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Python Pandas Operations for Data Science
Pandas is a powerful and widely-used open-source library in Python designed for data manipulation and analysis. It provides data structures like Series (1D) and DataFrame (2D) that make handling structured data fast and easy. In data science, Pandas is essential for tasks like data cleaning, exploration, transformation, and analysis.

Key Pandas Operations:
Data Loading:

read_csv(), read_excel(), read_json() to import data from various sources.

Data Inspection:

head(), tail(), info(), describe() for quick data overview and summaries.

Data Selection and Filtering:

Indexing with loc[] and iloc[] for label- and position-based access.

Conditional filtering for row selections.

Data Cleaning:

Handling missing values with isnull(), fillna(), dropna().

Renaming columns, changing data types, and removing duplicates.

Data Transformation:

Column operations (creating new columns, applying functions using apply()).

String operations and datetime manipulations.

Grouping and Aggregation:

groupby() for split-apply-combine strategy.

Aggregations like sum(), mean(), count(), and custom functions.

Merging and Joining:

Combining DataFrames with merge(), concat(), and join().

Sorting and Ranking:

sort_values() and rank() for organizing and prioritizing data.

Pivoting and Reshaping:

pivot_table(), melt(), and stack()/unstack() for flexible data restructuring.

Exporting Data:

Saving cleaned and processed data using to_csv(), to_excel(), etc.

Pandas enables data scientists to efficiently wrangle, explore, and prepare data for modeling, making it an indispensable tool in the data science workflow.