6_extra_features.ipynb

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   "source": [
    "<font size=6> <b> Advanced Python : week #7</b> </font>\n",
    "<div class=\"alert alert-block alert-success\">\n",
    "   Advanced Python features <br>\n",
    "    <ol>\n",
    "        <li> PEP </li>\n",
    "        <li> type hint </li>\n",
    "        <li> dir, inspect </li>\n",
    "        <li> code formatting </li>\n",
    "        <li> debugging </li>\n",
    "    </ol>\n",
    "</div>\n",
    "\n",
    "<p style=\"text-align:right;\"> sumyeon@gmail.com </p>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### - import frequently used modules"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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   "source": [
    "import sys\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PEP : Python Enhancement Proposals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {
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   "source": [
    "A PEP is a design document providing information to the Python community, <br> \n",
    "or describing a new feature for Python or its processes or environment. <br>\n",
    "The PEP should provide a concise technical specification of the feature and a rationale for the feature."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## PEP examples"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PEP 0 : Index of Python Enhancemnet Proposals\n",
    "> it contains the index of all Python Enhancement Proposals, known as PEPs. PEP numbers are assigned by the PEP editors, and once assigned are never changed. <br>\n",
    "\n",
    "> https://www.python.org/dev/peps/pep-0000/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PEP 8 style guides\n",
    "> it gives coding conventions for the Python code comprising the standard library in the main Python distribution.\n",
    "<br>\n",
    "\n",
    "> https://www.python.org/dev/peps/pep-0008/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### PEP 20 : The Zen of Python\n",
    "> The Zen of Python is a collection of 19 \"guiding principles\" for writing computer programs that influence the design of the Python programming language"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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     "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": "markdown",
   "metadata": {},
   "source": [
    "# Type hints (PEP 484)\n",
    "> type annotation convention for static analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "> <b> Pros </b> : imporve readability, catch certation errors, help document your code, imporve IDEs and linters <br>\n",
    "> <b> Cons </b> : take developer time and effort, introduce a slight penalty in start-up time"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### How-to"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Variables/Built-in types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "execution": {
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   "source": [
    "from typing import Dict, List, Optional, Set, Tuple\n",
    "\n",
    "# For simple built-in types, just use the name of the type\n",
    "x: int = 1\n",
    "x: float = 1.0\n",
    "x: bool = True\n",
    "x: str = \"test\"\n",
    "x: bytes = b\"test\"\n",
    "\n",
    "x: List[int] = [1]\n",
    "x: Set[int] = {6, 7}\n",
    "x: Dict[str, float] = {\"field\": 2.0}\n",
    "x: Tuple[int, str, float] = (3, \"yes\", 7.5)  # tuple of fixed size\n",
    "x: Tuple[int, ...] = (1, 2, 3)  # tuple of variable size\n",
    "x: Optional[str] = lambda x: x * X  # values that could be None"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Functions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "execution": {
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   "source": [
    "from typing import Callable, Iterator, Union, Optional, List\n",
    "\n",
    "\n",
    "def f(num1: int, my_float: float = 3.5) -> float:\n",
    "    return num1 + my_float\n",
    "\n",
    "\n",
    "x: Callable[[int, float], float] = f\n",
    "\n",
    "\n",
    "def g(n: int) -> Iterator[int]:\n",
    "    i = 0\n",
    "    while i < n:\n",
    "        yield i\n",
    "        i += 1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-13T15:13:49.008417Z",
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   "outputs": [],
   "source": [
    "class MyClass:\n",
    "\n",
    "    attr: int\n",
    "    charge_percent: int = 100\n",
    "\n",
    "    def __init__(self) -> None:\n",
    "        ...\n",
    "\n",
    "    def my_method(self, num: int, str1: str) -> str:\n",
    "        return num * str1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Note: type hint is just a HINT!!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "execution": {
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     "shell.execute_reply.started": "2021-10-13T15:13:49.462622Z"
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   },
   "outputs": [],
   "source": [
    "def add_two_int(x: int, y: int) -> int:\n",
    "    return x + y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "execution": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.69"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "add_two_int(3.7, 0.99)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Type checking tools : pydantic example"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "!pip install -U pydantic"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "execution": {
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from datetime import datetime\n",
    "from typing import List, Optional\n",
    "from dataclasses import dataclass\n",
    "\n",
    "\n",
    "@dataclass\n",
    "class User:\n",
    "    id: int\n",
    "    name: str\n",
    "    signup_ts: Optional[datetime] = None\n",
    "    friends: List[int] = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "execution": {
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    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "User(id='123', name='smkim', signup_ts='2017-06-01 12:22', friends=[1, '2', b'3'])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "User(id='123', name='smkim', signup_ts = '2017-06-01 12:22', friends = [1, '2', b'3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
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   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "__init__() missing 2 required positional arguments: 'id' and 'name'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-10-7a13cdd5b253>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mUser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msignup_ts\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'broken'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfriends\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'not number'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m: __init__() missing 2 required positional arguments: 'id' and 'name'"
     ]
    }
   ],
   "source": [
    "User(signup_ts='broken', friends=[1, 2, 'not number'])"
   ]
  },
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   "cell_type": "code",
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "User(id='123', name='smkim', signup_ts='broken', friends=[1, 2, 'not number'])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "User(id='123', name='smkim', signup_ts='broken', friends=[1, 2, 'not number'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
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   },
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   "source": [
    "from pydantic import BaseModel\n",
    "\n",
    "class PydanticUser(BaseModel):\n",
    "    id: int\n",
    "    name = 'John Doe'\n",
    "    signup_ts: Optional[datetime] = None\n",
    "    friends: List[int] = None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "execution": {
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    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "PydanticUser(id=123, signup_ts=datetime.datetime(2017, 6, 1, 12, 22), friends=[1, 2, 3], name='smkim')"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "PydanticUser(id='123', name='smkim', signup_ts = '2017-06-01 12:22', friends = [1, '2', b'3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "execution": {
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   },
   "outputs": [
    {
     "ename": "ValidationError",
     "evalue": "3 validation errors for PydanticUser\nid\n  field required (type=value_error.missing)\nsignup_ts\n  invalid datetime format (type=value_error.datetime)\nfriends -> 2\n  value is not a valid integer (type=type_error.integer)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValidationError\u001b[0m                           Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-14-86e2afd15a73>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mPydanticUser\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0msignup_ts\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'broken'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfriends\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'not number'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pydantic\\main.cp37-win_amd64.pyd\u001b[0m in \u001b[0;36mpydantic.main.BaseModel.__init__\u001b[1;34m()\u001b[0m\n",
      "\u001b[1;31mValidationError\u001b[0m: 3 validation errors for PydanticUser\nid\n  field required (type=value_error.missing)\nsignup_ts\n  invalid datetime format (type=value_error.datetime)\nfriends -> 2\n  value is not a valid integer (type=type_error.integer)"
     ]
    }
   ],
   "source": [
    "PydanticUser(signup_ts='broken', friends=[1, 2, 'not number'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# dir \n",
    "> Without arguments, return the list of names in the current local scope. <br>\n",
    "> With an argument, attempt to return a list of valid attributes for that object."
   ]
  },
  {
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    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pretty printing has been turned OFF\n"
     ]
    }
   ],
   "source": [
    "pprint"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-13T15:13:53.804354Z",
     "iopub.status.busy": "2021-10-13T15:13:53.803346Z",
     "iopub.status.idle": "2021-10-13T15:13:53.811345Z",
     "shell.execute_reply": "2021-10-13T15:13:53.810356Z",
     "shell.execute_reply.started": "2021-10-13T15:13:53.804354Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['ARIMA', 'BaseModel', 'Callable', 'CountVectorizer', 'Dict', 'ElasticNet', 'ElasticNetCV', 'GradientBoostingClassifier', 'GradientBoostingRegressor', 'GridSearchCV', 'Image', 'In', 'Iterator', 'KFold', 'KMeans', 'LabelEncoder', 'Lasso', 'LassoCV', 'LazyImport', 'LinearRegression', 'List', 'LogisticRegression', 'MinMaxScaler', 'MyClass', 'OneHotEncoder', 'Optional', 'Out', 'PCA', 'Path', 'PolynomialFeatures', 'Prophet', 'PydanticUser', 'RandomForestClassifier', 'RandomForestRegressor', 'RandomizedSearchCV', 'Ridge', 'RidgeCV', 'RobustScaler', 'Set', 'SimpleImputer', 'SparkContext', 'StandardScaler', 'StratifiedKFold', 'TSNE', 'TfidfVectorizer', 'Tuple', 'Union', 'User', '_', '_11', '_13', '_7', '_9', '__', '___', '__annotations__', '__builtin__', '__builtins__', '__cached__', '__doc__', '__loader__', '__name__', '__package__', '__path__', '__spec__', '_dh', '_i', '_i1', '_i10', '_i11', '_i12', '_i13', '_i14', '_i15', '_i16', '_i2', '_i3', '_i4', '_i5', '_i6', '_i7', '_i8', '_i9', '_ih', '_ii', '_iii', '_importable', '_imports', '_oh', 'active_imports', 'add_two_int', 'alt', 'bokeh', 'cross_val_score', 'cv2', 'dash', 'dataclass', 'datetime', 'dd', 'dt', 'exit', 'f', 'fastai', 'fbprophet', 'g', 'gensim', 'get_ipython', 'get_user_symbols', 'glob', 'go', 'imutils', 'install_extensions', 'install_labextension', 'install_nbextension', 'keras', 'lazy_imports', 'lgb', 'load_workbook', 'metrics', 'mpl', 'nltk', 'np', 'open_workbook', 'os', 'pd', 'pickle', 'plt', 'px', 'py', 'pydot', 'pyforest', 'quit', 're', 'sg', 'skimage', 'sklearn', 'sm', 'sns', 'spacy', 'statistics', 'stats', 'svm', 'sys', 'textblob', 'tf', 'this', 'torch', 'tqdm', 'train_test_split', 'user_specific_imports', 'user_symbols', 'utils', 'wr', 'x', 'xgb']"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-13T15:13:54.008692Z",
     "iopub.status.busy": "2021-10-13T15:13:54.007675Z",
     "iopub.status.idle": "2021-10-13T15:13:54.021675Z",
     "shell.execute_reply": "2021-10-13T15:13:54.020685Z",
     "shell.execute_reply.started": "2021-10-13T15:13:54.007675Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['__add__', '__class__', '__contains__', '__delattr__', '__dir__', '__doc__', '__eq__', '__format__', '__ge__', '__getattribute__', '__getitem__', '__getnewargs__', '__gt__', '__hash__', '__init__', '__init_subclass__', '__iter__', '__le__', '__len__', '__lt__', '__mod__', '__mul__', '__ne__', '__new__', '__reduce__', '__reduce_ex__', '__repr__', '__rmod__', '__rmul__', '__setattr__', '__sizeof__', '__str__', '__subclasshook__', 'capitalize', 'casefold', 'center', 'count', 'encode', 'endswith', 'expandtabs', 'find', 'format', 'format_map', 'index', 'isalnum', 'isalpha', 'isascii', 'isdecimal', 'isdigit', 'isidentifier', 'islower', 'isnumeric', 'isprintable', 'isspace', 'istitle', 'isupper', 'join', 'ljust', 'lower', 'lstrip', 'maketrans', 'partition', 'replace', 'rfind', 'rindex', 'rjust', 'rpartition', 'rsplit', 'rstrip', 'split', 'splitlines', 'startswith', 'strip', 'swapcase', 'title', 'translate', 'upper', 'zfill']"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dir('hello world')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-13T15:13:54.210677Z",
     "iopub.status.busy": "2021-10-13T15:13:54.210677Z",
     "iopub.status.idle": "2021-10-13T15:13:54.216686Z",
     "shell.execute_reply": "2021-10-13T15:13:54.215677Z",
     "shell.execute_reply.started": "2021-10-13T15:13:54.210677Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pretty printing has been turned ON\n"
     ]
    }
   ],
   "source": [
    "pprint"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# inspect"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-10T04:18:57.310140Z",
     "iopub.status.busy": "2021-10-10T04:18:57.309141Z",
     "iopub.status.idle": "2021-10-10T04:18:57.328139Z",
     "shell.execute_reply": "2021-10-10T04:18:57.326138Z",
     "shell.execute_reply.started": "2021-10-10T04:18:57.309141Z"
    }
   },
   "source": [
    "The inspect module provides several useful functions to help get information about live objects such as modules, classes, methods, functions, tracebacks, frame objects, and code objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-13T15:13:54.906167Z",
     "iopub.status.busy": "2021-10-13T15:13:54.905165Z",
     "iopub.status.idle": "2021-10-13T15:13:54.924165Z",
     "shell.execute_reply": "2021-10-13T15:13:54.923169Z",
     "shell.execute_reply.started": "2021-10-13T15:13:54.906167Z"
    }
   },
   "outputs": [],
   "source": [
    "import inspect"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-13T15:13:55.142162Z",
     "iopub.status.busy": "2021-10-13T15:13:55.142162Z",
     "iopub.status.idle": "2021-10-13T15:13:55.150162Z",
     "shell.execute_reply": "2021-10-13T15:13:55.149162Z",
     "shell.execute_reply.started": "2021-10-13T15:13:55.142162Z"
    }
   },
   "outputs": [],
   "source": [
    "class anyclass:\n",
    "    def any_func(self, x):\n",
    "        return x*x*x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-13T15:13:55.360391Z",
     "iopub.status.busy": "2021-10-13T15:13:55.359389Z",
     "iopub.status.idle": "2021-10-13T15:13:55.375389Z",
     "shell.execute_reply": "2021-10-13T15:13:55.374400Z",
     "shell.execute_reply.started": "2021-10-13T15:13:55.360391Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<module '__main__'>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inspect.getmodule(anyclass.any_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-13T15:13:55.571401Z",
     "iopub.status.busy": "2021-10-13T15:13:55.571401Z",
     "iopub.status.idle": "2021-10-13T15:13:55.589389Z",
     "shell.execute_reply": "2021-10-13T15:13:55.588393Z",
     "shell.execute_reply.started": "2021-10-13T15:13:55.571401Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'    def any_func(self, x):\\n        return x*x*x\\n'"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inspect.getsource(anyclass.any_func)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-13T15:13:55.781933Z",
     "iopub.status.busy": "2021-10-13T15:13:55.781933Z",
     "iopub.status.idle": "2021-10-13T15:13:55.800934Z",
     "shell.execute_reply": "2021-10-13T15:13:55.799929Z",
     "shell.execute_reply.started": "2021-10-13T15:13:55.781933Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "def getsource(object):\n",
      "    \"\"\"Return the text of the source code for an object.\n",
      "\n",
      "    The argument may be a module, class, method, function, traceback, frame,\n",
      "    or code object.  The source code is returned as a single string.  An\n",
      "    OSError is raised if the source code cannot be retrieved.\"\"\"\n",
      "    lines, lnum = getsourcelines(object)\n",
      "    return ''.join(lines)\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(inspect.getsource(inspect.getsource))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-13T15:13:55.987557Z",
     "iopub.status.busy": "2021-10-13T15:13:55.986558Z",
     "iopub.status.idle": "2021-10-13T15:13:55.996559Z",
     "shell.execute_reply": "2021-10-13T15:13:55.995565Z",
     "shell.execute_reply.started": "2021-10-13T15:13:55.987557Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[('__class__', type),\n",
       " ('__delattr__', <slot wrapper '__delattr__' of 'object' objects>),\n",
       " ('__dict__',\n",
       "  mappingproxy({'__module__': '__main__',\n",
       "                'any_func': <function __main__.anyclass.any_func(self, x)>,\n",
       "                '__dict__': <attribute '__dict__' of 'anyclass' objects>,\n",
       "                '__weakref__': <attribute '__weakref__' of 'anyclass' objects>,\n",
       "                '__doc__': None})),\n",
       " ('__dir__', <method '__dir__' of 'object' objects>),\n",
       " ('__doc__', None),\n",
       " ('__eq__', <slot wrapper '__eq__' of 'object' objects>),\n",
       " ('__format__', <method '__format__' of 'object' objects>),\n",
       " ('__ge__', <slot wrapper '__ge__' of 'object' objects>),\n",
       " ('__getattribute__', <slot wrapper '__getattribute__' of 'object' objects>),\n",
       " ('__gt__', <slot wrapper '__gt__' of 'object' objects>),\n",
       " ('__hash__', <slot wrapper '__hash__' of 'object' objects>),\n",
       " ('__init__', <slot wrapper '__init__' of 'object' objects>),\n",
       " ('__init_subclass__', <function anyclass.__init_subclass__>),\n",
       " ('__le__', <slot wrapper '__le__' of 'object' objects>),\n",
       " ('__lt__', <slot wrapper '__lt__' of 'object' objects>),\n",
       " ('__module__', '__main__'),\n",
       " ('__ne__', <slot wrapper '__ne__' of 'object' objects>),\n",
       " ('__new__', <function object.__new__(*args, **kwargs)>),\n",
       " ('__reduce__', <method '__reduce__' of 'object' objects>),\n",
       " ('__reduce_ex__', <method '__reduce_ex__' of 'object' objects>),\n",
       " ('__repr__', <slot wrapper '__repr__' of 'object' objects>),\n",
       " ('__setattr__', <slot wrapper '__setattr__' of 'object' objects>),\n",
       " ('__sizeof__', <method '__sizeof__' of 'object' objects>),\n",
       " ('__str__', <slot wrapper '__str__' of 'object' objects>),\n",
       " ('__subclasshook__', <function anyclass.__subclasshook__>),\n",
       " ('__weakref__', <attribute '__weakref__' of 'anyclass' objects>),\n",
       " ('any_func', <function __main__.anyclass.any_func(self, x)>)]"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inspect.getmembers(anyclass)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-13T15:13:56.189771Z",
     "iopub.status.busy": "2021-10-13T15:13:56.189771Z",
     "iopub.status.idle": "2021-10-13T15:13:56.206769Z",
     "shell.execute_reply": "2021-10-13T15:13:56.205770Z",
     "shell.execute_reply.started": "2021-10-13T15:13:56.189771Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(1)\n",
    "print(2)\n",
    "inspect.currentframe().f_lineno"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Code Formatting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pylint and Flake8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Pylint is a tool that checks for errors in Python. It tries to enforce a coding standard and looks for code smells. It can also look for certain type errors, it can recommend suggestions about how particular blocks can be refactored, and can offer you details about the code’s complexity."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Flake8 is a Python library that wraps PyFlakes, pycodestyle and Ned Batchelder’s McCabe script. It is a great toolkit for checking your code base against coding style (PEP8), programming errors like “library imported but unused”, “Undefined name” and code which is not indented."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jupyterlab Code Formatter\n",
    "> https://jupyterlab-code-formatter.readthedocs.io/en/latest/installation.html <br>\n",
    "> https://jupyterlab-code-formatter.readthedocs.io/en/latest/how-to-use.html#changing-formatter-parameter"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Debugging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-13T15:13:58.157646Z",
     "iopub.status.busy": "2021-10-13T15:13:58.157646Z",
     "iopub.status.idle": "2021-10-13T15:13:58.164629Z",
     "shell.execute_reply": "2021-10-13T15:13:58.163637Z",
     "shell.execute_reply.started": "2021-10-13T15:13:58.157646Z"
    }
   },
   "outputs": [],
   "source": [
    "def my_add(value_a, value_b):\n",
    "    plus_a = value_a+1\n",
    "    plus_b = value_b+1\n",
    "    return plus_a+plus_b\n",
    "\n",
    "def my_print(first, second):\n",
    "    \n",
    "    result = my_add(first,second)\n",
    "    print(int(result))\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-13T15:13:58.412644Z",
     "iopub.status.busy": "2021-10-13T15:13:58.412644Z",
     "iopub.status.idle": "2021-10-13T15:13:58.435631Z",
     "shell.execute_reply": "2021-10-13T15:13:58.433642Z",
     "shell.execute_reply.started": "2021-10-13T15:13:58.412644Z"
    }
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "can only concatenate str (not \"int\") to str",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-27-8626e4689a8a>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mmy_print\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'10'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m<ipython-input-26-4d38af6cd134>\u001b[0m in \u001b[0;36mmy_print\u001b[1;34m(first, second)\u001b[0m\n\u001b[0;32m      6\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mmy_print\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfirst\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msecond\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 8\u001b[1;33m     \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmy_add\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfirst\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0msecond\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      9\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     10\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m<ipython-input-26-4d38af6cd134>\u001b[0m in \u001b[0;36mmy_add\u001b[1;34m(value_a, value_b)\u001b[0m\n\u001b[0;32m      1\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0mmy_add\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue_a\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalue_b\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      2\u001b[0m     \u001b[0mplus_a\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue_a\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m     \u001b[0mplus_b\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mvalue_b\u001b[0m\u001b[1;33m+\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      4\u001b[0m     \u001b[1;32mreturn\u001b[0m \u001b[0mplus_a\u001b[0m\u001b[1;33m+\u001b[0m\u001b[0mplus_b\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mTypeError\u001b[0m: can only concatenate str (not \"int\") to str"
     ]
    }
   ],
   "source": [
    "my_print(10, '10')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-13T15:13:58.647745Z",
     "iopub.status.busy": "2021-10-13T15:13:58.646745Z",
     "iopub.status.idle": "2021-10-13T15:13:58.659748Z",
     "shell.execute_reply": "2021-10-13T15:13:58.658747Z",
     "shell.execute_reply.started": "2021-10-13T15:13:58.647745Z"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "UsageError: Line magic function `%` not found.\n"
     ]
    }
   ],
   "source": [
    "%debug"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Sorting Objects by Multiple Keys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-13T15:14:18.247403Z",
     "iopub.status.busy": "2021-10-13T15:14:18.246388Z",
     "iopub.status.idle": "2021-10-13T15:14:18.253389Z",
     "shell.execute_reply": "2021-10-13T15:14:18.252390Z",
     "shell.execute_reply.started": "2021-10-13T15:14:18.247403Z"
    }
   },
   "outputs": [],
   "source": [
    "people = [\n",
    "    {\"name\": \"John\", \"age\": 64},\n",
    "    {\"name\": \"Janet\", \"age\": 34},\n",
    "    {\"name\": \"Ed\", \"age\": 24},\n",
    "    {\"name\": \"Sara\", \"age\": 64},\n",
    "    {\"name\": \"John\", \"age\": 32},\n",
    "    {\"name\": \"Jane\", \"age\": 34},\n",
    "    {\"name\": \"John\", \"age\": 99},\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-13T15:14:18.920250Z",
     "iopub.status.busy": "2021-10-13T15:14:18.920250Z",
     "iopub.status.idle": "2021-10-13T15:14:18.931250Z",
     "shell.execute_reply": "2021-10-13T15:14:18.930266Z",
     "shell.execute_reply.started": "2021-10-13T15:14:18.920250Z"
    }
   },
   "outputs": [],
   "source": [
    "# SELECT * FROM people ORDER by name, age\n",
    "\n",
    "import operator\n",
    "\n",
    "people.sort(key=operator.itemgetter(\"age\"))\n",
    "people.sort(key=operator.itemgetter(\"name\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-13T15:14:27.403506Z",
     "iopub.status.busy": "2021-10-13T15:14:27.402505Z",
     "iopub.status.idle": "2021-10-13T15:14:27.418506Z",
     "shell.execute_reply": "2021-10-13T15:14:27.417505Z",
     "shell.execute_reply.started": "2021-10-13T15:14:27.403506Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'name': 'Ed', 'age': 24},\n",
       " {'name': 'Jane', 'age': 34},\n",
       " {'name': 'Janet', 'age': 34},\n",
       " {'name': 'John', 'age': 32},\n",
       " {'name': 'John', 'age': 64},\n",
       " {'name': 'John', 'age': 99},\n",
       " {'name': 'Sara', 'age': 64}]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Pickling and Unpicklong\n",
    "> a way of object serialization and deserialization"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-13T15:14:33.470172Z",
     "iopub.status.busy": "2021-10-13T15:14:33.470172Z",
     "iopub.status.idle": "2021-10-13T15:14:33.489172Z",
     "shell.execute_reply": "2021-10-13T15:14:33.487181Z",
     "shell.execute_reply.started": "2021-10-13T15:14:33.470172Z"
    }
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "people_serialized = pickle.dumps(people)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-13T15:14:34.131524Z",
     "iopub.status.busy": "2021-10-13T15:14:34.131524Z",
     "iopub.status.idle": "2021-10-13T15:14:34.151511Z",
     "shell.execute_reply": "2021-10-13T15:14:34.150527Z",
     "shell.execute_reply.started": "2021-10-13T15:14:34.131524Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'\\x80\\x03]q\\x00(}q\\x01(X\\x04\\x00\\x00\\x00nameq\\x02X\\x02\\x00\\x00\\x00Edq\\x03X\\x03\\x00\\x00\\x00ageq\\x04K\\x18u}q\\x05(h\\x02X\\x04\\x00\\x00\\x00Janeq\\x06h\\x04K\"u}q\\x07(h\\x02X\\x05\\x00\\x00\\x00Janetq\\x08h\\x04K\"u}q\\t(h\\x02X\\x04\\x00\\x00\\x00Johnq\\nh\\x04K u}q\\x0b(h\\x02h\\nh\\x04K@u}q\\x0c(h\\x02h\\nh\\x04Kcu}q\\r(h\\x02X\\x04\\x00\\x00\\x00Saraq\\x0eh\\x04K@ue.'"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "people_serialized"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-13T15:14:34.789326Z",
     "iopub.status.busy": "2021-10-13T15:14:34.789326Z",
     "iopub.status.idle": "2021-10-13T15:14:34.801340Z",
     "shell.execute_reply": "2021-10-13T15:14:34.800339Z",
     "shell.execute_reply.started": "2021-10-13T15:14:34.789326Z"
    }
   },
   "outputs": [],
   "source": [
    "new_people = pickle.loads(people_serialized)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-13T15:14:35.217511Z",
     "iopub.status.busy": "2021-10-13T15:14:35.217511Z",
     "iopub.status.idle": "2021-10-13T15:14:35.239512Z",
     "shell.execute_reply": "2021-10-13T15:14:35.238512Z",
     "shell.execute_reply.started": "2021-10-13T15:14:35.217511Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_people == people"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Today's Tip"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why should we explicitly add every time \"import\" module statements? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-13T15:14:36.619514Z",
     "iopub.status.busy": "2021-10-13T15:14:36.618525Z",
     "iopub.status.idle": "2021-10-13T15:14:36.642511Z",
     "shell.execute_reply": "2021-10-13T15:14:36.640520Z",
     "shell.execute_reply.started": "2021-10-13T15:14:36.619514Z"
    }
   },
   "outputs": [],
   "source": [
    "df = pd.DataFrame(data={'first' : [21,15,10], 'second' : [11, 12, 13]}, index=[0,1,2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-13T15:14:36.822513Z",
     "iopub.status.busy": "2021-10-13T15:14:36.821510Z",
     "iopub.status.idle": "2021-10-13T15:14:36.867512Z",
     "shell.execute_reply": "2021-10-13T15:14:36.866512Z",
     "shell.execute_reply.started": "2021-10-13T15:14:36.822513Z"
    }
   },
   "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>first</th>\n",
       "      <th>second</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>21</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>15</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   first  second\n",
       "0     21      11\n",
       "1     15      12\n",
       "2     10      13"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-13T15:14:37.018511Z",
     "iopub.status.busy": "2021-10-13T15:14:37.018511Z",
     "iopub.status.idle": "2021-10-13T15:14:37.309512Z",
     "shell.execute_reply": "2021-10-13T15:14:37.307511Z",
     "shell.execute_reply.started": "2021-10-13T15:14:37.018511Z"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-13T15:14:37.312511Z",
     "iopub.status.busy": "2021-10-13T15:14:37.312511Z",
     "iopub.status.idle": "2021-10-13T15:14:38.413690Z",
     "shell.execute_reply": "2021-10-13T15:14:38.411690Z",
     "shell.execute_reply.started": "2021-10-13T15:14:37.312511Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "AttributeError",
     "evalue": "module 'seaborn' has no attribute 'plot'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-41-52fde9146a1e>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pyforest\\_importable.py\u001b[0m in \u001b[0;36m__getattr__\u001b[1;34m(self, attribute)\u001b[0m\n\u001b[0;32m     69\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m__getattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mattribute\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     70\u001b[0m         \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__maybe_import__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 71\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0meval\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34mf\"{self.__imported_name__}.{attribute}\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     72\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     73\u001b[0m     \u001b[1;31m# called for callable objects, e.g. from pathlib import Path; Path(\".\")\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pyforest\\_importable.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n",
      "\u001b[1;31mAttributeError\u001b[0m: module 'seaborn' has no attribute 'plot'"
     ]
    }
   ],
   "source": [
    "sns.plot(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-13T15:14:53.074558Z",
     "iopub.status.busy": "2021-10-13T15:14:53.074558Z",
     "iopub.status.idle": "2021-10-13T15:14:53.081552Z",
     "shell.execute_reply": "2021-10-13T15:14:53.080551Z",
     "shell.execute_reply.started": "2021-10-13T15:14:53.074558Z"
    }
   },
   "outputs": [],
   "source": [
    "import pyforest"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "execution": {
     "iopub.execute_input": "2021-10-13T15:14:54.308889Z",
     "iopub.status.busy": "2021-10-13T15:14:54.307889Z",
     "iopub.status.idle": "2021-10-13T15:14:54.528886Z",
     "shell.execute_reply": "2021-10-13T15:14:54.527888Z",
     "shell.execute_reply.started": "2021-10-13T15:14:54.308889Z"
    }
   },
   "outputs": [
    {
     "data": {
      "application/javascript": [
       "\n",
       "        if (window._pyforest_update_imports_cell) { window._pyforest_update_imports_cell('import seaborn as sns'); }\n",
       "    "
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lineplot(data=df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Practical Coding Techniques"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "** 32 Advanced Techniques for Better Python Code ** <br>\n",
    "https://betterprogramming.pub/thirty-two-advanced-techniques-for-better-python-code-6717226eb611"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
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