Automating Low-Performing Content Identification with Python

As content creators, we strive to produce high-quality material that engages our audience. However, with the ever-growing volume of content being published online, it can be challenging to keep track of which pieces are performing well and which ones need improvement.

In this article, we’ll explore how to use Python to automate the identification of low-performing content in markdown format. We’ll discuss the steps involved in setting up a basic script, processing markdown files, extracting relevant metrics, and visualizing the results using Matplotlib.

Prerequisites

Before diving into the code, make sure you have:

• Python 3.x installed on your system
• Pandas, NumPy, and Matplotlib libraries (install them via pip if needed)
• markdown library for parsing markdown files (install it via pip)

Step 1: Set Up the Environment

Create a new Python project directory and add the following dependencies to your requirements.txt file:

bash
pandas==1.3.4
numpy==1.21.2
matplotlib==3.5.1
markdown==3.3.6

Run pip install -r requirements.txt to install the required libraries.

Step 2: Define the Script

Create a file named content_analyzer.py. This script will serve as our main function for processing markdown files and identifying low-performing content:

“`python
import os
import pandas as pd
from markdown import Markdown

def analyze_content(markdown_files_dir):
# Initialize lists to store metrics
pageviews = []
engagement = []
metadata = []

# Iterate through markdown files in the specified directory
for filename in os.listdir(markdown_files_dir):
    if filename.endswith(".md"):
        filepath = os.path.join(markdown_files_dir, filename)

        # Parse markdown file using Markdown library
        md = Markdown()
        content = md.convert(open(filepath).read())

        # Extract pageviews and engagement metrics (simulated for demonstration purposes)
        pageviews.append(100)  # Replace with actual data source or calculation
        engagement.append(0.5)  # Replace with actual data source or calculation

        # Store metadata (e.g., title, author, date)
        metadata.append({"title": "Example Title", "author": "John Doe", "date": "2022-01-01"})

# Create a Pandas DataFrame from the collected metrics
df = pd.DataFrame({
    "Pageviews": pageviews,
    "Engagement": engagement,
    "Metadata": metadata
})

return df

Example usage:

markdown_files_dir = “/path/to/markdown/files”
result_df = analyze_content(markdown_files_dir)
print(result_df.head())
“`

This script defines a function analyze_content that takes the directory containing markdown files as input. It parses each file using the Markdown library, extracts relevant metrics (pageviews and engagement), and stores metadata in a Pandas DataFrame.

Step 3: Visualize Results

Use Matplotlib to create a line plot of pageviews vs engagement for each markdown file:

“`python
import matplotlib.pyplot as plt

Create a line plot of pageviews vs engagement

plt.figure(figsize=(10, 6))
plt.plot(result_df[“Pageviews”], result_df[“Engagement”])
plt.xlabel(“Pageviews”)
plt.ylabel(“Engagement”)
plt.title(“Low-Performing Content Identification”)
plt.show()
“`

This code generates a line plot showing the relationship between pageviews and engagement for each markdown file.

Conclusion

In this article, we’ve demonstrated how to use Python to automate the identification of low-performing content in markdown format. By processing markdown files, extracting relevant metrics, and visualizing the results using Matplotlib, you can streamline your content analysis process and focus on improving underperforming content.