With the exponential growth in data volume and sources in recent years, organizations increasingly rely on data-driven decision making. However, raw data extracted from various sources is often messy, inconsistent, and unusable in its original form. This is where data wrangling comes in – the process of transforming raw data into a clean, structured, and analysis-ready format. In this comprehensive guide, we will understand what data wrangling involves, why it is a crucial step in the data workflow, and how to effectively wrangle data.
What is Data Wrangling?
Data wrangling, also referred to as data munging or data cleaning, is the process of taking raw data from one or more sources and converting it into a structured format that is more appropriate and valuable for downstream analytics and machine learning tasks.
In essence, data wrangling transforms disorganized data into actionable insights by:
- Identifying and dealing with missing values
- Fixing inconsistencies in formats and codes
- Detecting and removing errors and outliers
- Combining data from different sources
- Converting raw data into desired formats
- Reshaping datasets for usability
Data wrangling ensures that unwanted noise in data does not get passed down to models or analyses, thus preventing inaccurate outputs. According to CrowdFlower’s Data Science Report, data scientists spend about 60-80% of their time cleaning and organizing data. Thus, having strong data wrangling skills is a prerequisite for anyone looking to pursue a career in data analytics.
Key Steps in Data Wrangling
While the data wrangling workflow can differ based on the tools and techniques used, most processes follow a similar high-level approach:
1. Data Discovery
This first step involves getting to know the raw dataset(s) – number of rows, columns, size, schema, data types, distributions, quality checks etc. Profiling tools can help summarize key metadata to build an understanding.
2. Data Structuring
Raw data may come from various sources in incompatible formats. This step converts all data to a standard schema and data model for uniformity.
3. Data Cleaning
With structured data, issues like missing values, duplicate records, and outliers can be identified and fixed. Syntax errors and special characters are handled.
4. Data Enrichment
Additional data from other sources is combined with the main dataset to enhance insights, if needed. Common identifiers help merge the sources.
5. Data Validation
Once cleaning steps are applied, the wrangled dataset is validated to ensure no new issues crop up. Statistical checks confirm data quality.
6. Data Publishing
The final, cleaned dataset is exported to file formats like CSV, JSON, or a database for consumption by analysts, engineers, and decision makers.
Proper data documentation is also created.
Why is Data Wrangling Important?
High-quality data is the fuel that powers modern data science. Here are some key reasons why investing time in thorough data wrangling is critical:
- Accurate analytics: With incomplete, duplicate, or erroneous data, analysis results can be misleading or plain wrong. Data wrangling weed out issues that skew insights.
- Unbiased models: Machine learning models will pick up biases and inaccuracies in dirty data. Data wrangling avoids this by creating training data that correctly represents the problem space.
- Time savings: Clean data means less time spent troubleshooting errors and mismatches in reporting and analytics.
- Reduced costs: Bad data leads to wrong business decisions which is expensive to recover from. Data wrangling mitigates this risk.
- Improved productivity: Analysts can focus on high-value analysis rather than fighting data problems.
In summary, when data is wrangled well, teams get speed, accuracy, and confidence in their data products.
Data Wrangling Tools and Techniques
Data wrangling comprises many nuanced techniques that require going beyond basic spreadsheet editors. Here are some common tools and languages used:
With libraries like Pandas, NumPy, scipy, and deep learning frameworks, Python is ubiquitous in data wrangling:
- Pandas – offers highly optimized data structures and analysis routines for cleaning, munging, merging, reshaping, and transforming data.
- NumPy – provides Python‘s matrix data structures plus math/stat operations for numerical data munging.
- Scikit-learn – data preprocessing and imputation techniques for missing values and outliers.
R‘s strong data manipulation packages make it popular for wrangling tasks:
- dplyr – fast data transformations and aggregation with intuitive verbs like filter(), mutate(), group_by() etc.
- data.table – enhanced syntax and speed for large dataset munging.
- tidyr – reshaping data between wide and long formats for analysis.
- stringr – routines for text data wrangling and cleaning.
Tools like Informatica, Talend, Trifacta and Apache Airflow provide graphical ETL interfaces, workflows and automation for large-scale data wrangling.
Profiling tools like Google’s DataPrep, Trifacta Wrangler, and Pandas Profiling analyze datasets for metadata and anomalies before wrangling.
In addition to these tools, methodologies like EDA (Exploratory Data Analysis) help uncover nuances in data for more effective wrangling. Statistical analysis and visualization surface outliers, biases, and data quality issues.
Data wrangling is the intense heavy lifting stage of data analysis, requiring a strategic combination of programmatic skills, analytical thinking, and product awareness. Mastering data wrangling in tools like Python enables analysts to expedite the data-to-insight pipeline, uncovering treasures hidden amidst the data jungle. Robust wrangling skills are thus among the key things that set great data professionals apart.