Plotly & Cufflinks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# PLOTY AND CUFFLINKS\n",
    "Plotly is an interactive visualization library  \n",
    "Cufflinks connects plotly with pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import plotly\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.0.9\n"
     ]
    }
   ],
   "source": [
    "# Check version of plotly\n",
    "from plotly import __version__\n",
    "print(__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Import cufflink\n",
    "import cufflinks as cf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# Import libraries from plotly offine\n",
    "from plotly.offline import download_plotlyjs,init_notebook_mode,plot, iplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ],
      "text/vnd.plotly.v1+html": [
       "<script>requirejs.config({paths: { 'plotly': ['https://cdn.plot.ly/plotly-latest.min']},});if(!window.Plotly) {{require(['plotly'],function(plotly) {window.Plotly=plotly;});}}</script>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Connect the java script to the notebook\n",
    "init_notebook_mode(connected = True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "IOPub data rate exceeded.\n",
      "The notebook server will temporarily stop sending output\n",
      "to the client in order to avoid crashing it.\n",
      "To change this limit, set the config variable\n",
      "`--NotebookApp.iopub_data_rate_limit`.\n"
     ]
    }
   ],
   "source": [
    "cf.go_offline()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    .dataframe tbody tr th {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "      <th>C</th>\n",
       "      <th>D</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.321070</td>\n",
       "      <td>0.514164</td>\n",
       "      <td>0.097266</td>\n",
       "      <td>0.909704</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.811084</td>\n",
       "      <td>0.473747</td>\n",
       "      <td>0.738716</td>\n",
       "      <td>0.536735</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.167203</td>\n",
       "      <td>0.742098</td>\n",
       "      <td>0.757856</td>\n",
       "      <td>0.701057</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.129868</td>\n",
       "      <td>0.928874</td>\n",
       "      <td>0.715706</td>\n",
       "      <td>0.744325</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.697740</td>\n",
       "      <td>0.577430</td>\n",
       "      <td>0.381048</td>\n",
       "      <td>0.175554</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0  0.321070  0.514164  0.097266  0.909704\n",
       "1  0.811084  0.473747  0.738716  0.536735\n",
       "2  0.167203  0.742098  0.757856  0.701057\n",
       "3  0.129868  0.928874  0.715706  0.744325\n",
       "4  0.697740  0.577430  0.381048  0.175554"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create data frame\n",
    "df = pd.DataFrame(np.random.rand(100,4), columns = 'A B C D'.split())\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Category</th>\n",
       "      <th>Values</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Category  Values\n",
       "0        A      32\n",
       "1        B      43\n",
       "2        C      50"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Create second data frame\n",
    "df2 = pd.DataFrame({'Category':['A','B','C'],\n",
    "                   'Values':[32, 43,50]})\n",
    "df2.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Line Plots"
   ]
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  {
   "cell_type": "code",
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   ],
   "source": [
    "import warnings\n",
    "warnings.simplefilter(action = 'ignore', category = FutureWarning)\n",
    "\n",
    "# Create line plot\n",
    "df.iplot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scatter Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
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    "# Create scatter plot\n",
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    "# Bar Plots"
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    "# Create box plot\n",
    "df.iplot(kind = 'box')"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3D Surface Plots"
   ]
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   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Create data frame\n",
    "df3 = pd.DataFrame({'x':[1,2,3,4,5], \n",
    "                    'y':[10,20,30,20,10], \n",
    "                    'z':[500,400,300,200,100]})"
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   "source": [
    "# Create plot\n",
    "df[['A','B']].iplot(kind ='spread')"
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   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Bubble Plots"
   ]
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  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
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   "source": [
    "# Create bubble plot\n",
    "df.iplot(kind ='bubble', x = 'A', y = 'B', size = 'C')"
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Scatter Matrix Plots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
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