numpypandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NumPy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NumPy Arrays\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_list = [1,2,3]\n",
    "my_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(my_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[[1, 2, 3], [1, 2, 3], [1, 2, 3]]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "my_matrix = [[1,2,3],[1,2,3],[1,2,3]]\n",
    "my_matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [1, 2, 3],\n",
       "       [1, 2, 3]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.array(my_matrix)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(0,10)\n",
    "# od [0 , 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (<ipython-input-138-e409e3532706>, line 1)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-138-e409e3532706>\"\u001b[0;36m, line \u001b[0;32m1\u001b[0m\n\u001b[0;31m    np.arange(0,10,2) //\u001b[0m\n\u001b[0m                         ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "np.arange(0,10,2)\n",
    "# od [0 , 10) z krokiem 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0.])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1., 1.],\n",
       "       [1., 1., 1.],\n",
       "       [1., 1., 1.]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[9., 9., 9.],\n",
       "       [9., 9., 9.],\n",
       "       [9., 9., 9.]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((3,3))*9"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.        ,  0.34482759,  0.68965517,  1.03448276,  1.37931034,\n",
       "        1.72413793,  2.06896552,  2.4137931 ,  2.75862069,  3.10344828,\n",
       "        3.44827586,  3.79310345,  4.13793103,  4.48275862,  4.82758621,\n",
       "        5.17241379,  5.51724138,  5.86206897,  6.20689655,  6.55172414,\n",
       "        6.89655172,  7.24137931,  7.5862069 ,  7.93103448,  8.27586207,\n",
       "        8.62068966,  8.96551724,  9.31034483,  9.65517241, 10.        ])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linspace(0,10,30)\n",
    "# od [0 , 10) miedzy tym zakresem tworzymy 30 rownych przedzialow"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 1., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 1., 0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 1., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 1., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 1., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 0., 1., 0.],\n",
       "       [0., 0., 0., 0., 0., 0., 0., 0., 0., 1.]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.eye(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.89162104, 0.41494806, 0.18336246, 0.09852774],\n",
       "       [0.8301477 , 0.42299313, 0.01038311, 0.04976188],\n",
       "       [0.04090745, 0.03996818, 0.9592236 , 0.61789877],\n",
       "       [0.78019373, 0.40285564, 0.19538195, 0.13064767]])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.rand(4,4)\n",
    "# losowe od [0,1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.48668265, -1.486561  , -1.42747402, -1.41860115, -1.31379777,\n",
       "       -1.23263958, -1.14427282, -1.14078914, -1.12185548, -1.0845843 ,\n",
       "       -1.06231633, -0.91258113, -0.87382753, -0.85702277, -0.80493935,\n",
       "       -0.78311652, -0.77155865, -0.76670664, -0.75229497, -0.74571452,\n",
       "       -0.74259843, -0.65331349, -0.58820953, -0.57586975, -0.55654788,\n",
       "       -0.47918994, -0.46053359, -0.42131825, -0.40118693, -0.36446552,\n",
       "       -0.36272594, -0.32573029, -0.2972902 , -0.28865901, -0.27166636,\n",
       "       -0.12141271, -0.11087435, -0.09590103, -0.08819358, -0.08711881,\n",
       "       -0.08502432, -0.01151872,  0.00966231,  0.01784402,  0.04779674,\n",
       "        0.09934432,  0.10049182,  0.14827486,  0.1623482 ,  0.18609885,\n",
       "        0.18651601,  0.206983  ,  0.22514695,  0.22729327,  0.23070454,\n",
       "        0.24560586,  0.27072763,  0.27414554,  0.28627235,  0.30279781,\n",
       "        0.37048886,  0.3765498 ,  0.39763776,  0.43892435,  0.44117217,\n",
       "        0.44368958,  0.44745153,  0.45593795,  0.46690903,  0.50947501,\n",
       "        0.64285746,  0.66326346,  0.66331901,  0.68061728,  0.70692296,\n",
       "        0.71479515,  0.73711714,  0.7373997 ,  0.74827075,  0.79922505,\n",
       "        0.81129389,  0.85792817,  0.96644051,  0.97851959,  1.03036318,\n",
       "        1.04875067,  1.10859328,  1.17838482,  1.39082393,  1.40564247,\n",
       "        1.45347856,  1.49688984,  1.68727599,  1.68868654,  1.81770238,\n",
       "        1.89119276,  2.13350016,  2.19606924,  2.2936669 ,  2.64407182])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.random.randn(100)\n",
    "# losowe od [0,1) w rozkladzie normalnym\n",
    "x.sort()\n",
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1220217b8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Dodatkowe (ponad material) wyswietlenie danych rozkladu normalnego\n",
    "\n",
    "x = np.random.randn(100)\n",
    "x.sort()\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import norm\n",
    "std = np.std(x) \n",
    "mean = np.mean(x)    \n",
    "plt.plot(norm.pdf(x,mean,std))\n",
    "\n",
    "\n",
    "import scipy.stats as stats\n",
    "import pylab as pl\n",
    "fit = stats.norm.pdf(x, np.mean(x), np.std(x))  #this is a fitting indeed\n",
    "pl.plot(x,fit,'-o')\n",
    "pl.hist(x,normed=True)      #use this to draw histogram of your data\n",
    "pl.show()  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x122024a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Dodatkowe (ponad material) wyswietlenie danych rozkladu normalnego\n",
    "import scipy.stats as stats\n",
    "import pylab as pl\n",
    "\n",
    "\n",
    "\n",
    "fit = stats.norm.pdf(x, np.mean(x), np.std(x))  #this is a fitting indeed\n",
    "\n",
    "pl.plot(x,fit,'-o')\n",
    "\n",
    "pl.hist(x,normed=True)      #use this to draw histogram of your data\n",
    "\n",
    "pl.show()     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.40033229,  0.83363866,  0.07294056,  1.08669842, -0.87836213],\n",
       "       [ 1.62529463,  1.76478444,  2.30175133,  1.75048438, -0.11644845],\n",
       "       [ 1.13037744,  1.44339631,  0.85650771, -0.0450753 , -0.44207747],\n",
       "       [ 0.38744961,  0.62399843,  0.51009123, -0.53638943,  0.06817707],\n",
       "       [ 0.08322649, -1.25535727, -0.5413483 ,  0.98380657,  0.72502095]])"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randn(5,5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[79, 61, 20],\n",
       "       [38, 18, 69],\n",
       "       [27,  6, 51]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.random.randint(1,100,(3,3))\n",
    "# random integers w zakresie [1, 100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.arange(16)\n",
    "random_arr = np.random.randint(0,20,16)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11],\n",
       "       [12, 13, 14, 15]])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.reshape(4,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 8,  9, 19, 17],\n",
       "       [19,  6,  6,  3],\n",
       "       [14, 10,  7,  3],\n",
       "       [11, 14, 12,  1]])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix_arr = random_arr.reshape(4,4)\n",
    "matrix_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15])"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "matrix_arr.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.random.randint(0,10,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 5, 8, 5, 2, 3, 9, 6, 3, 7])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr[:3] = 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 4, 4, 5, 2, 3, 9, 6, 3, 7])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "slice_of_arr = arr[-3:]\n",
    "# jesli zrobimy tak to tworzymy referencje do Naszych prawdziwych danych, \n",
    "# wiec robiac operacje na slice_of_arr robimy zmiany w arr na tym kawalku danych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "slice_of_array = arr[-3:].copy()\n",
    "# tutaj kopiujemy dane, wiec wykonujac operacje nie zmienimy danych oryginalnych"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6, 3, 7])"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "slice_of_array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "slice_of_array[:] = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 4, 4, 5, 2, 3, 9, 6, 3, 7])"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 4, 4, 5, 2, 3, 9, 6, 3, 7])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 4, 4, 5, 2],\n",
       "       [3, 9, 6, 3, 7]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = arr.reshape(2,5)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_2d = np.zeros((10,10))\n",
    "arr_lenght = arr_2d.shape[1]\n",
    "arr_lenght"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "       [2., 2., 2., 2., 2., 2., 2., 2., 2., 2.],\n",
       "       [3., 3., 3., 3., 3., 3., 3., 3., 3., 3.],\n",
       "       [4., 4., 4., 4., 4., 4., 4., 4., 4., 4.],\n",
       "       [5., 5., 5., 5., 5., 5., 5., 5., 5., 5.],\n",
       "       [6., 6., 6., 6., 6., 6., 6., 6., 6., 6.],\n",
       "       [7., 7., 7., 7., 7., 7., 7., 7., 7., 7.],\n",
       "       [8., 8., 8., 8., 8., 8., 8., 8., 8., 8.],\n",
       "       [9., 9., 9., 9., 9., 9., 9., 9., 9., 9.]])"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "for i in range(arr_lenght):\n",
    "    arr_2d[i] = i\n",
    "arr_2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[6., 6., 6., 6., 6., 6., 6., 6., 6., 6.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "       [2., 2., 2., 2., 2., 2., 2., 2., 2., 2.]])"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_2d[[6,1,2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
       "       [1., 1., 1., 1., 1., 1., 1., 1., 1., 1.],\n",
       "       [2., 2., 2., 2., 2., 2., 2., 2., 2., 2.],\n",
       "       [3., 3., 3., 3., 3., 3., 3., 3., 3., 3.],\n",
       "       [4., 4., 4., 4., 4., 4., 4., 4., 4., 4.],\n",
       "       [5., 5., 5., 5., 5., 5., 5., 5., 5., 5.],\n",
       "       [6., 6., 6., 6., 6., 6., 6., 6., 6., 6.],\n",
       "       [7., 7., 7., 7., 7., 7., 7., 7., 7., 7.],\n",
       "       [8., 8., 8., 8., 8., 8., 8., 8., 8., 8.],\n",
       "       [9., 9., 9., 9., 9., 9., 9., 9., 9., 9.]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 139,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = np.array([1,0,0,0,1,0,1,0,1,1], dtype = bool)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_2d[mask,mask] = 222"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 141,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[222.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.,   0.],\n",
       "       [  1.,   1.,   1.,   1.,   1.,   1.,   1.,   1.,   1.,   1.],\n",
       "       [  2.,   2.,   2.,   2.,   2.,   2.,   2.,   2.,   2.,   2.],\n",
       "       [  3.,   3.,   3.,   3.,   3.,   3.,   3.,   3.,   3.,   3.],\n",
       "       [  4.,   4.,   4.,   4., 222.,   4.,   4.,   4.,   4.,   4.],\n",
       "       [  5.,   5.,   5.,   5.,   5.,   5.,   5.,   5.,   5.,   5.],\n",
       "       [  6.,   6.,   6.,   6.,   6.,   6., 222.,   6.,   6.,   6.],\n",
       "       [  7.,   7.,   7.,   7.,   7.,   7.,   7.,   7.,   7.,   7.],\n",
       "       [  8.,   8.,   8.,   8.,   8.,   8.,   8.,   8., 222.,   8.],\n",
       "       [  9.,   9.,   9.,   9.,   9.,   9.,   9.,   9.,   9., 222.]])"
      ]
     },
     "execution_count": 141,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_2d\n",
    "#zmieniaja Nam sie dane tam gdzie dziala maska\n",
    "# maske zastosowalismy teraz na indeks i kolumny jednoczesnie\n",
    "# co sie stanie jak zastosujemy tylko na indeks ? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(1,11)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = (arr > 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5,  6,  7,  8,  9, 10])"
      ]
     },
     "execution_count": 145,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5,  6,  7,  8,  9, 10])"
      ]
     },
     "execution_count": 146,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[arr>4]\n",
    "# indeksowanie poprzez operacje logiczne"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr = np.arange(0,10)\n",
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/romankarski/anaconda3/lib/python3.6/site-packages/ipykernel_launcher.py:1: RuntimeWarning: divide by zero encountered in log\n",
      "  \"\"\"Entry point for launching an IPython kernel.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([      -inf, 0.        , 0.69314718, 1.09861229, 1.38629436,\n",
       "       1.60943791, 1.79175947, 1.94591015, 2.07944154, 2.19722458])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.log(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "PANDAS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = ['a','b','c']\n",
    "my_list = [10,20,30]\n",
    "arr=np.array(my_list)\n",
    "d = dict(zip(labels,my_list))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    10\n",
       "b    20\n",
       "c    30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 76,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(arr, labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    10\n",
       "b    20\n",
       "c    30\n",
       "dtype: int64"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(d)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    a\n",
       "1    b\n",
       "2    c\n",
       "dtype: object"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.Series(labels)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "ser1 = pd.Series([1,2,3,4], index = ['apple','banana','plum','pear'])\n",
    "ser2 = pd.Series([1,2,5,4], index = ['apple','banana','orange','pear'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "apple     2.0\n",
       "banana    4.0\n",
       "orange    NaN\n",
       "pear      8.0\n",
       "plum      NaN\n",
       "dtype: float64"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser1 + ser2 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.random import randn\n",
    "np.random.seed(101)\n",
    "df = pd.DataFrame(randn(5,4), index = 'A B C D E'.split(), columns = 'W X Y Z'.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "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>W</th>\n",
       "      <th>Z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.503826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.651118</td>\n",
       "      <td>0.605965</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-2.018168</td>\n",
       "      <td>-0.589001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.188695</td>\n",
       "      <td>0.955057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.190794</td>\n",
       "      <td>0.683509</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         Z\n",
       "A  2.706850  0.503826\n",
       "B  0.651118  0.605965\n",
       "C -2.018168 -0.589001\n",
       "D  0.188695  0.955057\n",
       "E  0.190794  0.683509"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['W','Z']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\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>W</th>\n",
       "      <th>X</th>\n",
       "      <th>Y</th>\n",
       "      <th>Z</th>\n",
       "      <th>new_column</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>A</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "      <td>3.614819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>B</th>\n",
       "      <td>0.651118</td>\n",
       "      <td>-0.319318</td>\n",
       "      <td>-0.848077</td>\n",
       "      <td>0.605965</td>\n",
       "      <td>-0.196959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "      <td>-1.489355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "      <td>0.955057</td>\n",
       "      <td>-0.744542</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>E</th>\n",
       "      <td>0.190794</td>\n",
       "      <td>1.978757</td>\n",
       "      <td>2.605967</td>\n",
       "      <td>0.683509</td>\n",
       "      <td>2.796762</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          W         X         Y         Z  new_column\n",
       "A  2.706850  0.628133  0.907969  0.503826    3.614819\n",
       "B  0.651118 -0.319318 -0.848077  0.605965   -0.196959\n",
       "C -2.018168  0.740122  0.528813 -0.589001   -1.489355\n",
       "D  0.188695 -0.758872 -0.933237  0.955057   -0.744542\n",
       "E  0.190794  1.978757  2.605967  0.683509    2.796762"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['new_column'] = df['W'] + df['Y']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('E',axis=0, inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "      <td>3.614819</td>\n",
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       "      <th>B</th>\n",
       "      <td>0.651118</td>\n",
       "      <td>-0.319318</td>\n",
       "      <td>-0.848077</td>\n",
       "      <td>0.605965</td>\n",
       "      <td>-0.196959</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "      <td>-1.489355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "      <td>0.955057</td>\n",
       "      <td>-0.744542</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "          W         X         Y         Z  new_column\n",
       "A  2.706850  0.628133  0.907969  0.503826    3.614819\n",
       "B  0.651118 -0.319318 -0.848077  0.605965   -0.196959\n",
       "C -2.018168  0.740122  0.528813 -0.589001   -1.489355\n",
       "D  0.188695 -0.758872 -0.933237  0.955057   -0.744542"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
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    "df"
   ]
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   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
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       "          W         X\n",
       "A  2.706850  0.628133\n",
       "B  0.651118 -0.319318"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
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   "source": [
    "df.loc[['A','B'],['W','X']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
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       "      <th>A</th>\n",
       "      <td>2.706850</td>\n",
       "      <td>0.628133</td>\n",
       "      <td>0.907969</td>\n",
       "      <td>0.503826</td>\n",
       "      <td>3.614819</td>\n",
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       "      <th>B</th>\n",
       "      <td>0.651118</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.605965</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>C</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.740122</td>\n",
       "      <td>0.528813</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.188695</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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      ],
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       "          W         X         Y         Z  new_column\n",
       "A  2.706850  0.628133  0.907969  0.503826    3.614819\n",
       "B  0.651118       NaN       NaN  0.605965         NaN\n",
       "C       NaN  0.740122  0.528813       NaN         NaN\n",
       "D  0.188695       NaN       NaN  0.955057         NaN"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df>0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.605965</td>\n",
       "      <td>-0.196959</td>\n",
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       "      <th>C</th>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "      <td>-1.489355</td>\n",
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       "    <tr>\n",
       "      <th>D</th>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
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       "      <td>0.955057</td>\n",
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      ],
      "text/plain": [
       "          W         X         Y         Z  new_column\n",
       "B  0.651118 -0.319318 -0.848077  0.605965   -0.196959\n",
       "C -2.018168  0.740122  0.528813 -0.589001   -1.489355\n",
       "D  0.188695 -0.758872 -0.933237  0.955057   -0.744542"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[(df['W']<0) | (df['W']<2)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.reset_index(inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_column = 'AA BB CC DD'.split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['New_Column'] = new_column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "      <td>-1.489355</td>\n",
       "      <td>CC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "      <td>0.955057</td>\n",
       "      <td>-0.744542</td>\n",
       "      <td>DD</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  index         W         X         Y         Z  new_column New_Column\n",
       "0     A  2.706850  0.628133  0.907969  0.503826    3.614819         AA\n",
       "1     B  0.651118 -0.319318 -0.848077  0.605965   -0.196959         BB\n",
       "2     C -2.018168  0.740122  0.528813 -0.589001   -1.489355         CC\n",
       "3     D  0.188695 -0.758872 -0.933237  0.955057   -0.744542         DD"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.set_index('New_Column' , inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop('index',axis = 1,inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>BB</th>\n",
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       "      <th>CC</th>\n",
       "      <td>-2.018168</td>\n",
       "      <td>0.740122</td>\n",
       "      <td>0.528813</td>\n",
       "      <td>-0.589001</td>\n",
       "      <td>-1.489355</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>DD</th>\n",
       "      <td>0.188695</td>\n",
       "      <td>-0.758872</td>\n",
       "      <td>-0.933237</td>\n",
       "      <td>0.955057</td>\n",
       "      <td>-0.744542</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   W         X         Y         Z  new_column\n",
       "New_Column                                                    \n",
       "AA          2.706850  0.628133  0.907969  0.503826    3.614819\n",
       "BB          0.651118 -0.319318 -0.848077  0.605965   -0.196959\n",
       "CC         -2.018168  0.740122  0.528813 -0.589001   -1.489355\n",
       "DD          0.188695 -0.758872 -0.933237  0.955057   -0.744542"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [],
   "source": [
    "kierunki = ['Data_Science','Data_Science','Data_Science','Infa','Infa','Infa','IBM','IBM','IBM','EleTele','EleTele','EleTele','AiR','AiR','AiR']\n",
    "grupy = [1,2,3,1,2,3,1,2,3,1,2,3]\n",
    "multi_index = list(zip(kierunki,grupy))\n",
    "multi_index = pd.MultiIndex.from_tuples(multi_index)\n",
    "df = pd.DataFrame(np.random.randn(12,2),index = multi_index, columns = ['student','srednia'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(12,2),index = multi_index, columns = ['student','srednia'] )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>srednia</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Data_Science</th>\n",
       "      <th>1</th>\n",
       "      <td>1.025984</td>\n",
       "      <td>-0.156598</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.031579</td>\n",
       "      <td>0.649826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2.154846</td>\n",
       "      <td>-0.610259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Infa</th>\n",
       "      <th>1</th>\n",
       "      <td>-0.755325</td>\n",
       "      <td>-0.346419</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.147027</td>\n",
       "      <td>-0.479448</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.558769</td>\n",
       "      <td>1.024810</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">IBM</th>\n",
       "      <th>1</th>\n",
       "      <td>-0.925874</td>\n",
       "      <td>1.862864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.133817</td>\n",
       "      <td>0.610478</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.386030</td>\n",
       "      <td>2.084019</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">EleTele</th>\n",
       "      <th>1</th>\n",
       "      <td>-0.376519</td>\n",
       "      <td>0.230336</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.681209</td>\n",
       "      <td>1.035125</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-0.031160</td>\n",
       "      <td>1.939932</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 student   srednia\n",
       "Data_Science 1  1.025984 -0.156598\n",
       "             2 -0.031579  0.649826\n",
       "             3  2.154846 -0.610259\n",
       "Infa         1 -0.755325 -0.346419\n",
       "             2  0.147027 -0.479448\n",
       "             3  0.558769  1.024810\n",
       "IBM          1 -0.925874  1.862864\n",
       "             2 -1.133817  0.610478\n",
       "             3  0.386030  2.084019\n",
       "EleTele      1 -0.376519  0.230336\n",
       "             2  0.681209  1.035125\n",
       "             3 -0.031160  1.939932"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index.names = ['kierunek','ID']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "student   -0.755325\n",
       "srednia   -0.346419\n",
       "Name: (Infa, 1), dtype: float64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.xs(('Infa',1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DlugECTS</th>\n",
       "      <th>Student</th>\n",
       "      <th>Uniwerek</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>Tomek</td>\n",
       "      <td>PG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>Hania</td>\n",
       "      <td>PG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>Jan</td>\n",
       "      <td>UG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>Patrycja</td>\n",
       "      <td>UG</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Marek</td>\n",
       "      <td>GUMED</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>3</td>\n",
       "      <td>Ola</td>\n",
       "      <td>GUMED</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   DlugECTS   Student Uniwerek\n",
       "0         2     Tomek       PG\n",
       "1         4     Hania       PG\n",
       "2        16       Jan       UG\n",
       "3         8  Patrycja       UG\n",
       "4         5     Marek    GUMED\n",
       "5         3       Ola    GUMED"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = {'Uniwerek': 'PG PG UG UG GUMED GUMED'.split(),\n",
    "       'Student': 'Tomek Hania Jan Patrycja Marek Ola'.split(),\n",
    "       'DlugECTS': [2,4,16,8,5,3]}\n",
    "df = pd.DataFrame(data)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [],
   "source": [
    "po_uniwerku = df.groupby('Uniwerek')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>DlugECTS</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Uniwerek</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>GUMED</th>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PG</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>UG</th>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          DlugECTS\n",
       "Uniwerek          \n",
       "GUMED            4\n",
       "PG               3\n",
       "UG              12"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "po_uniwerku.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DlugECTS  count    2.000000\n",
       "          mean     3.000000\n",
       "          std      1.414214\n",
       "          min      2.000000\n",
       "          25%      2.500000\n",
       "          50%      3.000000\n",
       "          75%      3.500000\n",
       "          max      4.000000\n",
       "Name: PG, dtype: float64"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "po_uniwerku.describe().transpose()['PG']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {},
   "outputs": [
    {
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       "      <td>NaN</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>10</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A10</td>\n",
       "      <td>B10</td>\n",
       "      <td>C10</td>\n",
       "      <td>D10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A11</td>\n",
       "      <td>B11</td>\n",
       "      <td>C11</td>\n",
       "      <td>D11</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A    B    C    D    A    B    C    D    A    B    C    D\n",
       "0    A0   B0   C0   D0  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "1    A1   B1   C1   D1  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "2    A2   B2   C2   D2  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "3    A3   B3   C3   D3  NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN\n",
       "4   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   A4   B4   C4   D4\n",
       "5   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   A5   B5   C5   D5\n",
       "6   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   A6   B6   C6   D6\n",
       "7   NaN  NaN  NaN  NaN  NaN  NaN  NaN  NaN   A7   B7   C7   D7\n",
       "8   NaN  NaN  NaN  NaN   A8   B8   C8   D8  NaN  NaN  NaN  NaN\n",
       "9   NaN  NaN  NaN  NaN   A9   B9   C9   D9  NaN  NaN  NaN  NaN\n",
       "10  NaN  NaN  NaN  NaN  A10  B10  C10  D10  NaN  NaN  NaN  NaN\n",
       "11  NaN  NaN  NaN  NaN  A11  B11  C11  D11  NaN  NaN  NaN  NaN"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = pd.DataFrame({'A': ['A0', 'A1', 'A2', 'A3'],\n",
    "                        'B': ['B0', 'B1', 'B2', 'B3'],\n",
    "                        'C': ['C0', 'C1', 'C2', 'C3'],\n",
    "                        'D': ['D0', 'D1', 'D2', 'D3']},\n",
    "                        index=[0, 1, 2, 3])\n",
    "\n",
    "df2 = pd.DataFrame({'A': ['A4', 'A5', 'A6', 'A7'],\n",
    "                        'B': ['B4', 'B5', 'B6', 'B7'],\n",
    "                        'C': ['C4', 'C5', 'C6', 'C7'],\n",
    "                        'D': ['D4', 'D5', 'D6', 'D7']},\n",
    "                         index=[4, 5, 6, 7]) \n",
    "\n",
    "df3 = pd.DataFrame({'A': ['A8', 'A9', 'A10', 'A11'],\n",
    "                        'B': ['B8', 'B9', 'B10', 'B11'],\n",
    "                        'C': ['C8', 'C9', 'C10', 'C11'],\n",
    "                        'D': ['D8', 'D9', 'D10', 'D11']},\n",
    "                        index=[8, 9, 10, 11])\n",
    "\n",
    "\n",
    "\n",
    "pd.concat([df1,df3,df2],axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {},
   "outputs": [],
   "source": [
    "left = pd.DataFrame({'key': ['K0', 'K2', 'K2', 'K3'],\n",
    "                     'A': ['A0', 'A1', 'A2', 'A3'],\n",
    "                     'B': ['B0', 'B1', 'B2', 'B3']})\n",
    "   \n",
    "right = pd.DataFrame({'key': ['K0', 'K1', 'K2', 'K3'],\n",
    "                          'C': ['C0', 'C1', 'C2', 'C3'],\n",
    "                          'D': ['D0', 'D1', 'D2', 'D3']})    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
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       "      <th>B</th>\n",
       "      <th>key</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "      <td>K0</td>\n",
       "      <td>C0</td>\n",
       "      <td>D0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>K2</td>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>K2</td>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
       "      <td>K3</td>\n",
       "      <td>C3</td>\n",
       "      <td>D3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>K1</td>\n",
       "      <td>C1</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     A    B key   C   D\n",
       "0   A0   B0  K0  C0  D0\n",
       "1   A1   B1  K2  C2  D2\n",
       "2   A2   B2  K2  C2  D2\n",
       "3   A3   B3  K3  C3  D3\n",
       "4  NaN  NaN  K1  C1  D1"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left,right,how='outer',on = 'key')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "left = pd.DataFrame({'key1': ['K0', 'K0', 'K1', 'K2'],\n",
    "                     'key2': ['K0', 'K1', 'K0', 'K1'],\n",
    "                        'A': ['A0', 'A1', 'A2', 'A3'],\n",
    "                        'B': ['B0', 'B1', 'B2', 'B3']})\n",
    "    \n",
    "right = pd.DataFrame({'key1': ['K0', 'K1', 'K1', 'K2'],\n",
    "                               'key2': ['K0', 'K0', 'K0', 'K0'],\n",
    "                                  'C': ['C0', 'C1', 'C2', 'C3'],\n",
    "                                  'D': ['D0', 'D1', 'D2', 'D3']})\n",
    "                                        "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>key1</th>\n",
       "      <th>key2</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "      <td>K0</td>\n",
       "      <td>K0</td>\n",
       "      <td>C0</td>\n",
       "      <td>D0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>K0</td>\n",
       "      <td>K1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>K1</td>\n",
       "      <td>K0</td>\n",
       "      <td>C1</td>\n",
       "      <td>D1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>K1</td>\n",
       "      <td>K0</td>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>A3</td>\n",
       "      <td>B3</td>\n",
       "      <td>K2</td>\n",
       "      <td>K1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A   B key1 key2    C    D\n",
       "0  A0  B0   K0   K0   C0   D0\n",
       "1  A1  B1   K0   K1  NaN  NaN\n",
       "2  A2  B2   K1   K0   C1   D1\n",
       "3  A2  B2   K1   K0   C2   D2\n",
       "4  A3  B3   K2   K1  NaN  NaN"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(left,right, how = 'left',on = ['key1','key2'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "left = pd.DataFrame({'A': ['A0', 'A1', 'A2'],\n",
    "                     'B': ['B0', 'B1', 'B2']},\n",
    "                      index=['K0', 'K1', 'K2']) \n",
    "\n",
    "right = pd.DataFrame({'C': ['C0', 'C2', 'C3'],\n",
    "                    'D': ['D0', 'D2', 'D3']},\n",
    "                      index=['K0', 'K2', 'K3'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>K0</th>\n",
       "      <td>A0</td>\n",
       "      <td>B0</td>\n",
       "      <td>C0</td>\n",
       "      <td>D0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K1</th>\n",
       "      <td>A1</td>\n",
       "      <td>B1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K2</th>\n",
       "      <td>A2</td>\n",
       "      <td>B2</td>\n",
       "      <td>C2</td>\n",
       "      <td>D2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>K3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>C3</td>\n",
       "      <td>D3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      A    B    C    D\n",
       "K0   A0   B0   C0   D0\n",
       "K1   A1   B1  NaN  NaN\n",
       "K2   A2   B2   C2   D2\n",
       "K3  NaN  NaN   C3   D3"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "left.join(right,how='outer')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {},
   "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",
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       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
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       "      <th></th>\n",
       "      <th>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>444</td>\n",
       "      <td>abc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>555</td>\n",
       "      <td>def</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>666</td>\n",
       "      <td>ghi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "      <td>xyz</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col1  col2 col3\n",
       "0     1   444  abc\n",
       "1     2   555  def\n",
       "2     3   666  ghi\n",
       "3     4   444  xyz"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({'col1':[1,2,3,4],\n",
    "                   'col2':[444,555,666,444],\n",
    "                   'col3':['abc','def','ghi','xyz']})\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {},
   "outputs": [],
   "source": [
    "new_one = df[(df['col1']>2) & (df['col2']==444)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {},
   "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>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>444</td>\n",
       "      <td>xyz</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col1  col2 col3\n",
       "3     4   444  xyz"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "new_one"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {},
   "outputs": [],
   "source": [
    "def times2(x):\n",
    "    return x*2\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['col1'].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [],
   "source": [
    "del df['col1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.sort_values(by='col2',inplace = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "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>col2</th>\n",
       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>444</td>\n",
       "      <td>abc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>444</td>\n",
       "      <td>xyz</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>555</td>\n",
       "      <td>def</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>666</td>\n",
       "      <td>ghi</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col2 col3\n",
       "0   444  abc\n",
       "3   444  xyz\n",
       "1   555  def\n",
       "2   666  ghi"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "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>col2</th>\n",
       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    col2   col3\n",
       "0  False  False\n",
       "3  False  False\n",
       "1  False  False\n",
       "2  False  False"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame({'col1':[1,2,3,np.nan],\n",
    "                   'col2':[np.nan,np.nan,666,444],\n",
    "                   'col3':['abc',np.nan,'ghi','xyz']})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\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>col1</th>\n",
       "      <th>col2</th>\n",
       "      <th>col3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>abc</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3.0</td>\n",
       "      <td>666.0</td>\n",
       "      <td>ghi</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>NaN</td>\n",
       "      <td>444.0</td>\n",
       "      <td>xyz</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   col1   col2 col3\n",
       "0   1.0    NaN  abc\n",
       "1   2.0    NaN  NaN\n",
       "2   3.0  666.0  ghi\n",
       "3   NaN  444.0  xyz"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>3</th>\n",
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      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [0, 1, 2, 3]"
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     },
     "execution_count": 124,
     "metadata": {},
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   ],
   "source": [
    "df.dropna(axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "         col1        col2        col3\n",
       "0           1  FILL VALUE         abc\n",
       "1           2  FILL VALUE  FILL VALUE\n",
       "2           3         666         ghi\n",
       "3  FILL VALUE         444         xyz"
      ]
     },
     "execution_count": 125,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(value='FILL VALUE')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    1.0\n",
       "1    2.0\n",
       "2    3.0\n",
       "3    2.0\n",
       "Name: col1, dtype: float64"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['col1'].fillna(value=df['col1'].mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.loc[df['col2'].isna(),'col2'] = 999"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "   col1   col2 col3\n",
       "0   1.0  999.0  abc\n",
       "1   2.0  999.0  NaN\n",
       "2   3.0  666.0  ghi\n",
       "3   NaN  444.0  xyz"
      ]
     },
     "execution_count": 128,
     "metadata": {},
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    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "C          x    y\n",
       "A   B            \n",
       "bar one  4.0  1.0\n",
       "    two  NaN  5.0\n",
       "foo one  1.0  3.0\n",
       "    two  2.0  NaN"
      ]
     },
     "execution_count": 129,
     "metadata": {},
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   ],
   "source": [
    "data = {'A':['foo','foo','foo','bar','bar','bar'],\n",
    "     'B':['one','one','two','two','one','one'],\n",
    "       'C':['x','y','x','y','x','y'],\n",
    "       'D':[1,3,2,5,4,1]}\n",
    "\n",
    "df = pd.DataFrame(data)\n",
    "\n",
    "df.pivot_table(values = 'D', index = ['A','B'], columns=['C'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
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      "text/plain": [
       "     A    B  C  D\n",
       "0  foo  one  x  1\n",
       "1  foo  one  y  3\n",
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       "3  bar  two  y  5\n",
       "4  bar  one  x  4\n",
       "5  bar  one  y  1"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.arange(16).reshape(4,4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(index = ['0','1', '2', '3'],\n",
    "                  columns = 'a b c d'.split(),\n",
    "                  data = data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 133,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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      "text/plain": [
       "    a   b   c   d\n",
       "0   0   1   2   3\n",
       "1   4   5   6   7\n",
       "2   8   9  10  11\n",
       "3  12  13  14  15"
      ]
     },
     "execution_count": 133,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_html('https://www.premierleague.com/matchweek/3291/table')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Position</th>\n",
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       "      <th>GD</th>\n",
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       "      <td>1</td>\n",
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       "      <td>79</td>\n",
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       "      <td>Spurs</td>\n",
       "      <td>32</td>\n",
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       "      <td>63</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>Chelsea</td>\n",
       "      <td>32</td>\n",
       "      <td>21</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>Man Utd</td>\n",
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       "      <td>18</td>\n",
       "      <td>61</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
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       "      <td>32</td>\n",
       "      <td>1</td>\n",
       "      <td>47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>Watford</td>\n",
       "      <td>32</td>\n",
       "      <td>0</td>\n",
       "      <td>46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>Leicester</td>\n",
       "      <td>32</td>\n",
       "      <td>-1</td>\n",
       "      <td>44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>Everton</td>\n",
       "      <td>32</td>\n",
       "      <td>3</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>West Ham</td>\n",
       "      <td>32</td>\n",
       "      <td>-7</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>AFC Bournemouth</td>\n",
       "      <td>32</td>\n",
       "      <td>-15</td>\n",
       "      <td>38</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>Crystal Palace</td>\n",
       "      <td>32</td>\n",
       "      <td>-5</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>Newcastle</td>\n",
       "      <td>32</td>\n",
       "      <td>-11</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>Brighton</td>\n",
       "      <td>31</td>\n",
       "      <td>-14</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>Southampton</td>\n",
       "      <td>31</td>\n",
       "      <td>-15</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>Burnley</td>\n",
       "      <td>32</td>\n",
       "      <td>-22</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>Cardiff</td>\n",
       "      <td>32</td>\n",
       "      <td>-33</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>Fulham</td>\n",
       "      <td>33</td>\n",
       "      <td>-46</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>Huddersfield</td>\n",
       "      <td>32</td>\n",
       "      <td>-41</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    Position             Club  Pl  GD  Pts\n",
       "0          1         Man City  32  62   80\n",
       "1          2        Liverpool  32  53   79\n",
       "2          3            Spurs  32  26   64\n",
       "3          4          Arsenal  31  26   63\n",
       "4          5          Chelsea  32  21   63\n",
       "5          6          Man Utd  32  18   61\n",
       "6          7           Wolves  32   1   47\n",
       "7          8          Watford  32   0   46\n",
       "8          9        Leicester  32  -1   44\n",
       "9         10          Everton  32   3   43\n",
       "10        11         West Ham  32  -7   42\n",
       "11        12  AFC Bournemouth  32 -15   38\n",
       "12        13   Crystal Palace  32  -5   36\n",
       "13        14        Newcastle  32 -11   35\n",
       "14        15         Brighton  31 -14   33\n",
       "15        16      Southampton  31 -15   33\n",
       "16        17          Burnley  32 -22   33\n",
       "17        18          Cardiff  32 -33   28\n",
       "18        19           Fulham  33 -46   17\n",
       "19        20     Huddersfield  32 -41   14"
      ]
     },
     "execution_count": 135,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "ecom = pd.read_csv('Ecommerce Purchases')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        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>Address</th>\n",
       "      <th>Lot</th>\n",
       "      <th>AM or PM</th>\n",
       "      <th>Browser Info</th>\n",
       "      <th>Company</th>\n",
       "      <th>Credit Card</th>\n",
       "      <th>CC Exp Date</th>\n",
       "      <th>CC Security Code</th>\n",
       "      <th>CC Provider</th>\n",
       "      <th>Email</th>\n",
       "      <th>Job</th>\n",
       "      <th>IP Address</th>\n",
       "      <th>Language</th>\n",
       "      <th>Purchase Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16629 Pace Camp Apt. 448\\nAlexisborough, NE 77...</td>\n",
       "      <td>46 in</td>\n",
       "      <td>PM</td>\n",
       "      <td>Opera/9.56.(X11; Linux x86_64; sl-SI) Presto/2...</td>\n",
       "      <td>Martinez-Herman</td>\n",
       "      <td>6011929061123406</td>\n",
       "      <td>02/20</td>\n",
       "      <td>900</td>\n",
       "      <td>JCB 16 digit</td>\n",
       "      <td>pdunlap@yahoo.com</td>\n",
       "      <td>Scientist, product/process development</td>\n",
       "      <td>149.146.147.205</td>\n",
       "      <td>el</td>\n",
       "      <td>98.14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>9374 Jasmine Spurs Suite 508\\nSouth John, TN 8...</td>\n",
       "      <td>28 rn</td>\n",
       "      <td>PM</td>\n",
       "      <td>Opera/8.93.(Windows 98; Win 9x 4.90; en-US) Pr...</td>\n",
       "      <td>Fletcher, Richards and Whitaker</td>\n",
       "      <td>3337758169645356</td>\n",
       "      <td>11/18</td>\n",
       "      <td>561</td>\n",
       "      <td>Mastercard</td>\n",
       "      <td>anthony41@reed.com</td>\n",
       "      <td>Drilling engineer</td>\n",
       "      <td>15.160.41.51</td>\n",
       "      <td>fr</td>\n",
       "      <td>70.73</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Unit 0065 Box 5052\\nDPO AP 27450</td>\n",
       "      <td>94 vE</td>\n",
       "      <td>PM</td>\n",
       "      <td>Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ...</td>\n",
       "      <td>Simpson, Williams and Pham</td>\n",
       "      <td>675957666125</td>\n",
       "      <td>08/19</td>\n",
       "      <td>699</td>\n",
       "      <td>JCB 16 digit</td>\n",
       "      <td>amymiller@morales-harrison.com</td>\n",
       "      <td>Customer service manager</td>\n",
       "      <td>132.207.160.22</td>\n",
       "      <td>de</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7780 Julia Fords\\nNew Stacy, WA 45798</td>\n",
       "      <td>36 vm</td>\n",
       "      <td>PM</td>\n",
       "      <td>Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0 ...</td>\n",
       "      <td>Williams, Marshall and Buchanan</td>\n",
       "      <td>6011578504430710</td>\n",
       "      <td>02/24</td>\n",
       "      <td>384</td>\n",
       "      <td>Discover</td>\n",
       "      <td>brent16@olson-robinson.info</td>\n",
       "      <td>Drilling engineer</td>\n",
       "      <td>30.250.74.19</td>\n",
       "      <td>es</td>\n",
       "      <td>78.04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>23012 Munoz Drive Suite 337\\nNew Cynthia, TX 5...</td>\n",
       "      <td>20 IE</td>\n",
       "      <td>AM</td>\n",
       "      <td>Opera/9.58.(X11; Linux x86_64; it-IT) Presto/2...</td>\n",
       "      <td>Brown, Watson and Andrews</td>\n",
       "      <td>6011456623207998</td>\n",
       "      <td>10/25</td>\n",
       "      <td>678</td>\n",
       "      <td>Diners Club / Carte Blanche</td>\n",
       "      <td>christopherwright@gmail.com</td>\n",
       "      <td>Fine artist</td>\n",
       "      <td>24.140.33.94</td>\n",
       "      <td>es</td>\n",
       "      <td>77.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                             Address    Lot AM or PM  \\\n",
       "0  16629 Pace Camp Apt. 448\\nAlexisborough, NE 77...  46 in       PM   \n",
       "1  9374 Jasmine Spurs Suite 508\\nSouth John, TN 8...  28 rn       PM   \n",
       "2                   Unit 0065 Box 5052\\nDPO AP 27450  94 vE       PM   \n",
       "3              7780 Julia Fords\\nNew Stacy, WA 45798  36 vm       PM   \n",
       "4  23012 Munoz Drive Suite 337\\nNew Cynthia, TX 5...  20 IE       AM   \n",
       "\n",
       "                                        Browser Info  \\\n",
       "0  Opera/9.56.(X11; Linux x86_64; sl-SI) Presto/2...   \n",
       "1  Opera/8.93.(Windows 98; Win 9x 4.90; en-US) Pr...   \n",
       "2  Mozilla/5.0 (compatible; MSIE 9.0; Windows NT ...   \n",
       "3  Mozilla/5.0 (Macintosh; Intel Mac OS X 10_8_0 ...   \n",
       "4  Opera/9.58.(X11; Linux x86_64; it-IT) Presto/2...   \n",
       "\n",
       "                           Company       Credit Card CC Exp Date  \\\n",
       "0                  Martinez-Herman  6011929061123406       02/20   \n",
       "1  Fletcher, Richards and Whitaker  3337758169645356       11/18   \n",
       "2       Simpson, Williams and Pham      675957666125       08/19   \n",
       "3  Williams, Marshall and Buchanan  6011578504430710       02/24   \n",
       "4        Brown, Watson and Andrews  6011456623207998       10/25   \n",
       "\n",
       "   CC Security Code                  CC Provider  \\\n",
       "0               900                 JCB 16 digit   \n",
       "1               561                   Mastercard   \n",
       "2               699                 JCB 16 digit   \n",
       "3               384                     Discover   \n",
       "4               678  Diners Club / Carte Blanche   \n",
       "\n",
       "                            Email                                     Job  \\\n",
       "0               pdunlap@yahoo.com  Scientist, product/process development   \n",
       "1              anthony41@reed.com                       Drilling engineer   \n",
       "2  amymiller@morales-harrison.com                Customer service manager   \n",
       "3     brent16@olson-robinson.info                       Drilling engineer   \n",
       "4     christopherwright@gmail.com                             Fine artist   \n",
       "\n",
       "        IP Address Language  Purchase Price  \n",
       "0  149.146.147.205       el           98.14  \n",
       "1     15.160.41.51       fr           70.73  \n",
       "2   132.207.160.22       de            0.95  \n",
       "3     30.250.74.19       es           78.04  \n",
       "4     24.140.33.94       es           77.82  "
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ecom.head()"
   ]
  },
  {
   "cell_type": "code",
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