Pandas for Data Analysts: Leveraging Python with Confidence

Data analysts who work primarily in Excel often hit invisible walls: datasets too large to scroll through, analyses too repetitive to run manually, and charts that take more time to format than they took to build. Pandas, the Python package designed from the ground up for tabular data analysis, removes those walls. With a working knowledge of Pandas, you can filter a million-row dataset, join two data sources, and visualize results in the same script, reproducibly, in minutes. In this course, you'll write real code from the first lesson. You'll import data from Excel workbooks, profile DataFrames with summary statistics and charts, add calculated columns, filter and sort rows, aggregate with groupby, merge tables, handle missing values, reshape data with melt and pivot_table, build rolling window functions for time series, and apply all of those skills to a real dataset from start to finish. By the end of this course, you'll be able to build a complete, automated data analysis pipeline in Pandas that takes raw data from an Excel file to a clean, aggregated, and visualized output ready to share with stakeholders.