w2_d3-numpy,pandas.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# numerical python\n",
    "# intro numpy\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.81805331, 0.00131026, 0.38445213, 0.36135483],\n",
       "       [0.02598711, 0.32775142, 0.7944947 , 0.06603137],\n",
       "       [0.86200367, 0.29047727, 0.28350181, 0.39170528],\n",
       "       [0.24803027, 0.43448069, 0.50095574, 0.65057252],\n",
       "       [0.03564857, 0.44462084, 0.51369448, 0.64486957],\n",
       "       [0.61117216, 0.03806863, 0.80877654, 0.60618041],\n",
       "       [0.52355301, 0.07983054, 0.56153577, 0.6702878 ],\n",
       "       [0.81861034, 0.06097769, 0.35633461, 0.88199294],\n",
       "       [0.99543901, 0.0487111 , 0.63938097, 0.84863154],\n",
       "       [0.29392857, 0.6100925 , 0.72360484, 0.76734773]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a=np.random.random((10,4))\n",
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 4)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.38617133, 0.49583158],\n",
       "        [0.65556131, 0.22236554],\n",
       "        [0.81900969, 0.05993894]],\n",
       "\n",
       "       [[0.34847829, 0.02002571],\n",
       "        [0.188309  , 0.00148331],\n",
       "        [0.87704838, 0.12667634]],\n",
       "\n",
       "       [[0.27349373, 0.25877654],\n",
       "        [0.69283863, 0.91265228],\n",
       "        [0.7348062 , 0.35393443]],\n",
       "\n",
       "       [[0.50510289, 0.69811121],\n",
       "        [0.94068754, 0.19399909],\n",
       "        [0.74421527, 0.00998148]],\n",
       "\n",
       "       [[0.72090576, 0.77391406],\n",
       "        [0.64214939, 0.85171128],\n",
       "        [0.92807408, 0.58534788]]])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b=np.random.random((5,3,2))\n",
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 3, 2)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "30"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[0.4319117 , 0.54720836, 0.81942646, 0.86468318, 0.64343912],\n",
       "         [0.78733071, 0.35477817, 0.61622504, 0.92651945, 0.7409542 ],\n",
       "         [0.50397198, 0.16562731, 0.48498944, 0.45011381, 0.02216435],\n",
       "         [0.07230035, 0.00708465, 0.33736293, 0.16397362, 0.72320998]],\n",
       "\n",
       "        [[0.16504132, 0.23208632, 0.8219859 , 0.2395323 , 0.99825774],\n",
       "         [0.25788903, 0.86204471, 0.13663858, 0.37540695, 0.62114172],\n",
       "         [0.52636443, 0.0598118 , 0.68594944, 0.10713766, 0.80859816],\n",
       "         [0.80819313, 0.00539469, 0.22739683, 0.89731546, 0.25666523]],\n",
       "\n",
       "        [[0.22387244, 0.7830175 , 0.31577628, 0.67193488, 0.95602027],\n",
       "         [0.66026131, 0.20679455, 0.64735723, 0.63235128, 0.44253174],\n",
       "         [0.21811835, 0.93651399, 0.0440713 , 0.107314  , 0.87280986],\n",
       "         [0.12900406, 0.93639479, 0.55405933, 0.60809647, 0.55350206]]],\n",
       "\n",
       "\n",
       "       [[[0.78610239, 0.23258523, 0.83445931, 0.95502782, 0.21256326],\n",
       "         [0.37373718, 0.26570886, 0.16744959, 0.64047826, 0.53999186],\n",
       "         [0.0525771 , 0.21965873, 0.82752951, 0.6117277 , 0.01900437],\n",
       "         [0.15957123, 0.67285616, 0.28997401, 0.12198459, 0.62184131]],\n",
       "\n",
       "        [[0.09094832, 0.97296939, 0.55740973, 0.36223865, 0.82586056],\n",
       "         [0.26012144, 0.18183821, 0.10038432, 0.39855786, 0.00749757],\n",
       "         [0.15081168, 0.8161303 , 0.52962832, 0.18342545, 0.259588  ],\n",
       "         [0.3969853 , 0.19275119, 0.62668936, 0.0403198 , 0.64452374]],\n",
       "\n",
       "        [[0.51209464, 0.9287911 , 0.72493376, 0.15635718, 0.70768161],\n",
       "         [0.9927604 , 0.05369321, 0.69230072, 0.2139759 , 0.55179432],\n",
       "         [0.33215515, 0.20648645, 0.11225379, 0.60402326, 0.94169845],\n",
       "         [0.86477003, 0.50793142, 0.67844136, 0.41283173, 0.46363874]]]])"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c=np.random.random((2,3,4,5))\n",
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.81805331, 0.00131026, 0.38445213, 0.36135483],\n",
       "       [0.02598711, 0.32775142, 0.7944947 , 0.06603137],\n",
       "       [0.86200367, 0.29047727, 0.28350181, 0.39170528],\n",
       "       [0.24803027, 0.43448069, 0.50095574, 0.65057252],\n",
       "       [0.03564857, 0.44462084, 0.51369448, 0.64486957],\n",
       "       [0.61117216, 0.03806863, 0.80877654, 0.60618041],\n",
       "       [0.52355301, 0.07983054, 0.56153577, 0.6702878 ],\n",
       "       [0.81861034, 0.06097769, 0.35633461, 0.88199294],\n",
       "       [0.99543901, 0.0487111 , 0.63938097, 0.84863154],\n",
       "       [0.29392857, 0.6100925 , 0.72360484, 0.76734773]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81805331, 0.00131026, 0.38445213, 0.36135483])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.81805331, 0.02598711, 0.86200367, 0.24803027, 0.03564857,\n",
       "       0.61117216, 0.52355301, 0.81861034, 0.99543901, 0.29392857])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.513694481889146"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[4,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[0.4319117 , 0.54720836, 0.81942646, 0.86468318, 0.64343912],\n",
       "         [0.78733071, 0.35477817, 0.61622504, 0.92651945, 0.7409542 ],\n",
       "         [0.50397198, 0.16562731, 0.48498944, 0.45011381, 0.02216435],\n",
       "         [0.07230035, 0.00708465, 0.33736293, 0.16397362, 0.72320998]],\n",
       "\n",
       "        [[0.16504132, 0.23208632, 0.8219859 , 0.2395323 , 0.99825774],\n",
       "         [0.25788903, 0.86204471, 0.13663858, 0.37540695, 0.62114172],\n",
       "         [0.52636443, 0.0598118 , 0.68594944, 0.10713766, 0.80859816],\n",
       "         [0.80819313, 0.00539469, 0.22739683, 0.89731546, 0.25666523]],\n",
       "\n",
       "        [[0.22387244, 0.7830175 , 0.31577628, 0.67193488, 0.95602027],\n",
       "         [0.66026131, 0.20679455, 0.64735723, 0.63235128, 0.44253174],\n",
       "         [0.21811835, 0.93651399, 0.0440713 , 0.107314  , 0.87280986],\n",
       "         [0.12900406, 0.93639479, 0.55405933, 0.60809647, 0.55350206]]],\n",
       "\n",
       "\n",
       "       [[[0.78610239, 0.23258523, 0.83445931, 0.95502782, 0.21256326],\n",
       "         [0.37373718, 0.26570886, 0.16744959, 0.64047826, 0.53999186],\n",
       "         [0.0525771 , 0.21965873, 0.82752951, 0.6117277 , 0.01900437],\n",
       "         [0.15957123, 0.67285616, 0.28997401, 0.12198459, 0.62184131]],\n",
       "\n",
       "        [[0.09094832, 0.97296939, 0.55740973, 0.36223865, 0.82586056],\n",
       "         [0.26012144, 0.18183821, 0.10038432, 0.39855786, 0.00749757],\n",
       "         [0.15081168, 0.8161303 , 0.52962832, 0.18342545, 0.259588  ],\n",
       "         [0.3969853 , 0.19275119, 0.62668936, 0.0403198 , 0.64452374]],\n",
       "\n",
       "        [[0.51209464, 0.9287911 , 0.72493376, 0.15635718, 0.70768161],\n",
       "         [0.9927604 , 0.05369321, 0.69230072, 0.2139759 , 0.55179432],\n",
       "         [0.33215515, 0.20648645, 0.11225379, 0.60402326, 0.94169845],\n",
       "         [0.86477003, 0.50793142, 0.67844136, 0.41283173, 0.46363874]]]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3, 4, 5)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.4319117 , 0.54720836, 0.81942646, 0.86468318, 0.64343912],\n",
       "        [0.78733071, 0.35477817, 0.61622504, 0.92651945, 0.7409542 ],\n",
       "        [0.50397198, 0.16562731, 0.48498944, 0.45011381, 0.02216435],\n",
       "        [0.07230035, 0.00708465, 0.33736293, 0.16397362, 0.72320998]],\n",
       "\n",
       "       [[0.16504132, 0.23208632, 0.8219859 , 0.2395323 , 0.99825774],\n",
       "        [0.25788903, 0.86204471, 0.13663858, 0.37540695, 0.62114172],\n",
       "        [0.52636443, 0.0598118 , 0.68594944, 0.10713766, 0.80859816],\n",
       "        [0.80819313, 0.00539469, 0.22739683, 0.89731546, 0.25666523]],\n",
       "\n",
       "       [[0.22387244, 0.7830175 , 0.31577628, 0.67193488, 0.95602027],\n",
       "        [0.66026131, 0.20679455, 0.64735723, 0.63235128, 0.44253174],\n",
       "        [0.21811835, 0.93651399, 0.0440713 , 0.107314  , 0.87280986],\n",
       "        [0.12900406, 0.93639479, 0.55405933, 0.60809647, 0.55350206]]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.16504132, 0.23208632, 0.8219859 , 0.2395323 , 0.99825774],\n",
       "       [0.25788903, 0.86204471, 0.13663858, 0.37540695, 0.62114172],\n",
       "       [0.52636443, 0.0598118 , 0.68594944, 0.10713766, 0.80859816],\n",
       "       [0.80819313, 0.00539469, 0.22739683, 0.89731546, 0.25666523]])"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.16504132, 0.23208632, 0.8219859 , 0.2395323 , 0.99825774])"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[0,1,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2320863154198869"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c[0,1,0,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst_lst=[[1,2,3], [4,5,6], [7,8,9]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 4, 7]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d2=[e[0] for e in lst_lst]\n",
    "d2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [7, 8, 9]])"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d=np.array(lst_lst)\n",
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 4, 7])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d[:,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[0.81805331 0.00131026 0.38445213 0.36135483]\n",
      " [0.02598711 0.32775142 0.7944947  0.06603137]\n",
      " [0.86200367 0.29047727 0.28350181 0.39170528]\n",
      " [0.24803027 0.43448069 0.50095574 0.65057252]\n",
      " [0.03564857 0.44462084 0.51369448 0.64486957]\n",
      " [0.61117216 0.03806863 0.80877654 0.60618041]\n",
      " [0.52355301 0.07983054 0.56153577 0.6702878 ]\n",
      " [0.81861034 0.06097769 0.35633461 0.88199294]\n",
      " [0.99543901 0.0487111  0.63938097 0.84863154]\n",
      " [0.29392857 0.6100925  0.72360484 0.76734773]]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "19.024452498101795"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print (a)\n",
    "np.sum(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5.232426  , 2.33632093, 5.56673159, 5.88897398])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(a, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.56517052, 1.2142646 , 1.82768803, 1.83403921, 1.63883346,\n",
       "       2.06419773, 1.83520712, 2.11791557, 2.53216262, 2.39497364])"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(a, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[0.38617133, 0.49583158],\n",
       "        [0.65556131, 0.22236554],\n",
       "        [0.81900969, 0.05993894]],\n",
       "\n",
       "       [[0.34847829, 0.02002571],\n",
       "        [0.188309  , 0.00148331],\n",
       "        [0.87704838, 0.12667634]],\n",
       "\n",
       "       [[0.27349373, 0.25877654],\n",
       "        [0.69283863, 0.91265228],\n",
       "        [0.7348062 , 0.35393443]],\n",
       "\n",
       "       [[0.50510289, 0.69811121],\n",
       "        [0.94068754, 0.19399909],\n",
       "        [0.74421527, 0.00998148]],\n",
       "\n",
       "       [[0.72090576, 0.77391406],\n",
       "        [0.64214939, 0.85171128],\n",
       "        [0.92807408, 0.58534788]]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3, 2)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "b[:2].shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4.20089940933224"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(b[:2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.47561131245254484"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.47561131245254484"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sum(a)/a.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.5232426 , 0.23363209, 0.55667316, 0.5888974 ])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(a, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.39129263, 0.30356615, 0.45692201, 0.4585098 , 0.40970836,\n",
       "       0.51604943, 0.45880178, 0.52947889, 0.63304065, 0.59874341])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(a, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.random.random((2,3))\n",
    "y=np.random.random((2,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.42149101, 0.51555467, 0.05789328],\n",
       "       [0.56992372, 0.44140933, 0.38511909]])"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.5556223 , 0.10149137, 0.64052922],\n",
       "       [0.59902501, 0.99363723, 0.12471804]])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.9771133 , 0.61704604, 0.6984225 ],\n",
       "       [1.16894873, 1.43504656, 0.50983713]])"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.add(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-0.13413129,  0.4140633 , -0.58263594],\n",
       "       [-0.0291013 , -0.55222789,  0.26040105]])"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.subtract(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.2341898 , 0.05232435, 0.03708234],\n",
       "       [0.34139856, 0.43860075, 0.0480313 ]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.multiply(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.75859269, 5.0797882 , 0.09038351],\n",
       "       [0.9514189 , 0.44423591, 3.08791809]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.divide(x,y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.2837741629611615"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.33659373, 0.20458431, 0.17441772, 0.23730158])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(a, axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.28946243, 0.30621425, 0.23776185, 0.14454133, 0.22762552,\n",
       "       0.28780494, 0.22532895, 0.33813291, 0.36031001, 0.18510719])"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.std(a, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.81805331,  0.81936357,  1.20381569,  1.56517052,  1.59115763,\n",
       "        1.91890905,  2.71340375,  2.77943512,  3.64143879,  3.93191606,\n",
       "        4.21541787,  4.60712314,  4.85515341,  5.2896341 ,  5.79058984,\n",
       "        6.44116236,  6.47681093,  6.92143177,  7.43512625,  8.07999582,\n",
       "        8.69116797,  8.7292366 ,  9.53801314, 10.14419355, 10.66774656,\n",
       "       10.7475771 , 11.30911287, 11.97940067, 12.79801101, 12.85898869,\n",
       "       13.21532331, 14.09731624, 15.09275525, 15.14146635, 15.78084732,\n",
       "       16.62947886, 16.92340743, 17.53349992, 18.25710476, 19.0244525 ])"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cumsum(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([8.18053311e-01, 1.07186033e-03, 4.12078982e-04, 1.48906729e-04,\n",
       "       3.86965512e-06, 1.26828498e-06, 1.00764569e-06, 6.65362268e-08,\n",
       "       5.73544719e-08, 1.66601702e-08, 4.72318841e-09, 1.85009782e-09,\n",
       "       4.58880252e-10, 1.99374607e-10, 9.98778545e-11, 6.49777873e-11,\n",
       "       2.31636504e-12, 1.02990418e-12, 5.29056094e-13, 3.41172173e-13,\n",
       "       2.08514933e-13, 7.93787710e-15, 6.41996877e-15, 3.89165929e-15,\n",
       "       2.03748994e-15, 1.62653918e-16, 9.13359937e-17, 6.12214024e-17,\n",
       "       5.01164729e-17, 3.05598654e-18, 1.08895378e-18, 9.60449538e-19,\n",
       "       9.56068935e-19, 4.65711682e-20, 2.97767186e-20, 2.52694626e-20,\n",
       "       7.42741695e-21, 4.53141137e-21, 3.27895120e-21, 2.51609577e-21])"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.cumprod(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(10, 4)"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(40,)"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.flatten().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3, 4, 5)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(120,)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.flatten().shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9954390075374463"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0013102573081298363"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[[0.4319117 , 0.54720836, 0.81942646],\n",
       "         [0.86468318, 0.64343912, 0.78733071]],\n",
       "\n",
       "        [[0.35477817, 0.61622504, 0.92651945],\n",
       "         [0.7409542 , 0.50397198, 0.16562731]],\n",
       "\n",
       "        [[0.48498944, 0.45011381, 0.02216435],\n",
       "         [0.07230035, 0.00708465, 0.33736293]],\n",
       "\n",
       "        [[0.16397362, 0.72320998, 0.16504132],\n",
       "         [0.23208632, 0.8219859 , 0.2395323 ]],\n",
       "\n",
       "        [[0.99825774, 0.25788903, 0.86204471],\n",
       "         [0.13663858, 0.37540695, 0.62114172]]],\n",
       "\n",
       "\n",
       "       [[[0.52636443, 0.0598118 , 0.68594944],\n",
       "         [0.10713766, 0.80859816, 0.80819313]],\n",
       "\n",
       "        [[0.00539469, 0.22739683, 0.89731546],\n",
       "         [0.25666523, 0.22387244, 0.7830175 ]],\n",
       "\n",
       "        [[0.31577628, 0.67193488, 0.95602027],\n",
       "         [0.66026131, 0.20679455, 0.64735723]],\n",
       "\n",
       "        [[0.63235128, 0.44253174, 0.21811835],\n",
       "         [0.93651399, 0.0440713 , 0.107314  ]],\n",
       "\n",
       "        [[0.87280986, 0.12900406, 0.93639479],\n",
       "         [0.55405933, 0.60809647, 0.55350206]]],\n",
       "\n",
       "\n",
       "       [[[0.78610239, 0.23258523, 0.83445931],\n",
       "         [0.95502782, 0.21256326, 0.37373718]],\n",
       "\n",
       "        [[0.26570886, 0.16744959, 0.64047826],\n",
       "         [0.53999186, 0.0525771 , 0.21965873]],\n",
       "\n",
       "        [[0.82752951, 0.6117277 , 0.01900437],\n",
       "         [0.15957123, 0.67285616, 0.28997401]],\n",
       "\n",
       "        [[0.12198459, 0.62184131, 0.09094832],\n",
       "         [0.97296939, 0.55740973, 0.36223865]],\n",
       "\n",
       "        [[0.82586056, 0.26012144, 0.18183821],\n",
       "         [0.10038432, 0.39855786, 0.00749757]]],\n",
       "\n",
       "\n",
       "       [[[0.15081168, 0.8161303 , 0.52962832],\n",
       "         [0.18342545, 0.259588  , 0.3969853 ]],\n",
       "\n",
       "        [[0.19275119, 0.62668936, 0.0403198 ],\n",
       "         [0.64452374, 0.51209464, 0.9287911 ]],\n",
       "\n",
       "        [[0.72493376, 0.15635718, 0.70768161],\n",
       "         [0.9927604 , 0.05369321, 0.69230072]],\n",
       "\n",
       "        [[0.2139759 , 0.55179432, 0.33215515],\n",
       "         [0.20648645, 0.11225379, 0.60402326]],\n",
       "\n",
       "        [[0.94169845, 0.86477003, 0.50793142],\n",
       "         [0.67844136, 0.41283173, 0.46363874]]]])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.reshape((4,5,2,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "list"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(a.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([0.42149101, 0.51555467, 0.05789328]),\n",
       " array([0.5556223 , 0.10149137, 0.64052922]))"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(x[0], y[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.323596489499722"
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(x[0], y[0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.32359649, 0.77197831],\n",
       "       [0.60814159, 0.8280306 ]])"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.matmul(x,y.T)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.42149101, 0.51555467, 0.05789328],\n",
       "       [0.56992372, 0.44140933, 0.38511909]])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.42149101, 0.56992372],\n",
       "       [0.51555467, 0.44140933],\n",
       "       [0.05789328, 0.38511909]])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.42149101, 0.56992372],\n",
       "       [0.51555467, 0.44140933],\n",
       "       [0.05789328, 0.38511909]])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3, 4, 5)"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(5, 4, 3, 2)"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "c.T.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.08052777556430782"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [],
   "source": [
    "z=np.random.random((3,3,3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.30865043,  0.08375861, -0.28404425])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.linalg.det(z)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://docs.scipy.org/doc/numpy-1.14.0/reference/generated/numpy.ndarray.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0.],\n",
       "       [0., 0.]])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((2,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 1.],\n",
       "       [1., 1.]])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.ones((2,2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 0., 0., 0.],\n",
       "       [0., 1., 0., 0.],\n",
       "       [0., 0., 1., 0.],\n",
       "       [0., 0., 0., 1.]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.eye(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    0.736188\n",
       "1    0.820570\n",
       "2    0.712149\n",
       "3    0.405599\n",
       "4    0.780832\n",
       "5    0.335020\n",
       "6    0.971200\n",
       "7    0.433387\n",
       "8    0.631416\n",
       "9    0.408595\n",
       "dtype: float64"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s=pd.Series(np.random.random(10))\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.736188\n",
       "b    0.820570\n",
       "c    0.712149\n",
       "d    0.405599\n",
       "e    0.780832\n",
       "f    0.335020\n",
       "g    0.971200\n",
       "h    0.433387\n",
       "i    0.631416\n",
       "j    0.408595\n",
       "dtype: float64"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s.index=['a','b','c','d', 'e', 'f','g','h','i','j']\n",
    "s"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Col5</th>\n",
       "    </tr>\n",
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       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>0.855014</td>\n",
       "      <td>0.273018</td>\n",
       "      <td>0.550010</td>\n",
       "      <td>0.421354</td>\n",
       "      <td>0.487759</td>\n",
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       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>0.387329</td>\n",
       "      <td>0.583692</td>\n",
       "      <td>0.879277</td>\n",
       "      <td>0.852492</td>\n",
       "      <td>0.968689</td>\n",
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       "      <td>2</td>\n",
       "      <td>0.103042</td>\n",
       "      <td>0.479367</td>\n",
       "      <td>0.686740</td>\n",
       "      <td>0.197307</td>\n",
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       "      <td>0.503015</td>\n",
       "      <td>0.570675</td>\n",
       "      <td>0.144457</td>\n",
       "      <td>0.861365</td>\n",
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       "      <td>0.876827</td>\n",
       "      <td>0.279039</td>\n",
       "      <td>0.832960</td>\n",
       "      <td>0.358493</td>\n",
       "      <td>0.549032</td>\n",
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       "      <td>5</td>\n",
       "      <td>0.510392</td>\n",
       "      <td>0.288067</td>\n",
       "      <td>0.700452</td>\n",
       "      <td>0.254227</td>\n",
       "      <td>0.262606</td>\n",
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       "    <tr>\n",
       "      <td>6</td>\n",
       "      <td>0.859924</td>\n",
       "      <td>0.948815</td>\n",
       "      <td>0.795078</td>\n",
       "      <td>0.936383</td>\n",
       "      <td>0.416116</td>\n",
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       "      <td>7</td>\n",
       "      <td>0.280786</td>\n",
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       "      <td>0.847269</td>\n",
       "      <td>0.162491</td>\n",
       "      <td>0.447651</td>\n",
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       "      <td>8</td>\n",
       "      <td>0.194424</td>\n",
       "      <td>0.959520</td>\n",
       "      <td>0.613985</td>\n",
       "      <td>0.588174</td>\n",
       "      <td>0.594704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>9</td>\n",
       "      <td>0.643705</td>\n",
       "      <td>0.278851</td>\n",
       "      <td>0.508543</td>\n",
       "      <td>0.071959</td>\n",
       "      <td>0.965102</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Col1      Col2      Col3      Col4      Col5\n",
       "0  0.855014  0.273018  0.550010  0.421354  0.487759\n",
       "1  0.387329  0.583692  0.879277  0.852492  0.968689\n",
       "2  0.103042  0.479367  0.686740  0.197307  0.422101\n",
       "3  0.503015  0.570675  0.144457  0.861365  0.603069\n",
       "4  0.876827  0.279039  0.832960  0.358493  0.549032\n",
       "5  0.510392  0.288067  0.700452  0.254227  0.262606\n",
       "6  0.859924  0.948815  0.795078  0.936383  0.416116\n",
       "7  0.280786  0.757206  0.847269  0.162491  0.447651\n",
       "8  0.194424  0.959520  0.613985  0.588174  0.594704\n",
       "9  0.643705  0.278851  0.508543  0.071959  0.965102"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columnas=['Col1','Col2','Col3','Col4','Col5']\n",
    "df=pd.DataFrame(np.random.random((10,5)), columns=columnas)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>Col5</th>\n",
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       "      <td>0.855014</td>\n",
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       "      <td>0.421354</td>\n",
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       "      <td>b</td>\n",
       "      <td>0.387329</td>\n",
       "      <td>0.583692</td>\n",
       "      <td>0.879277</td>\n",
       "      <td>0.852492</td>\n",
       "      <td>0.968689</td>\n",
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       "    <tr>\n",
       "      <td>c</td>\n",
       "      <td>0.103042</td>\n",
       "      <td>0.479367</td>\n",
       "      <td>0.686740</td>\n",
       "      <td>0.197307</td>\n",
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       "      <td>0.861365</td>\n",
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       "      <td>0.279039</td>\n",
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       "      <td>0.358493</td>\n",
       "      <td>0.549032</td>\n",
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       "    <tr>\n",
       "      <td>f</td>\n",
       "      <td>0.510392</td>\n",
       "      <td>0.288067</td>\n",
       "      <td>0.700452</td>\n",
       "      <td>0.254227</td>\n",
       "      <td>0.262606</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>g</td>\n",
       "      <td>0.859924</td>\n",
       "      <td>0.948815</td>\n",
       "      <td>0.795078</td>\n",
       "      <td>0.936383</td>\n",
       "      <td>0.416116</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>h</td>\n",
       "      <td>0.280786</td>\n",
       "      <td>0.757206</td>\n",
       "      <td>0.847269</td>\n",
       "      <td>0.162491</td>\n",
       "      <td>0.447651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>i</td>\n",
       "      <td>0.194424</td>\n",
       "      <td>0.959520</td>\n",
       "      <td>0.613985</td>\n",
       "      <td>0.588174</td>\n",
       "      <td>0.594704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>j</td>\n",
       "      <td>0.643705</td>\n",
       "      <td>0.278851</td>\n",
       "      <td>0.508543</td>\n",
       "      <td>0.071959</td>\n",
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      ],
      "text/plain": [
       "       Col1      Col2      Col3      Col4      Col5\n",
       "a  0.855014  0.273018  0.550010  0.421354  0.487759\n",
       "b  0.387329  0.583692  0.879277  0.852492  0.968689\n",
       "c  0.103042  0.479367  0.686740  0.197307  0.422101\n",
       "d  0.503015  0.570675  0.144457  0.861365  0.603069\n",
       "e  0.876827  0.279039  0.832960  0.358493  0.549032\n",
       "f  0.510392  0.288067  0.700452  0.254227  0.262606\n",
       "g  0.859924  0.948815  0.795078  0.936383  0.416116\n",
       "h  0.280786  0.757206  0.847269  0.162491  0.447651\n",
       "i  0.194424  0.959520  0.613985  0.588174  0.594704\n",
       "j  0.643705  0.278851  0.508543  0.071959  0.965102"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.index=['a','b','c','d', 'e', 'f','g','h','i','j']\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.855014\n",
       "b    0.387329\n",
       "c    0.103042\n",
       "d    0.503015\n",
       "e    0.876827\n",
       "f    0.510392\n",
       "g    0.859924\n",
       "h    0.280786\n",
       "i    0.194424\n",
       "j    0.643705\n",
       "Name: Col1, dtype: float64"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Col1']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.855014\n",
       "b    0.387329\n",
       "c    0.103042\n",
       "d    0.503015\n",
       "e    0.876827\n",
       "f    0.510392\n",
       "g    0.859924\n",
       "h    0.280786\n",
       "i    0.194424\n",
       "j    0.643705\n",
       "Name: Col1, dtype: float64"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.Col1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "a    0.649159\n",
       "b    0.896868\n",
       "c    0.829197\n",
       "d    0.138914\n",
       "e    0.450032\n",
       "f    0.328920\n",
       "g    0.358087\n",
       "h    0.744604\n",
       "i    0.034886\n",
       "j    0.576436\n",
       "Name: Col6, dtype: float64"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Col6']=np.random.random((10,1))\n",
    "df.Col6"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Col7</th>\n",
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       "    <tr>\n",
       "      <td>a</td>\n",
       "      <td>0.855014</td>\n",
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       "      <td>0.421354</td>\n",
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       "      <td>0.649159</td>\n",
       "      <td>-0.187919</td>\n",
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       "      <td>b</td>\n",
       "      <td>0.387329</td>\n",
       "      <td>0.583692</td>\n",
       "      <td>0.879277</td>\n",
       "      <td>0.852492</td>\n",
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       "      <td>c</td>\n",
       "      <td>0.103042</td>\n",
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       "      <td>0.686740</td>\n",
       "      <td>0.197307</td>\n",
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       "      <td>d</td>\n",
       "      <td>0.503015</td>\n",
       "      <td>0.570675</td>\n",
       "      <td>0.144457</td>\n",
       "      <td>0.861365</td>\n",
       "      <td>0.603069</td>\n",
       "      <td>0.138914</td>\n",
       "      <td>-0.574307</td>\n",
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       "    <tr>\n",
       "      <td>e</td>\n",
       "      <td>0.876827</td>\n",
       "      <td>0.279039</td>\n",
       "      <td>0.832960</td>\n",
       "      <td>0.358493</td>\n",
       "      <td>0.549032</td>\n",
       "      <td>0.450032</td>\n",
       "      <td>-0.113824</td>\n",
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       "    <tr>\n",
       "      <td>f</td>\n",
       "      <td>0.510392</td>\n",
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       "      <td>0.700452</td>\n",
       "      <td>0.254227</td>\n",
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       "      <td>0.328920</td>\n",
       "      <td>-0.107200</td>\n",
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       "    <tr>\n",
       "      <td>g</td>\n",
       "      <td>0.859924</td>\n",
       "      <td>0.948815</td>\n",
       "      <td>0.795078</td>\n",
       "      <td>0.936383</td>\n",
       "      <td>0.416116</td>\n",
       "      <td>0.358087</td>\n",
       "      <td>-0.120474</td>\n",
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       "    <tr>\n",
       "      <td>h</td>\n",
       "      <td>0.280786</td>\n",
       "      <td>0.757206</td>\n",
       "      <td>0.847269</td>\n",
       "      <td>0.162491</td>\n",
       "      <td>0.447651</td>\n",
       "      <td>0.744604</td>\n",
       "      <td>0.050122</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>i</td>\n",
       "      <td>0.194424</td>\n",
       "      <td>0.959520</td>\n",
       "      <td>0.613985</td>\n",
       "      <td>0.588174</td>\n",
       "      <td>0.594704</td>\n",
       "      <td>0.034886</td>\n",
       "      <td>-0.401620</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>j</td>\n",
       "      <td>0.643705</td>\n",
       "      <td>0.278851</td>\n",
       "      <td>0.508543</td>\n",
       "      <td>0.071959</td>\n",
       "      <td>0.965102</td>\n",
       "      <td>0.576436</td>\n",
       "      <td>0.107539</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Col1      Col2      Col3      Col4      Col5      Col6      Col7\n",
       "a  0.855014  0.273018  0.550010  0.421354  0.487759  0.649159 -0.187919\n",
       "b  0.387329  0.583692  0.879277  0.852492  0.968689  0.896868 -0.626412\n",
       "c  0.103042  0.479367  0.686740  0.197307  0.422101  0.829197 -0.147912\n",
       "d  0.503015  0.570675  0.144457  0.861365  0.603069  0.138914 -0.574307\n",
       "e  0.876827  0.279039  0.832960  0.358493  0.549032  0.450032 -0.113824\n",
       "f  0.510392  0.288067  0.700452  0.254227  0.262606  0.328920 -0.107200\n",
       "g  0.859924  0.948815  0.795078  0.936383  0.416116  0.358087 -0.120474\n",
       "h  0.280786  0.757206  0.847269  0.162491  0.447651  0.744604  0.050122\n",
       "i  0.194424  0.959520  0.613985  0.588174  0.594704  0.034886 -0.401620\n",
       "j  0.643705  0.278851  0.508543  0.071959  0.965102  0.576436  0.107539"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Col7']=df.Col1*df.Col2-df.Col4\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[-0.18791940837337084,\n",
       " -0.6264116893354664,\n",
       " -0.14791218879976165,\n",
       " -0.5743069155102667,\n",
       " -0.1138241594883663,\n",
       " -0.10720036863956434,\n",
       " -0.12047413499524096,\n",
       " 0.05012224871081994,\n",
       " -0.40162011091705885,\n",
       " 0.10753875963666495]"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[e for e in df.Col7]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "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>Price</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>32423</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>534523</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>63426246</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>632364466</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Price\n",
       "0      32423\n",
       "1     534523\n",
       "2   63426246\n",
       "3  632364466"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lst=[32423,534523,63426246,632364466]\n",
    "price_df=pd.DataFrame(lst, columns=['Price'])\n",
    "price_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "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>Price</th>\n",
       "      <th>P2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>32423</td>\n",
       "      <td>2412</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>534523</td>\n",
       "      <td>43242</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>63426246</td>\n",
       "      <td>4234</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>632364466</td>\n",
       "      <td>43452543</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Price        P2\n",
       "0      32423      2412\n",
       "1     534523     43242\n",
       "2   63426246      4234\n",
       "3  632364466  43452543"
      ]
     },
     "execution_count": 106,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "price_df['P2']=[2412,43242,4234,43452543]\n",
    "price_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [],
   "source": [
    "lst_lst=[[873512, 'Aqui', 2010, 20400],\n",
    "       [8732, 'Nop', 2021, 2000],\n",
    "       [8512, 'Ai', 2000, 20900],\n",
    "       [3512, 'Hola', 2003, 10400]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [],
   "source": [
    "columnas=['lote', 'lugar','año', 'precio']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "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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       "    }\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>lote</th>\n",
       "      <th>lugar</th>\n",
       "      <th>año</th>\n",
       "      <th>precio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>873512</td>\n",
       "      <td>Aqui</td>\n",
       "      <td>2010</td>\n",
       "      <td>20400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>8732</td>\n",
       "      <td>Nop</td>\n",
       "      <td>2021</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>8512</td>\n",
       "      <td>Ai</td>\n",
       "      <td>2000</td>\n",
       "      <td>20900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>3512</td>\n",
       "      <td>Hola</td>\n",
       "      <td>2003</td>\n",
       "      <td>10400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     lote lugar   año  precio\n",
       "0  873512  Aqui  2010   20400\n",
       "1    8732   Nop  2021    2000\n",
       "2    8512    Ai  2000   20900\n",
       "3    3512  Hola  2003   10400"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(lst_lst, columns=columnas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [],
   "source": [
    "dic={'CasaA':lst_lst[0],'CasaB':lst_lst[1],'CasaC':lst_lst[2],'CasaD':lst_lst[3]}"
   ]
  },
  {
   "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>CasaA</th>\n",
       "      <th>CasaB</th>\n",
       "      <th>CasaC</th>\n",
       "      <th>CasaD</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>873512</td>\n",
       "      <td>8732</td>\n",
       "      <td>8512</td>\n",
       "      <td>3512</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Aqui</td>\n",
       "      <td>Nop</td>\n",
       "      <td>Ai</td>\n",
       "      <td>Hola</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>2010</td>\n",
       "      <td>2021</td>\n",
       "      <td>2000</td>\n",
       "      <td>2003</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>20400</td>\n",
       "      <td>2000</td>\n",
       "      <td>20900</td>\n",
       "      <td>10400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    CasaA CasaB  CasaC  CasaD\n",
       "0  873512  8732   8512   3512\n",
       "1    Aqui   Nop     Ai   Hola\n",
       "2    2010  2021   2000   2003\n",
       "3   20400  2000  20900  10400"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casas=pd.DataFrame(dic)\n",
    "casas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    873512\n",
       "1      Aqui\n",
       "2      2010\n",
       "3     20400\n",
       "Name: CasaA, dtype: object"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casas['CasaA']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>CasaA</td>\n",
       "      <td>873512</td>\n",
       "      <td>Aqui</td>\n",
       "      <td>2010</td>\n",
       "      <td>20400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CasaB</td>\n",
       "      <td>8732</td>\n",
       "      <td>Nop</td>\n",
       "      <td>2021</td>\n",
       "      <td>2000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CasaC</td>\n",
       "      <td>8512</td>\n",
       "      <td>Ai</td>\n",
       "      <td>2000</td>\n",
       "      <td>20900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>CasaD</td>\n",
       "      <td>3512</td>\n",
       "      <td>Hola</td>\n",
       "      <td>2003</td>\n",
       "      <td>10400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            0     1     2      3\n",
       "CasaA  873512  Aqui  2010  20400\n",
       "CasaB    8732   Nop  2021   2000\n",
       "CasaC    8512    Ai  2000  20900\n",
       "CasaD    3512  Hola  2003  10400"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casas.T #transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "894268"
      ]
     },
     "execution_count": 119,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casas.T[0].sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "223567.0"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casas.T[0].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "433303.37190010416"
      ]
     },
     "execution_count": 121,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casas.T[0].std()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "873512"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "casas.T[0].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: []"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2=pd.DataFrame()\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>a</td>\n",
       "      <td>0.421354</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>b</td>\n",
       "      <td>0.852492</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>c</td>\n",
       "      <td>0.197307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>d</td>\n",
       "      <td>0.861365</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>e</td>\n",
       "      <td>0.358493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>f</td>\n",
       "      <td>0.254227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>g</td>\n",
       "      <td>0.936383</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>h</td>\n",
       "      <td>0.162491</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>i</td>\n",
       "      <td>0.588174</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>j</td>\n",
       "      <td>0.071959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Col1\n",
       "a  0.421354\n",
       "b  0.852492\n",
       "c  0.197307\n",
       "d  0.861365\n",
       "e  0.358493\n",
       "f  0.254227\n",
       "g  0.936383\n",
       "h  0.162491\n",
       "i  0.588174\n",
       "j  0.071959"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2['Col1']=df['Col4']\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 125,
   "metadata": {},
   "outputs": [],
   "source": [
    "# https://pandas.pydata.org/pandas-docs/stable/"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "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>Nombre</th>\n",
       "      <th>Edad</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Pepe</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Juan</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Luis</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>3</td>\n",
       "      <td>Fran</td>\n",
       "      <td>15xº</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Nombre  Edad\n",
       "0   Pepe    10\n",
       "1   Juan    20\n",
       "2   Luis    25\n",
       "3   Fran  15xº"
      ]
     },
     "execution_count": 128,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alumnos=pd.read_csv('data/alumnos.csv')\n",
    "alumnos"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    [{'name': 'Beisbol', 'level': 2}, {'name': 'Fu...\n",
       "Name: aficiones, dtype: object"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alumnos2=pd.read_json('data/alumnos.json')\n",
    "alumnos2.aficiones"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'name': 'Beisbol', 'level': 2}"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "alumnos2.aficiones[0][0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 135,
   "metadata": {},
   "outputs": [],
   "source": [
    "from pandas.io.json import json_normalize"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 136,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[{'name': 'Beisbol', 'level': 2},\n",
       " {'name': 'Futbol', 'level': 1},\n",
       " {'name': 'Esgrima', 'level': 10}]"
      ]
     },
     "execution_count": 136,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=alumnos2.aficiones[0]\n",
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 137,
   "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>name</th>\n",
       "      <th>level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>0</td>\n",
       "      <td>Beisbol</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>1</td>\n",
       "      <td>Futbol</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>2</td>\n",
       "      <td>Esgrima</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      name  level\n",
       "0  Beisbol      2\n",
       "1   Futbol      1\n",
       "2  Esgrima     10"
      ]
     },
     "execution_count": 137,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "json_normalize(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 143,
   "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",
       "      <th>Col4</th>\n",
       "      <th>Col5</th>\n",
       "      <th>Col6</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <td>a</td>\n",
       "      <td>0.855014</td>\n",
       "      <td>0.273018</td>\n",
       "      <td>0.550010</td>\n",
       "      <td>0.421354</td>\n",
       "      <td>0.487759</td>\n",
       "      <td>0.649159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>b</td>\n",
       "      <td>0.387329</td>\n",
       "      <td>0.583692</td>\n",
       "      <td>0.879277</td>\n",
       "      <td>0.852492</td>\n",
       "      <td>0.968689</td>\n",
       "      <td>0.896868</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>c</td>\n",
       "      <td>0.103042</td>\n",
       "      <td>0.479367</td>\n",
       "      <td>0.686740</td>\n",
       "      <td>0.197307</td>\n",
       "      <td>0.422101</td>\n",
       "      <td>0.829197</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>d</td>\n",
       "      <td>0.503015</td>\n",
       "      <td>0.570675</td>\n",
       "      <td>0.144457</td>\n",
       "      <td>0.861365</td>\n",
       "      <td>0.603069</td>\n",
       "      <td>0.138914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>e</td>\n",
       "      <td>0.876827</td>\n",
       "      <td>0.279039</td>\n",
       "      <td>0.832960</td>\n",
       "      <td>0.358493</td>\n",
       "      <td>0.549032</td>\n",
       "      <td>0.450032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>f</td>\n",
       "      <td>0.510392</td>\n",
       "      <td>0.288067</td>\n",
       "      <td>0.700452</td>\n",
       "      <td>0.254227</td>\n",
       "      <td>0.262606</td>\n",
       "      <td>0.328920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>g</td>\n",
       "      <td>0.859924</td>\n",
       "      <td>0.948815</td>\n",
       "      <td>0.795078</td>\n",
       "      <td>0.936383</td>\n",
       "      <td>0.416116</td>\n",
       "      <td>0.358087</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>h</td>\n",
       "      <td>0.280786</td>\n",
       "      <td>0.757206</td>\n",
       "      <td>0.847269</td>\n",
       "      <td>0.162491</td>\n",
       "      <td>0.447651</td>\n",
       "      <td>0.744604</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>i</td>\n",
       "      <td>0.194424</td>\n",
       "      <td>0.959520</td>\n",
       "      <td>0.613985</td>\n",
       "      <td>0.588174</td>\n",
       "      <td>0.594704</td>\n",
       "      <td>0.034886</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <td>j</td>\n",
       "      <td>0.643705</td>\n",
       "      <td>0.278851</td>\n",
       "      <td>0.508543</td>\n",
       "      <td>0.071959</td>\n",
       "      <td>0.965102</td>\n",
       "      <td>0.576436</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Col1      Col2      Col3      Col4      Col5      Col6\n",
       "a  0.855014  0.273018  0.550010  0.421354  0.487759  0.649159\n",
       "b  0.387329  0.583692  0.879277  0.852492  0.968689  0.896868\n",
       "c  0.103042  0.479367  0.686740  0.197307  0.422101  0.829197\n",
       "d  0.503015  0.570675  0.144457  0.861365  0.603069  0.138914\n",
       "e  0.876827  0.279039  0.832960  0.358493  0.549032  0.450032\n",
       "f  0.510392  0.288067  0.700452  0.254227  0.262606  0.328920\n",
       "g  0.859924  0.948815  0.795078  0.936383  0.416116  0.358087\n",
       "h  0.280786  0.757206  0.847269  0.162491  0.447651  0.744604\n",
       "i  0.194424  0.959520  0.613985  0.588174  0.594704  0.034886\n",
       "j  0.643705  0.278851  0.508543  0.071959  0.965102  0.576436"
      ]
     },
     "execution_count": 143,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.drop(columns=['Col7'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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