Section 5 Python for Data Analysis - NumPy.ipynb

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    "# NumPy Arrays"
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    "my_list = [1,2,3]"
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    "my_list"
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   "execution_count":42,
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     "data":{
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       "[1, 2, 3]"
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   "cell_type":"code",
   "source":[
    "import numpy as np"
   ],
   "execution_count":43,
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    "arr = np.array(my_list)"
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   "execution_count":44,
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   "execution_count":45,
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       "<pre>array([1, 2, 3])<\/pre>"
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   "source":[
    "my_mat = [[1,2,3], [4,5,6], [7, 8, 9]]"
   ],
   "execution_count":46,
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   ],
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   "source":[
    "np.array(my_mat)"
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   "execution_count":47,
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       "<pre>array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [7, 8, 9]])<\/pre>"
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   "source":[
    "np.arange(0, 10) # similar to python range function"
   ],
   "execution_count":48,
   "outputs":[
    {
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       "<pre>array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])<\/pre>"
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   "source":[
    "np.arange(0, 11, 2) "
   ],
   "execution_count":49,
   "outputs":[
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       "<pre>array([ 0,  2,  4,  6,  8, 10])<\/pre>"
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   "cell_type":"code",
   "source":[
    "np.zeros(3)"
   ],
   "execution_count":50,
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       "<pre>array([0., 0., 0.])<\/pre>"
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   "cell_type":"code",
   "source":[
    "np.zeros((2, 3)) # (rows, columns)"
   ],
   "execution_count":51,
   "outputs":[
    {
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       "<pre>array([[0., 0., 0.],\n",
       "       [0., 0., 0.]])<\/pre>"
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   "cell_type":"code",
   "source":[
    "np.ones(4)"
   ],
   "execution_count":52,
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     "data":{
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       "<pre>array([1., 1., 1., 1.])<\/pre>"
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   "cell_type":"code",
   "source":[
    "np.ones((3, 4))"
   ],
   "execution_count":53,
   "outputs":[
    {
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       "<pre>array([[1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.],\n",
       "       [1., 1., 1., 1.]])<\/pre>"
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   "cell_type":"code",
   "source":[
    "np.linspace(0, 5, 10) # returns one dimensional vector and ten evenly points from 0 to 5"
   ],
   "execution_count":54,
   "outputs":[
    {
     "data":{
      "text\/html":[
       "<pre>array([0.        , 0.55555556, 1.11111111, 1.66666667, 2.22222222,\n",
       "       2.77777778, 3.33333333, 3.88888889, 4.44444444, 5.        ])<\/pre>"
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  {
   "cell_type":"code",
   "source":[
    "np.linspace(0, 5, 100)"
   ],
   "execution_count":55,
   "outputs":[
    {
     "data":{
      "text\/html":[
       "<pre>array([0.        , 0.05050505, 0.1010101 , 0.15151515, 0.2020202 ,\n",
       "       0.25252525, 0.3030303 , 0.35353535, 0.4040404 , 0.45454545,\n",
       "       0.50505051, 0.55555556, 0.60606061, 0.65656566, 0.70707071,\n",
       "       0.75757576, 0.80808081, 0.85858586, 0.90909091, 0.95959596,\n",
       "       1.01010101, 1.06060606, 1.11111111, 1.16161616, 1.21212121,\n",
       "       1.26262626, 1.31313131, 1.36363636, 1.41414141, 1.46464646,\n",
       "       1.51515152, 1.56565657, 1.61616162, 1.66666667, 1.71717172,\n",
       "       1.76767677, 1.81818182, 1.86868687, 1.91919192, 1.96969697,\n",
       "       2.02020202, 2.07070707, 2.12121212, 2.17171717, 2.22222222,\n",
       "       2.27272727, 2.32323232, 2.37373737, 2.42424242, 2.47474747,\n",
       "       2.52525253, 2.57575758, 2.62626263, 2.67676768, 2.72727273,\n",
       "       2.77777778, 2.82828283, 2.87878788, 2.92929293, 2.97979798,\n",
       "       3.03030303, 3.08080808, 3.13131313, 3.18181818, 3.23232323,\n",
       "       3.28282828, 3.33333333, 3.38383838, 3.43434343, 3.48484848,\n",
       "       3.53535354, 3.58585859, 3.63636364, 3.68686869, 3.73737374,\n",
       "       3.78787879, 3.83838384, 3.88888889, 3.93939394, 3.98989899,\n",
       "       4.04040404, 4.09090909, 4.14141414, 4.19191919, 4.24242424,\n",
       "       4.29292929, 4.34343434, 4.39393939, 4.44444444, 4.49494949,\n",
       "       4.54545455, 4.5959596 , 4.64646465, 4.6969697 , 4.74747475,\n",
       "       4.7979798 , 4.84848485, 4.8989899 , 4.94949495, 5.        ])<\/pre>"
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   "cell_type":"code",
   "source":[
    "# create identity matrix - useful linear algebra problem \n",
    "np.eye(4) "
   ],
   "execution_count":56,
   "outputs":[
    {
     "data":{
      "text\/html":[
       "<pre>array([[1., 0., 0., 0.],\n",
       "       [0., 1., 0., 0.],\n",
       "       [0., 0., 1., 0.],\n",
       "       [0., 0., 0., 1.]])<\/pre>"
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  {
   "cell_type":"code",
   "source":[
    "np.random.rand(5)"
   ],
   "execution_count":57,
   "outputs":[
    {
     "data":{
      "text\/html":[
       "<pre>array([0.95851676, 0.32150029, 0.71368466, 0.32893694, 0.6520969 ])<\/pre>"
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     "hide_output_from_viewers":true
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   }
  },
  {
   "cell_type":"code",
   "source":[
    "np.random.rand(5,5) # 5v5 matrix"
   ],
   "execution_count":58,
   "outputs":[
    {
     "data":{
      "text\/html":[
       "<pre>array([[0.03096549, 0.59232787, 0.11691732, 0.84207371, 0.27148218],\n",
       "       [0.26013153, 0.95240324, 0.79259888, 0.50588033, 0.98127688],\n",
       "       [0.9943849 , 0.4999976 , 0.08991209, 0.65019886, 0.93092801],\n",
       "       [0.74891602, 0.89487891, 0.44824743, 0.604662  , 0.19529187],\n",
       "       [0.83506403, 0.20803755, 0.18988757, 0.61805874, 0.23862785]])<\/pre>"
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     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "np.random.randn(2) # normal distribution"
   ],
   "execution_count":59,
   "outputs":[
    {
     "data":{
      "text\/html":[
       "<pre>array([ 0.32238506, -1.7091718 ])<\/pre>"
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     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "np.random.rand(4, 4)"
   ],
   "execution_count":60,
   "outputs":[
    {
     "data":{
      "text\/html":[
       "<pre>array([[0.75538328, 0.37789442, 0.87056957, 0.81040865],\n",
       "       [0.9222525 , 0.28439391, 0.3827665 , 0.88750191],\n",
       "       [0.27973316, 0.42226162, 0.1093088 , 0.10512155],\n",
       "       [0.30027613, 0.1347196 , 0.71007699, 0.94175297]])<\/pre>"
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     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
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   }
  },
  {
   "cell_type":"code",
   "source":[
    "np.random.randint(1, 100) # generate random from low to high number. incluvise on the lowend but exclusive on the highend"
   ],
   "execution_count":61,
   "outputs":[
    {
     "data":{
      "text\/plain":[
       "37"
      ]
     },
     "metadata":{
      
     },
     "output_type":"display_data"
    }
   ],
   "metadata":{
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    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "np.random.randint(1, 100, 10)"
   ],
   "execution_count":62,
   "outputs":[
    {
     "data":{
      "text\/html":[
       "<pre>array([97, 92, 32, 51, 83, 35, 89, 94, 24, 14])<\/pre>"
      ]
     },
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     },
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    }
   ],
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     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "arr = np.arange(25)"
   ],
   "execution_count":63,
   "outputs":[
    
   ],
   "metadata":{
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     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "arr"
   ],
   "execution_count":64,
   "outputs":[
    {
     "data":{
      "text\/html":[
       "<pre>array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
       "       17, 18, 19, 20, 21, 22, 23, 24])<\/pre>"
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     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "ranarr = np.random.randint(0, 50, 10)"
   ],
   "execution_count":65,
   "outputs":[
    
   ],
   "metadata":{
    "datalore":{
     "node_id":"hY8dOGEzMezjMJe0hACvFc",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "ranarr"
   ],
   "execution_count":66,
   "outputs":[
    {
     "data":{
      "text\/html":[
       "<pre>array([32, 44, 41, 41, 25, 14, 39, 16,  8, 37])<\/pre>"
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   "cell_type":"code",
   "source":[
    "# reshape method = return the same data in a new shape\n",
    "# number of total rows should be the same as the row x columns\n",
    "arr.reshape(5,5)"
   ],
   "execution_count":67,
   "outputs":[
    {
     "data":{
      "text\/html":[
       "<pre>array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14],\n",
       "       [15, 16, 17, 18, 19],\n",
       "       [20, 21, 22, 23, 24]])<\/pre>"
      ]
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     "output_type":"display_data"
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     "hide_output_from_viewers":true
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   }
  },
  {
   "cell_type":"code",
   "source":[
    "ranarr"
   ],
   "execution_count":68,
   "outputs":[
    {
     "data":{
      "text\/html":[
       "<pre>array([32, 44, 41, 41, 25, 14, 39, 16,  8, 37])<\/pre>"
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  {
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   "source":[
    "ranarr.max()"
   ],
   "execution_count":69,
   "outputs":[
    {
     "data":{
      "text\/plain":[
       "44"
      ]
     },
     "metadata":{
      
     },
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   ],
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     "hide_output_from_viewers":true
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   }
  },
  {
   "cell_type":"code",
   "source":[
    "ranarr.min()"
   ],
   "execution_count":70,
   "outputs":[
    {
     "data":{
      "text\/plain":[
       "8"
      ]
     },
     "metadata":{
      
     },
     "output_type":"display_data"
    }
   ],
   "metadata":{
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     "hide_output_from_viewers":true
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   }
  },
  {
   "cell_type":"code",
   "source":[
    "ranarr.argmax() # return the index location"
   ],
   "execution_count":71,
   "outputs":[
    {
     "data":{
      "text\/plain":[
       "1"
      ]
     },
     "metadata":{
      
     },
     "output_type":"display_data"
    }
   ],
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   }
  },
  {
   "cell_type":"code",
   "source":[
    "ranarr.argmin() # return the index location"
   ],
   "execution_count":72,
   "outputs":[
    {
     "data":{
      "text\/plain":[
       "8"
      ]
     },
     "metadata":{
      
     },
     "output_type":"display_data"
    }
   ],
   "metadata":{
    "datalore":{
     "node_id":"QpsPgEbJnbRkF1xoz5qgUZ",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "arr.shape"
   ],
   "execution_count":73,
   "outputs":[
    {
     "data":{
      "text\/plain":[
       "(25,)"
      ]
     },
     "metadata":{
      
     },
     "output_type":"display_data"
    }
   ],
   "metadata":{
    "datalore":{
     "node_id":"ocmvwGH7QGWqeu0qXhGZyk",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "arr = arr.reshape(5,5)"
   ],
   "execution_count":74,
   "outputs":[
    
   ],
   "metadata":{
    "datalore":{
     "node_id":"6N9xOQ1XT2K51F6aY4R0LX",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "arr.shape"
   ],
   "execution_count":75,
   "outputs":[
    {
     "data":{
      "text\/plain":[
       "(5, 5)"
      ]
     },
     "metadata":{
      
     },
     "output_type":"display_data"
    }
   ],
   "metadata":{
    "datalore":{
     "node_id":"wC1QnclMPSISwEQp6D2fXf",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "arr.dtype # data type of the array"
   ],
   "execution_count":76,
   "outputs":[
    {
     "data":{
      "text\/plain":[
       "dtype('int64')"
      ]
     },
     "metadata":{
      
     },
     "output_type":"display_data"
    }
   ],
   "metadata":{
    "datalore":{
     "node_id":"cioJmfHa4zbYTZPRY3Kafk",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "from numpy.random import randint"
   ],
   "execution_count":77,
   "outputs":[
    
   ],
   "metadata":{
    "datalore":{
     "node_id":"muPQMlrJVKaiFfri0IaZ2F",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "randint(2, 10)"
   ],
   "execution_count":78,
   "outputs":[
    {
     "data":{
      "text\/plain":[
       "6"
      ]
     },
     "metadata":{
      
     },
     "output_type":"display_data"
    }
   ],
   "metadata":{
    "datalore":{
     "node_id":"mrbxn2BwG7BdfuglauskFD",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    
   ],
   "execution_count":null,
   "outputs":[
    
   ],
   "metadata":{
    "datalore":{
     "node_id":"OP9sBKIsaolmsSxotS6k1G",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  }
 ],
 "metadata":{
  "kernelspec":{
   "display_name":"Python",
   "language":"python",
   "name":"python"
  },
  "datalore":{
   "computation_mode":"JUPYTER",
   "package_manager":"pip",
   "base_environment":"default",
   "packages":[
    
   ],
   "report_row_ids":[
    
   ],
   "version":3
  }
 },
 "nbformat":4,
 "nbformat_minor":4
}