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10 Surprising Things You Can Do with Python’s collections Module

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Counting Hashable Objects Effortlessly with Counter A common task in almost any data analysis project is counting the occurrences of items in a sequence. As managing editor of KDnuggets & Statology , and contributing editor at Machine Learning Mastery , Matthew aims to make complex data science concepts accessible.

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Integrating DuckDB & Python: An Analytics Guide

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By Josep Ferrer , KDnuggets AI Content Specialist on June 10, 2025 in Python Image by Author DuckDB is a fast, in-process analytical database designed for modern data analysis. Its tight integration with Python and R makes it ideal for interactive data analysis. EXCLUDE, REPLACE, and ALL) to simplify query writing.

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7 Python Statistics Tools That Data Scientists Actually Use in 2025 - KDnuggets

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NumPy offers powerful array operations, mathematical functions, and random number capabilities, making it essential for statistical analysis and data manipulation. Pandas: Data Analysis and Manipulation Made Simple Pandas is the go-to library for data manipulation and analysis. Learn more: [link] 3.

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How to Combine Streamlit, Pandas, and Plotly for Interactive Data Apps

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Conclusion The combination of Streamlit, Pandas, and Plotly transforms data analysis from static reports into interactive web applications. The free tier supports multiple apps and handles reasonable traffic loads, making it perfect for sharing dashboards with colleagues or showcasing your work in a portfolio.

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Go vs. Python for Modern Data Workflows: Need Help Deciding?

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Python works best for: Exploratory data analysis and prototyping Machine learning model development Complex ETL with business logic Statistical analysis and research Data visualization and reporting Go: Built for Scale and Speed Go takes a different approach to data processing, focusing on performance and reliability from the start.

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The 7 Most Useful Jupyter Notebook Extensions for Data Scientists

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Conclusion Jupyter Notebook is a platform used by many data scientists for data analysis and collaborative work. In this article, we have explored seven different Jupyter Notebook extensions that data scientists should not miss: I hope this has helped!

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Building End-to-End Data Pipelines: From Data Ingestion to Analysis

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By subscribing you accept KDnuggets Privacy Policy Leave this field empty if youre human: Get the FREE ebook The Great Big Natural Language Processing Primer and The Complete Collection of Data Science Cheat Sheets along with the leading newsletter on Data Science, Machine Learning, AI & Analytics straight to your inbox.