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JoeHelbing/network-graph-short-python-intro-via-runnable-code.ipynb

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network-graph-short-python-intro-via-runnable-code.ipynb
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network-graph-short-python-intro-via-runnable-code.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {
"colab_type": "text",
"id": "view-in-github"
},
"source": [
"<a href=\"https://colab.research.google.com/gist/JoeHelbing/ed328158c450d708083dfe5d5400cb4c/network-graph-short-python-intro-via-runnable-code.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
]
},
{
"cell_type": "markdown",
"metadata": {
"id": "QslKXDO3xMTD"
},
"source": [
"# Network Graph Short Python Intro via Runnable Code\n",
"To run each code block you can use:\n",
"- ctrl+enter to run the specific code cell\n",
"- shift+enter to run the selected cell, and automatically move down to the next cell\n",
"- The play button on the left side will also run the code on a mouseclick"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"id": "zuoj0A4us6IG"
},
"outputs": [],
"source": [
"# Use BASH commands to install packages into the development environment\n",
"# !pip install networkx pandas matplotlib seaborn plotly -q\n",
"# !pip install pyvis -q # -q is a flag to make the install \"quiet\" i.e. not show a bunch of status logging"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"id": "1SV2yqzeqewN"
},
"outputs": [],
"source": [
"# Import the main packages for data manipulation\n",
"import pandas as pd # Dataframes in Python\n",
"import networkx as nx # Handles graph objects, the datastructures, not the actual visualizatons\n",
"import numpy as np # Matrix operations\n",
"from collections import Counter # Python standard package to create dictionaries with counts\n",
"\n",
"# Create random data to graph\n",
"from random import randint\n",
"\n",
"# Graphing packages\n",
"import matplotlib.pyplot as plt # Graph plotting package used as the foundation that many graphing packages build off of\n",
"import seaborn as sns # Good static graph package, simple and straightforward, easy to work with\n",
"import plotly.graph_objects as go # More extensible graphing package, bit less user friendly, can export .html\n",
"from pyvis.network import Network # Less popular and orphaned network graphing package, but still fantastic at what it does"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"id": "d9RqKc5Tvv8m"
},
"outputs": [],
"source": [
"# Number of data points to generate\n",
"num_points = 20 # Choose however large you'd like your graph to be.\n",
"\n",
"# Generate random data to create a Sankey Diagram\n",
"# We want to have different source and target values aka one thing leads to a different thing\n",
"source = [int(randint(0, 4)) for _ in range(num_points)] # Sources 0-4\n",
"target = [int(randint(5, 9)) for _ in range(num_points)] # Targets 5-9\n",
"value = value=[1] * len(source) # We will start with all flows of weight=1 (unweighted)\n",
"\n",
"# If you want to create new data and graph again, you can run this cell and the\n",
"# cells below again and it will produce new graphs with the new data."
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"id": "K-4jKa3qyGKf"
},
"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>source</th>\n",
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" <th>value</th>\n",
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" <td>0</td>\n",
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" <tr>\n",
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" <td>7</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>3</td>\n",
" <td>7</td>\n",
" <td>1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" source target value\n",
"0 1 8 1\n",
"1 3 8 1\n",
"2 0 5 1\n",
"3 3 7 1\n",
"4 3 7 1"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# Let's inspect the data.\n",
"# Create a dictionary with the lists to convert the list values into a dataframe\n",
"data = {'source': source, 'target': target, 'value': value}\n",
"\n",
"# Create the pandas DataFrame\n",
"df = pd.DataFrame(data)\n",
"\n",
"# Display the DataFrame first 5 rows\n",
"df.head()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {
"id": "_sOiqbYdq3dE"
},
"outputs": [
{
"data": {
"text/html": [
" <script type=\"text/javascript\">\n",
" window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
" if (window.MathJax && window.MathJax.Hub && window.MathJax.Hub.Config) {window.MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
" if (typeof require !== 'undefined') {\n",
" require.undef(\"plotly\");\n",
" define('plotly', function(require, exports, module) {\n",
" /**\n",
"* plotly.js v2.35.2\n",
"* Copyright 2012-2024, Plotly, Inc.\n",
"* All rights reserved.\n",
"* Licensed under the MIT license\n",
"*/\n",
"/*! For license information please see plotly.min.js.LICENSE.txt */\n",
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