data_cleaning.md

Data Cleaning Best Practices

Data Cleaning Best Practices

• Remove duplicate rows to avoid data leakage.
• Standardize column names (lowercase, underscores).
• Handle missing values using median/mean or domain logic.
• Convert date columns to proper datetime format.
• Validate data types before modeling.

Python Example

import pandas as pd

df = pd.read_csv("data.csv")

df = df.drop_duplicates() df.columns = [c.lower().replace(" ", "_") for c in df.columns]

num_cols = df.select_dtypes(include="number").columns df[num_cols] = df[num_cols].fillna(df[num_cols].median())

if "date" in df.columns: df["date"] = pd.to_datetime(df["date"])