Created visualization and reports using extensive python libraries, Pandas, Numpy, Matplotlib.

Pandas is a mature, open-source Python library for data manipulation and analysis. Its core components, `DataFrame` and `Series`, provide robust abstractions for handling structured, labeled data. Here’s what stands out from a developer’s perspective: ✅ Expressive Data Structures • `DataFrame`: Two-dimensional, size-mutable, heterogeneous tabular data structure with labeled axes (rows and columns). • `Series`: One-dimensional labeled array, capable of holding any data type. ✅ Comprehensive I/O Support • Native functions for reading/writing CSV, Excel, SQL, JSON, Parquet, HDF5, and more. Methods like `read_csv()`, `to_excel()`, and `read_sql()` streamline integration with external data sources. ✅ Efficient Data Manipulation • Powerful indexing, slicing, and subsetting using intuitive label-based or integer-based selectors. • Vectorized operations built on top of NumPy enable fast, memory-efficient computations on large datasets. • Built-in support for handling missing data (`NaN`, `NA`, `NaT`) without breaking workflows. ✅ Advanced Grouping and Aggregation • Flexible `groupby` operations for split-apply-combine workflows, supporting complex aggregations and transformations. ✅ Time Series and Categorical Data • Specialized types and methods for time series (e.g., `Timestamp`, `Period`, resampling) and categorical data, improving both performance and memory usage. ✅ Interoperability • Seamless integration with the broader Python data stack: NumPy for numerical operations, Matplotlib and Seaborn for visualization, and scikit-learn for machine learning pipelines. ✅ Reshape, Merge, and Pivot • Functions like `pivot_table`, `melt`, `merge`, and `concat` enable flexible data reshaping and joining. ✅ Extensive Documentation and Community • Large, active community and extensive documentation, with a wealth of tutorials and examples for most use cases.

Usability and Graphical representation of various data sets