Create a blogging frequency analyzer

Here’s a simple Blogging Frequency Analyzer script written in Python. It takes a CSV file of blog post dates (or accepts direct input), analyzes how often blog posts are published, and gives useful frequency metrics such as:

• Total posts

• Average posts per week/month

• Busiest months/weeks

python
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import pandas as pd
from collections import Counter
from datetime import datetime

def analyze_blogging_frequency(file_path):
    # Load CSV with a 'date' column
    try:
        df = pd.read_csv(file_path, parse_dates=['date'])
    except Exception as e:
        print(f"Error loading file: {e}")
        return

    if 'date' not in df.columns:
        print("CSV must contain a 'date' column with publication dates.")
        return

    # Ensure dates are in datetime format
    df['date'] = pd.to_datetime(df['date'])

    # Basic statistics
    total_posts = len(df)
    date_range = df['date'].max() - df['date'].min()
    weeks_range = max(date_range.days / 7, 1)
    months_range = max(date_range.days / 30.44, 1)

    posts_per_week = total_posts / weeks_range
    posts_per_month = total_posts / months_range

    # Posts by month
    df['month'] = df['date'].dt.to_period('M')
    posts_by_month = df['month'].value_counts().sort_index()

    # Posts by week
    df['week'] = df['date'].dt.to_period('W')
    posts_by_week = df['week'].value_counts().sort_index()

    busiest_month = posts_by_month.idxmax().strftime('%Y-%m')
    busiest_week = posts_by_week.idxmax().strftime('%Y-%m-%d')

    # Output
    print(f"Total blog posts: {total_posts}")
    print(f"Date range: {df['date'].min().date()} to {df['date'].max().date()}")
    print(f"Average posts per week: {posts_per_week:.2f}")
    print(f"Average posts per month: {posts_per_month:.2f}")
    print(f"Busiest month: {busiest_month} with {posts_by_month.max()} posts")
    print(f"Busiest week: {busiest_week} with {posts_by_week.max()} postsn")

    print("Posts per Month:")
    for period, count in posts_by_month.items():
        print(f"{period}: {count}")

    print("nPosts per Week:")
    for period, count in posts_by_week.items():
        print(f"{period.start_time.date()} - {period.end_time.date()}: {count}")

# Example usage:
# analyze_blogging_frequency("your_blog_posts.csv")

Requirements:

• Your CSV must include a date column with valid publication dates (format like 2023-10-05).

• Install pandas via pip if needed: pip install pandas

This script is ideal for tracking blogging consistency and identifying high-activity periods. If you need a web-based version or want to input data manually, I can also help adapt it.