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Chapter 7: Graph I/O


%load_ext autoreload
%autoreload 2
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import warnings


from IPython.display import YouTubeVideo

YouTubeVideo(id="3sJnTpeFXZ4", width="100%")

In order to get you familiar with graph ideas, I have deliberately chosen to steer away from the more pedantic matters of loading graph data to and from disk. That said, the following scenario will eventually happen, where a graph dataset lands on your lap, and you'll need to load it in memory and start analyzing it.

Thus, we're going to go through graph I/O, specifically the APIs on how to convert graph data that comes to you into that magical NetworkX object G.

Let's get going!

Graph Data as Tables

Let's recall what we've learned in the introductory chapters. Graphs can be represented using two sets:

  • Node set
  • Edge set

Node set as tables

Let's say we had a graph with 3 nodes in it: A, B, C. We could represent it in plain text, computer-readable format:


Suppose the nodes also had metadata. Then, we could tag on metadata as well:

A, circle, 5
B, circle, 7
C, square, 9

Does this look familiar to you? Yes, node sets can be stored in CSV format, with one of the columns being node ID, and the rest of the columns being metadata.

Edge set as tables

If, between the nodes, we had 4 edges (this is a directed graph), we can also represent those edges in plain text, computer-readable format:

A, C
B, C
A, B
C, A

And let's say we also had other metadata, we can represent it in the same CSV format:

A, C, red
B, C, orange
A, B, yellow
C, A, green

If you've been in the data world for a while, this should not look foreign to you. Yes, edge sets can be stored in CSV format too! Two of the columns represent the nodes involved in an edge, and the rest of the columns represent the metadata.

Combined Representation

In fact, one might also choose to combine the node set and edge set tables together in a merged format:

n1, n2, colour, shape1, num1, shape2, num2
A,  C,  red,    circle, 5,    square, 9
B,  C,  orange, circle, 7,    square, 9
A,  B,  yellow, circle, 5,    circle, 7
C,  A,  green,  square, 9,    circle, 5

In this chapter, the datasets that we will be looking at are going to be formatted in both ways. Let's get going.


We will be working with the Divvy bike sharing dataset.

Divvy is a bike sharing service in Chicago. Since 2013, Divvy has released their bike sharing dataset to the public. The 2013 dataset is comprised of two files: - Divvy_Stations_2013.csv, containing the stations in the system, and - DivvyTrips_2013.csv, containing the trips.

Let's dig into the data!

from pyprojroot import here

Firstly, we need to unzip the dataset:

import zipfile
import os
from nams.load_data import datasets

# This block of code checks to make sure that a particular directory is present.
if "divvy_2013" not in os.listdir(datasets):
    print('Unzipping the file in the datasets folder.')
    with zipfile.ZipFile(datasets / "","r") as zip_ref:

Now, let's load in both tables.

First is the stations table:

import pandas as pd

stations = pd.read_csv(datasets / 'divvy_2013/Divvy_Stations_2013.csv', parse_dates=['online date'], encoding='utf-8')
id name latitude longitude dpcapacity landmark online date
0 5 State St & Harrison St 41.874 -87.6277 19 30 2013-06-28
1 13 Wilton Ave & Diversey Pkwy 41.9325 -87.6527 19 66 2013-06-28
2 14 Morgan St & 18th St 41.8581 -87.6511 15 163 2013-06-28
3 15 Racine Ave & 18th St 41.8582 -87.6565 15 164 2013-06-28
4 16 Wood St & North Ave 41.9103 -87.6725 15 223 2013-08-12
id latitude longitude dpcapacity landmark online date
count 300 300 300 300 300 300
mean 189.063 41.8963 -87.6482 16.8 192.013 2013-08-13 22:48:00
min 5 41.7887 -87.7079 11 1 2013-06-28 00:00:00
25% 108.75 41.8718 -87.6658 15 83.75 2013-07-23 00:00:00
50% 196.5 41.8946 -87.6486 15 184.5 2013-08-15 00:00:00
75% 276.25 41.9264 -87.6318 19 288.25 2013-09-13 00:00:00
max 351 41.9784 -87.5807 47 440 2013-10-29 00:00:00
std 99.4845 0.0409522 0.0230011 4.67399 120.535 nan

Now, let's load in the trips table.

trips = pd.read_csv(datasets / 'divvy_2013/Divvy_Trips_2013.csv', 
                    parse_dates=['starttime', 'stoptime'])
trip_id starttime stoptime bikeid tripduration from_station_id from_station_name to_station_id to_station_name usertype gender birthday
0 4118 2013-06-27 12:11:00 2013-06-27 12:16:00 480 316 85 Michigan Ave & Oak St 28 Larrabee St & Menomonee St Customer nan nan
1 4275 2013-06-27 14:44:00 2013-06-27 14:45:00 77 64 32 Racine Ave & Congress Pkwy 32 Racine Ave & Congress Pkwy Customer nan nan
2 4291 2013-06-27 14:58:00 2013-06-27 15:05:00 77 433 32 Racine Ave & Congress Pkwy 19 Loomis St & Taylor St Customer nan nan
3 4316 2013-06-27 15:06:00 2013-06-27 15:09:00 77 123 19 Loomis St & Taylor St 19 Loomis St & Taylor St Customer nan nan
4 4342 2013-06-27 15:13:00 2013-06-27 15:27:00 77 852 19 Loomis St & Taylor St 55 Halsted St & James M Rochford St Customer nan nan
import janitor
trips_summary = (
    .groupby(["from_station_id", "to_station_id"])
    .rename_column("trip_id", "num_trips")
from_station_id to_station_id num_trips
0 5 5 232
1 5 13 1
2 5 14 15
3 5 15 9
4 5 16 4

Graph Model

Given the data, if we wished to use a graph as a data model for the number of trips between stations, then naturally, nodes would be the stations, and edges would be trips between them.

This graph would be directed, as one could have more trips from station A to B and less in the reverse.

With this definition, we can begin graph construction!

Create NetworkX graph from pandas edgelist

NetworkX provides an extremely convenient way to load data from a pandas DataFrame:

import networkx as nx

G = nx.from_pandas_edgelist(

Inspect the graph

Once the graph is in memory, we can inspect it to get out summary graph statistics.

DiGraph with 300 nodes and 44422 edges

You'll notice that the edge metadata have been added correctly: we have recorded in there the number of trips between stations.

[(5, 5, {'num_trips': 232}),
 (5, 13, {'num_trips': 1}),
 (5, 14, {'num_trips': 15}),
 (5, 15, {'num_trips': 9}),
 (5, 16, {'num_trips': 4})]

However, the node metadata is not present:

[(5, {}), (13, {}), (14, {}), (15, {}), (16, {})]

Annotate node metadata

We have rich station data on hand, such as the longitude and latitude of each station, and it would be a pity to discard it, especially when we can potentially use it as part of the analysis or for visualization purposes. Let's see how we can add this information in.

Firstly, recall what the stations dataframe looked like:

id name latitude longitude dpcapacity landmark online date
0 5 State St & Harrison St 41.874 -87.6277 19 30 2013-06-28
1 13 Wilton Ave & Diversey Pkwy 41.9325 -87.6527 19 66 2013-06-28
2 14 Morgan St & 18th St 41.8581 -87.6511 15 163 2013-06-28
3 15 Racine Ave & 18th St 41.8582 -87.6565 15 164 2013-06-28
4 16 Wood St & North Ave 41.9103 -87.6725 15 223 2013-08-12

The id column gives us the node ID in the graph, so if we set id to be the index, if we then also loop over each row, we can treat the rest of the columns as dictionary keys and values as dictionary values, and add the information into the graph.

Let's see this in action.

for node, metadata in stations.set_index("id").iterrows():
    for key, val in metadata.items():
        G.nodes[node][key] = val

Now, our node metadata should be populated.

  {'name': 'State St & Harrison St',
   'latitude': 41.87395806,
   'longitude': -87.62773949,
   'dpcapacity': 19,
   'landmark': 30,
   'online date': Timestamp('2013-06-28 00:00:00')}),
  {'name': 'Wilton Ave & Diversey Pkwy',
   'latitude': 41.93250008,
   'longitude': -87.65268082,
   'dpcapacity': 19,
   'landmark': 66,
   'online date': Timestamp('2013-06-28 00:00:00')}),
  {'name': 'Morgan St & 18th St',
   'latitude': 41.858086,
   'longitude': -87.651073,
   'dpcapacity': 15,
   'landmark': 163,
   'online date': Timestamp('2013-06-28 00:00:00')}),
  {'name': 'Racine Ave & 18th St',
   'latitude': 41.85818061,
   'longitude': -87.65648665,
   'dpcapacity': 15,
   'landmark': 164,
   'online date': Timestamp('2013-06-28 00:00:00')}),
  {'name': 'Wood St & North Ave',
   'latitude': 41.910329,
   'longitude': -87.672516,
   'dpcapacity': 15,
   'landmark': 223,
   'online date': Timestamp('2013-08-12 00:00:00')})]

In nxviz, a GeoPlot object is available that allows you to quickly visualize a graph that has geographic data. However, being matplotlib-based, it is going to be quickly overwhelmed by the sheer number of edges.

As such, we are going to first filter the edges.

Exercise: Filter graph edges

Leveraging what you know about how to manipulate graphs, now try filtering edges.

Hint: NetworkX graph objects can be deep-copied using G.copy():

G_copy = G.copy()

Hint: NetworkX graph objects also let you remove edges:

G.remove_edge(node1, node2)  # does not return anything
def filter_graph(G, minimum_num_trips):
    Filter the graph such that 
    only edges that have minimum_num_trips or more
    are present.
    G_filtered = G.____()
    for _, _, _ in G._____(data=____):
        if d[___________] < ___:
            G_________.___________(_, _)
    return G_filtered

from import filter_graph

G_filtered = filter_graph(G, 50)

Visualize using GeoPlot

nxviz provides a GeoPlot object that lets you quickly visualize geospatial graph data.

A note on geospatial visualizations:

As the creator of nxviz, I would recommend using proper geospatial packages to build custom geospatial graph viz, such as pysal.)

That said, nxviz can probably do what you need for a quick-and-dirty view of the data.

import nxviz as nv

c = nv.geo(G_filtered, node_color_by="dpcapacity")

Does that look familiar to you? Looks quite a bit like Chicago, I'd say :)

Jesting aside, this visualization does help illustrate that the majority of trips occur between stations that are near the city center.

Pickling Graphs

Since NetworkX graphs are Python objects, the canonical way to save them is by pickling them.

Here's an example in action:

import pickle
with open("/tmp/divvy.pkl", "wb") as f:
    pickle.dump(G, f, pickle.HIGHEST_PROTOCOL)

And just to show that it can be loaded back into memory:

with open("/tmp/divvy.pkl", "rb") as f:
    G_loaded = pickle.load(f)

Exercise: checking graph integrity

If you get a graph dataset as a pickle, you should always check it against reference properties to make sure of its data integrity.

Write a function that tests that the graph has the correct number of nodes and edges inside it.

def test_graph_integrity(G):
    """Test integrity of raw Divvy graph."""
    # Your solution here

from import test_graph_integrity


Other text formats

CSV files and pandas DataFrames give us a convenient way to store graph data, and if possible, do insist with your data collaborators that they provide you with graph data that are in this format. If they don't, however, no sweat! After all, Python is super versatile.

In this ebook, we have loaded data in from non-CSV sources, sometimes by parsing text files raw, sometimes by treating special characters as delimiters in a CSV-like file, and sometimes by resorting to parsing JSON.

You can see other examples of how we load data by browsing through the source file of and studying how we construct graph objects.


The solutions to this chapter's exercises are below

from import io
import inspect

"""Solutions to I/O chapter"""

def filter_graph(G, minimum_num_trips):
    Filter the graph such that
    only edges that have minimum_num_trips or more
    are present.
    G_filtered = G.copy()
    for u, v, d in G.edges(data=True):
        if d["num_trips"] < minimum_num_trips:
            G_filtered.remove_edge(u, v)
    return G_filtered

def test_graph_integrity(G):
    """Test integrity of raw Divvy graph."""
    assert len(G.nodes()) == 300
    assert len(G.edges()) == 44422