In today’s data-driven world, businesses are collecting vast amounts of information from various sources to make informed decisions, gain insights, and achieve their goals.
But the raw data we have is very messy, inconsistent, and riddled with errors. This is where data profiling steps in which is a crucial process that allows organizations to gain a comprehensive understanding of their data before diving into analysis or decision-making.
What is Data Profiling ?
Data profiling is the process of examining and analyzing the data to gain insights to its structure, quality, completeness, and other characteristics.
Typically, it involves tasks like identifying data types, analyzing data distributions, checking data quality and visualizing data patterns.
Importance of Data Profiling:
• Collecting Descriptive Statistics such as minimum and maximum values, count of values, etc., along with any other attributes that can be used to describe the basic features of the data going through the Data Profiling process.
• Performing data quality assessment.
• Identifying data types, recurring patterns, etc.
• Tagging data with descriptions and keywords.
• Group data into categories.
• Identifying the metadata and its accuracy.
Data Profiling Examples
It’s important to understand that data profiling is not just about creating definitions for tables, columns and fields; it’s also about creating definitions for the information that we store in those tables, columns and fields ( “data”). When we do this properly, we can use these definitions later when we need them–for example:
When someone needs to know what kind of data they should enter into a particular field on a form or report (e.g., “Is this email address valid?”)
When someone needs to know which reports should be run against certain datasets because they contain interesting pieces of information (e.g., “Which customers bought product X last month?”
Data profiling libraries in python:
1. Y-data profiling:
ydata-profiling is not a built-in Python package. You need to install it within your terminal with the
pip install ydata-profiling command.
Key features of ydata-profiling library:
• Type inference: automatic detection of columns’ data types (Categorical, Numerical, Date, etc.)
• Warnings: A summary of the problems/challenges in the data that you might need to work on (missing data, inaccuracies, skewness, etc.)
• Univariate analysis: including descriptive statistics (mean, median, mode, etc) and informative visualizations such as distribution histograms
• Multivariate analysis: including correlations, a detailed analysis of missing data, duplicate rows, and visual support for variables’ pairwise interaction
• Time-Series: including different statistical information relative to time-dependent data such as auto-correlation and seasonality, along ACF and PACF plots.
• Text analysis: most common categories (uppercase, lowercase, separator), scripts (Latin, Cyrillic), and blocks (ASCII, Cyrilic)
• File and Image analysis: file sizes, creation dates, dimensions, an indication of truncated images, and the existence of EXIF metadata
• Compare datasets: one-line solution to enable a fast and complete report on the comparison of datasets
• Flexible output formats: all analysis can be exported to an HTML report that can be easily shared with different parties, as JSON for easy integration in automated systems and as a widget in a Jupyter Notebook.
• Integrations: automating the profiling operation in various steps is crucial for ongoing operations. The library supports integrations with the other major open-source tools in the modern data stack; Great Expectations, Alitflow, Prefect, etc.
Data profiling is a fundamental process that helps in successful data analysis, reporting, and decision-making. Data Profiling Tools provide a clear picture of data structure, content, and rules. Data Profiling Tools can improve users’ understanding of the gathered data.
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