxsfMpK0Y/uskn+Ne1z36sR4LVFPq0zOPwEaf41zbFxQ17Di1cd/Cuzb37Jecvpexs/imJtbP6L8Fu9o+9C9nBUzcpD1SAcsH5tkyEexfHtWi3PEAPOtZl4sawLriJNdfDYOjh45aUFFdFYyVK06jvNt95McR4l71rt7eZ868J7kmsOR602KmwlevKnE000CGmkoNBqJIRqbTjTTSGIaSg0hoGIKV+lIKG6UkM6k7WYFrYJn+jsrVdvaCCsLg12Bvn3J9vOsG5vllih3xi1tseQ3ghOaiYJsjSfv8ALNbauskzmUXZNE7xntcDlYwz+WRso+bH/wDXNaLxqGd8kkLnyGT18vKt1sLNMbAV7twoH3qqWpcmUjxHgMuc7H+H51hHhMv7B+4j86vG54CD5V4Hs35Y8qrtYn2jKn7OWjK0moFcxOFYjOH2KHA6+IA/Me1OSy1KUETR60jRiTnxRKCshVV2yw0kDP2i2+KtuPs1jfTn516R9n8b4/n7/wAKmpO1iObiUtbcMnLK5iYt4tepwBICCvVTkHSSC3XPizk1Lrwid95F3MusnWBrBXQQwVMBtOo6h1LHarasOBlmxjp/rUxb9nohjWM/f86lBSIyq3Kw7KdjpXLAFQDgM6xqgwNWMkA6n339cDzyTZ3BuGRWqaEBz1ZsfabYDPkFAGkKNgAB5VLS3kSLpBCgdAMDz8v8ahb7jSnOB8vX+f58qvTyopleW8y/j9f2/I/xp11bNIM8zlq3Qgq8vzUsNMY8s4duv2cZrRuIX4znmYPoMfxFRk/aIrs0xA3OwAOwyTu2NsHOff02M/Mik+Ba/Z0W9qBhEbdftZYnJGpixJd3OMlmJJwMkdBuXDuKWbDBREJG42GepJPv5/nXNw46jMVDuzggbtupLrGBpBAB1sq7+ZrxsOKyTf0UhdQyrknBUsVVMhnJUEkAEjBOw3GKshWa0SJZeLOoz2PtpF1ZG/TTgA58ts5HzzioK37vIUk+pZmJyx1sHA3AAGwIB3qk34/dw9ZJREnJ5shIVVaUSEIoYhpdlC82JZEDN5jSXzH7S3KTvAJ2iuiqy2qSTQSJd6i+YmmiOIpXUKYlfKsV0+Auhax1EtbEcty7j2akiJbQjZ3zyxq/Hr028qg+NwXAGqPQrDoGg1Yxjf7f5+VVmnexfxF2aXVCJ5beAmNkkupItIb6lyDCi6oy7SHKawoV2DKsvad/zjw3VqisnNEhjZXIeEqrLjUFOSw0+LDZG+4NNV4PiTVHob32eub1cEXFuZc5VnBEK9AFaFZVBB6HcH0I2xy/3jdk7mz4i8N2iLJI5uByjmJ0leRg0eSSE1BlCtuNJG/U7r3l96IuJ457FZLVwhWYssYDsCNDooeTT4dQIOP1fStA4xxme5uY7i4leZzpQs5yQBqCrtgBdzgfOsmInGdi+mmrmx8MtanYNtqjLV8CpC03qdPQxz1M+zXPWnXL42/CmNMB5j+f8613tFx1VYgeJhsB5Z9Tjy8vU1KbSRGEXJ6Hh254p4Viz1Ot/kM6Qfmcn7hWpEiie4ZmLHck5Jpuqs9zoRjlVjJtpBXR3cJ3xrGiWd8/1agLBcN+ovQRzH9heiy/qjAbYahzQpqQsbrFc7aezaOPoulWWnB8U+a96mnDYidCeaP/ACfRJHBAIIIIBBG4IO4IPmD60ZrkDuy71bqywikSwf8AYSE6R/wn3aI+wyvUlSd66H7Gd6Vjd4UScmU7cqYhST6I+eW+fIA6v6or5Jtb4XxeCbklnh/uj91vX06nq8LtKlW0bs+T+zN4pM0hpCa82dIUmkJpM0lIdisPpGOwtoMZ0c/xemrlyaM/drrnjiF3iuxu0PCIrmJ4ZV1I43xsQRuGU+TKcEH/ABGRVGcc7grhnxFdQmPPWRHVwPdUDKxHrlc+gr6P8K/EGEw+F7CvLK027tOzT14cVu8jz21MBVqVc8FdP0NM7i7t24paBM51SFsdAghn1avbG2/mR54rrQmtA7pu7GDhgZw5nuZFCvMyhQE2PLiTJ0IWAY5ZmYgZOFULv1eb+KNq0sfi1Oj+2KUU919W792uh0tl4WdClae9u/cU79JTi/ht7YHqTO4z5DKR/cSZP7ornPjjirF72+Oi5vJ5FbVGpEUZHQpGNOR6qzanB9GqrONzda+nfD+C/S4GnTa1td98tfTd4HmcfW7WvKXDcvDQgrtxmsTVXpO25rwJrtmE9BJ70c2vImmmgZ6mX3pjPTQaRqQwJphxTiaYWpCA00mlNNqLZIKRjS5ppoGhDTc0pNJSGFIaXNNNJgIKUV48yjmGo3JWLqQssVqR52lqevpFGP8ALH8msNLo1o0XbecJGmmIiKMRqSHyVXVjPjxkA42A2Arxl7Xyn9SIfIP/AOurZVUzKqEkW7wniPvW0cOvc1z7B2zmXosf4N/66zrfvIuV6LD96v8A9ShVUg7GR0daqG39ay44x6D+c/z+Fc6w97l4uwS3/uSf9WvdO+a9H6lv/ck/6tWKtAToSOjjZj0/n+f59cG6twMkkfKqHHflf/sW39yX/rViXXfFev1S3+5JP+rT7aBH9PULtvuJIvTY+o/06ZqC4l2gO+Dn3zjeqbuu8e6bqIh8lYf/AL1GXnauZ10nSDqDa11q4wSRgh9IHyHkKO3Qv00i0OLdoio1tIEXOM7tvjOAANzjfA3xUdL2liYqvNPjXIYlY4x4ZGCs7Z0MxXAGAckdM5qs5eOTMVZnLshBRnJYqQVOQSTn7K/azsB6VlQdp5VLldKiTHMVdQWTDa11gNvpfxD7wcgkE7aF+hL9M7G2l3bGUcFgMRrl5dw2EUBcmRsHC+x8ldlh+F2RkLSMDhg0aLnJ0vqjOC3XqY1ZtjIWZiRFLUbP2rmYhvCHU6kdTIrId9RGJMEuDpbWG8IAGMnL5O10rNqZIm8auQysVbDZ0sC+6lPqyOugyb5llZ261MnGlJGx2UkKkyAkEkO0o16N5n8XVmWNZok0OsZZSELrLqaNZCHgQaRJIJFgZnVX8OqPRKdJKxrrVgy6j8MNcVwFYQFzm3j0eLtO6tqWOFcKVVQhKJ/tBuVIVmIOiTACtqUqoDBstnI//m1wTIX5cnMd3IdMqHkYNIVUEDTIftRHMbnDMpZEZZfqIEXRmWrwbiA1RuU8SyTGxnePSHkgJR3h5mssviUSQsWKMVcF9LSRxttwuNBJCgLRTsirwvnAzXUyRSFZmkhjDiMOrOMBE1NHsoZkj0ri3eJNKJAYLZQ6ctPBLIbcao3Y25nmkKSOy6mmJaTxbMNMejCue21w6xhsF4Y3jhlV5o5IubI8kjrypVQySa3V3ZW1h2LamwwcsVBkI4efv09+Rv7yXEltM73FvcWsFsXWZk0yyyxTcNRZAuRc8wXF1HbpcTKysi+JQrNWjcIDSyKpY45ejIBJQNqhiQY8TR65IxoOfAcbjOcGTtTJiULHBGJoYoHEUSoOXE1u46frs8KSM5yWbfOQuMa2446O0iqiltGwUhRy3t5BpAIx4olz826ZGKXWTL4UmrktxC15bFc5CnTqHQsoXXjr4Q+oA+YANeCkfd/OPvHUGo+64474yEADO2ACN5CGbz9gB7AV4fpJvRf4/nSlVjfQapytqb3wriWQAeo9PP5eh9RUqL7HT+fnVYpxRx0A/j+dZT9pJSMYQe4Bz/5qksQkUywzb0Nu4txojZT4vM9dP3+ta8T86h/0m3ov4H86P0m3ov4H86TrJlkaLjuJfal2qIHFG9F/A/nR+lG9F/A/nS7WJLs2S+1ORqhf0o3ov4H86P0o3ov4H86O1iHZs2a1ucVM2XEK0IcWf0X8D+deqcdkHkn4H/1UnUiPIy9OyHeFeW2BFO4Qf7t/rI8egR8hR+5pNWn2f78CQBcW4Pq8DY/+3IT/APkrkOHtbKvRY/wb/wBdZkXb2cfqQ/3X/wCpXHxuxsBi9atNX5rR+atfxNlHF16X7ZeG/wCp3TwnvLsJf99yz6TKyY+bYMf/ADVs1jxGKUZjkjkHqjq4/wCUmvnuvePc/sQ/3X/6lL//AGPcf9nB/df/AKleZxHwPhpP+VUlHvSl/wCp06e2qq/dFP0/J9DzSV8/Ie9y/X7Lhf3WnX/Cakn7279tmkyPQvOR+BmrB/8ARZ3/ANZf2v8AJo/ja/2ev+DvLjfH7e3GZ5o4vZmAY/up9tj7KCapzvJ71jOjQWgZI2BV5mGl3U9VjXqikbFm8RBIwvU8v/8A8+n/AGIf7r/9Snf/ANg3H7EP91/+pXb2Z8I4TCTVSo3OS3XVop87a38XboYsTtWrVWWKyr18zeOJvgVpvFpdzUfc9s5m6rF9wf8A9dRk/GXbqF+4H869gqiOO4My5DXm1YJv29B/H86T45vQfx/On2iEoMzjSVhG9b2/j+dJ8Yfb+P50u0Q8jM4UhrC+MPt/H86T4s+38fzozoMjMwmmmsX4s+38/fSfEn2/n76WdBkZlGkrF+IPtR8QfalnQ8rMk0014fEH2pOefajMh5WexpK8ucfakMppZkGVnqTSGvLmGjmUsw7DKKKKgSCiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigAooooA//Z\n",
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/nxjwB8up2gI\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "            \n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x1265e9590>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "YouTubeVideo(\"nxjwB8up2gI\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7da6479b-b097-45ee-b4bf-413a64712544",
   "metadata": {},
   "source": [
    "#### [ Session 14 ]  Recap: EDA on Titanic Data Set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "3beeb8c9-0bc6-4b37-88d5-2811e9eb1751",
   "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>survived</th>\n",
       "      <th>pclass</th>\n",
       "      <th>sex</th>\n",
       "      <th>age</th>\n",
       "      <th>sibsp</th>\n",
       "      <th>parch</th>\n",
       "      <th>fare</th>\n",
       "      <th>embarked</th>\n",
       "      <th>class</th>\n",
       "      <th>who</th>\n",
       "      <th>adult_male</th>\n",
       "      <th>deck</th>\n",
       "      <th>embark_town</th>\n",
       "      <th>alive</th>\n",
       "      <th>alone</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Cherbourg</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>S</td>\n",
       "      <td>First</td>\n",
       "      <td>woman</td>\n",
       "      <td>False</td>\n",
       "      <td>C</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>yes</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>S</td>\n",
       "      <td>Third</td>\n",
       "      <td>man</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>no</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   survived  pclass     sex   age  sibsp  parch     fare embarked  class  \\\n",
       "0         0       3    male  22.0      1      0   7.2500        S  Third   \n",
       "1         1       1  female  38.0      1      0  71.2833        C  First   \n",
       "2         1       3  female  26.0      0      0   7.9250        S  Third   \n",
       "3         1       1  female  35.0      1      0  53.1000        S  First   \n",
       "4         0       3    male  35.0      0      0   8.0500        S  Third   \n",
       "\n",
       "     who  adult_male deck  embark_town alive  alone  \n",
       "0    man        True  NaN  Southampton    no  False  \n",
       "1  woman       False    C    Cherbourg   yes  False  \n",
       "2  woman       False  NaN  Southampton   yes   True  \n",
       "3  woman       False    C  Southampton   yes  False  \n",
       "4    man        True  NaN  Southampton    no   True  "
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = sns.load_dataset('titanic')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9aca12ac-bd71-4eb5-a025-ca01b40fbe25",
   "metadata": {},
   "source": [
    "<hr />"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "55ac3d13-30fc-4ae0-959b-c47d8080e4fa",
   "metadata": {},
   "source": [
    "## Let's Look at Git\n",
    "\n",
    "<a data-flickr-embed=\"true\" href=\"https://www.flickr.com/photos/kirbyurner/52567606601/in/dateposted-public/\" title=\"Screen Shot 2022-12-16 at 12.44.11 PM\"><img src=\"https://live.staticflickr.com/65535/52567606601_22a034eb5f_z.jpg\" width=\"640\" height=\"215\" alt=\"Screen Shot 2022-12-16 at 12.44.11 PM\"></a><script async src=\"//embedr.flickr.com/assets/client-code.js\" charset=\"utf-8\"></script>\n",
    "\n",
    "The instructor updates these notebooks(s) locally (localhost), then adds (stages) the notebook(s) for uploading, then commits them and pushes them.  These are Git actions."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "022d1cbe-2392-4bf3-b7fc-2239ad018efe",
   "metadata": {},
   "source": [
    "<hr />"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d6eb3446-7b45-4556-ba92-cde37f041ae7",
   "metadata": {},
   "source": [
    "# Topics in Python...\n",
    "\n",
    "The rest of this Notebook is devoted to topics we took up in class."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da96bd34-e903-4f54-9b74-91af19f2b51f",
   "metadata": {},
   "source": [
    "### Formatting Strings\n",
    "\n",
    "[Main Documentation and tutorial](https://docs.python.org/3/tutorial/inputoutput.html)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc7327cf-cdc8-4112-acff-5db562c1beaa",
   "metadata": {},
   "source": [
    "### Designing Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "085063d0-a5a1-474c-ab53-3001b23783c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "By K.. Urner Date\n",
    "Explain what this is.\n",
    "\n",
    "\"\"\"\n",
    "\n",
    "class Python:\n",
    "    \"\"\"\n",
    "    Talk to the end user about the class\n",
    "    \"\"\"\n",
    "    \n",
    "    def __init__(self, nm):\n",
    "        \"\"\"birth method\"\"\"\n",
    "        self.name = nm\n",
    "        self.stomach = [ ]\n",
    "        \n",
    "    def eat(self, food):\n",
    "        self.stomach.append(food)\n",
    "        \n",
    "    def __call__(self, food):     # <-- __rib__\n",
    "        self.eat(food)\n",
    "        \n",
    "    def __getitem__(self, idx):   # <-- __rib__\n",
    "        # print(type(idx))\n",
    "        # optional:  blah blah\n",
    "        return self.stomach[idx]\n",
    "    \n",
    "    def __repr__(self):           # <-- __rib__\n",
    "        return f'Python named {self.name}'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "98446e59-79fa-4a69-9cef-f13ceca277a5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on class Python in module __main__:\n",
      "\n",
      "class Python(builtins.object)\n",
      " |  Python(nm)\n",
      " |  \n",
      " |  Talk to the end user about the class\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __call__(self, food)\n",
      " |      Call self as a function.\n",
      " |  \n",
      " |  __getitem__(self, idx)\n",
      " |  \n",
      " |  __init__(self, nm)\n",
      " |      birth method\n",
      " |  \n",
      " |  __repr__(self)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  eat(self, food)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors defined here:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "help(Python)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "e09871d1-31c0-4acc-8ddb-730b90451b5d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Python named Monty"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snake = Python(\"Monty\")\n",
    "snake"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e52f6b13-6879-4b07-9c1b-0669dc9d1df8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Python named Jerry"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snake2 = Python(\"Jerry\")\n",
    "snake2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "e4d2ce2c-1326-49a9-9717-2f67737b3e3b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Monty'"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snake.name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "91d6daed-9d30-4ecd-b9b0-489dea307af8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Jerry', 'stomach': []}"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snake2.__dict__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "6658ec42-651e-46b6-9246-dfaa58e7a44f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Monty', 'stomach': []}"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snake.__dict__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "207bf46e-0370-4b42-be47-f0f94d7c71ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[None, None, None, None, None]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[snake.eat(\"cupcake\") for _ in range(5)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c0d5a279-80e7-4eb2-9b25-4f480b317ea1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['cupcake', 'cupcake', 'cupcake', 'cupcake', 'cupcake']\n"
     ]
    }
   ],
   "source": [
    "print(snake.stomach)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "6dab7cdb-a74f-41a3-81dc-a19d244aeea8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'cupcake'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snake[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "6227f23d-7a0d-4d11-a0f2-574a9a78d6a1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['__abs__', '__add__', '__and__', '__bool__', '__ceil__', '__class__', '__delattr__', '__dir__', '__divmod__', '__doc__', '__eq__', '__float__', '__floor__', '__floordiv__', '__format__', '__ge__', '__getattribute__', '__getnewargs__', '__getstate__', '__gt__', '__hash__', '__index__', '__init__', '__init_subclass__', '__int__', '__invert__', '__le__', '__lshift__', '__lt__', '__mod__', '__mul__', '__ne__', '__neg__', '__new__', '__or__', '__pos__', '__pow__', '__radd__', '__rand__', '__rdivmod__', '__reduce__', '__reduce_ex__', '__repr__', '__rfloordiv__', '__rlshift__', '__rmod__', '__rmul__', '__ror__', '__round__', '__rpow__', '__rrshift__', '__rshift__', '__rsub__', '__rtruediv__', '__rxor__', '__setattr__', '__sizeof__', '__str__', '__sub__', '__subclasshook__', '__truediv__', '__trunc__', '__xor__', 'as_integer_ratio', 'bit_count', 'bit_length', 'conjugate', 'denominator', 'from_bytes', 'imag', 'numerator', 'real', 'to_bytes']\n"
     ]
    }
   ],
   "source": [
    "print(dir(1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "600e2d73-e840-4bc4-9f23-ba975b58ce57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 .__add__(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "4634c1df-8a4d-40f0-af23-e9c5bd5ec293",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "callable(snake)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "904c6598-2e2f-4f85-9d19-65f667e52e5c",
   "metadata": {},
   "outputs": [],
   "source": [
    "snake('tapioca')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "ae716adb-f390-42d0-8e1d-986d1e33efe9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cupcake', 'cupcake', 'cupcake', 'cupcake', 'cupcake', 'tapioca']"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snake.stomach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "460c7e83-bdfa-4165-8c32-b6fc7ece6e98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cupcake', 'cupcake', 'cupcake', 'cupcake', 'cupcake', 'tapioca']"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snake[slice(None,None)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "c9b7e305-0bd3-4f62-bbe0-8cc944d803b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "Python.eat(snake, \"cake\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "bbc5998d-7343-403d-bb9b-982398452c96",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['cupcake', 'cupcake', 'cupcake', 'cupcake', 'cupcake', 'tapioca', 'cake']"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snake.stomach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "38fe470c-2930-4b27-a442-8de61af69ae3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snake2.stomach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "401bb16b-2e2e-4fd7-a98a-a08c9188443d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'HELLO, WORLD'"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s = \"hello, world\"\n",
    "str.upper(s)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "3e974838-c727-449b-a1ad-200a9b8fe068",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'HELLO, WORLD'"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.upper()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "6236a6af-9e3b-4472-b2c6-69f90f0665d9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Python named Monty'"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "repr(snake)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "1be3f0b0-e2d2-4538-9f7f-e605249516f1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Python named Jerry'"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "snake2.__repr__()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b7221adb-30d0-4627-b91b-7a307093ab1a",
   "metadata": {},
   "source": [
    "### Sudoku Project\n",
    "\n",
    "A puzzle with numbers.  \n",
    "\n",
    "The goal here is to work with numpy in an IDE, writing a longish program, then importing it here in a Notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "e857e540-c319-4e62-93f0-68852c9a1449",
   "metadata": {},
   "outputs": [],
   "source": [
    "import sudoku"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "15f43796-3ae0-4ea4-a494-c13a727fe09a",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Complete; Time: 1.0845320224761963\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[4, 7, 6, 3, 1, 8, 5, 9, 2],\n",
       "       [5, 2, 9, 4, 6, 7, 8, 3, 1],\n",
       "       [8, 1, 3, 9, 5, 2, 6, 7, 4],\n",
       "       [3, 8, 2, 1, 9, 5, 4, 6, 7],\n",
       "       [7, 4, 1, 6, 8, 3, 2, 5, 9],\n",
       "       [9, 6, 5, 2, 7, 4, 1, 8, 3],\n",
       "       [1, 5, 8, 7, 2, 9, 3, 4, 6],\n",
       "       [6, 9, 4, 5, 3, 1, 7, 2, 8],\n",
       "       [2, 3, 7, 8, 4, 6, 9, 1, 5]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sudoku.generate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "fa458f79-1339-4cb6-90c4-997a27f419de",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Complete; Time: 1.5201680660247803\n"
     ]
    }
   ],
   "source": [
    "B = sudoku.generate()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3a43814f-a1c6-4b9a-a7ad-ed0f9dc2fdba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 4, 6, 8, 7, 5, 2, 9, 3],\n",
       "       [3, 7, 5, 2, 6, 9, 8, 4, 1],\n",
       "       [2, 8, 9, 3, 1, 4, 5, 6, 7],\n",
       "       [9, 5, 4, 6, 2, 7, 1, 3, 8],\n",
       "       [6, 3, 2, 1, 9, 8, 7, 5, 4],\n",
       "       [8, 1, 7, 4, 5, 3, 6, 2, 9],\n",
       "       [5, 6, 3, 9, 8, 1, 4, 7, 2],\n",
       "       [7, 9, 1, 5, 4, 2, 3, 8, 6],\n",
       "       [4, 2, 8, 7, 3, 6, 9, 1, 5]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "67434d64-402f-4b56-b36a-d537e2a75781",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[45 45 45 45 45 45 45 45 45]\n",
      "[45 45 45 45 45 45 45 45 45]\n",
      "[45, 45, 45, 45, 45, 45, 45, 45, 45]\n"
     ]
    }
   ],
   "source": [
    "sudoku.verify(B)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "8ce72b15-ef29-4ca5-9a11-509d813cb2a6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'1.0'"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str(1.000000000000000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "453a1dae-7692-4dd1-b794-1791a01228c6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(str(1.1230000000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "3e8c2fa3-b9d5-4c1c-89e9-f5d2f67cc26c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "nan"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "np.NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b6bd1984-2ffe-44e9-9635-e7aa939c6635",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'nan'"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "str(np.NaN)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d2fb0d5-61ae-402b-a733-43144237da62",
   "metadata": {},
   "source": [
    "## Bookmarks Project\n",
    "\n",
    "When casting about for some challenge, that also pays off, think about \"curating bookmarks\", mainly hyperlinks, using SQLite."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "d408d731-cd3a-4f95-a317-dce5ea606a4d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import bookmarks as bk"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "61fd820d-1c04-43a3-b277-58db0d9f5497",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'New Math' by Tom Lehrer (animated)\n",
      "https://youtu.be/UIKGV2cTgqA\n",
      "[comedy]\n",
      "\n",
      "In Defense of Ada\n",
      "http://www.grunch.net/synergetics/adaessay.html\n",
      "[comedy, history]\n",
      "\n",
      "Warriors of the Net\n",
      "https://www.youtube.com/watch?v=PBWhzz_Gn10\n",
      "[docs, glossary, comedy]\n",
      "\n",
      "Will Geeks Rule? CBS News (world domination meme)\n",
      "http://www.cbsnews.com/news/will-geeks-rule-the-world/\n",
      "[glossary, comedy]\n",
      "\n",
      "World Domination meme\n",
      "https://www.google.com/search?q=linux+world+domination&safe=off&source=lnms&tbm=isch\n",
      "[glossary, comedy, culture]\n",
      "\n",
      "XKCD\n",
      "http://xkcd.com\n",
      "[comedy]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# try %comedy%  %docs% %tool% and %glossary% too\n",
    "for record in bk.tag_filter(\"%comedy%\"):\n",
    "    print( \"{}\\n{}\\n[{}]\\n\".format(*record))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "937a0c9f-e125-4c7d-978e-fccbb7b12d07",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Anaconda.org\n",
      "http://anaconda.org\n",
      "[tool]\n",
      "\n",
      "PythonAnywhere\n",
      "https://www.pythonanywhere.com/\n",
      "[tool]\n",
      "\n",
      "What Python IDE is best?\n",
      "https://www.quora.com/What-is-your-favorite-IDE-for-Python-programming-and-why\n",
      "[tool]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "for record in bk.tag_filter(\"%tool%\"):\n",
    "    print( \"{}\\n{}\\n[{}]\\n\".format(*record))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ca3ee4e5-d5ba-4353-bc5e-b4c77a9b79b4",
   "metadata": {},
   "source": [
    "Lets use a different formatted string to show data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "a9e39512-e02f-44c1-92b5-f3d2883561c7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "'New Math' by Tom Lehrer (animated)      https://youtu.be/UIKGV2cTgqA\n",
      "---\n",
      "Anaconda.org                             http://anaconda.org \n",
      "---\n",
      "CodeAcademy: Python                      https://www.codecademy.com/learn/python\n",
      "---\n",
      "Django                                   https://docs.djangoproject.com/\n",
      "---\n",
      "Find string in string in SQLite          https://stackoverflow.com/questions/3498844/sqlite-string-contains-other-string-query\n",
      "---\n"
     ]
    }
   ],
   "source": [
    "import sqlite3 as sql\n",
    "\n",
    "conn = sql.connect(\"bookmarks.db\")\n",
    "curs = conn.cursor()\n",
    "curs.execute(\"SELECT * FROM Bookmarks\")\n",
    "for rec in curs.fetchmany(5):\n",
    "    print(\"{:40} {:20}\".format(*rec), end=\"\\n---\\n\")\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6cd6615-478d-402f-bc67-9dc3df15c3f3",
   "metadata": {},
   "source": [
    "The code below consults the schema table to come back with the SQL CREATE syntax used to define the Bookmarks table in the first place."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "42e1e4c0-0913-4e53-9f28-35c66ff268dd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "CREATE TABLE \"Bookmarks\" (\n",
      "\"bk_place\" TEXT,\n",
      "  \"bk_url\" TEXT,\n",
      "  \"bk_tags\" TEXT\n",
      ")\n"
     ]
    }
   ],
   "source": [
    "conn = sql.connect(\"bookmarks.db\")\n",
    "curs = conn.cursor()\n",
    "curs.execute(\"\"\"SELECT sql \n",
    "             FROM sqlite_schema \n",
    "             WHERE name = 'Bookmarks'\"\"\")\n",
    "rec = curs.fetchone()\n",
    "conn.close()\n",
    "print(rec[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "f80665ac-1f1f-454f-a2b3-9e476f09d981",
   "metadata": {},
   "outputs": [],
   "source": [
    "# ? pd.read_sql"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc284f32-2f36-469e-a3ba-67e858453e13",
   "metadata": {},
   "source": [
    "We can read a Sqlite3 database table directly into a DataFrame.  We may use any database supported by SQLAlchemy."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "f89283cd-16f2-42f5-9f4e-525001d40f3f",
   "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>bk_place</th>\n",
       "      <th>bk_url</th>\n",
       "      <th>bk_tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>'New Math' by Tom Lehrer (animated)</td>\n",
       "      <td>https://youtu.be/UIKGV2cTgqA</td>\n",
       "      <td>comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Anaconda.org</td>\n",
       "      <td>http://anaconda.org</td>\n",
       "      <td>tool</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>CodeAcademy: Python</td>\n",
       "      <td>https://www.codecademy.com/learn/python</td>\n",
       "      <td>docs</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Django</td>\n",
       "      <td>https://docs.djangoproject.com/</td>\n",
       "      <td>source</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Find string in string in SQLite</td>\n",
       "      <td>https://stackoverflow.com/questions/3498844/sq...</td>\n",
       "      <td>docs</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              bk_place  \\\n",
       "0  'New Math' by Tom Lehrer (animated)   \n",
       "1                         Anaconda.org   \n",
       "2                  CodeAcademy: Python   \n",
       "3                               Django   \n",
       "4      Find string in string in SQLite   \n",
       "\n",
       "                                              bk_url bk_tags  \n",
       "0                       https://youtu.be/UIKGV2cTgqA  comedy  \n",
       "1                                http://anaconda.org    tool  \n",
       "2            https://www.codecademy.com/learn/python    docs  \n",
       "3                    https://docs.djangoproject.com/  source  \n",
       "4  https://stackoverflow.com/questions/3498844/sq...    docs  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "conn = sql.connect(\"bookmarks.db\")\n",
    "df = pd.read_sql(\"SELECT * FROM Bookmarks\", conn)\n",
    "conn.close()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "fe80851f-1143-4a61-a167-1487653dc921",
   "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>bk_place</th>\n",
       "      <th>bk_url</th>\n",
       "      <th>bk_tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>Warriors of the Net</td>\n",
       "      <td>https://www.youtube.com/watch?v=PBWhzz_Gn10</td>\n",
       "      <td>docs, glossary, comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>What Python IDE is best?</td>\n",
       "      <td>https://www.quora.com/What-is-your-favorite-ID...</td>\n",
       "      <td>tool</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Will Geeks Rule? CBS News (world domination meme)</td>\n",
       "      <td>http://www.cbsnews.com/news/will-geeks-rule-th...</td>\n",
       "      <td>glossary, comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>World Domination meme</td>\n",
       "      <td>https://www.google.com/search?q=linux+world+do...</td>\n",
       "      <td>glossary, comedy, culture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>XKCD</td>\n",
       "      <td>http://xkcd.com</td>\n",
       "      <td>comedy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             bk_place  \\\n",
       "18                                Warriors of the Net   \n",
       "19                           What Python IDE is best?   \n",
       "20  Will Geeks Rule? CBS News (world domination meme)   \n",
       "21                              World Domination meme   \n",
       "22                                               XKCD   \n",
       "\n",
       "                                               bk_url  \\\n",
       "18        https://www.youtube.com/watch?v=PBWhzz_Gn10   \n",
       "19  https://www.quora.com/What-is-your-favorite-ID...   \n",
       "20  http://www.cbsnews.com/news/will-geeks-rule-th...   \n",
       "21  https://www.google.com/search?q=linux+world+do...   \n",
       "22                                    http://xkcd.com   \n",
       "\n",
       "                      bk_tags  \n",
       "18     docs, glossary, comedy  \n",
       "19                       tool  \n",
       "20           glossary, comedy  \n",
       "21  glossary, comedy, culture  \n",
       "22                     comedy  "
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "45285ce1-8b96-4618-ba36-ed9d80ce0d9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['bk_place', 'bk_url', 'bk_tags'], dtype='object')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3090ff75-bacc-4d65-905f-a6fb5000dee6",
   "metadata": {},
   "source": [
    "Lets use `pd.concat` to add one or more records to the database, starting with a new DataFrame we will concatenate to the original."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "2ced9c7f-31b0-4d49-b85d-e8c5b7a77f1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "add_df = pd.DataFrame({'bk_place':'SQLAlchemy Home Page', \n",
    "                       'bk_url':'https://www.sqlalchemy.org/', \n",
    "                       'bk_tags':'tool'}, index = [23])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "8537eb7c-1a43-4cad-a786-d7fcbc724c37",
   "metadata": {},
   "outputs": [],
   "source": [
    "new_df = pd.concat([df, add_df], axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "e99cc1cd-4a6d-41be-afce-4ae451b2dc7f",
   "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>bk_place</th>\n",
       "      <th>bk_url</th>\n",
       "      <th>bk_tags</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>What Python IDE is best?</td>\n",
       "      <td>https://www.quora.com/What-is-your-favorite-ID...</td>\n",
       "      <td>tool</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>Will Geeks Rule? CBS News (world domination meme)</td>\n",
       "      <td>http://www.cbsnews.com/news/will-geeks-rule-th...</td>\n",
       "      <td>glossary, comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>World Domination meme</td>\n",
       "      <td>https://www.google.com/search?q=linux+world+do...</td>\n",
       "      <td>glossary, comedy, culture</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>XKCD</td>\n",
       "      <td>http://xkcd.com</td>\n",
       "      <td>comedy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>SQLAlchemy Home Page</td>\n",
       "      <td>https://www.sqlalchemy.org/</td>\n",
       "      <td>tool</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             bk_place  \\\n",
       "19                           What Python IDE is best?   \n",
       "20  Will Geeks Rule? CBS News (world domination meme)   \n",
       "21                              World Domination meme   \n",
       "22                                               XKCD   \n",
       "23                               SQLAlchemy Home Page   \n",
       "\n",
       "                                               bk_url  \\\n",
       "19  https://www.quora.com/What-is-your-favorite-ID...   \n",
       "20  http://www.cbsnews.com/news/will-geeks-rule-th...   \n",
       "21  https://www.google.com/search?q=linux+world+do...   \n",
       "22                                    http://xkcd.com   \n",
       "23                        https://www.sqlalchemy.org/   \n",
       "\n",
       "                      bk_tags  \n",
       "19                       tool  \n",
       "20           glossary, comedy  \n",
       "21  glossary, comedy, culture  \n",
       "22                     comedy  \n",
       "23                       tool  "
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_df.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e9aeb056-b4a7-4c80-8542-995dfedd8b3e",
   "metadata": {},
   "source": [
    "Below, change `df` to `new_df` if wanting to included the most recently appended record."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "9a13be1d-353e-4e1a-a593-c1910a30df0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "conn = sql.connect(\"bookmarks.db\")\n",
    "df.to_sql('Bookmarks', conn, if_exists='replace', index=False)\n",
    "conn.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2476cb65-631c-4fc1-a322-d04582d66382",
   "metadata": {},
   "source": [
    "## Regular Expressions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "e67a5578-b440-406d-96b5-8bce31344874",
   "metadata": {},
   "outputs": [],
   "source": [
    "import re"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e3cffd8d-f9e8-4334-beef-006012d5c5d2",
   "metadata": {},
   "source": [
    "## Bonus Extras"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "e8998004-1c01-4f00-84ee-c9b32f68d3bb",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The Zen of Python, by Tim Peters\n",
      "\n",
      "Beautiful is better than ugly.\n",
      "Explicit is better than implicit.\n",
      "Simple is better than complex.\n",
      "Complex is better than complicated.\n",
      "Flat is better than nested.\n",
      "Sparse is better than dense.\n",
      "Readability counts.\n",
      "Special cases aren't special enough to break the rules.\n",
      "Although practicality beats purity.\n",
      "Errors should never pass silently.\n",
      "Unless explicitly silenced.\n",
      "In the face of ambiguity, refuse the temptation to guess.\n",
      "There should be one-- and preferably only one --obvious way to do it.\n",
      "Although that way may not be obvious at first unless you're Dutch.\n",
      "Now is better than never.\n",
      "Although never is often better than *right* now.\n",
      "If the implementation is hard to explain, it's a bad idea.\n",
      "If the implementation is easy to explain, it may be a good idea.\n",
      "Namespaces are one honking great idea -- let's do more of those!\n"
     ]
    }
   ],
   "source": [
    "import this"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "0eb7fe1b-767c-4893-8d92-3345309e035b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# import antigravity  # try this "
   ]
  }
 ],
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