Aula_13_paths_environment_numpy_matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "IEEE VTS Centro-Norte Brasil Chapter\n",
    "==\n",
    "Curso de Verão - Introdução à Programação com a Linguagem Python\n",
    "--"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Explorando Bibliotecas do Python"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "O que usamos até então:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import math\n",
    "import random #https://docs.python.org/3.4/library/random.html\n",
    "import time\n",
    "import datetime\n",
    "import pickle\n",
    "import string\n",
    "import nltk #http://www.nltk.org/\n",
    "from functools import reduce\n",
    "import requests #ver também urllib2, mechanize, selenium\n",
    "from bs4 import BeautifulSoup\n",
    "import collections #from collections import Counter\n",
    "from numpy import mean\n",
    "import numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Outras bibliotecas úteis:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os #https://docs.python.org/3.4/library/os.html\n",
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Chave: CLUTTER_IM_MODULE, valor: xim\n",
      "Chave: CONDA_SHLVL, valor: 1\n",
      "Chave: LS_COLORS, valor: rs=0:di=01;34:ln=01;36:mh=00:pi=40;33:so=01;35:do=01;35:bd=40;33;01:cd=40;33;01:or=40;31;01:mi=00:su=37;41:sg=30;43:ca=30;41:tw=30;42:ow=34;42:st=37;44:ex=01;32:*.tar=01;31:*.tgz=01;31:*.arc=01;31:*.arj=01;31:*.taz=01;31:*.lha=01;31:*.lz4=01;31:*.lzh=01;31:*.lzma=01;31:*.tlz=01;31:*.txz=01;31:*.tzo=01;31:*.t7z=01;31:*.zip=01;31:*.z=01;31:*.Z=01;31:*.dz=01;31:*.gz=01;31:*.lrz=01;31:*.lz=01;31:*.lzo=01;31:*.xz=01;31:*.zst=01;31:*.tzst=01;31:*.bz2=01;31:*.bz=01;31:*.tbz=01;31:*.tbz2=01;31:*.tz=01;31:*.deb=01;31:*.rpm=01;31:*.jar=01;31:*.war=01;31:*.ear=01;31:*.sar=01;31:*.rar=01;31:*.alz=01;31:*.ace=01;31:*.zoo=01;31:*.cpio=01;31:*.7z=01;31:*.rz=01;31:*.cab=01;31:*.wim=01;31:*.swm=01;31:*.dwm=01;31:*.esd=01;31:*.jpg=01;35:*.jpeg=01;35:*.mjpg=01;35:*.mjpeg=01;35:*.gif=01;35:*.bmp=01;35:*.pbm=01;35:*.pgm=01;35:*.ppm=01;35:*.tga=01;35:*.xbm=01;35:*.xpm=01;35:*.tif=01;35:*.tiff=01;35:*.png=01;35:*.svg=01;35:*.svgz=01;35:*.mng=01;35:*.pcx=01;35:*.mov=01;35:*.mpg=01;35:*.mpeg=01;35:*.m2v=01;35:*.mkv=01;35:*.webm=01;35:*.ogm=01;35:*.mp4=01;35:*.m4v=01;35:*.mp4v=01;35:*.vob=01;35:*.qt=01;35:*.nuv=01;35:*.wmv=01;35:*.asf=01;35:*.rm=01;35:*.rmvb=01;35:*.flc=01;35:*.avi=01;35:*.fli=01;35:*.flv=01;35:*.gl=01;35:*.dl=01;35:*.xcf=01;35:*.xwd=01;35:*.yuv=01;35:*.cgm=01;35:*.emf=01;35:*.ogv=01;35:*.ogx=01;35:*.aac=00;36:*.au=00;36:*.flac=00;36:*.m4a=00;36:*.mid=00;36:*.midi=00;36:*.mka=00;36:*.mp3=00;36:*.mpc=00;36:*.ogg=00;36:*.ra=00;36:*.wav=00;36:*.oga=00;36:*.opus=00;36:*.spx=00;36:*.xspf=00;36:\n",
      "Chave: CONDA_EXE, valor: /home/bruno/anaconda3/bin/conda\n",
      "Chave: LESSCLOSE, valor: /usr/bin/lesspipe %s %s\n",
      "Chave: ANDROID_HOME, valor: /home/bruno/Android/Sdk\n",
      "Chave: XDG_MENU_PREFIX, valor: gnome-\n",
      "Chave: LANG, valor: pt_BR.UTF-8\n",
      "Chave: MANAGERPID, valor: 1489\n",
      "Chave: DISPLAY, valor: :0\n",
      "Chave: INVOCATION_ID, valor: eaeefeb64a754c3a9b22c99cbc7b3000\n",
      "Chave: COLORTERM, valor: truecolor\n",
      "Chave: DESKTOP_AUTOSTART_ID, valor: 102b6f47b37ac29c84156502809440428900000025490011\n",
      "Chave: USERNAME, valor: bruno\n",
      "Chave: CONDA_PREFIX, valor: /home/bruno/anaconda3\n",
      "Chave: JAVA_HOME, valor: /usr/lib/jvm/java-11-openjdk\n",
      "Chave: SSH_AUTH_SOCK, valor: /run/user/1000/keyring/ssh\n",
      "Chave: MANDATORY_PATH, valor: /usr/share/gconf/gnome-flashback-metacity.mandatory.path\n",
      "Chave: S_COLORS, valor: auto\n",
      "Chave: _CE_M, valor: \n",
      "Chave: USER, valor: bruno\n",
      "Chave: DESKTOP_SESSION, valor: gnome-flashback-metacity\n",
      "Chave: QT4_IM_MODULE, valor: xim\n",
      "Chave: TEXTDOMAINDIR, valor: /usr/share/locale/\n",
      "Chave: GNOME_TERMINAL_SCREEN, valor: /org/gnome/Terminal/screen/3f06a500_610c_4fdf_9aa1_7d1da556d158\n",
      "Chave: DEFAULTS_PATH, valor: /usr/share/gconf/gnome-flashback-metacity.default.path\n",
      "Chave: PWD, valor: /home/bruno/cursoPython\n",
      "Chave: HOME, valor: /home/bruno\n",
      "Chave: CONDA_PYTHON_EXE, valor: /home/bruno/anaconda3/bin/python\n",
      "Chave: JOURNAL_STREAM, valor: 9:37265\n",
      "Chave: TEXTDOMAIN, valor: im-config\n",
      "Chave: QT_ACCESSIBILITY, valor: 1\n",
      "Chave: XDG_SESSION_TYPE, valor: x11\n",
      "Chave: XDG_DATA_DIRS, valor: /usr/share/gnome-flashback-metacity:/home/bruno/.local/share/flatpak/exports/share:/var/lib/flatpak/exports/share:/usr/local/share:/usr/share:/var/lib/snapd/desktop\n",
      "Chave: _CE_CONDA, valor: \n",
      "Chave: XDG_SESSION_DESKTOP, valor: gnome-flashback-metacity\n",
      "Chave: SSH_AGENT_LAUNCHER, valor: gnome-keyring\n",
      "Chave: CONDA_PROMPT_MODIFIER, valor: (base) \n",
      "Chave: GTK_MODULES, valor: gail:atk-bridge\n",
      "Chave: WINDOWPATH, valor: 1\n",
      "Chave: GNOME_SESSION_XDG_SESSION_PATH, valor: \n",
      "Chave: TERM, valor: xterm-color\n",
      "Chave: SHELL, valor: /bin/bash\n",
      "Chave: VTE_VERSION, valor: 5202\n",
      "Chave: QT_IM_MODULE, valor: ibus\n",
      "Chave: XMODIFIERS, valor: @im=ibus\n",
      "Chave: IM_CONFIG_PHASE, valor: 2\n",
      "Chave: XDG_CURRENT_DESKTOP, valor: GNOME-Flashback:GNOME\n",
      "Chave: GPG_AGENT_INFO, valor: /run/user/1000/gnupg/S.gpg-agent:0:1\n",
      "Chave: GNOME_TERMINAL_SERVICE, valor: :1.109\n",
      "Chave: SHLVL, valor: 2\n",
      "Chave: LANGUAGE, valor: pt_BR:pt:en\n",
      "Chave: GDMSESSION, valor: gnome-flashback-metacity\n",
      "Chave: GNOME_DESKTOP_SESSION_ID, valor: this-is-deprecated\n",
      "Chave: LOGNAME, valor: bruno\n",
      "Chave: DBUS_SESSION_BUS_ADDRESS, valor: unix:path=/run/user/1000/bus\n",
      "Chave: XDG_RUNTIME_DIR, valor: /run/user/1000\n",
      "Chave: XAUTHORITY, valor: /run/user/1000/gdm/Xauthority\n",
      "Chave: XDG_CONFIG_DIRS, valor: /etc/xdg/xdg-gnome-flashback-metacity:/etc/xdg\n",
      "Chave: PATH, valor: /home/bruno/anaconda3/bin:/home/bruno/anaconda3/bin:/home/bruno/anaconda3/condabin:/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin:/usr/games:/usr/local/games:/snap/bin:/home/bruno/Android/Sdk/emulator:/home/bruno/Android/Sdk/tools:/home/bruno/Android/Sdk/tools/bin:/home/bruno/Android/Sdk/platform-tools\n",
      "Chave: CONDA_DEFAULT_ENV, valor: base\n",
      "Chave: SESSION_MANAGER, valor: local/reborn:@/tmp/.ICE-unix/2549,unix/reborn:/tmp/.ICE-unix/2549\n",
      "Chave: LESSOPEN, valor: | /usr/bin/lesspipe %s\n",
      "Chave: GTK_IM_MODULE, valor: ibus\n",
      "Chave: _, valor: /home/bruno/anaconda3/bin/jupyter\n",
      "Chave: OLDPWD, valor: /home/bruno\n",
      "Chave: JPY_PARENT_PID, valor: 6020\n",
      "Chave: CLICOLOR, valor: 1\n",
      "Chave: PAGER, valor: cat\n",
      "Chave: GIT_PAGER, valor: cat\n",
      "Chave: MPLBACKEND, valor: module://ipykernel.pylab.backend_inline\n"
     ]
    }
   ],
   "source": [
    "for key, value in os.environ.items():\n",
    "    print('Chave: {0}, valor: {1}'.format(key, value))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/bruno/cursoPython'"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: 'c:\\\\Users\\\\renato.souza\\\\Documents\\\\'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m\u001b[0m",
      "\u001b[1;31mFileNotFoundError\u001b[0mTraceback (most recent call last)",
      "\u001b[1;32m<ipython-input-9-3827eadd8de4>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mos\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mchdir\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'c:\\\\Users\\\\renato.souza\\\\Documents\\\\'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;31m#os.chdir('c:\\\\Users\\\\renato.souza\\\\')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'c:\\\\Users\\\\renato.souza\\\\Documents\\\\'"
     ]
    }
   ],
   "source": [
    "os.chdir('c:\\\\Users\\\\renato.souza\\\\Documents\\\\')\n",
    "#os.chdir('c:\\\\Users\\\\renato.souza\\\\')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/bruno/cursoPython'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    " os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'bruno'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getlogin()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'/home/bruno/cursoPython/meu arquivo.txt'"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nome_arquivo = os.path.join(os.getcwd(), 'meu arquivo.txt')\n",
    "nome_arquivo"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['/home/bruno/anaconda3/lib/python3.7/site-packages/ipykernel_launcher.py',\n",
       " '-f',\n",
       " '/run/user/1000/jupyter/kernel-1795285f-8d3d-4f02-964f-881499c382e3.json']"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sys.argv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "module 'sys' has no attribute 'getwindowsversion'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0mTraceback (most recent call last)",
      "\u001b[1;32m<ipython-input-16-a1416e8c9c76>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgetwindowsversion\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mAttributeError\u001b[0m: module 'sys' has no attribute 'getwindowsversion'"
     ]
    }
   ],
   "source": [
    "sys.getwindowsversion()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['',\n",
       " '/home/bruno/cursoPython',\n",
       " '/home/bruno/anaconda3/lib/python37.zip',\n",
       " '/home/bruno/anaconda3/lib/python3.7',\n",
       " '/home/bruno/anaconda3/lib/python3.7/lib-dynload',\n",
       " '/home/bruno/anaconda3/lib/python3.7/site-packages',\n",
       " '/home/bruno/clients_simulator',\n",
       " '/home/bruno/dims',\n",
       " '/home/bruno/pasta/raise-p2p',\n",
       " '/home/bruno/BLEHeartRateLogger',\n",
       " '/home/bruno/anaconda3/lib/python3.7/site-packages/IPython/extensions',\n",
       " '/home/bruno/.ipython']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sys.path"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Biblioteca CSV:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "with open('meu_arquivo.csv', mode='w', newline='') as csvfile:\n",
    "    writer = csv.writer(csvfile, delimiter=',', quotechar='|', quoting=csv.QUOTE_MINIMAL)\n",
    "    writer.writerow(['Peso','Altura','Idade'])\n",
    "    writer.writerow(['72', '183', '44'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Peso, Altura, Idade\n",
      "72, 183, 44\n"
     ]
    }
   ],
   "source": [
    "with open('meu_arquivo.csv', mode='r', newline='') as csvfile:\n",
    "    reader = csv.reader(csvfile, delimiter=',', quotechar='|')\n",
    "    for row in reader:\n",
    "        print(', '.join(row))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Bibliotecas Numpy e MatPlotLib:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array(range(10000))\n",
    "l = list(range(10000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10000,)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 14.44 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "10000 loops, best of 3: 19.4 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit 3 * x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1000 loops, best of 3: 751 µs per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit [3*elem for elem in l]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The slowest run took 10.00 times longer than the fastest. This could mean that an intermediate result is being cached.\n",
      "1000000 loops, best of 3: 340 ns per loop\n"
     ]
    }
   ],
   "source": [
    "%timeit map(lambda x:x*3, l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "x[0] = 23"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 9],\n",
       "       [4, 6, 7, 7],\n",
       "       [7, 8, 9, 6]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = np.array([[1,2,3,9],[4,6,7,7],[7,8,9,6]])\n",
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 4)"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(24,)"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = np.arange(1,25)\n",
    "z.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
       "       18, 19, 20, 21, 22, 23, 24])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 1,  2],\n",
       "        [ 3,  4],\n",
       "        [ 5,  6]],\n",
       "\n",
       "       [[ 7,  8],\n",
       "        [ 9, 10],\n",
       "        [11, 12]],\n",
       "\n",
       "       [[13, 14],\n",
       "        [15, 16],\n",
       "        [17, 18]],\n",
       "\n",
       "       [[19, 20],\n",
       "        [21, 22],\n",
       "        [23, 24]]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z2 = z.reshape(4,3,2)\n",
    "z2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 1,  2,  3,  4],\n",
       "        [ 5,  6,  7,  8],\n",
       "        [ 9, 10, 11, 12]],\n",
       "\n",
       "       [[13, 14, 15, 16],\n",
       "        [17, 18, 19, 20],\n",
       "        [21, 22, 23, 24]]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z3 = z.reshape(2,3,4)\n",
    "z3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7,  8],\n",
       "       [11, 12]])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z3[0,1:,-2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z3[0,1:,-2:].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([19, 20])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z3[1,1,2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2,)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z3[1,1,2:].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1,    8,   27,   64,  125,  216,  343,  512,  729, 1000])"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z[0:10] ** z[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [],
   "source": [
    "q = np.arange(64).reshape(2,4,8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 0,  1,  2,  3,  4,  5,  6,  7],\n",
       "        [ 8,  9, 10, 11, 12, 13, 14, 15],\n",
       "        [16, 17, 18, 19, 20, 21, 22, 23],\n",
       "        [24, 25, 26, 27, 28, 29, 30, 31]],\n",
       "\n",
       "       [[32, 33, 34, 35, 36, 37, 38, 39],\n",
       "        [40, 41, 42, 43, 44, 45, 46, 47],\n",
       "        [48, 49, 50, 51, 52, 53, 54, 55],\n",
       "        [56, 57, 58, 59, 60, 61, 62, 63]]])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "q"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.]])"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = np.zeros((3,3))\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  1.,  1.,  1.],\n",
       "       [ 1.,  1.,  1.,  1.],\n",
       "       [ 1.,  1.,  1.,  1.],\n",
       "       [ 1.,  1.,  1.,  1.]])"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "o = np.ones((4,4))\n",
    "o"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  0.,  0.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  1.,  0.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  1.,  0.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  1.,  0.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  1.,  0.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  0.,  1.,  0.],\n",
       "       [ 0.,  0.,  0.,  0.,  0.,  0.,  1.]])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ident = np.identity(7)\n",
    "ident"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([   1.        ,    5.30434783,    9.60869565,   13.91304348,\n",
       "         18.2173913 ,   22.52173913,   26.82608696,   31.13043478,\n",
       "         35.43478261,   39.73913043,   44.04347826,   48.34782609,\n",
       "         52.65217391,   56.95652174,   61.26086957,   65.56521739,\n",
       "         69.86956522,   74.17391304,   78.47826087,   82.7826087 ,\n",
       "         87.08695652,   91.39130435,   95.69565217,  100.        ])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "l = np.linspace(1,100,24)\n",
    "l"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0. ,  0.1,  0.2,  0.3,  0.4,  0.5,  0.6,  0.7,  0.8,  0.9,  1. ,\n",
       "        1.1,  1.2,  1.3,  1.4,  1.5,  1.6,  1.7,  1.8,  1.9,  2. ,  2.1,\n",
       "        2.2,  2.3,  2.4,  2.5,  2.6,  2.7,  2.8,  2.9,  3. ,  3.1,  3.2,\n",
       "        3.3,  3.4,  3.5,  3.6,  3.7,  3.8,  3.9,  4. ,  4.1,  4.2,  4.3,\n",
       "        4.4,  4.5,  4.6,  4.7,  4.8,  4.9,  5. ,  5.1,  5.2,  5.3,  5.4,\n",
       "        5.5,  5.6,  5.7,  5.8,  5.9,  6. ,  6.1,  6.2,  6.3,  6.4,  6.5,\n",
       "        6.6,  6.7,  6.8,  6.9,  7. ,  7.1,  7.2,  7.3,  7.4,  7.5,  7.6,\n",
       "        7.7,  7.8,  7.9,  8. ,  8.1,  8.2,  8.3,  8.4,  8.5,  8.6,  8.7,\n",
       "        8.8,  8.9,  9. ,  9.1,  9.2,  9.3,  9.4,  9.5,  9.6,  9.7,  9.8,\n",
       "        9.9])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "outro = np.arange(0,10,0.1)\n",
    "outro"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 1])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "meuarray = np.array([0,0,0,0,1])\n",
    "meuarray"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.any(meuarray)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.all(meuarray)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False, False], dtype=bool)"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array([1,2,3]) == np.array([1,5,4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5, -4, -3, -2, -1,  0,  1,  2,  3,  4,  5])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z = np.arange(-5,6,1)\n",
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a1 = z[z>=0]\n",
    "a1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 2, 4])"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a2 = a1[a1%2==0]\n",
    "a2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 4, 5],\n",
       "       [2, 9, 4],\n",
       "       [1, 7, 8]])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M = np.array([[3,4,5],[2,9,4],[1,7,8]])\n",
    "M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 2, 1],\n",
       "       [4, 9, 7],\n",
       "       [5, 4, 8]])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[3, 2, 1],\n",
       "       [4, 9, 7],\n",
       "       [5, 4, 8]])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 9, 16, 25],\n",
       "       [ 4, 81, 16],\n",
       "       [ 1, 49, 64]])"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M * M"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "43"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [],
   "source": [
    "M2 = np.matrix(M)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[3, 4, 5],\n",
       "        [2, 9, 4],\n",
       "        [1, 7, 8]])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 22,  83,  71],\n",
       "        [ 28, 117,  78],\n",
       "        [ 25, 123,  97]])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M2 * M2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "matrix([[ 0.40366972,  0.02752294, -0.26605505],\n",
       "        [-0.11009174,  0.17431193, -0.01834862],\n",
       "        [ 0.04587156, -0.1559633 ,  0.17431193]])"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "M2.I"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.linspace(0,2*np.pi,100)\n",
    "y = np.sin(x)\n",
    "y2 = np.cos(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5c405dd780>]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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G3ok8lCXTVIUqA30HoqiiCKdST4mOopOUEcp7XTFpKaZe0UjBZ4wNYozdYIzd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\nqx0bAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5c40395c18>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y,'g')\n",
    "plt.plot(x,y2,'m')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 209,
   "metadata": {},
   "outputs": [],
   "source": [
    "x = random.random(1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0xb64c438>]"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NVHuJyTh/+AOw4orVoKcLqs9ReZWScSR14zL11JdpKjou4wCyCSqxtCj4GBdn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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbd40080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  98.,   77.,  111.,  109.,  103.,  100.,   98.,   88.,  117.,   99.]),\n",
       " array([  3.48729599e-04,   1.00122928e-01,   1.99897126e-01,\n",
       "          2.99671325e-01,   3.99445523e-01,   4.99219722e-01,\n",
       "          5.98993920e-01,   6.98768118e-01,   7.98542317e-01,\n",
       "          8.98316515e-01,   9.98090714e-01]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kPwW8BXjV2pUzqFH64gPAtqqqJOH7+Q3KKxulL04AXsLiW5JPBj6X5PNV9cCaVjZ9o/TF\nu4G7q2ouyXOA+SQvqqrH17i2WbSq3FyrkB/lpqjl+2zu2loz0g1i3Yut1wFbqmqlP9e+n43SFz8O\nfHQx3zkN+Lkkh6rqlumUODWj9MVDwDeq6jvAd5L8A/AioLWQH6UvXgn8LkBVfTXJvwHPA+6cSoWz\nY9W5uVbTNXcCz01ydhY/v+B1wPIn6S3ArwAkeTnwrapaWKN6hnTMvkjyI8AngDdU1YMD1Dgtx+yL\nqvrRqjqnqs5hcV7+1xsMeBjtOfI3wE8mWZ/kZBZfaNsz5TqnYZS+2AtcCNDNQT8P+NepVjkbVp2b\nazKSr6e5KSrJr3WP/2lV3ZbkoiQPAv8NvHktahnaKH0BvBc4BfhQN4I9VFUXDFXzWhmxL44LIz5H\n9ib5JPBl4DBwXVU1F/Ij/lxcA9yQ5B4WB6e/XVWPDVb0GklyI/Aa4LQkDwFXsTht1zs3vRlKkhrm\n1/9JUsMMeUlqmCEvSQ0z5CWpYYa8JDXMkJekhhnyktQwQ16SGvZ/sgrmLpuGr7EAAAAASUVORK5C\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xa9dc940>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Exercício: simular 100.000 jogos da mega sena e plotar a distribuição dos números sorteados"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}