{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Clustergrammer DataFrame Visualization Example\n",
    "This example shows how to visualize a Pandas DataFrame using the [Clustergrammer Jupyter Widget](http://clustergrammer.readthedocs.io/clustergrammer_widget.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "from clustergrammer_widget import *"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Generate a dataframe of random data with row and column labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# generate random matrix\n",
    "num_rows = 500\n",
    "num_cols = 10\n",
    "np.random.seed(seed=100)\n",
    "mat = np.random.rand(num_rows, num_cols)\n",
    "\n",
    "# make row and col labels\n",
    "rows = range(num_rows)\n",
    "cols = range(num_cols)\n",
    "rows = [str(i) for i in rows]\n",
    "cols = [str(i) for i in cols]\n",
    "\n",
    "# make dataframe \n",
    "df = pd.DataFrame(data=mat, columns=cols, index=rows)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Initialize the network object, load the dataframe, hierarchically cluster the rows and columns using default parameters, and finally visualize using clustergrammer_widget."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "9714c35f1c734febb7b45633c5bd4792"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# initialize network object\n",
    "net = Network(clustergrammer_widget)\n",
    "# load dataframe\n",
    "net.load_df(df)\n",
    "# cluster using default parameters\n",
    "net.cluster(enrichrgram=False)\n",
    "# make the visualization\n",
    "net.widget()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The network object can also be used to normalize and filter your data. Here we will Z-score normalize the columns and make an updated visualization."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "427cb1bcfb794f0fbdc87b0deea6d97f"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net.normalize(axis='col', norm_type='zscore', keep_orig=True)\n",
    "net.cluster(enrichrgram=False)\n",
    "net.widget()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we will filter rows based on their sum. We will keep the top 20 rows based on their absolute value sum. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "800575243c434f01ae47ac912e38e069"
      }
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "net.filter_N_top('row', 20, 'sum')\n",
    "net.cluster(enrichrgram=False)\n",
    "net.widget()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [Root]",
   "language": "python",
   "name": "Python [Root]"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  },
  "widgets": {
   "application/vnd.jupyter.widget-state+json": {
    "state": {
     "427cb1bcfb794f0fbdc87b0deea6d97f": {
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      "model_module_version": "*",
      "model_name": "hello_model",
      "state": {
       "_dom_classes": [],
       "_model_module_version": "*",
       "_view_module_version": "*",
       "layout": "IPY_MODEL_6d3cecaf91df4ba6ae7e6375f07a1e23",
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       "viz_title": "updating python by restarting kernel"
      }
     },
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