Data Wrangling in Data Science

Shailendra Chauhan 12 min read

24 May 2023

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Data Wrangling in Data Science

Introduction

The process of changing and processing raw data into a format appropriate for analysis and modeling is known as "data wrangling," and it is an essential step in the field of data science. It is frequently regarded as one of the most difficult and time-consuming parts of the data science workflow. Cleaning, integrating, transforming, and enriching data are just a few of the many jobs that fall under the umbrella term "data wrangling."

In data science tasks, the raw data is frequently illegible, incompatible, or includes errors or missing numbers. Data wrangling uses a variety of methods to clean and preprocess the data in an effort to solve these problems. This involves activities like eliminating duplication, managing missing data, fixing mistakes, and guaranteeing data quality.

Another key component of data wrangling is data integration, especially when working with various data sources. It entails fusing information from several sources and formats into a single dataset. To build a coherent dataset for analysis, this method entails locating common variables, resolving nomenclature conflicts, and addressing data inconsistencies.

Changing data's representation or structure is known as data transformation. To do this, you might aggregate data, divide columns, add new variables, or use mathematical operations to get useful insights. Data scientists can create useful features and extract important information from raw data by transforming the data.

Data wrangling also includes a process called data enrichment, which involves adding more pertinent data to the dataset. This may entail combining data from different external sources, incorporating demographic or geographic information, or using APIs to access additional data. The ability to analyze data and generate information from it is enhanced by data enrichment.

Statistical tools, data manipulation packages, and computer languages (such as Python or R) are frequently used in data-wrangling tasks. With the use of these tools, data scientists may effectively alter, clean, and restructure the data using a wide range of features and techniques.

Data wrangling in data science

Data cleaning, transformation, and preparation for evaluation in data science is known as data wrangling in data science, also referred to as data munging and data preprocessing. Making the data more organized, consistent, and appropriate for additional study and modeling entails a number of tasks.

The first step in data manipulation is to understand the qualities and characteristics of the original data. Examining data kinds, spotting values that are missing, outliers, and abnormalities, as well as determining data integrity, are all part of this process. After the initial evaluation is complete, a number of procedures are used to clean and process the data.

Handling missing numbers, getting rid of duplicate records, and fixing mistakes or discrepancies are all part of cleaning the data. Utilizing methods like mean, median, and regression-based imputation, values that are missing can be replaced. By contrasting important variables, unnecessary entries can be found and removed. Statistical techniques or the use of data validation standards can be used to fix inconsistent or inaccurate data.

Outliers, which are exceptional values that can affect analytical results, must also be dealt with when dealing with data. Outliers can be found using statistical approaches, and they can be eliminated entirely if they are thought to represent errors by utilizing techniques like data compression, which replaces extreme values with less extreme ones.

Importance of Data Wrangling

Importance of Data Wrangling to data research for a number of reasons.

Quality of Data: Data completeness, consistency, and error rates are common characteristics of raw data. By cleaning and preparing the data, addressing missing values, fixing mistakes, and removing duplicates, data wrangling helps to ensure data quality. For appropriate analysis and modeling, high-quality data is necessary since judgments based on defective or unreliable data might result in wrong results.

Preparation of Data: Raw data is transformed into an analysis-ready format by data wrangling. The data must be reshaped, various data sources must be integrated, and new variables or characteristics must be created. Data scientists can get valuable insights and make well-informed decisions when the data they are working with has been properly prepared.
Data exploration: Data scientists frequently obtain a deeper grasp of the data through the data wrangling process. Patterns, linkages, and potential problems can be found by exploring the data using visualization, summary statistics, & exploratory analysis. Data exploration yields knowledge that directs later analysis and modeling operations.
Feature Engineering: The process of creating new variables and features from existing data is known as feature engineering. These manufactured features can enable more accurate predictions, enhance model performance, and gather crucial data. The relevance and quality of features have a substantial impact on the accuracy and performance of models, making feature engineering an essential step in machine learning tasks.
Efficiency & Scalability: Data scientists may work with huge, complicated data sets effectively by using data wrangling. Unneeded or redundant details can be eliminated by cleaning, converting, and organizing the data, creating a more understandable and simplified dataset. As a result, analysis and modeling on large data platforms are made possible and processing performance is increased.
Data Integrity and Consistency: In many data science initiatives, data is sourced from diverse databases, systems, or files with various structures and coding. Data wrangling unifies and harmonizes these various data sources to guarantee data consistency. Data scientists can provide a uniform dataset for analysis & modeling by matching variables, resolving nomenclature conflicts, and addressing data inconsistencies.
Data Governance & Compliance: Monitoring data compliance with laws and privacy standards is known as data wrangling. It entails processing sensitive data, anonymizing data, and putting suitable data security measures in place. Data handling procedures that are ethical and responsible are ensured by following data governance standards.

Benefits of Data Wrangling

In data research, data wrangling has a number of advantages:

Accurate Analysis: By correcting values that are missing, errors, and inconsistencies, data wrangling maintains data quality. Data scientists can feel confident in the correctness of their analysis by cleaning & pre-processing the data. High-quality and trustworthy data enables more precise insights and improved decision-making.
Workflow Efficiency: Data wrangling improves workflow efficiency by streamlining the data preparation stage. Data scientists can concentrate on analyzing and understanding the information rather than spending unnecessary time with data cleaning & formatting duties by transforming, cleaning, and integrating the data.
Enhanced Feature Engineering: By delivering a well-organized and standardised dataset, data wrangling facilitates efficient feature engineering. This makes it possible for data scientists to develop new features and variables that collect pertinent data and enhance the effectiveness of predictive models. Well-designed features can make models more reliable and accurate.
Better Data Exploration: Data scientists learn about the properties, distributions, and connections of the data throughout the data-wrangling process. This knowledge facilitates the search for and detection of patterns, trends, as well as abnormalities. Data wrangling makes data visualization and summary statistics possible, which improves the effectiveness of the exploration process.
Data Integration: The integration of information from several sources is made possible by data wrangling, which offers an extensive picture for analysis. Data scientists can use a wider range of facts to discover hidden connections and correlations that could not be obvious in standalone datasets by merging various data sources, such as files, databases, or APIs.
Big data scalability: Data wrangling techniques make it possible to analyze and analyze large-scale, complicated datasets, which are frequently seen in big data scenarios. Data wrangling aids in effectively managing the amount, speed, and variety of large data by optimizing data storage and organization. On big data platforms, this scalability enables efficient analysis and modeling.
Collaboration and Reproducibility: Data wrangling entails recording the procedures and data transformations used. Because other data scientists may duplicate the data wrangling procedure and verify the results, this documentation encourages reproducibility. By fostering transparency & enabling teammates to understand and expand on one another's work, it also promotes collaboration.
Resource efficiency and cost: Data wrangling lowers the risk of losing resources on analyzing or modeling erroneous or incomplete data by cleaning and manipulating the data early in the process. In the long term, it saves time and effort by preventing possible problems from spreading throughout the information science workflow.

Data Wrangling Tools

In data science, the term "data wrangling tools" refers to software programs, libraries, and platforms that make it easier to clean, manipulate, and prepare raw data for analysis & modeling. These tools offer functionality and features that make certain data-wrangling jobs easier and more automated, enabling data scientists to successfully modify and shape data. Based on their features and intended use, data-wrangling tools can be divided into various types. Typical examples of data-wrangling tools are:

Data Manipulation Libraries: Libraries for programming that allow for the manipulation and modification of data include those called "data manipulation libraries." Examples include the Python libraries Pandas and NumPy, which have a wide range of tools for collecting, cleaning, filtering, and manipulating data.
Query Languages: Data scientists can connect with databases and carry out activities including querying, filtering, joining, & aggregating data using query languages like SQL. For activities involving relational databases and data wrangling, SQL is frequently employed.
Spreadsheet software: Programmes like Google Sheets and Microsoft Excel provide the fundamental data-wrangling features of sorting, filtering, cleaning, & manipulating data. These tools are appropriate for smaller datasets and frequently have a graphic user interface for data manipulation activities.
Platforms for Data Wrangling: There are platforms for data wrangling that are both open-source and available for purchase. To make difficult data-wrangling jobs simpler, these platforms offer visual interfaces, drag-and-drop elements, and pre-built functionalities. Examples are RapidMiner, KNIME, and Trifacta Wrangler.
Tools for Specialised Data Cleaning and Transformation: OpenRefine, previously Google Refine, is one example of a tool that focuses on cleaning and standardizing data. They offer tools for text editing, deduplication, and processing jumbled data.

Various data-wrangling tools are frequently employed in data research. Here are a few well-known examples:

Pandas: A popular Python package for data analysis and manipulation is called Pandas. Powerful data structures like DataFrame and Series are provided, which enable flexible and effective operations for data-wrangling jobs. Pandas provides tools for aggregating, cleaning, filtering, combining, and manipulating data. For data analysis & visualization, it also works well with other Python packages.
NumPy: NumPy is a necessary Python package for numerical computation. Although it is primarily concerned with mathematical operations, it also offers multidimensional arrays and data-wrangling-friendly mathematical algorithms. NumPy is frequently used for reshaping arrays and doing calculations on them, which is useful for applications involving numerical data wrangling.
Dplyr and tidyr: These two R packages offer simple and effective methods for handling data. Data filtering, selection, grouping, and summarization are all made simple using dplyr. It provides tools like group_by(), filter(), select(), and summarise(). While gathering and spreading data between broad and long formats are among the data-cleaning activities that tidyr concentrates on.
SQL: Relational database management and querying are both possible with SQL, or Structured Query Language. SQL is frequently used to filter, join, aggregate, and alter data that is stored in databases, among other data wrangling and manipulation operations. It has strong querying capabilities and effectively manages big datasets.
Excel: Microsoft Excel is a well-known spreadsheet program with basic data manipulation features. Excel has tools for filtering, cleaning, converting, and sorting data. In particular, when a visual interface is favored over coding, it is frequently utilized for smaller datasets and easy tasks.
OpenRefine: OpenRefine is a free data-wrangling tool (formerly known as Google Refine). It offers an easy-to-use interface for looking through and organizing disorganized data. Data standardization, deduplication, text editing, and format conversion are functions available in OpenRefine. Large dataset processing and data cleaning jobs make good use of it.
Trifacta Wrangler: Trifacta Wrangler is a for-profit data wrangling program that features a visual interface for cleaning and preparing data. It offers sophisticated data integration, transformation, and profiling capabilities. Without substantial programming skills, Trifacta Wrangler provides interactive exploration of data and modification. Workflows for cooperative data wrangling are also supported.
KNIME: A number of data wrangling tools are included in the open-source data analytics platform known as KNIME (Konstanz Information Miner). It provides a visual workflow interface that enables users to drag and drop components to design and carry out data-wrangling activities. KNIME is appropriate for both beginning and experienced data scientists since it offers a large number of pre-built nodes for data transformation, cleansing, and integration.

Summary:

Data cleansing, transformation, and preparation for analysis and modeling are all part of the crucial data science process known as "data wrangling." It fixes problems with the data, such as missing values, mistakes, and inconsistencies, ensuring the accuracy and quality of the data. By facilitating accurate analysis, effective workflow, improved feature engineering, enhanced information exploration, data integration, scalability of big data, reproducibility, collaboration, & resource and cost efficiency, data wrangling is essential to data science. There are several tools available for handling data, including platforms like Trifacta Wrangler and KNIME, data manipulation libraries like Pandas and NumPy, query languages like SQL, spreadsheet programs like Excel, and specialized tools like OpenRefine. These tools offer features that make data cleansing, transformation, and integration chores easier and more automated, enabling data scientists to successfully edit and shape data. Data scientists can save time, enhance data quality, and gain insightful knowledge from their data by using data-wrangling technologies.