Chapter 7: Graph I/O
%load_ext autoreload
%autoreload 2
%matplotlib inline
%config InlineBackend.figure_format = 'retina'
import warnings
warnings.filterwarnings('ignore')
Introduction
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:
A
B
C
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.
Dataset
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 divvy_2013.zip file in the datasets folder.')
with zipfile.ZipFile(datasets / "divvy_2013.zip","r") as zip_ref:
zip_ref.extractall(datasets)
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')
stations.head()
| | 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 |
stations.describe()
| | id | latitude | longitude | dpcapacity | landmark | |:------|---------:|------------:|------------:|-------------:|-----------:| | count | 300 | 300 | 300 | 300 | 300 | | mean | 189.063 | 41.8963 | -87.6482 | 16.8 | 192.013 | | std | 99.4845 | 0.0409522 | 0.0230011 | 4.67399 | 120.535 | | min | 5 | 41.7887 | -87.7079 | 11 | 1 | | 25% | 108.75 | 41.8718 | -87.6658 | 15 | 83.75 | | 50% | 196.5 | 41.8946 | -87.6486 | 15 | 184.5 | | 75% | 276.25 | 41.9264 | -87.6318 | 19 | 288.25 | | max | 351 | 41.9784 | -87.5807 | 47 | 440 |
Now, let's load in the trips table.
trips = pd.read_csv(datasets / 'divvy_2013/Divvy_Trips_2013.csv',
parse_dates=['starttime', 'stoptime'])
trips.head()
| | 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 = (
trips
.groupby(["from_station_id", "to_station_id"])
.count()
.reset_index()
.select_columns(
[
"from_station_id",
"to_station_id",
"trip_id"
]
)
.rename_column("trip_id", "num_trips")
)
trips_summary.head()
| | 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(
df=trips_summary,
source="from_station_id",
target="to_station_id",
edge_attr=["num_trips"],
create_using=nx.DiGraph
)
Inspect the graph
Once the graph is in memory, we can inspect it to get out summary graph statistics.
print(nx.info(G))
You'll notice that the edge metadata have been added correctly: we have recorded in there the number of trips between stations.
list(G.edges(data=True))[0:5]
However, the node metadata is not present:
list(G.nodes(data=True))[0:5]
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:
stations.head()
| | 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.
list(G.nodes(data=True))[0:5]
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 nams.solutions.io 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 aspysal.)That said,
nxvizcan 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. You can do this using:
nx.write_gpickle(G, file_path)
Here's an example in action:
nx.write_gpickle(G, "/tmp/divvy.pkl")
And just to show that it can be loaded back into memory:
G_loaded = nx.read_gpickle("/tmp/divvy.pkl")
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
pass
from nams.solutions.io import test_graph_integrity
test_graph_integrity(G)
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 load_data.py
and studying how we construct graph objects.
Solutions
The solutions to this chapter's exercises are below
from nams.solutions import io
import inspect
print(inspect.getsource(io))