How to Write a Script to Parse and Clean Data

Below is a clean, step-by-step explanation you can follow (plus a sample script in Python).

✅ 1. Understand Your Data

Before writing any code, check:

• What format is the data? (CSV, JSON, TXT, HTML, Excel…)
• What problems exist?
  • Missing values
  • Duplicates
  • Wrong data types
  • Extra spaces or symbols
  • Irrelevant columns
  • Inconsistent formatting (e.g., “Yes”/”YES”/”Y” )

✅ 2. Choose a Tool or Language

Most common:

• Python (best for automation)
• R
• Excel PowerQuery
• JavaScript (if working with web data)

Below we use Python + Pandas because it’s simple and powerful.

✅ 3. Write a Basic Script Structure

📌 Example: Python Script to Parse & Clean CSV Data

import pandas as pd

# 1. Load the data
df = pd.read_csv("raw_data.csv")  # use read_json(), read_excel() if needed

# 2. Remove duplicate rows
df = df.drop_duplicates()

# 3. Handle missing values
df = df.fillna({
    "name": "Unknown",
    "age": 0,
    "email": "no-email@example.com"
})

# 4. Clean text fields (strip spaces, lowercase)
df["name"] = df["name"].str.strip().str.title()
df["email"] = df["email"].str.strip().str.lower()

# 5. Convert data types
df["age"] = df["age"].astype(int)

# 6. Remove unwanted columns
df = df.drop(columns=["temp", "unused_column"])

# 7. Save the cleaned data
df.to_csv("cleaned_data.csv", index=False)

print("Data cleaning complete!")

✅ 4. Automation (Optional But Powerful)

You can automate the script to run:

• Daily
• Weekly
• Whenever new data arrives