🚀 Keyword Research Tool – Your Secret SEO Weapon in Python!

Want to unlock the hidden SEO treasures buried in search engines? Say hello to your very own Keyword Research Tool, built with nothing but Python and some clever API magic!

🔮 Whether you’re an SEO enthusiast, content strategist, or digital marketing pro, this simple tool helps you find powerful keywords that can skyrocket your rankings.

 

🔍 Why You Need a Keyword Research Tool

A great keyword isn’t just a word — it’s insight, opportunity, and a shortcut to traffic. This tool helps you discover:

✅ High-traffic keywords.

✅ Low-competition gems.

✅ Related search terms people are typing.

✅ Export-ready data for quick content planning.

🛠️ Features That Pack a Punch

✨ Fetch keyword suggestions from Google Autocomplete.

📊 Check real-time search volume, CPC & competition (via Ubersuggest API).

📈 Analyze keyword difficulty (customizable with third-party APIs).

📁 Export everything to a clean, ready-to-use CSV or Excel file.

💻 Let’s Build It – Step-by-Step

🔧 1. Install the Essentials

sh

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pip install requests beautifulsoup4 pandas

 

🧠 2. The Python Magic Begins

python

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import requests

from bs4 import BeautifulSoup

import pandas as pd

def get_google_suggestions(keyword):

url = f”http://suggestqueries.google.com/complete/search?client=firefox&q={keyword}”

response = requests.get(url)

suggestions = response.json()[1]

return suggestions

def get_keyword_data(keyword):

api_url = f”https://api.neilpatel.com/ubersuggest/keyword-data?keyword={keyword}&country=us”

headers = {“User-Agent”: “Mozilla/5.0”}

try:

response = requests.get(api_url, headers=headers)

data = response.json()

if ‘search_volume’ in data:

return {

“Keyword”: keyword,

“Search Volume”: data[“search_volume”],

“CPC ($)”: data[“cpc”],

“Competition”: data[“competition”]

}

except:

return Nonedef keyword_research(seed_keyword):

print(f”🔍 Searching for: {seed_keyword}…”)

suggestions = get_google_suggestions(seed_keyword)

keyword_data_list = []

for keyword in suggestions:

data = get_keyword_data(keyword)

if data:

keyword_data_list.append(data)

df = pd.DataFrame(keyword_data_list)

df.to_csv(“keyword_research.csv”, index=False)

print(“✅ Done! Results saved to ‘keyword_research.csv'”)

return df

# 🔥 Example Run

seed_keyword = “best SEO expert in Bangladesh”

df_results = keyword_research(seed_keyword)

print(df_results)

🧬 How It Works

Here’s the simple genius behind it:

• You enter a seed keyword like “best SEO expert in Bangladesh”.
• The tool fetches autocomplete suggestions directly from Google.
• It queries the Ubersuggest API for:
  • 📈 Monthly Search Volume
  • 💵 Cost Per Click (CPC)
  • ⚔️ Keyword Competition
• Finally, it exports everything to a CSV, ready for analysis, planning, or client reporting.

🔧 Level Up: Make It Smarter!

🔹 Add Moz or Ahrefs API for more in-depth insights

🔹 Calculate Keyword Difficulty Score for smarter targeting

🔹 Create a web app with Flask or Django to make it interactive

🔹 Enable location filters for hyper-targeted keyword research

👨‍💻 Want to Build a Web-Based Version?

Let’s turn this Python script into a beautiful web app with a dashboard, charts, filters, and maybe even a sprinkle of AI. 💡 Just say the word, and I’ll guide you step-by-step.

Ready to dominate the SERPs? 🌐💥

 

🌐 Web-Based Keyword Research Tool – Game Plan

✅ Core Features

🔍 Input field for seed keyword.

📥 Fetch keyword suggestions via Google Autocomplete

📊 Pull keyword metrics (volume, CPC, competition) from API

📈 Display data in an interactive table + charts

📁 Export to CSV

🌍 Optional: Select location/country for keyword targeting

 

⚙️ Tech Stack

• Frontend: HTML + Tailwind CSS + Chart.js + Vanilla JS or HTMX (for real-time updates).
• Backend: Python + Flask.
• Data Layer: Pandas for processing, CSV export.

🧩 Folder Structure

csharp

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keyword-research-tool/

│

├── app.py # Flask backend

├── templates/

│ └── index.html # Main UI

├── static/

│ ├── css/ # Tailwind styles

│ └── js/ # Chart.js scripts

├── utils/

│ └── keyword_api.py # API functions

├── output/

│ └── keyword_data.csv # Exported CSV

└── requirements.txt

🛠️ Let’s Start Building

Would you like me to:

• Generate the full Flask project with frontend UI (ready-to-run)?
• Focus on just the frontend design first (with search input, table & chart)?
• Or build the backend + keyword processing logic first?

🚧 Phase 1: Basic Flask Web App Setup

• Homepage with a keyword input form.
• Backend processes the keyword.
• Fetches suggestions + data.
• Displays results in a styled HTML table.
• CSV export button.

🎨 Phase 2: Polished UI with Tailwind + Charts

• Tailwind CSS for a modern design.
• Keyword metrics visualized using Chart.js (bar or line charts).
• Add a loading animation during API calls.
• Optional: dropdown to select target country.

🧠 Phase 3: Advanced Features (Optional)

• Ahrefs/Moz API integration for KD (keyword difficulty).
• User login for saved searches.
• Historical trend graphs.

I’ll now begin by generating Phase 1: A working Flask web app with all your existing logic integrated and styled simply. Let’s do it. 🎯

Keyword Research App

✅ Flask backend is ready! Now I’ll set up the frontend HTML with Tailwind CSS that includes:

• A clean input form.
• Table to display keyword results.
• Download button.

Next up: the UI! Hang tight. 🎨✨

 

from flask import Flask, render_template, request, send_file

import requests

import pandas as pd

import os

app = Flask(__name__)

# Function to get Google autocomplete suggestions

def get_google_suggestions(keyword):

url = f”http://suggestqueries.google.com/complete/search?client=firefox&q={keyword}”

response = requests.get(url)

suggestions = response.json()[1]

return suggestions

Final wrapped-up Flask backend for the Keyword Research Tool:

🧠 Features Included:

• Takes a seed keyword input.
• Pulls Google autocomplete suggestions.
• Generates dummy SEO metrics (search volume, CPC, competition).
• Saves the results to a CSV file.
• Allows CSV download from /download.
• No multiprocessing issues (debug mode is off).

✅ Next Step: Frontend HTML (index.html)

Would you like me to now give you a ready-made index.html with:

• Tailwind CSS styling.
• Keyword input form.
• Results table with clean UI.
• Export button.