Kurz_Python_Den1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "2df4aa21",
   "metadata": {},
   "source": [
    "# 01. Komentáre, kódovanie, výpis, docstringy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "d925b447",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python je skvely jazyk\n",
      "Python je skvely jazyk\n",
      "Python \n",
      "je skvely jazyk\n",
      "Toto je docstring (dokumentacny string)\n",
      "😀 ❤ 💚 ✔\n"
     ]
    }
   ],
   "source": [
    "# -*- coding: utf-8 -*-\n",
    "# Toto je jednoriadkovy komentar ALT + 35 \n",
    "\"\"\"Toto je docstring (dokumentacny string)\"\"\"\n",
    "\n",
    "print(\"Python je skvely jazyk\")\n",
    "print(\"Python \" \n",
    "      \"je skvely jazyk\")\n",
    "print(\"Python \\nje skvely jazyk\")\n",
    "print(__doc__)\n",
    "print(\"😀 ❤ 💚 ✔\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6e312877",
   "metadata": {},
   "source": [
    "# 02. Premenné a dátové typy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "60660882",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Adam\n",
      "<class 'str'>\n",
      "36\n",
      "<class 'int'>\n",
      "False\n",
      "<class 'bool'>\n",
      "29.99\n",
      "<class 'float'>\n",
      "None\n",
      "<class 'NoneType'>\n",
      "meno klienta --> Adam typ premennej --> <class 'str'>\n"
     ]
    }
   ],
   "source": [
    "# str - string - retazec\n",
    "meno_klient = \"Adam\"\n",
    "print(meno_klient)\n",
    "print(type(meno_klient))\n",
    "\n",
    "# int - integer - cele cislo\n",
    "vek_klient = 36\n",
    "print(vek_klient)\n",
    "print(type(vek_klient))\n",
    "\n",
    "# bool/boolean - True/False\n",
    "jePN = False\n",
    "print(jePN)\n",
    "print(type(jePN))\n",
    "\n",
    "# float - cislo s plavajucou desatinnou ciarkou - real. cislo\n",
    "cena_produkt = 29.99\n",
    "print(cena_produkt)\n",
    "print(type(cena_produkt))\n",
    "\n",
    "kosik = None \n",
    "print(kosik)\n",
    "print(type(kosik))\n",
    "\n",
    "# Kombinovat spravu + obsah premennej + typ premennej\n",
    "print(\"meno klienta --> \" + meno_klient + \" typ premennej --> \" + str(type(meno_klient)))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2198f4b6",
   "metadata": {},
   "source": [
    "# 03. Operácie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "808b6944",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "8\n",
      "32\n",
      "2.5\n",
      "2\n",
      "1\n",
      "True\n",
      "False\n",
      "True\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "# + - * /\n",
    "print(5+3)\n",
    "\n",
    "# Mocnina 2^5\n",
    "print(2**5)\n",
    "\n",
    "# Realne delenie\n",
    "print(5/2)\n",
    "\n",
    "# Celociselne delenie\n",
    "print(5//2)\n",
    "\n",
    "# Delenie so zvyskom (modulo)\n",
    "print(5%2)\n",
    "\n",
    "# and (log. sucin), or (log. sucet), not (log. negacia), xor (log. exluz. sucet)\n",
    "print(True and True)\n",
    "print(False and True)\n",
    "print(True or True)\n",
    "print(False or True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c6131d17",
   "metadata": {},
   "source": [
    "# 04. Funkcie zabudovane (definicie)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "7d210346",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "369\n",
      "-100\n",
      "6\n",
      "275\n",
      "Co bolo skorej? -->  🥚\n"
     ]
    }
   ],
   "source": [
    "print(max(-5,9,-100,0,369,2))\n",
    "print(min(-5,9,-100,0,369,2))\n",
    "print(len([-5,9,-100,0,369,2]))\n",
    "print(sum([-5,9,-100,0,369,2]))\n",
    "# print(avg([-5,9,-100,0,369,2]))\n",
    "\n",
    "# tab - inteligentne doplnanie kodu/zobrazenie prikazov/premennych/funkcii (intellisense)\n",
    "# %mkdir laco\n",
    "# %rmdir laco\n",
    "\n",
    "print(\"Co bolo skorej? --> \", min(['\\N{CHICKEN}', '\\N{EGG}']))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e273485b",
   "metadata": {},
   "source": [
    "# 05. Vlastne vypisy, pretypovanie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "b0d38423",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "Nina Adam\n",
      "Milujem Python\n",
      "Milujem Python\n",
      "Milujem Python\n",
      "Milujem Python\n",
      "\n",
      "\n",
      "Ahoj Karol \n",
      "Ahoj Peter \n",
      "Ahoj Monika \n",
      "Meno \t Priezvisko \t Mzda \t Email \t Kontakt\n",
      "C:\\temp\n",
      "\"Chod domov\", povedala mama\n",
      "WTF is this (ಠ_ಠ) shit?\n",
      "\n",
      "Mzda zamestnanec --> 1923.56 , je to typ -->  <class 'float'>\n",
      "Mzda zamestnanec --> 1923.56 , je to typ -->  <class 'str'>\n",
      "Mzda zamestnanec --> True , je to typ -->  <class 'bool'>\n"
     ]
    }
   ],
   "source": [
    "# Sucet - cisla (int, float), Spajanie retazcov/zretazenie - concatenate/text.join\n",
    "print(2+3)\n",
    "print(\"Nina\" + \" \" + \"Adam\")\n",
    "\n",
    "jazyk_programovaci = \"Python\"\n",
    "print(\"Milujem \" + jazyk_programovaci)\n",
    "print(\"Milujem\", jazyk_programovaci)\n",
    "print(\"Milujem {}\" .format(jazyk_programovaci))\n",
    "print(f\"Milujem {jazyk_programovaci}\")\n",
    "\n",
    "# Ukoncovacie sekvencie escaped sequences\n",
    "# \\n - new line - novy riadok - vbNewLine, vbCrLf, vbCr\n",
    "# \\t - tab - vbTab\n",
    "# \\\\ - \\ - backslash - spatne lomitka\n",
    "# \\' '\n",
    "# \\\" \"\n",
    "# \n",
    "print(\"\\n\\nAhoj Karol \\nAhoj Peter \\nAhoj Monika \")\n",
    "print(\"Meno \\t Priezvisko \\t Mzda \\t Email \\t Kontakt\")\n",
    "print(\"C:\\\\temp\")\n",
    "print(\"\\\"Chod domov\\\", povedala mama\")\n",
    "\n",
    "# Nestandardne znaky ako identifikator\n",
    "ಠ_ಠ = \"WTF is this (ಠ_ಠ) shit?\"\n",
    "print(ಠ_ಠ)\n",
    "\n",
    "# 🎲_🎲 = \"Som hodil kockou\"\n",
    "# print(🎲_🎲)\n",
    "\n",
    "# Pretypovanie (casting)\n",
    "# 0, \"\", None --> False\n",
    "mzda_zamestnanec = 1923.56\n",
    "print(\"\\nMzda zamestnanec -->\", mzda_zamestnanec, \", je to typ --> \", type(mzda_zamestnanec))\n",
    "\n",
    "mzda_zamestnanec_string = str(mzda_zamestnanec)\n",
    "print(\"Mzda zamestnanec -->\", mzda_zamestnanec_string, \", je to typ --> \", type(mzda_zamestnanec_string))\n",
    "\n",
    "mzda_zamestnanec_bool = bool(mzda_zamestnanec)\n",
    "print(\"Mzda zamestnanec -->\", mzda_zamestnanec_bool, \", je to typ --> \", type(mzda_zamestnanec_bool))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4df62475",
   "metadata": {},
   "source": [
    "# 06. Modul math (matematicka knižnica)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "0763eaf5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3.141592653589793\n",
      "2.718281828459045\n",
      "math.ceil(2.0001) -->  3\n",
      "math.ceil(2.999) -->  3\n",
      "math.ceil(2.000) -->  2\n",
      "\n",
      "math.floor(2.0001) -->  2\n",
      "math.floor(2.999) -->  2\n",
      "math.floor(2.000) -->  2\n",
      "\n",
      "round(2.0001) -->  2\n",
      "round(2.999) -->  3\n",
      "round(2.000) -->  2\n",
      "\n",
      "math.trunc(2.0001) -->  2\n",
      "math.trunc(2.999) -->  2\n",
      "math.trunc(2.000) -->  2\n",
      "math.pow(2,5) -->  32.0\n",
      "math.sqrt(81) -->  9.0\n"
     ]
    }
   ],
   "source": [
    "import math\n",
    "print(math.pi)\n",
    "print(math.e)\n",
    "\n",
    "# Ceil (roundup) - zaokruhli smerom nahor na najblizsie cele cislo\n",
    "print(\"math.ceil(2.0001) --> \", math.ceil(2.0001))\n",
    "print(\"math.ceil(2.999) --> \",math.ceil(2.999))\n",
    "print(\"math.ceil(2.000) --> \",math.ceil(2.0))\n",
    "\n",
    "# Floor (rounddown) - zaokruhli smerom nadol na najblizsie cele cislo\n",
    "print(\"\\nmath.floor(2.0001) --> \", math.floor(2.0001))\n",
    "print(\"math.floor(2.999) --> \",math.floor(2.999))\n",
    "print(\"math.floor(2.000) --> \",math.floor(2.0))\n",
    "\n",
    "# Round (Round) - zaokruhli\n",
    "print(\"\\nround(2.0001) --> \", round(2.0001))\n",
    "print(\"round(2.999) --> \", round(2.999))\n",
    "print(\"round(2.000) --> \", round(2.0))\n",
    "\n",
    "# Trunc (Trunc) - orezanie hodnoty\n",
    "print(\"\\nmath.trunc(2.0001) --> \", math.trunc(2.0001))\n",
    "print(\"math.trunc(2.999) --> \", math.trunc(2.999))\n",
    "print(\"math.trunc(2.000) --> \", math.trunc(2.0))\n",
    "\n",
    "print(\"math.pow(2,5) --> \", math.pow(2,5))\n",
    "print(\"math.sqrt(81) --> \", math.sqrt(81))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0abba7f6",
   "metadata": {},
   "source": [
    "# 07. Moduly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "64d4a030",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Co je v builtin module --> ['In', 'Out', '_', '__', '___', '__builtin__', '__builtins__', '__doc__', '__loader__', '__name__', '__package__', '__spec__', '_dh', '_i', '_i1', '_i10', '_i11', '_i12', '_i13', '_i14', '_i15', '_i16', '_i17', '_i18', '_i2', '_i3', '_i4', '_i5', '_i6', '_i7', '_i8', '_i9', '_ih', '_ii', '_iii', '_oh', 'builtins', 'exit', 'get_ipython', 'math', 'os', 'quit', 're', 'sys']\n",
      "\n",
      "Co je v matematickom module --> ['__doc__', '__loader__', '__name__', '__package__', '__spec__', 'acos', 'acosh', 'asin', 'asinh', 'atan', 'atan2', 'atanh', 'ceil', 'comb', 'copysign', 'cos', 'cosh', 'degrees', 'dist', 'e', 'erf', 'erfc', 'exp', 'expm1', 'fabs', 'factorial', 'floor', 'fmod', 'frexp', 'fsum', 'gamma', 'gcd', 'hypot', 'inf', 'isclose', 'isfinite', 'isinf', 'isnan', 'isqrt', 'lcm', 'ldexp', 'lgamma', 'log', 'log10', 'log1p', 'log2', 'modf', 'nan', 'nextafter', 'perm', 'pi', 'pow', 'prod', 'radians', 'remainder', 'sin', 'sinh', 'sqrt', 'tan', 'tanh', 'tau', 'trunc', 'ulp']\n",
      "\n",
      "Co je v sys module --> ['__breakpointhook__', '__displayhook__', '__doc__', '__excepthook__', '__interactivehook__', '__loader__', '__name__', '__package__', '__spec__', '__stderr__', '__stdin__', '__stdout__', '__unraisablehook__', '_base_executable', '_clear_type_cache', '_current_frames', '_debugmallocstats', '_enablelegacywindowsfsencoding', '_framework', '_getframe', '_git', '_home', '_xoptions', 'addaudithook', 'api_version', 'argv', 'audit', 'base_exec_prefix', 'base_prefix', 'breakpointhook', 'builtin_module_names', 'byteorder', 'call_tracing', 'copyright', 'displayhook', 'dllhandle', 'dont_write_bytecode', 'exc_info', 'excepthook', 'exec_prefix', 'executable', 'exit', 'flags', 'float_info', 'float_repr_style', 'get_asyncgen_hooks', 'get_coroutine_origin_tracking_depth', 'getallocatedblocks', 'getdefaultencoding', 'getfilesystemencodeerrors', 'getfilesystemencoding', 'getprofile', 'getrecursionlimit', 'getrefcount', 'getsizeof', 'getswitchinterval', 'gettrace', 'getwindowsversion', 'hash_info', 'hexversion', 'implementation', 'int_info', 'intern', 'is_finalizing', 'last_traceback', 'last_type', 'last_value', 'maxsize', 'maxunicode', 'meta_path', 'modules', 'path', 'path_hooks', 'path_importer_cache', 'platform', 'platlibdir', 'prefix', 'ps1', 'ps2', 'ps3', 'pycache_prefix', 'set_asyncgen_hooks', 'set_coroutine_origin_tracking_depth', 'setprofile', 'setrecursionlimit', 'setswitchinterval', 'settrace', 'stderr', 'stdin', 'stdout', 'thread_info', 'unraisablehook', 'version', 'version_info', 'warnoptions', 'winver']\n",
      "\n",
      "Co je v os module --> ['DirEntry', 'F_OK', 'GenericAlias', 'Mapping', 'MutableMapping', 'O_APPEND', 'O_BINARY', 'O_CREAT', 'O_EXCL', 'O_NOINHERIT', 'O_RANDOM', 'O_RDONLY', 'O_RDWR', 'O_SEQUENTIAL', 'O_SHORT_LIVED', 'O_TEMPORARY', 'O_TEXT', 'O_TRUNC', 'O_WRONLY', 'P_DETACH', 'P_NOWAIT', 'P_NOWAITO', 'P_OVERLAY', 'P_WAIT', 'PathLike', 'R_OK', 'SEEK_CUR', 'SEEK_END', 'SEEK_SET', 'TMP_MAX', 'W_OK', 'X_OK', '_AddedDllDirectory', '_Environ', '__all__', '__builtins__', '__cached__', '__doc__', '__file__', '__loader__', '__name__', '__package__', '__spec__', '_check_methods', '_execvpe', '_exists', '_exit', '_fspath', '_get_exports_list', '_walk', '_wrap_close', 'abc', 'abort', 'access', 'add_dll_directory', 'altsep', 'chdir', 'chmod', 'close', 'closerange', 'cpu_count', 'curdir', 'defpath', 'device_encoding', 'devnull', 'dup', 'dup2', 'environ', 'error', 'execl', 'execle', 'execlp', 'execlpe', 'execv', 'execve', 'execvp', 'execvpe', 'extsep', 'fdopen', 'fsdecode', 'fsencode', 'fspath', 'fstat', 'fsync', 'ftruncate', 'get_exec_path', 'get_handle_inheritable', 'get_inheritable', 'get_terminal_size', 'getcwd', 'getcwdb', 'getenv', 'getlogin', 'getpid', 'getppid', 'isatty', 'kill', 'linesep', 'link', 'listdir', 'lseek', 'lstat', 'makedirs', 'mkdir', 'name', 'open', 'pardir', 'path', 'pathsep', 'pipe', 'popen', 'putenv', 'read', 'readlink', 'remove', 'removedirs', 'rename', 'renames', 'replace', 'rmdir', 'scandir', 'sep', 'set_handle_inheritable', 'set_inheritable', 'spawnl', 'spawnle', 'spawnv', 'spawnve', 'st', 'startfile', 'stat', 'stat_result', 'statvfs_result', 'strerror', 'supports_bytes_environ', 'supports_dir_fd', 'supports_effective_ids', 'supports_fd', 'supports_follow_symlinks', 'symlink', 'sys', 'system', 'terminal_size', 'times', 'times_result', 'truncate', 'umask', 'uname_result', 'unlink', 'unsetenv', 'urandom', 'utime', 'waitpid', 'waitstatus_to_exitcode', 'walk', 'write']\n",
      "Help on built-in function modf in module math:\n",
      "\n",
      "modf(x, /)\n",
      "    Return the fractional and integer parts of x.\n",
      "    \n",
      "    Both results carry the sign of x and are floats.\n",
      "\n",
      "\n",
      "Pomocnik matematicky modul --> None\n"
     ]
    }
   ],
   "source": [
    "# Nemusis import zabudovane veci...\n",
    "import builtins\n",
    "import math\n",
    "# Regularne vyrazy regular expressions\n",
    "# import re\n",
    "import sys\n",
    "import os\n",
    "\n",
    "# print(dir())\n",
    "# print(__doc__)\n",
    "# print(__builtins__)\n",
    "# print(__name__)\n",
    "\n",
    "# print(math.__doc__)\n",
    "# print(re.__doc__)\n",
    "\n",
    "# print(os.__doc__)\n",
    "# print(sys.__doc__)\n",
    "# print(sys.__package__)\n",
    "print(\"Co je v builtin module -->\", dir())\n",
    "print(\"\\nCo je v matematickom module -->\", dir(math))\n",
    "print(\"\\nCo je v sys module -->\", dir(sys))\n",
    "print(\"\\nCo je v os module -->\", dir(os))\n",
    "\n",
    "# print(\"\\nPomocnik matematicky modul -->\", help(math))\n",
    "print(\"\\nPomocnik matematicky modul -->\", help(math.modf))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45908d15",
   "metadata": {},
   "source": [
    "# 08. Moduly pre datascience (numpy, pandas, matplotlib)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "bafb91f8",
   "metadata": {},
   "outputs": [
    {
     "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": [
    "# PIP \n",
    "# !pip install numpy\n",
    "# !dir\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# print(matplotlib.pyplot.__doc__)\n",
    "# array range\n",
    "x = np.arange(-10, 10, 0.1)\n",
    "y = np.sin(x)\n",
    "plt.title(\"Sinus...\")\n",
    "plt.xlabel(\"Cas [s]\")\n",
    "plt.ylabel(\"Napätie [V]\")\n",
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9cc81df5",
   "metadata": {},
   "source": [
    "# 09. Grafy, zoznamy, polia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "id": "6dd2972f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'list'>\n",
      "[1, 2, 3, 'Kapusta', None, True, 1690.23, [], [5, 6, 7]]\n",
      "<class 'numpy.ndarray'>\n",
      "[35 25 20 20]\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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kNcqAl6RGGfCS1CgDXpIaZcBLUqMMeElqlAEvSY0y4CWpUYMFfJL7JDkrycVJLkpyyFDnkiT9oU0HPPatwGuq6oIkdwHOT/L5qvrugOeUJPUG68FX1VVVdUH/+HrgYuDeQ51PkrSmIXvwv5dkObALcN4U2w4CDgLYbrvtZnyO5YedMeN9W3DpkXsudAmSFpnB32RNshXwceDVVXXd5O1VdXxVraiqFcuWLRu6HEnaaAwa8EnuRBfuJ1XVJ4Y8lyRpTUPeRRPgA8DFVfUPQ51HkjS1IXvwjweeD+ye5ML+Z48BzydJGjHYm6xVdS6QoY4vSVo3P8kqSY0y4CWpUQa8JDXKgJekRhnwktQoA16SGmXAS1KjDHhJapQBL0mNMuAlqVEGvCQ1yoCXpEYZ8JLUKANekhplwEtSowx4SWqUAS9JjRrsG52kjcnyw85Y6BIW1KVH7rnQJWgK9uAlqVEGvCQ1yoCXpEYZ8JLUKANekhplwEtSowx4SWqUAS9JjTLgJalRBrwkNcqAl6RGGfCS1CgDXpIaZcBLUqMMeElqlAEvSY0y4CWpUQa8JDXKgJekRg0a8EmenuT7SX6U5LAhzyVJWtNgAZ9kE+B9wDOABwP7J3nwUOeTJK1pyB78o4AfVdWPq+p3wEeBZw94PknSiFTVMAdO9gGeXlUv65efDzy6ql45qd1BwEH94o7A9wcpaHhLgWsWuogNmNdvdrx+s7MhX7/7VtWyqTZsOuBJM8W6P/htUlXHA8cPWMe8SLKyqlYsdB0bKq/f7Hj9ZqfV6zfkEM0VwH1GlrcFfjbg+SRJI4YM+G8AD0hyvySbAfsBpw94PknSiMGGaKrq1iSvBD4LbAKcUFUXDXW+RWCDH2ZaYF6/2fH6zU6T12+wN1klSQvLT7JKUqMMeElqlAE/piQ39H8uT3JTkguTfCvJV5Ps2G/bLUkleenIfrv06167ULUvJhPXcWT5RUn+qX/85iRX9tf2O0n2WpgqF17/b+bdI8uvTfLm/vHodZr42SbJlklOSrKqv37nJtmq3+dNSS5K8u2+/aMX6KnNSn9d/m1kedMkq5P8n355r4WYFiXJc5NcnOSsPgceN981TMWAn5lLqurhVbUz8EHgb0e2rQL2HVneD/jWfBa3gXtPVT0ceC5wQpKN9d/ozcB/S7J0Ldvf0/8bnPj5DXAI8POqemhV7QS8FLglyWOBZwK7VtXDgKcCl8/DcxjCjcBOSbbol58GXDmxsapOr6ojxzlQOnP17+ulwF9V1ZOB3QADvhFLgF+PLP8U2DzJPZIEeDrwmQWpbANWVRcDt9J9wnBjdCvdnR2Hrsc+92TNsPt+Vd3cr7+mf0xVXVNVG/JnUj4D7Nk/3h84eWLDpFeE90jyyf6V9reSPK5/BX5xkn8GLgDuk+So/hXPqiT79vveM8mXR15NPrFfv//IK6R39usOB54AHJfkVOC/A4f2+z5xnq7JlAz4mdmh/8u7BPgb4B8mbT+Nrgf6OLp/RDfPc32L2RajQwvAW6dq1A8h3A6sns/iFpn3Ac9LsvUU2w4duY5n9etOAN6Q5D+TvC3JA/r1n6MLsh8k+eckfzofxQ/oo8B+STYHHgact5Z27wXO6V9p7wpM3Ka9I/ChqtoFWAE8HNiZ7pXNUUnuCRwAfLZ/NbkzcGGSewHvBHbv93lkkr2r6q3ASuB5VfVc4DjueIX1lTl95uvJgJ+ZiSGaHYBX84f30J5CF/Br9C4EwE2jQwvA4ZO2H9oH/9HAvrUR38dbVdcBHwJeNcXm0SGaJ/ftLwS2B44C7gZ8I8mDquoG4BF0cz6tBj6W5EXz8BQGUVXfBpbT/f/69Dqa7g78S7/PbVV1bb/+sqr6Wv/4CcDJ/fafA+cAj6T7oOaL+/c9HlpV1/frz66q1VV1K3AS8KQ5fXJzzICfvdOZ9JdcVVcDt9CND35xIYragE0E1xMXuvezSPwj3fjuncdpXFU3VNUnquqvgA8De/Trb6uqs6vqCOCVwJ8PVO98OZ2uEzCTDtSNI4+nmjOLqvoy3f/rK4F/S/KCtbVdzAz42XsCcMkU6w8H3lBVt81zPWpIVf2K7hXhS6drm+TxSe7aP96M7nsYLkuy48hwDXTDC5cNUO58OgF4a1WtWkebLwJ/Cd33UyRZMkWbLwP79tuX0YX615PcF/hFVb0f+ADdEM95wJ8mWZru+y72p+vxT3Y9cJeZPrG5NORski3boR9GCPA74GWTG1TVV+e7KDXr3XS97lGHJjlwZHlvYAfgX/o39/8IOAP4OF04HZtkG7o3b3/EHVN0b5Cq6grgmGmaHQIcn+625dvowv6qSW0+CTyW7k63Al5fVVcneSHwuiS3ADcAL6iqq5K8ETiL7v/+p6vqP6Y476eA05I8Gzh4IV+JOlWBJDXKIRpJapQBL0mNMuAlqVEGvCQ1yoCXpEYZ8NroJLlt0kyMy5N4W6ua422S2ugkuaGqthqj3SZ+UE0bMnvwEmvM979bP6f3R4BV/fI5SU7pJ+s6Msnzkny9n1Vwh36/ZyU5L8k3k3whyT0W9AlJGPDaOI3OaPnJKbY/CnhTVT24X96Z7lORDwWeDzywqh4F/CtwcN/mXOAx/QyFHwVeP+gzkMbgVAXaGN3Uz2S5Nl+vqp+MLH+jqq4C6KeI/ly/fhXw5P7xtnSzNN4T2AwY3V9aEPbgpT9046Tl0fn8bx9Zvp07OknHAv9UVQ8FXgFsPmiF0hgMeGlubM0d36b0woUsRJpgwEtz483AqUm+AlyzwLVIgLdJSlKz7MFLUqMMeElqlAEvSY0y4CWpUQa8JDXKgJekRhnwktSo/w8v2ZRPWXHd+gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 91.32035773  85.99398929 114.75312113  85.80233075 101.29810368\n",
      "  96.04826897  94.05196419  99.51127217 114.86460141  85.8850128\n",
      "  78.30744701  95.11112935  99.4600333   89.36998026  96.68965295\n",
      "  87.11434649  84.58317545  98.99464186  77.79514899 114.60774773\n",
      "  93.8918182   83.43714376  90.17954681 101.15545536  98.1919261\n",
      "  92.5966096  113.48658965 104.41455209  96.92912917  86.89147306\n",
      " 102.49342568  96.96768516 106.56192958  80.87471581  96.98719093\n",
      "  79.90830837 102.75880073 105.09416154  90.61873918  83.49557606\n",
      "  82.90227     92.45745839 100.52677316 109.47284736 100.93576016\n",
      " 113.81856052  76.92174773  97.72638094 116.592905    92.04389698\n",
      " 110.6903511   95.37987203  89.71912645 103.45963585 103.9876971\n",
      " 125.18285398 111.61937923  91.50600854 109.22950648 105.47207265\n",
      " 102.59833386 119.55614302  96.7175334  102.74824999 110.3671241\n",
      "  80.97055072  95.08966472 101.74855573  95.99821261 114.32965332\n",
      "  96.36735521 100.87144166  90.69900395 101.73250028 110.78617386\n",
      "  78.10930445 114.23392081  99.3432223  109.00171601  93.79213738\n",
      "  83.06824305  97.28457611  88.62210691  93.40045049  92.76549716\n",
      "  72.71267407 106.03096285 105.65740693 115.23563946 105.14221339\n",
      "  93.34902703 116.85500931  87.68089562  97.81611228  98.23283103\n",
      "  96.19890442 105.44239033 119.43665692 100.33488629  89.85935165\n",
      " 112.17508528 107.78590517 110.89215471  98.11535646 111.72251818\n",
      " 101.88358775  95.51301088  97.66332493  80.43971889  90.27886708\n",
      "  99.96776974 107.40577742 106.77359778 103.07174368  88.75012642\n",
      "  93.24007149  82.76274057 100.82164318 101.17939952  94.73581161\n",
      " 104.04383976  94.84302781 104.45437971  96.3917096   98.93814859\n",
      " 111.58342258  94.21975384  93.52113472  94.50792645  91.1038205\n",
      " 107.73410952 100.16772432 108.4250571   93.82072961 106.21025108\n",
      " 117.60729455  92.40061697  88.51403978  93.07850748 100.0747983\n",
      " 102.59636289 109.91527826 105.18535047  78.45818975  95.01288871\n",
      "  98.61044624 106.29823538 108.2304663   99.02956735  93.01238486\n",
      "  97.72546118 102.06035651 103.9414654  114.28498038  99.66401349\n",
      " 102.7907721  103.13662414 101.57048983 103.65688813 111.12104575\n",
      "  99.04828318  97.41344043  94.8281665   98.39515423 111.72739165\n",
      "  97.35179262  96.06996566 105.72944808 104.73456645 107.15165613\n",
      "  97.81595717 100.30005187 108.45416303  94.47105192  94.90270901\n",
      "  88.8899554   81.78602329  92.94238437  89.29475442 109.86381514\n",
      "  99.99125146 101.49498654  89.28485025 110.04894189  91.46761272\n",
      " 100.51195638 102.75495406 102.92114692  91.11840936 105.88665583\n",
      " 102.55358578 106.98646709 103.87940111 100.58478646  93.87380503\n",
      "  87.873792    82.10376808  92.7014631   89.3664396   92.82090706]\n"
     ]
    },
    {
     "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": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# Zoznam / list, heterogenne, menitelne\n",
    "zoznam_cisla = [1,2,3,\"Kapusta\", None, True, 1690.23, [], [5,6,7]]\n",
    "print(type(zoznam_cisla))\n",
    "print(zoznam_cisla)\n",
    "\n",
    "# Array / pole, homogenne\n",
    "y = np.array([35, 25, 20, 20])\n",
    "menovky = [\"Notebooky\", \"Tlaciaren\", \"Mysky\", \"Projektory\"]\n",
    "vykrojenie = [0.1, 0, 0, 0]\n",
    "tiene = True\n",
    "# r g b, c m y k, w\n",
    "farby_vlastne = [\"black\",\"pink\", \"b\", \"#CCCC00\"]\n",
    "\n",
    "print(type(y))\n",
    "print(y)\n",
    "\n",
    "plt.pie(y, labels = menovky, startangle = 0, explode = vykrojenie, shadow = tiene, colors = farby_vlastne)\n",
    "plt.legend(title = \"Produkty ABC s.r.o.\")\n",
    "plt.title(\"Analyza predaja produktov za Q1-2022\")\n",
    "plt.show()\n",
    "\n",
    "x1 = np.array([\"IBM\", \"HP\", \"NESS\", \"Microsoft\"])\n",
    "y1 = np.array([2, 5, 1, 9])\n",
    "\n",
    "plt.bar(x1, y1)\n",
    "plt.title(\"Porovnanie IT firiem 2022\")\n",
    "plt.xlabel(\"Firma\")\n",
    "plt.ylabel(\"Obrat v [mil]\")\n",
    "plt.show()\n",
    "\n",
    "nahoda = np.random.normal(100, 10, 200)\n",
    "print(nahoda)\n",
    "plt.hist(nahoda)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "04a0fef5",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}