02_EDA_Solutions.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "# Exploratory Data Analysis - Solution\n",
    "\n",
    "Make sure the libraries are imported by executing this code (remember to unset Reset all runtimes before running):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "from py2neo import Graph\n",
    "import pandas as pd\n",
    "\n",
    "import matplotlib \n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "plt.style.use('fivethirtyeight')\n",
    "pd.set_option('display.float_format', lambda x: '%.3f' % x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "Update the cell below to use the IP Address, Bolt Port, and Password, as you did previously."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "pycharm": {}
   },
   "outputs": [],
   "source": [
    "# Change the line of code below to use the Bolt URL,  and Password of your Neo4j Database instance.\n",
    "# graph = Graph(\"<Bolt URL>\", auth=(\"neo4j\", \"<Password>\")) \n",
    " \n",
    "graph = Graph(\"bolt://localhost:7687\", auth=(\"neo4j\", \"letmein\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "## Exercise\n",
    "\n",
    "1. Create a similar query showing the articles that cite the most other papers? Note that you will only need to do a slight tweak of the Cypher query to do this."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "pycharm": {}
   },
   "outputs": [
    {
     "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>cited</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>51956.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90%</th>\n",
       "      <td>2.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99%</th>\n",
       "      <td>6.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>51.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          cited\n",
       "count 51956.000\n",
       "mean      0.553\n",
       "std       1.301\n",
       "min       0.000\n",
       "25%       0.000\n",
       "50%       0.000\n",
       "75%       1.000\n",
       "90%       2.000\n",
       "99%       6.000\n",
       "max      51.000"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "MATCH (a:Article)\n",
    "RETURN size((a)-[:CITED]->()) AS cited\n",
    "\"\"\"\n",
    "\n",
    "cited_df = graph.run(query).to_data_frame()\n",
    "cited_df.describe([.25, .5, .75, .9, .99])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "pycharm": {}
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig1, ax1 = plt.subplots()\n",
    "ax1.hist(pd.Series(cited_df['cited'].dropna()), 50, density=True, facecolor='g', alpha=0.75)\n",
    "plt.tight_layout()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "pycharm": {}
   },
   "source": [
    "2. What about a distribution of the number of articles published by authors?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "pycharm": {}
   },
   "outputs": [
    {
     "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>published</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>80299.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>1.751</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>2.064</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>1.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>2.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90%</th>\n",
       "      <td>3.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99%</th>\n",
       "      <td>10.000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>89.000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       published\n",
       "count  80299.000\n",
       "mean       1.751\n",
       "std        2.064\n",
       "min        1.000\n",
       "25%        1.000\n",
       "50%        1.000\n",
       "75%        2.000\n",
       "90%        3.000\n",
       "99%       10.000\n",
       "max       89.000"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "query = \"\"\"\n",
    "MATCH (a:Author)\n",
    "RETURN size((a)<-[:AUTHOR]-()) AS published\n",
    "\"\"\"\n",
    "\n",
    "published_df = graph.run(query).to_data_frame()\n",
    "published_df.describe([.25, .5, .75, .9, .99])\n",
    "\n"
   ]
  }
 ],
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