PANDAS FOR DATA ANALYTICS

Learn the core architecture of Pandas DataFrames and Series, bracket notation vs dot notation, and selecting rows and columns using .loc[] and .iloc[].

Understand how to import and export CSV, Excel, and JSON files using pandas—your first step into real datasets.

Learn to filter rows, select columns, and sort datasets to extract meaningful insights.

Master techniques to detect, clean, replace, or drop missing data—one of the most common real-world problems.

Complete data cleaning toolkit: dropping missing rows/cols with dropna, median/mean and forward-fill imputation with fillna, masking, and astype conversions.

Learn pandas’ datetime features to parse dates, extract components, and work with time-series data.

Using a provided CSV dataset, create a Jupyter Notebook that demonstrates core pandas skills. Your submission should include: loading the data with read_csv, exploring it with head/info/describe, filtering rows using boolean conditions, sorting by one or more columns, handling missing values (identify, drop, or fill), and working with date columns. Include at least 3 insights you discovered from the data.

Master relational database-style joins in Pandas using pd.merge(): inner, outer, left, and right joins, join keys, indicator flags, and custom suffixes.

Learn to summarize and aggregate data efficiently using groupby—one of the most powerful operations in pandas.

Combine DataFrames with pd.concat and pd.merge, multi-column groupbys with .reset_index(), and named aggregation mappings.

Complete a final pandas assignment that combines merging and aggregation. Your submission should include: merging two or more DataFrames using merge/join, groupby operations with multiple aggregation functions (sum, mean, count), creating pivot tables or cross-tabulations, and at least one visualization using pandas plot. Summarize your findings in markdown cells.