python_for_DA.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "b=[1,2,3]\n",
    "b?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_numbers(a,b):\n",
    "    return a+b\n",
    "\n",
    "add_numbers(1,2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "add_numbers?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "add_numbers??"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "np.*load*?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "ERROR:root:Line magic function `%paste` not found.\n"
     ]
    }
   ],
   "source": [
    "%paste"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "a=np.random.randn(100,100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "51.3 µs ± 4.94 µs per loop (mean ± std. dev. of 7 runs, 10000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit np.dot(a,a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x24ff142b278>]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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eNx586tKmF75z81G854u/xqnpZfzd+w/hHQean9ZYK4YT9nyBI50rVDg8rT/h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\ngiAIg0HCThAEYTD+fxudG6wiuj3BAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x24ff01392b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "plt.plot(np.random.randn(50))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=[1,2,3,4]\n",
    "b=a\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<list_iterator at 0x24ff141c898>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=[1,2,3]\n",
    "iter(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x=5\n",
    "isinstance(x,int)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['foo', 2, [4, 5]]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=[\"foo\",2,[4,5]]\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[4, 5]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2][1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "template=\"{0:.2f} {1:s} are worth US${2:d}\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'4.56 ABC are worth US$1'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "template.format(4.556,'ABC', 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "b'123e'"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val=\"123e\"\n",
    "val_1=val.encode('utf-8')\n",
    "val_1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'123e'"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val_2=val_1.decode('utf-8')\n",
    "val_2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s='123'\n",
    "type(int(s))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from datetime import datetime,date,time\n",
    "dt=datetime(2011,10,29,20,39,21)\n",
    "dt.hour"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'10/29/2011 20:39'"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.strftime('%m/%d/%Y %H:%M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2011/10/29 20:39'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt.strftime('%Y/%m/%d %H:%M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "seq=[1,2,3,4]\n",
    "for i in range(len(seq)):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n"
     ]
    }
   ],
   "source": [
    "for i in seq:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "23331668\n"
     ]
    }
   ],
   "source": [
    "num=0\n",
    "for i in range(10000):\n",
    "    if i%3==0 or i%5==0:\n",
    "        num=num+i\n",
    "print(num)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
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   "file_extension": ".py",
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