Data Wrangling Projects
Libraries for cleaning, transforming, and preparing data.
3 projects available
How to Choose the Right Data Wrangling Tool for Python?
When deciding among the three most popular data wrangling tools in Python - Pandas, Dask, and NumPy - your choice largely depends on the specific requirements of your data and the task at hand. Opt for Pandas when you're working with tabular data that fits into memory, ideal for data cleaning, transformation, and analysis tasks on medium-sized datasets. Choose Dask when you need to handle larger-than-memory datasets or require parallel computing to speed up your data processing. NumPy is the preferred choice when you need to perform numerical computations, especially on arrays, with heavy mathematical operations.
Sales Data Analysis with Pandas
beginnerMaster essential data wrangling tasks with Pandas through a practical sales data analysis project. Learn to load CSV files, clean messy data, handle missing values, engineer new features, and perform powerful grouping and aggregation operations that form the foundation of any data pipeline.
Large-Scale Log Processing with Dask
intermediateLearn to process datasets larger than memory using Dask's parallel computing capabilities. This project demonstrates how to read multiple log files, perform distributed aggregations, and efficiently process big data that would be impossible with standard Pandas.
Sensor Data Analysis with NumPy
intermediateProcess and analyze time-series sensor data using NumPy's powerful array operations. Learn to perform statistical analysis, smooth data with rolling averages, detect anomalies, and visualize results - essential skills for IoT and monitoring applications.