13. Python Crash Course - Part 4.ipynb

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       "12"
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   "source": [
    "def times2(var):\n",
    "    return var*2\n",
    "\n",
    "def times2(var): return var*2\n",
    "\n",
    "t = lambda var:var*2 #lambda expression\n",
    "\n",
    "t(6)"
   ]
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     "execution_count": 2,
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   "source": [
    "times2(5)"
   ]
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   "source": [
    "# map() function\n",
    "seq = [1, 2, 3, 4, 5]"
   ]
  },
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   "execution_count": 4,
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     "data": {
      "text/plain": [
       "<map at 0x21f84271ac0>"
      ]
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     "execution_count": 4,
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   "source": [
    "map(times2, seq)"
   ]
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     "data": {
      "text/plain": [
       "[2, 4, 6, 8, 10]"
      ]
     },
     "execution_count": 5,
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   ],
   "source": [
    "list(map(times2, seq))"
   ]
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   "execution_count": 6,
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     "data": {
      "text/plain": [
       "[3, 6, 9, 12, 15]"
      ]
     },
     "execution_count": 6,
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   "source": [
    "list(map(lambda num: num*3, seq))"
   ]
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   "execution_count": 7,
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     "data": {
      "text/plain": [
       "<filter at 0x21f842716a0>"
      ]
     },
     "execution_count": 7,
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   ],
   "source": [
    "# filter function\n",
    "filter(lambda num: num%2 == 0, seq)"
   ]
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   "execution_count": 8,
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     "data": {
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       "[2, 4]"
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    "list(filter(lambda num: num%2 == 0, seq))"
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   "source": [
    "# methods\n",
    "s = 'hello my name is Sam'"
   ]
  },
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   "execution_count": 10,
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     "data": {
      "text/plain": [
       "'hello my name is sam'"
      ]
     },
     "execution_count": 10,
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   ],
   "source": [
    "s.lower()"
   ]
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   "execution_count": 11,
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     "data": {
      "text/plain": [
       "'HELLO MY NAME IS SAM'"
      ]
     },
     "execution_count": 11,
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   "source": [
    "s.upper()"
   ]
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   "execution_count": 12,
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     "data": {
      "text/plain": [
       "['hello', 'my', 'name', 'is', 'Sam']"
      ]
     },
     "execution_count": 12,
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   ],
   "source": [
    "s.split()"
   ]
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   "execution_count": 13,
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   "outputs": [],
   "source": [
    "tweet = 'Go Sports! #Sports'"
   ]
  },
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   "execution_count": 14,
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     "data": {
      "text/plain": [
       "['Go', 'Sports!', '#Sports']"
      ]
     },
     "execution_count": 14,
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   ],
   "source": [
    "tweet.split()"
   ]
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   "execution_count": 15,
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    {
     "data": {
      "text/plain": [
       "['Go Sports! ', 'Sports']"
      ]
     },
     "execution_count": 15,
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   "source": [
    "tweet.split('#')"
   ]
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   "execution_count": 16,
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   "source": [
    "d =  {'k1': 1, 'k2': 2}"
   ]
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       "dict_items([('k1', 1), ('k2', 2)])"
      ]
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     "execution_count": 18,
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   "source": [
    "lst = [1,2,3]"
   ]
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     "data": {
      "text/plain": [
       "3"
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     "execution_count": 21,
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       "[1, 2]"
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     "execution_count": 22,
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   "source": [
    "lst = [1,2,3,4,5]"
   ]
  },
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   "cell_type": "code",
   "execution_count": 24,
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   "source": [
    "item = lst.pop()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 25,
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     "data": {
      "text/plain": [
       "[1, 2, 3, 4]"
      ]
     },
     "execution_count": 25,
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     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 26,
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   "source": [
    "item"
   ]
  },
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   "cell_type": "code",
   "execution_count": 27,
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     "node_id": "towCGHqFYVMe2bREacGONk",
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   "outputs": [],
   "source": [
    "first = lst.pop(0)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 28,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 3, 4]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "lst"
   ]
  },
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   "cell_type": "code",
   "execution_count": 29,
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     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 29,
     "metadata": {},
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   "source": [
    "first"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "datalore": {
     "hide_input_from_viewers": true,
     "hide_output_from_viewers": true,
     "node_id": "IInYnPa7YSozzOCJ5wO18Z",
     "type": "CODE"
    }
   },
   "outputs": [],
   "source": [
    "lst.append('new')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "datalore": {
     "hide_input_from_viewers": true,
     "hide_output_from_viewers": true,
     "node_id": "9CN4vFR3f4BXLFSvIkss39",
     "type": "CODE"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 3, 4, 'new']"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
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     "hide_output_from_viewers": true,
     "node_id": "unjmicSGvcyv6kLCjtwhqu",
     "type": "CODE"
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   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'x' in [1,2,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "datalore": {
     "hide_input_from_viewers": true,
     "hide_output_from_viewers": true,
     "node_id": "JoAQp9kMaqjIcG9jUw9q8y",
     "type": "CODE"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'x' in ['x', 'y', 'z']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "datalore": {
     "hide_input_from_viewers": true,
     "hide_output_from_viewers": true,
     "node_id": "mJcLqTBaW3iWH7MmeBzfr0",
     "type": "CODE"
    }
   },
   "outputs": [],
   "source": [
    "# Tuple unpacking -- allow to unpack tuples as we go along\n",
    "x = [(1,2), (3,4), (5, 6)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "datalore": {
     "hide_input_from_viewers": true,
     "hide_output_from_viewers": true,
     "node_id": "xNuoFOjaIU8oDPl0VqeddC",
     "type": "CODE"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "datalore": {
     "hide_input_from_viewers": true,
     "hide_output_from_viewers": true,
     "node_id": "NvyTyR8WOI9WMFeGWBgrdp",
     "type": "CODE"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1, 2)\n",
      "(3, 4)\n",
      "(5, 6)\n"
     ]
    }
   ],
   "source": [
    "for item in x:\n",
    "    print(item)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
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   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "4\n",
      "6\n"
     ]
    }
   ],
   "source": [
    "# tuple unpacking\n",
    "for a, b in x:\n",
    "    print(b)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
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   "outputs": [],
   "source": []
  }
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