Tell about your experience using the Pandas library for data processing.

In my experience with Pandas, I have used this library for processing and analyzing tabular data. For example, loading data from CSV files, cleaning missing values, filtering based on conditions, and creating new columns with calculations.

Typical operations:

• reading data: pd.read_csv("file.csv")
• filtering: df[df["age"] > 30]
• grouping and aggregation: df.groupby("category").sum()
• handling missing values: df.fillna(0) or df.dropna()

Pandas is convenient for quick data preparation before analysis or visualization, as well as for data format transformation.