Data wrangling and exploratory data analysis are the two most discussed components of the data science pipeline. Though they are discussed together, they have different functions. This article delves into what data wrangling is, its importance, and how it differs from EDA.
Here is a detailed guide on how to perform Exploratory Data Analysis (EDA) Explained with Python.
What is Data Wrangling?
Data wrangling or data munging refers to the process of cleaning and prepping raw data for analysis or modeling. Importantly, during the data science workflow, since raw data arises mostly from sources greater than one that includes databases, APIs, and more, this most often tends to be quite messy, and full of errors and inconsistencies.
Suppose you have data from multiple sources, but all in different formats, with some entries missing or duplicated. Data wrangling addresses these issues by cleaning the data to correct errors and handle missing values, transforming it into a consistent format, and combining multiple datasets into a unified structure.
Without proper data wrangling, any analysis or model based on the data may be wrong or misleading. Data wrangling is aimed at ensuring that the data is accurate and complete so that it can be used for further analysis. Good data wrangling, in a nutshell, is the foundation of producing insightful and reliable results from your data.
Key Processes in Data Wrangling
Data wrangling involves several processes that prepare data for analysis:
- Data Collection
Data wrangling is the process of gathering data from various sources, such as databases, APIs, web scraping, or file formats like CSV, Excel, or JSON. These datasets are mostly in different formats and hence need to be harmonized into a consistent structure.
- Data Cleaning
Cleaning ensures that the data is accurate and free from errors. This includes:
- Handling Missing Values: Filling gaps with appropriate values or removing incomplete records.
- Removing Duplicates: Removes duplicated entries.
- Correcting Errors: Corrects typos and wrong data
- Standardizing Formats: Normalizes the date and time formats
- Data Transformation
Transformation reshapes data to make it more analytically friendly. This includes:
- Normalization: Scaling all the numerical data into a fixed range.
- Encoding: Converts the categorical variables into numerical formats
- Aggregation: Summation of data through groupings and values summation
- Feature Engineering: Construction of new features to enhance exploratory analysis
- Data Integration
Once data is extracted from various systems, integration of data merges different datasets into a single uniform format with the help of techniques like join or merge.
- Data Validation
Data validation ensures the quality of transformed and cleaned data for analysis; this includes verification of accuracy, consistency, and integrity.
Exploratory Data Analysis (EDA)?
After wrangling, the EDA comes into the limelight. Now, this process is focused primarily on the exploration and understanding of data. It identifies the models through statistical and visual methods. In this step, patterns, relationships, and anomalies of data are identified.
Unlike wrangling, the EDA technique does not envision cleaning or manipulating the data to draw insights into the characteristics being investigated. So, whereas some wrangling involves correcting missing values, the outcomes of the following EDA are trends, distribution, and relations in the prepared dataset.
Process in EDA
The various processes of carrying out EDA include:
- Statistics: Computing means, medians, variance, and standard deviation.
- Data Visualization: Using graphics such as histograms, scatter plots, and box plots to provide a visual sense of relationships and distributions.
- Outlier Detection: Identifying unusual data points that might affect the analysis.
- Trend Analysis: Detecting seasonal patterns, correlations, or other trends in the data.
Differences Between Data Wrangling from EDA
The following table summarizes the differences between data wrangling and exploratory data analysis:
| Aspect | Data Wrangling | Exploratory Data Analysis (EDA) |
| Objective | Preparing data for analysis. | Exploring data to uncover patterns and insights. |
| Processes | Cleaning, structuring, transforming, and integrating data. | Analyzing data distributions, correlations, and trends. |
| Tools | Pandas, dplyr, SQL, ETL tools. | Matplotlib, Seaborn, ggplot2, Tableau, Power BI. |
| Output | A clean and structured dataset. | Insights, visualizations, and patterns. |
| Focus | Data quality and usability. | Data characteristics and interpretation. |
Complementary Roles of Data Wrangling and EDA
While data wrangling and EDA have different purposes, they rely on one another. Wrangling ensures that the data is clean, accurate, and ready, while EDA uses that prepared data to gain insights and make decisions.
For instance, wrangling may address missing values and standard formats, helping the EDA unveil seasonal patterns or correlations in the data. Without wrangling, EDA might produce unreliable or skewed results.
Conclusion
Data wrangling and EDA are two significant components of the data science workflow. Each serves a different purpose. Wrangling deals with data preparation, whereas EDA further explores and analyzes. Both ensure that the insights extracted from data are correct, reliable, and actionable.
With mastery over both, data professionals can leverage the best of their datasets to make informed decisions with complete confidence.
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