Python is a popular high-level programming language known for its readability and efficiency. In the realm of Search Engine Optimization (SEO), Python offers powerful tools for web scraping, data analysis, automation, and much more. In this article, we will look at a Python script for creating a graph to analyze relationships between keywords and URLs.
This Python script uses the Pandas, NetworkX, and Plotly libraries. Here is the code:
import pandas as pd import networkx as nx import plotly.graph_objects as go # Load data from CSV or ODS file filepath = "your_file_path" data = pd.read_excel(filepath, usecols=['Keyword', 'Current URL']) # Create an empty graph graph = nx.Graph() # Add nodes to the graph graph.add_nodes_from(data['Keyword']) graph.add_nodes_from(data['Current URL']) # Add edges to the graph edges = zip(data['Keyword'], data['Current URL']) graph.add_edges_from(edges) # Create the graph layout positions = nx.spring_layout(graph) # Get the degree of each node node_degrees = dict(graph.degree) # Normalize node sizes min_size = min(node_degrees.values()) max_size = max(node_degrees.values()) fig = go.Figure() for node in graph.nodes: x, y = positions[node] size = node_degrees[node] # Unscaled size size_normalized = (size - min_size) / (max_size - min_size) # Scale size in the range [0, 1] node_size = size_normalized * 20 # Adjust the size as you like fig.add_trace(go.Scatter(x=[x], y=[y], mode='markers', name=node, marker=dict(size=node_size), text=node, hoverinfo='text')) for edge in graph.edges: x0, y0 = positions[edge] x1, y1 = positions[edge] fig.add_trace(go.Scatter(x=[x0, x1], y=[y0, y1], mode='lines', name='Connection', hoverinfo='none')) fig.update_layout(title='Interactive Graph', showlegend=False, hovermode='closest') fig.show()
your_file_path with the path to your own ODS or CSV file.
Installation and Execution
To successfully run this script, you need to install a few Python packages. The first, pandas, is a data manipulation library. The second, networkx, is used for creation, manipulation, and study of complex networks. The third package, plotly, is used for interactive graphing.
You can install these packages using pip, which is Python’s package manager. If you've Python installed, you likely have pip on your machine. In your terminal or command line, type the following commands:
pip install pandas pip install networkx pip install plotly
After installing these packages, you can run the script in your Python environment. Don't forget to replace the file path with your own CSV or ODS file.
This script's primary use is to analyze relationships between various keywords and URLs of a website. From an SEO perspective, understanding these connections is crucial. With this visual graph, you can identify which keywords are linked to which URLs. This could be particularly beneficial for content creation, targeting specific keywords, and improving the website's overall SEO.
The graph's nodes represent Keywords and URLs, with their size proportionate to their degree (number of connections). The lines between nodes represent connections based on the data from your input file.
In the field of SEO, Python can be a powerful ally. Whether you need to extract keywords, analyze sentiment, auto-complete suggestions, or visualize relationships between URLs and keywords, Python has all the tools needed. With a bit of coding knowledge, the world of SEO data analysis can well be at your fingertips.
We hope this guide has been helpful. If you have any questions or issues, don't hesitate to reach out.
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