2_Basic-NetworkX.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To start working with NetworkX, we're going to build a graph for a single event. We can do this in a couple of different ways: adding attributes piece-by-piece, building it from list of edges or through a dataframe. Then, we'll play with the measures and do basic plotting.  \n",
    "\n",
    "The event we'll be working with is:\n",
    "\n",
    "```\n",
    "event_id: 233228364\n",
    "name: Tomato Art Festival\n",
    "date: 2016-08-13 20:00:00\n",
    "group: 20s in Nashville\n",
    "yes_rsvps: 10\n",
    "```\n",
    "\n",
    "The member information will come from the `rsvps` data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The members attending this event are:\n",
      "Alexandra, Andrea, Christina, Cory, Jermaine, Kealy, Lifeavinash, Shealyn, Tsion, Zack\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "example = pd.read_csv('data\\\\example_members.csv', index_col='member_id')\n",
    "\n",
    "print('The members attending this event are:')\n",
    "print(', '.join(example.name))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. Creating a graph via declarative syntax.\n",
    "\n",
    "The first way to create a graph is to explicitly add nodes and edges. \n",
    "\n",
    "```\n",
    "g.add_node(node_id)\n",
    "g.add_edge(node_id, node_id)\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import networkx as nx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Instantiate an undirected graph\n",
    "g = nx.Graph()\n",
    "\n",
    "# Add member nodes\n",
    "for member in example.name:\n",
    "    g.add_node(member)\n",
    "    \n",
    "# Add event node\n",
    "g.add_node('tomato-festival')\n",
    "\n",
    "# Add edges\n",
    "for member in example.name:\n",
    "    g.add_edge(member, 'tomato-festival')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<networkx.classes.graph.Graph at 0x225fc283f60>"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can access a \"view\" of the nodes and edges view the `g.nodes` and `g.edges` methods. Applying a `dict` to the view or passing `data=True` will expose the attributes of the underlying objects."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NodeView(('Alexandra', 'Andrea', 'Christina', 'Cory', 'Jermaine', 'Kealy', 'Lifeavinash', 'Shealyn', 'Tsion', 'Zack', 'tomato-festival'))"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.nodes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "NodeDataView({'Alexandra': {}, 'Andrea': {}, 'Christina': {}, 'Cory': {}, 'Jermaine': {}, 'Kealy': {}, 'Lifeavinash': {}, 'Shealyn': {}, 'Tsion': {}, 'Zack': {}, 'tomato-festival': {}})"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g.nodes(data=True) # Notice each node has an empty attribute dict"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Alexandra': {},\n",
       " 'Andrea': {},\n",
       " 'Christina': {},\n",
       " 'Cory': {},\n",
       " 'Jermaine': {},\n",
       " 'Kealy': {},\n",
       " 'Lifeavinash': {},\n",
       " 'Shealyn': {},\n",
       " 'Tsion': {},\n",
       " 'Zack': {},\n",
       " 'tomato-festival': {}}"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(g.nodes) # Similarly, access by coercing into a dict"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Attributes\n",
    "You can add attributes in two ways:\n",
    "\n",
    "1. Assign them directly, like you would a dict.\n",
    "2. Use the `nx.set_XXXX_attributes` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# We can assign attributes in single statements or for loops\n",
    "g.nodes['tomato-festival']['node_type'] = 'event'\n",
    "\n",
    "for member in example.name:\n",
    "    g.nodes[member]['node_type'] = 'person'\n",
    "\n",
    "# ...but a combination of listcomps and the \"set_node/edge_attributes\" function is more succinct.\n",
    "nx.set_edge_attributes(G = g, \n",
    "                       values = {e: 'yes_rsvp' for e in g.edges}, \n",
    "                       name = 'edge_type')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Alexandra': {'node_type': 'person'},\n",
       " 'Andrea': {'node_type': 'person'},\n",
       " 'Christina': {'node_type': 'person'},\n",
       " 'Cory': {'node_type': 'person'},\n",
       " 'Jermaine': {'node_type': 'person'},\n",
       " 'Kealy': {'node_type': 'person'},\n",
       " 'Lifeavinash': {'node_type': 'person'},\n",
       " 'Shealyn': {'node_type': 'person'},\n",
       " 'Tsion': {'node_type': 'person'},\n",
       " 'Zack': {'node_type': 'person'},\n",
       " 'tomato-festival': {'node_type': 'event'}}"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(g.nodes)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{('Alexandra', 'tomato-festival'): {'edge_type': 'yes_rsvp'},\n",
       " ('Andrea', 'tomato-festival'): {'edge_type': 'yes_rsvp'},\n",
       " ('Christina', 'tomato-festival'): {'edge_type': 'yes_rsvp'},\n",
       " ('Cory', 'tomato-festival'): {'edge_type': 'yes_rsvp'},\n",
       " ('Jermaine', 'tomato-festival'): {'edge_type': 'yes_rsvp'},\n",
       " ('Kealy', 'tomato-festival'): {'edge_type': 'yes_rsvp'},\n",
       " ('Lifeavinash', 'tomato-festival'): {'edge_type': 'yes_rsvp'},\n",
       " ('Shealyn', 'tomato-festival'): {'edge_type': 'yes_rsvp'},\n",
       " ('Tsion', 'tomato-festival'): {'edge_type': 'yes_rsvp'},\n",
       " ('Zack', 'tomato-festival'): {'edge_type': 'yes_rsvp'}}"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(g.edges)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Building graphs from lists and dataframes\n",
    "\n",
    "There are many other methods for building graphs which might take advantage of your existing data structures. These include building it from a list of edges (which can be useful when combined with listcomps) or from a pandas dataframe."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# From an edge_list\n",
    "g = nx.from_edgelist([(n, 'tomato-festival') for n in example.name])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# From a dataframe\n",
    "example['event_name'] = 'tomato-festival'\n",
    "example['edge_type'] = 'yes_rsvp'\n",
    "g = nx.from_pandas_dataframe(example, \n",
    "                             source='name', \n",
    "                             target='event_name', \n",
    "                             edge_attr='edge_type')\n",
    "\n",
    "# Set node attributes\n",
    "node_type_dict = dict([(n, 'member') if n in example.name.tolist()\n",
    "                           else (n, 'event') for n in g.nodes])\n",
    "nx.set_node_attributes(g, node_type_dict, 'node_type')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "... and there are many other methods available."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['from_dict_of_dicts',\n",
       " 'from_dict_of_lists',\n",
       " 'from_edgelist',\n",
       " 'from_nested_tuple',\n",
       " 'from_numpy_array',\n",
       " 'from_numpy_matrix',\n",
       " 'from_pandas_adjacency',\n",
       " 'from_pandas_dataframe',\n",
       " 'from_pandas_edgelist',\n",
       " 'from_prufer_sequence',\n",
       " 'from_scipy_sparse_matrix']"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "[m for m in dir(nx) if 'from_' in m]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. Getting graph metrics\n",
    "\n",
    "There are many built-in functions for calculating graph measures. They typically take the form of `nx.some_measure(g)` and return a dictionary of values.\n",
    "\n",
    "Degree is one exception -- it is so common and useful that it has its own special view. (It can be coerced into a dictionary, however.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DegreeView({'Alexandra': 1, 'tomato-festival': 10, 'Andrea': 1, 'Christina': 1, 'Cory': 1, 'Jermaine': 1, 'Kealy': 1, 'Lifeavinash': 1, 'Shealyn': 1, 'Tsion': 1, 'Zack': 1})"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.degree(g)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Alexandra': 1,\n",
       " 'Andrea': 1,\n",
       " 'Christina': 1,\n",
       " 'Cory': 1,\n",
       " 'Jermaine': 1,\n",
       " 'Kealy': 1,\n",
       " 'Lifeavinash': 1,\n",
       " 'Shealyn': 1,\n",
       " 'Tsion': 1,\n",
       " 'Zack': 1,\n",
       " 'tomato-festival': 10}"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dict(nx.degree(g))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'Alexandra': 0.0,\n",
       " 'Andrea': 0.0,\n",
       " 'Christina': 0.0,\n",
       " 'Cory': 0.0,\n",
       " 'Jermaine': 0.0,\n",
       " 'Kealy': 0.0,\n",
       " 'Lifeavinash': 0.0,\n",
       " 'Shealyn': 0.0,\n",
       " 'Tsion': 0.0,\n",
       " 'Zack': 0.0,\n",
       " 'tomato-festival': 1.0}"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "nx.betweenness_centrality(g)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. Drawing simple graphs.\n",
    "\n",
    "NetworkX includes functionality for plotting graphs quickly and easily using `matplotlib`. There are two fundamental aspects to plotting graphs:\n",
    "\n",
    "1. Getting a layout dictionary.\n",
    "    - To plot a graph, each node must have a defined position in the x-y plane. Several functions can provide these for you, including `nx.circular_layout`, `nx.spring_layout` and `nx.random_layout`.\n",
    "2. Plotting nodes, edges and labels.\n",
    "    - The plotting call can be done all at once (via `nx.draw_networkx`) or piecemeal through `nx.draw_networkx_nodes`, `nx.draw_networkx_edges`, etc. In each case, you must provide the graph and position dictionary."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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f3M/xWnxapnByH2lIE1zUblFvkhYxmKkyIhDKEw5tR1ZQpxCEFHjMKujSFlmU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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x225fa3a6c50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from utils import setup_graph_plot   # Configures useful matplotlib settings\n",
    "\n",
    "fig, ax = setup_graph_plot()\n",
    "\n",
    "pos = nx.spring_layout(g)\n",
    "nx.draw_networkx(g, pos)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x225faf6ad30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting nodes and edges separately can be helpful\n",
    "\n",
    "fig, ax = setup_graph_plot()\n",
    "\n",
    "colors = ['g' if g.nodes[n]['node_type'] == 'member' else 'b' for n in g.nodes]\n",
    "\n",
    "pos = nx.circular_layout(g)\n",
    "nx.draw_networkx_nodes(g, pos, node_color=colors) # No labels are plotted either\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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xg4DjJScblLkmzynYNNn3e2lnZsS7QD+DLT4MwApVrBn/fBQwqcq/RVV4L9KYRVjJnSQG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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x225fafc2550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plotting nodes and edges separately can be helpful\n",
    "\n",
    "fig, ax = setup_graph_plot()\n",
    "\n",
    "colors = ['g' if g.nodes[n]['node_type'] == 'member' else 'b' for n in g.nodes]\n",
    "sizes = [100*g.degree[n] for n in g.nodes]\n",
    "\n",
    "pos = nx.random_layout(g)\n",
    "nx.draw_networkx_nodes(g, pos, node_color=colors, node_size=sizes) \n",
    "nx.draw_networkx_edges(g, pos)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Great! We have now seen how to build a simple graph, access measurements from it and make a basic plot. In the next post, we'll see how to do this on the MeetUp data."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda env:py3]",
   "language": "python",
   "name": "conda-env-py3-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}