Pandas is one of the most powerful and widely used libraries in Python for data manipulation and analysis. It provides easy-to-use data structures like Series and DataFrames that make handling tabular data intuitive and efficient.

Key features of Pandas:

• Import data from CSV, Excel, SQL, or JSON.
• Clean and transform datasets.
• Perform statistical analysis.
• Handle missing data and outliers.
• Merge, join, and reshape datasets.

The core of Pandas is the DataFrame—a two-dimensional, labeled data structure similar to a spreadsheet or SQL table. You can easily filter rows, compute aggregates, and apply functions to columns.

Example:

pythonКопироватьРедактироватьimport pandas as pd
df = pd.read_csv("sales.csv")
df['Revenue'] = df['Price'] * df['Quantity']
df.groupby('Region')['Revenue'].sum()

Pandas integrates seamlessly with other libraries such as Matplotlib for visualization, NumPy for numerical operations, and Scikit-learn for machine learning.

Typical use cases:

• Analyzing customer or sales data.
• Cleaning messy datasets for machine learning.
• Aggregating and summarizing large datasets.
• Time series analysis with built-in date functions.

Whether you’re a data analyst, scientist, or engineer, Pandas is an essential skill. Its flexibility and simplicity make it ideal for exploratory data analysis, reporting, and even production pipelines.