Data Wrangling Basics

Study Notes

Data wrangling, often referred to as data munging, is a crucial step in the data analysis process that involves transforming and mapping raw data into a more refined and usable state. This process is essential for ensuring that the data is accurate, consistent, and structured in a way that makes it easier to analyze efficiently.
Within a typical data project, data wrangling can consume a significant amount of time—often 70% to 80% of the total project duration. This extensive time commitment highlights the complexity and importance of preparing data correctly before analysis and modeling can take place. As a result, only 20% to 30% of the time is left for actual analysis, modeling, and deriving insights, which emphasizes the need for thoroughness during the wrangling phase.
The final output of the data wrangling process is a highly organized dataset that is not only clean and structured but also ready for various applications such as analysis, visualization, or machine learning. This transformation facilitates improved decision-making based on reliable and well-understood data.

Data gathering is the initial step in the data wrangling process, where data is collected from a myriad of sources. These sources can range from databases, online repositories, and APIs, to spreadsheets, web scraping, and surveys. Given the diversity of potential sources, the data may be present in various formats, including structured forms like CSV or SQL databases, as well as unstructured forms such as text files, images, or JSON data.
The challenge during this phase often arises from the disparate nature of the sources, which can lead to difficulties in locating, accessing, and integrating the data. Therefore, it becomes imperative to utilize efficient collection methods and tools, such as data integration platforms or ETL (Extract, Transform, Load) processes, to streamline the process and ensure that all relevant data is captured accurately.

Following data gathering, the next critical phase is data assessment. This involves a detailed evaluation of the collected data to identify issues concerning its quality and tidiness. This step is foundational because any deficiencies in the dataset can lead to misleading conclusions and affect the overall analysis.
Key indicators of poor data quality generally include factors such as missing values, duplicate entries, inconsistent data formats, and incorrect data types. For instance, missing values can cause computation errors or biases, while duplicates can skew results and analyses.
Data assessment can be conducted using two main approaches: visually and programmatically. The visual assessment may involve manually inspecting the dataset to identify irregularities, whereas programmatic assessment leverages various data profiling tools, scripts, and analytics software to automate the evaluation process, making it more efficient and less prone to human error.

Data cleaning constitutes a pivotal part of data wrangling as it involves addressing and rectifying the quality issues identified during the assessment phase. This can often be a painstaking task, but it is necessary to ensure the integrity and reliability of the data.
Some of the key techniques used in data cleaning include:
- Checking variable correlations to discern which variables are important and which may be irrelevant for the analysis, thus enhancing the efficiency of the subsequent analysis.
- Merging similar datasets to create a comprehensive dataset while eliminating duplicate entries to avoid redundancy.
- Ensuring that the data types are appropriate for the values contained within them, such as confirming that numerical data is stored as integers or floats, and that dates are appropriately classified as date objects, facilitating correct computations and analyses.
- Removing irrelevant variables that do not contribute to the analysis or insights, while potentially adding new variables that can enhance the depth of the analysis.
The overall quality and structure of the data are paramount for deriving better insights, as clean data leads to more trustworthy analysis results. Subsequently, it is advisable to reassess the data after cleaning to ensure that the issues have been effectively resolved and the dataset is now organized.