Data-Visualisation with Python and Javascript - Crafting a Data-Viz Toolchain for the Web
Explore data visualization using Python and JavaScript, from scraping and cleaning data to creating engaging web visualizations with tools like Scrapy, Pandas, Flask, and D3.js.
Scale Your Data, Not Your Process - Welcome to the Blaze Ecosystem
Explore the Blaze ecosystem for scaling data processing across various technologies. Learn about tools for querying, migrating, and analyzing large datasets efficiently.
How Can Machine Learning Help to Predict Changes in Size of Atlantic Herring
Explore machine learning techniques to predict Atlantic herring size changes using environmental and fishing data, with implications for conservation and sustainable fisheries management.
How to Use Pandas the Wrong Way
Explore common pitfalls in pandas usage, focusing on performance and robustness. Learn efficient indexing, data type handling, and optimized group operations for better data manipulation.
Neat Analytics with Pandas Indexes
Explore advanced Pandas indexing techniques for efficient data analysis, covering index types, time-series resampling, and multi-indexing to enhance your productivity in Python analytics.
Pandas - Not Just for Data Scientists
Discover how Pandas can enhance Python performance and overcome limitations, with practical examples for experienced developers beyond data science applications.
Using Pandas and Dask to Work with Large Columnar Datasets in Apache Parquet
Explore efficient techniques for handling large columnar datasets using Pandas and Dask with Apache Parquet, including partitioning, row group skipping, and optimized data layout for faster queries.
All You Need is Pandas - Unexpected Success Stories
Discover efficient data processing with Pandas through real-world examples, from SVG stroke extraction to letter composition, showcasing improved performance and code maintainability.
The Rise of Python in the Data Communities
Explore Python's growing influence in data-driven industries, its impact on the ecosystem, and future challenges in scaling for massive data processing.
Air Quality & Python - Developing Online Analysis Tools
Explore Python-based online tools for analyzing air quality data, integrating libraries like Pandas and NumPy with Django and Plot.ly to create interactive visualizations and support research and education.
Practical Optimisations for Pandas
Discover techniques for optimizing Pandas code, including bottleneck identification, computational efficiency, and memory management for improved performance in data processing tasks.
Top 15 Python Tips for Data Cleaning - Understanding
Discover 15 essential Python tips for effective data cleaning and understanding, focusing on Pandas and NumPy libraries to enhance your data analysis skills.
Making Pandas Fly
Optimize Pandas for larger datasets: compression techniques, faster operations, and alternatives like Dask and Vaex. Learn to process more data efficiently and boost your data analysis capabilities.
From Telemetry Data to CSVs with Python, Spark and Azure Databricks
Transforming industrial telemetry data into CSV format using Python, Spark, and Azure Databricks for analytics and AI model retraining in the context of Industry 4.0.
Darts - Unifying Time Series Forecasting Models from ARIMA to Deep Learning
Explore time series forecasting with Darts, a Python library unifying traditional and deep learning models. Learn best practices for preprocessing, backtesting, and achieving quick results in forecasting problems.
Using Python to Predict Asset Price Reversals
Learn to predict potential trend reversals in stocks and cryptocurrencies using Python, Pandas, and Plotly. Discover how to identify Fibonacci retracement levels for low-risk trade entries.
Creating the Next Generation of Billionaires - Part 4
Explore how young people use Python to analyze real-world issues like climate change and population growth, showcasing the impact of early programming education on problem-solving skills.
PyArrow and the Future of Data Analytics
Explore PyArrow's transformative impact on data analytics, covering its in-memory format, disk format, network protocol, dataframe library, and query engine integration with NumPy and Pandas.
I Have to Confess, I Still Love Pandas
Discover the power of Pandas for data science workflows. Learn advantages, efficient usage techniques, and fundamental concepts to enhance your data manipulation skills.
DuckDB - Bringing Analytical SQL Directly to Your Python Shell
Explore DuckDB's seamless integration with PyData ecosystem, enabling efficient analytical SQL queries without a server. Live demo showcases performance and usability alongside Pandas for data science tasks.
Polars vs Pandas - What's the Difference?
Explore the differences, pros, and cons of Polars and Pandas data manipulation libraries. Gain insights into their unique features and understand when to use each for optimal data analysis.