Plotly_N_Cufflinks.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plotly_N_Cufflinks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cufflinks allows to link plotly to pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from plotly import __version__\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.2.1\n"
     ]
    }
   ],
   "source": [
    "print(__version__)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [],
   "source": [
    "import cufflinks as cf\n",
    "import plotly\n",
    "from plotly.offline import download_plotlyjs, init_notebook_mode, plot, iplot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": [
    "init_notebook_mode(connected=True) #connects interactive js to notebook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "cf.go_offline() # it allows to use cufflinks offiline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.randn(100, 4), columns ='A B C D'.split())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</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>-1.596089</td>\n",
       "      <td>-1.645613</td>\n",
       "      <td>0.538873</td>\n",
       "      <td>-0.234858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2.259580</td>\n",
       "      <td>0.065265</td>\n",
       "      <td>1.835243</td>\n",
       "      <td>0.054295</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.247259</td>\n",
       "      <td>-0.078529</td>\n",
       "      <td>-0.263771</td>\n",
       "      <td>-0.529021</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-2.162654</td>\n",
       "      <td>0.696583</td>\n",
       "      <td>-0.549176</td>\n",
       "      <td>-0.258758</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.038627</td>\n",
       "      <td>-0.477123</td>\n",
       "      <td>0.377293</td>\n",
       "      <td>-0.402667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          A         B         C         D\n",
       "0 -1.596089 -1.645613  0.538873 -0.234858\n",
       "1  2.259580  0.065265  1.835243  0.054295\n",
       "2  0.247259 -0.078529 -0.263771 -0.529021\n",
       "3 -2.162654  0.696583 -0.549176 -0.258758\n",
       "4  1.038627 -0.477123  0.377293 -0.402667"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "df2 = pd.DataFrame({'Category':['A', 'B', 'C'], 'Values': [32, 43, 50]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x21cf5934128>"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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iKhlXrhTOI3RxFRnWSiNS9cbhoMd+4QkoN2Hqzv4DfIOyDS9Kte49/5z8w5mH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\nzp49e4tv/FUQ1YPyz7Wn5J93iLGDtP4No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      "text/plain": [
       "<matplotlib.figure.Figure at 0x21cf5917cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [],
   "source": [
    "plotly.tools.set_credentials_file(username='MateenSA', api_key='ZExzIKbTNeO2xhW65wMd')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~MateenSA/2.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~MateenSA/10.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iplot(kind='scatter', x='A', y='B',  mode='markers', size=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~MateenSA/12.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2.iplot(kind='bar', x='Category', y='Values')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~MateenSA/18.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sum().iplot(kind='bar')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~MateenSA/22.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iplot(kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df3 = pd.DataFrame({'x':[1, 2, 3, 4, 5], 'y':[10, 20, 30, 20, 10], 'z':[5, 4, 3, 2, 1]})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~MateenSA/26.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.iplot(kind='surface')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~MateenSA/32.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['A'].iplot(kind='hist', bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~MateenSA/34.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iplot(kind='hist', bins=10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~MateenSA/38.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[['A','B']].iplot(kind='spread')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~MateenSA/40.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iplot(kind='bubble', x='A', y='B', size='C')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~MateenSA/42.embed\" height=\"525px\" width=\"100%\"></iframe>"
      ],
      "text/plain": [
       "<plotly.tools.PlotlyDisplay object>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.scatter_matrix()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
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