Data Wrangling Techniques Every Data Scientist Should Know

Data wrangling (also called data munging) is the process of cleaning, transforming, and organizing raw data into a format suitable for analysis. It’s a crucial skill for data scientists because real-world data is often messy and unstructured.

1. Handling Missing Data

Techniques:

Remove missing values: Drop rows or columns with NaNs (use with caution).

Imputation: Fill missing values with:

Mean/Median/Mode

Forward-fill/backward-fill

Interpolation

Flagging: Create indicator columns to flag missing values.

2. Removing Duplicates

Detect duplicates: Use functions like .duplicated() in pandas.

Remove duplicates: Drop using .drop_duplicates() to avoid skewed analysis.

3. Data Type Conversion

Convert types: Ensure numerical data isn’t stored as strings.

Datetime parsing: Convert strings to datetime objects for time series analysis.

Example: pd.to_datetime()

4. Filtering and Subsetting Data

Use conditional logic: Select rows based on conditions (e.g., values > 100).

Select relevant features: Remove unnecessary columns to reduce noise.

5. Data Normalization and Scaling

Techniques:

Min-Max Scaling

Z-score Standardization

Log Transformation

Why? Brings features to a similar scale, crucial for algorithms like KNN and SVM.

6. Encoding Categorical Variables

Label Encoding: Assigns an integer to each category.

One-Hot Encoding: Converts categorical variables into binary columns.

Example: pd.get_dummies()

Ordinal Encoding: Used when categories have a logical order.

7. Binning

Purpose: Convert continuous variables into categorical bins.

Methods:

Equal-width bins

Quantile-based bins

Custom bins

8. Text Data Cleaning

Lowercasing

Removing punctuation and stopwords

Tokenization and Lemmatization

Removing special characters or numbers (if needed)

9. Aggregation and Grouping

Use .groupby(): Aggregate data by categories.

Example: df.groupby('region')['sales'].sum()

Pivot Tables: Summarize data across multiple dimensions.

10. Merging and Joining Datasets

Types of joins:

Inner join

Left join

Right join

Outer join

Tools: merge(), concat(), join() in pandas

11. Reshaping Data

Melting: Convert wide format to long format.

Pivoting: Convert long format to wide format.

Stack/Unstack: Manipulate multi-level indexes.

12. Dealing with Outliers

Detection:

Boxplots

Z-score

IQR method

Handling:

Cap/floor values

Remove outliers

Log transformation

Best Practices

Always visualize your data during wrangling.

Document each transformation step.

Maintain a raw data backup.

Automate wrangling with scripts or pipelines for repeatability.

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