plotly-NLU.ipynb

{
    "nbformat_minor": 1, 
    "cells": [
        {
            "execution_count": 40, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "output_type": "stream", 
                    "name": "stdout", 
                    "text": "retrieved_url => https://www.ibm.com/us-en/\nlanguage => en\nusage => {'features': 1, 'text_units': 1, 'text_characters': 1536}\nkeywords => [{'sentiment': {'score': 0.792454}, 'relevance': 0.864624, 'count': 3, 'emotions': {'joy': 0.522781, 'sadness': 0.188625, 'fear': 0.12012, 'disgust': 0.103212, 'anger': 0.106669}, 'text': 'curated online courses'}, {'sentiment': {'score': 0.664726}, 'relevance': 0.864593, 'count': 1, 'emotions': {'joy': 0.532354, 'sadness': 0.265225, 'fear': 0.07773, 'disgust': 0.090112, 'anger': 0.102242}, 'text': 'free virtual server'}]\n"
                }, 
                {
                    "execution_count": 40, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>count</th>\n      <th>emotions.anger</th>\n      <th>emotions.disgust</th>\n      <th>emotions.fear</th>\n      <th>emotions.joy</th>\n      <th>emotions.sadness</th>\n      <th>relevance</th>\n      <th>sentiment.score</th>\n      <th>text</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>3</td>\n      <td>0.106669</td>\n      <td>0.103212</td>\n      <td>0.12012</td>\n      <td>0.522781</td>\n      <td>0.188625</td>\n      <td>0.864624</td>\n      <td>0.792454</td>\n      <td>curated online courses</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>1</td>\n      <td>0.102242</td>\n      <td>0.090112</td>\n      <td>0.07773</td>\n      <td>0.532354</td>\n      <td>0.265225</td>\n      <td>0.864593</td>\n      <td>0.664726</td>\n      <td>free virtual server</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "   count  emotions.anger  emotions.disgust  emotions.fear  emotions.joy  \\\n0      3        0.106669          0.103212        0.12012      0.522781   \n1      1        0.102242          0.090112        0.07773      0.532354   \n\n   emotions.sadness  relevance  sentiment.score                    text  \n0          0.188625   0.864624         0.792454  curated online courses  \n1          0.265225   0.864593         0.664726     free virtual server  "
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "import pandas as pd\nfrom pandas.io.json import json_normalize\nimport json\n\n\njsoninput = {\n  \"usage\": {\n    \"text_units\": 1,\n    \"text_characters\": 1536,\n    \"features\": 1\n  },\n  \"keywords\": [\n    {\n      \"text\": \"curated online courses\",\n      \"count\": 3,\n      \"sentiment\": {\n        \"score\": 0.792454\n      },\n      \"relevance\": 0.864624,\n      \"emotions\": {\n        \"sadness\": 0.188625,\n        \"joy\": 0.522781,\n        \"fear\": 0.12012,\n        \"disgust\": 0.103212,\n        \"anger\": 0.106669\n      }\n    },\n    {\n      \"text\": \"free virtual server\",\n      \"count\": 1,\n      \"sentiment\": {\n        \"score\": 0.664726\n      },\n      \"relevance\": 0.864593,\n      \"emotions\": {\n        \"sadness\": 0.265225,\n        \"joy\": 0.532354,\n        \"fear\": 0.07773,\n        \"disgust\": 0.090112,\n        \"anger\": 0.102242\n      }\n    }\n  ],\n  \"language\": \"en\",\n  \"retrieved_url\": \"https://www.ibm.com/us-en/\"\n}\n\nfor key,val in jsoninput.items():\n    print(key, \"=>\", val)\nmydata = json_normalize(jsoninput['keywords'])\nmydata.head(3)"
        }, 
        {
            "execution_count": 74, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 74, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>text</th>\n      <th>count</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>curated online courses</td>\n      <td>3</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>free virtual server</td>\n      <td>1</td>\n    </tr>\n  </tbody>\n</table>\n</div>", 
                        "text/plain": "                     text  count\n0  curated online courses      3\n1     free virtual server      1"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "NLU_df = pd.DataFrame(mydata, columns=['text', 'count'])\nNLU_df"
        }, 
        {
            "execution_count": 79, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [
                {
                    "execution_count": 79, 
                    "metadata": {}, 
                    "data": {
                        "text/html": "<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\" seamless=\"seamless\" src=\"https://plot.ly/~aficionadovmac/6.embed\" height=\"525px\" width=\"100%\"></iframe>", 
                        "text/plain": "<plotly.tools.PlotlyDisplay object>"
                    }, 
                    "output_type": "execute_result"
                }
            ], 
            "source": "import plotly\nimport plotly.plotly as py\nimport plotly.graph_objs as go\n\nNLU_data = [go.Bar(x=NLU_df.text, y=NLU_df['count'].tolist())]\n\npy.iplot({ 'data': NLU_data,\n            'layout': {\n               'title': 'Distribution',\n               'xaxis': {\n                 'title': 'text'},\n               'yaxis': {\n                'title': 'count'}\n        }})"
        }, 
        {
            "execution_count": null, 
            "cell_type": "code", 
            "metadata": {}, 
            "outputs": [], 
            "source": ""
        }
    ], 
    "metadata": {
        "kernelspec": {
            "display_name": "Python 3.5", 
            "name": "python3", 
            "language": "python"
        }, 
        "language_info": {
            "mimetype": "text/x-python", 
            "nbconvert_exporter": "python", 
            "version": "3.5.5", 
            "name": "python", 
            "file_extension": ".py", 
            "pygments_lexer": "ipython3", 
            "codemirror_mode": {
                "version": 3, 
                "name": "ipython"
            }
        }
    }, 
    "nbformat": 4
}

Plotted Bar chart