# Legal Text Analysis Project Documentation - Part 2*Continued from Previous Setup Documentation*
## Database Creation VerificationWe've successfully created our SQLite database with all required tables and structures. Let's review what we've accomplished and how to verify each component.
### Current Project Status1. **Database Location and Access**
Our database is located at:
```bashD:/Projects/LegalTextAnalysis/data/legal_text.db

We confirmed its existence and read/write permissions with:

sqlite3 D:/Projects/LegalTextAnalysis/data/legal_text.db ".databases"# Output: main: D:\\Projects\\LegalTextAnalysis\\data\\legal_text.db r/w

1. Table Structure Creation We successfully created three interconnected tables:

sqlite3 D:/Projects/LegalTextAnalysis/data/legal_text.db ".tables"# Output: legal_cases    text_metrics   tf_idf_scores

Each table serves a specific purpose in our analysis pipeline:

• legal_cases: Stores raw case data and basic metrics
• text_metrics: Holds advanced text analysis measurements
• tf_idf_scores: Contains term frequency-inverse document frequency analysis

Working With Our Database

To interact with the database and dataset, we have several options:

1. Direct SQLite Commands

# Open SQLite consolesqlite3 D:/Projects/LegalTextAnalysis/data/legal_text.db
# View table structure.schema legal_cases
.schema text_metrics
.schema tf_idf_scores
# Exit SQLite console.quit

1. Python Integration Create a new file database_utils.py in your scripts directory:

import sqlite3
import pandas as pd
def connect_db():
    """Establish connection to our SQLite database."""    return sqlite3.connect('D:/Projects/LegalTextAnalysis/data/legal_text.db')
def read_csv_to_df(csv_path):
    """Read our legal text CSV file into a pandas DataFrame."""    return pd.read_csv(csv_path)
def insert_legal_cases(df, conn):
    """Insert data from DataFrame into legal_cases table."""    df.to_sql('legal_cases', conn, if_exists='append', index=False)

Next Steps for Data Processing

Now that our database structure is set up, we can proceed with our text analysis pipeline:

1. Data Import
   • Load the legal text classification CSV
   • Preprocess the data according to our requirements
   • Insert into appropriate tables
2. Text Analysis
   • Calculate basic metrics (word count, text length)
   • Compute advanced metrics (sentence complexity, citation density)
   • Generate TF-IDF scores
3. Quality Verification
   • Check data integrity
   • Verify foreign key constraints
   • Validate metric calculations

Useful Commands for Data Exploration

Here are some helpful SQLite commands for exploring our data:

-- Count total casesSELECT COUNT(*) FROM legal_cases;
-- View data distributionSELECT length_category, COUNT(*)
FROM text_metrics
GROUP BY length_category;
-- Find top TF-IDF termsSELECT term, AVG(score) as avg_score
FROM tf_idf_scores
GROUP BY term
ORDER BY avg_score DESC
LIMIT 10;