Section 8-42 Python for Data Visualization - Matplotlib Part 1.ipynb

{
 "cells":[
  {
   "cell_type":"code",
   "source":[
    "import matplotlib.pyplot as plt"
   ],
   "execution_count":1,
   "outputs":[
    
   ],
   "metadata":{
    "datalore":{
     "node_id":"GePWc4Oc01XUv4DOsIKPKs",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "%matplotlib inline"
   ],
   "execution_count":2,
   "outputs":[
    
   ],
   "metadata":{
    "datalore":{
     "node_id":"XLWHpjQtPyOOoyehHiComd",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "import numpy as np\n",
    "x = np.linspace(0, 5, 11)\n",
    "y = x ** 2"
   ],
   "execution_count":3,
   "outputs":[
    
   ],
   "metadata":{
    "datalore":{
     "node_id":"fsPkhEoUyKOnaF6Dd4HrjE",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "x"
   ],
   "execution_count":4,
   "outputs":[
    {
     "data":{
      "text\/html":[
       "<pre>array([0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. , 4.5, 5. ])<\/pre>"
      ]
     },
     "metadata":{
      
     },
     "output_type":"display_data"
    }
   ],
   "metadata":{
    "datalore":{
     "node_id":"FAP5vluY7yQVhu728QBAqO",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "y"
   ],
   "execution_count":5,
   "outputs":[
    {
     "data":{
      "text\/html":[
       "<pre>array([ 0.  ,  0.25,  1.  ,  2.25,  4.  ,  6.25,  9.  , 12.25, 16.  ,\n",
       "       20.25, 25.  ])<\/pre>"
      ]
     },
     "metadata":{
      
     },
     "output_type":"display_data"
    }
   ],
   "metadata":{
    "datalore":{
     "node_id":"rrQKoy3h5dbAmJjG6m2bwP",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "# functional method\n",
    "plt.plot(x,y)\n",
    "# plt.show() if not in jupyter notebook"
   ],
   "execution_count":6,
   "outputs":[
    {
     "data":{
      "text\/plain":[
       "[<matplotlib.lines.Line2D at 0x7fcdb41c5dc0>]"
      ]
     },
     "metadata":{
      
     },
     "output_type":"display_data"
    }
   ],
   "metadata":{
    "datalore":{
     "node_id":"bn7GlZjbfDOrOVSwrFtWEb",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "'string'"
   ],
   "execution_count":7,
   "outputs":[
    {
     "data":{
      "text\/plain":[
       "'string'"
      ]
     },
     "metadata":{
      
     },
     "output_type":"display_data"
    }
   ],
   "metadata":{
    "datalore":{
     "node_id":"6aD7bYwNU9DMzUljXwfC3g",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "print('string') #similar to plt.show()"
   ],
   "execution_count":8,
   "outputs":[
    {
     "name":"stdout",
     "text":[
      "string\n"
     ],
     "output_type":"stream"
    }
   ],
   "metadata":{
    "datalore":{
     "node_id":"tETHoEKw8bRXbF63eP1HpP",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "plt.plot(x, y)\n",
    "plt.xlabel('X Label')\n",
    "plt.ylabel('Y Label')\n",
    "plt.title('Title')"
   ],
   "execution_count":11,
   "outputs":[
    {
     "data":{
      "text\/plain":[
       "Text(0.5, 1.0, 'Title')"
      ]
     },
     "metadata":{
      
     },
     "output_type":"display_data"
    },
    {
     "data":{
      "image\/png":[
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\n"
      ]
     },
     "metadata":{
      "image\/png":{
       
      }
     },
     "output_type":"display_data"
    }
   ],
   "metadata":{
    "datalore":{
     "node_id":"295MedmQmUolS8qRB4joMp",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "# multiple subplots on the same canvas\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.plot(x, y,'r')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.plot(y, x, 'b')"
   ],
   "execution_count":12,
   "outputs":[
    {
     "data":{
      "text\/plain":[
       "[<matplotlib.lines.Line2D at 0x7fcdadf84d00>]"
      ]
     },
     "metadata":{
      
     },
     "output_type":"display_data"
    },
    {
     "data":{
      "image\/png":[
       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\n"
      ]
     },
     "metadata":{
      "image\/png":{
       
      }
     },
     "output_type":"display_data"
    }
   ],
   "metadata":{
    "datalore":{
     "node_id":"DL3aMm3qOzvmg5sb5Uaq20",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "# object oriented method\n",
    "fig = plt.figure() # imaginary canvas\n",
    "\n",
    "axes = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # L B W H\n",
    "\n",
    "axes.plot(x, y)\n",
    "axes.set_xlabel('X Label')\n",
    "axes.set_ylabel('Y Labale')\n",
    "axes.set_title('Set Title')"
   ],
   "execution_count":17,
   "outputs":[
    {
     "data":{
      "text\/plain":[
       "Text(0.5, 1.0, 'Set Title')"
      ]
     },
     "metadata":{
      
     },
     "output_type":"display_data"
    },
    {
     "data":{
      "image\/png":[
       "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\n"
      ]
     },
     "metadata":{
      "image\/png":{
       
      }
     },
     "output_type":"display_data"
    }
   ],
   "metadata":{
    "datalore":{
     "node_id":"uMzh8LaSaTKSYp46TChYCf",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    "fig = plt.figure()\n",
    "\n",
    "axes1 = fig.add_axes([0.1, 0.1, 0.8, 0.8]) # number should be between 0 and 1\n",
    "axes2 = fig.add_axes([0.2, 0.5, 0.4, 0.3])\n",
    "\n",
    "\n",
    "axes1.plot(x, y)\n",
    "axes1.set_title('LARGER PLOT')\n",
    "\n",
    "axes2.plot(y, x)\n",
    "axes2.set_title('SMALLER PLOT')"
   ],
   "execution_count":24,
   "outputs":[
    {
     "data":{
      "text\/plain":[
       "Text(0.5, 1.0, 'SMALLER PLOT')"
      ]
     },
     "metadata":{
      
     },
     "output_type":"display_data"
    },
    {
     "data":{
      "image\/png":[
       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\n"
      ]
     },
     "metadata":{
      "image\/png":{
       
      }
     },
     "output_type":"display_data"
    }
   ],
   "metadata":{
    "datalore":{
     "node_id":"FMGzb8Ey0EXg96qtgcltny",
     "type":"CODE",
     "hide_input_from_viewers":true,
     "hide_output_from_viewers":true
    }
   }
  },
  {
   "cell_type":"code",
   "source":[
    
   ],
   "execution_count":null,
   "outputs":[
    
   ],
   "metadata":{
    "datalore":{
     "node_id":"CKG27QxtJ0XG6QqFhTBKtS",
     "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
}