Bridging Theory and Practice: ML Solutions for Today’s Challenges
3 days, 20+ experts, and 25+ tech sessions and talks covering critical aspects of:
- Agentic and Generative AI
- Applied Machine Learning in the Real World
- ML Engineering and Optimization
The future of AI is unfolding. Don’t fall behind.
Stay ahead with DataPro, the free weekly newsletter for data scientists, AI/ML researchers, and data engineers.
From trending tools like PyTorch, scikit-learn, XGBoost, and BentoML to hands-on insights on database optimization and real-world ML workflows, you’ll get what matters, fast.
Stay sharp with DataPro. Join 115K+ data professionals who never miss a beat.
Business runs on data. Make sure yours tells the right story.
BIPro is your free weekly newsletter for BI professionals, analysts, and data leaders.
Get practical tips on dashboarding, data visualization, and analytics strategy with tools like Power BI, Tableau, Looker, SQL, and dbt.
Get smarter with BIPro. Trusted by 35K+ BI professionals, see what you’re missing.
B21621 - Polars Cookbook - Available as an ebook and a physical copy on Amazon
- Introducing Key Features in Polars
- The Polars DataFrame
- The Polars Series
- The Polars LazyFrame
- Selecting columns and filtering data
- Creating, modifying, and deleting columns
- Understanding method chaining
- Processing datasets larger than RAM
- Reading and writing CSV files
- Reading and writing parquet files
- Reading and writing delta tables
- Reading and writing JSON files
- Reading and writing excel files
- Reading and writing other data file formats
- Reading and writing multiple files
- Working with databases
- Inspecting the DataFrame
- Casting data types
- Handling duplicate values
- Masking sensitive data
- Visualizing data using Plotly
- Detecting and handling outliers
- Exploring basic aggregations
- Using group by aggregations
- Aggregating values across multiple columns
- Computing with window functions
- Applying UDFs
- Using SQL for data transformations
- Identifying missing data
- Deleting rows and columns containing missing data
- Filling missing data
- Filtering strings
- Converting strings into a Date/Datetime/Time
- Extracting substrings
- Cleaning strings
- Splitting strings into lists and structs
- Concatenating and combining strings
- Creating lists
- Aggregating elements in lists
- Accessing and selecting elements in lists
- Applying logic to each element in lists
- Working with structs and JSON data
- Turning columns into rows
- Turning rows into columns
- Joining DataFrames
- Concatenating DataFrames
- Other reshaping techniques
- Working with date and time
- Applying rolling windows calculations
- Resampling techniques
- Time series forecasting with the functime library
- Converting to and from a pandas DataFrame
- Converting to and from NumPy arrays
- Interoperating with PyArrow
- Integration with DuckDB
- Amazon S3
- Azure Blog Storage
- Google Cloud Storage
- BigQuery
- Snowflake
- Debugging chained operations
- Inspecting and optimizing the query plan
- Testing data quality with cuallee
- Running unit tests with Pytest
- Version 1.6.0
- Use Altair in DataFrame.plot (#17995).