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Exploratory Data Analysis (EDA) Tools

8 posts · Sep 2023 – Jan 2026

What this trail covers

Hex has evolved its data analysis capabilities by providing a comprehensive guide to Exploratory Data Analysis (EDA). This includes detailing EDA techniques such as data profiling, statistical summaries, and visualization, and emphasizing their importance in understanding data before building models or dashboards. The post also highlights how collaborative tools and AI assistants are transforming EDA workflows, enabling faster iteration and insight generation. Best practices for conducting effec

All Hex trails
  1. What is exploratory data analysis? A practical guideThis post details the importance of Exploratory Data Analysis (EDA) as a foundational step before building models or dashboards. It outlines key EDA techniques including data profiling (checking dimensions, data types, missing values), statistical summaries (distribution analysis, outlier detection, correlation analysis), and visualization (histograms, scatter plots, box plots). The post also discusses how AI assistants and collaborative tools like Hex are transforming EDA workflows by enabling faster iteration, insight generation, and real-time collaboration. It provides best practices for conducting effective EDA, such as starting with specific questions, documenting reasoning, systematic profiling, and sharing findings early.
  2. snowpark under the hoodThis post details how Hex's integration with Snowpark enables users to perform Exploratory Data Analysis (EDA) on large datasets within Snowflake. It explains the underlying mechanism of Snowpark queries generated by Hex, including the use of lazy evaluation and previews (e.g., LIMIT 10) to avoid materializing entire datasets. The post analyzes Snowpark query profiles in Snowflake, highlighting the efficiency gains and the role of result caching. It demonstrates how Hex facilitates seamless transitions between SQL and Python cells for Snowpark operations, and how visual cells leverage Snowflake for aggregation before returning results to Hex. The core contribution is showcasing how Hex empowers users of all skill levels to harness Snowpark for ad-hoc analytics, visualizations, and answering business questions without the need for external tools like Spark.
  3. Exploratory Product Analysis for Product & Engineering TeamsThis post details how Hex empowers product and engineering teams to perform their own product analytics and exploratory data analysis. It highlights the challenges of data context and inflexible tools, and showcases how Hex's warehouse-native, polyglot query capabilities, and collaborative features enable self-serve data exploration. Specific use cases like tracking feature adoption, ad-hoc investigations, and overall product reporting are discussed, emphasizing the ability to move beyond static dashboards to interactive analysis.
  4. The Essential Steps in Data Preprocessing for Different Data FormatsThis post details essential data preprocessing steps for various data formats (structured, textual, temporal, image) within the context of exploratory data analysis. It provides code snippets for handling missing values, normalization, one-hot encoding, feature selection for structured data; tokenization, stop word removal, stemming/lemmatization, and text vectorization for textual data; parsing dates, handling time gaps, and feature engineering for temporal data; and resizing, normalization, and augmentation for image data. It emphasizes the importance of these steps for accurate analysis and model training.
  5. The Importance of Data Cleaning in EDAThis post details the critical importance of data cleaning within the context of Exploratory Data Analysis (EDA). It outlines seven key reasons why data cleaning is crucial: Accuracy, Consistency, Efficiency, Reliability, Relevance, Interpretability, and Optimization. For each reason, the post explains the impact of poor data quality and provides practical Python/pandas code snippets demonstrating techniques for cleaning data. These techniques include outlier detection and removal using IQR, correcting erroneous values (e.g., negative ages), handling typographical errors in text data, standardizing text case (e.g., to lowercase), converting date columns to a consistent datetime format, using categorical data types for efficiency, and optimizing memory usage by converting integer types to smaller formats and using sparse data structures. The post emphasizes that clean data forms the foundation for valid EDA results and reliable downstream decisions.
  7. Exploring Data in Jupyter with Python and PandasThis post provides a detailed guide to data exploration using Jupyter Notebooks with Python and Pandas. It covers setting up the Jupyter environment, installing Pandas, loading data (specifically the Titanic dataset), previewing data with head(), tail(), and sample() methods, gathering basic information with info(), and calculating descriptive statistics with describe(). It also details filtering and subsetting data by selecting specific columns and rows, and introduces basic data exploration techniques like checking column values and data types. Finally, it touches upon data cleaning and preprocessing, pivot tables, and cross-tabulation.