How is Pandas better than regular lists and dictionaries in Python?

Pandas provides convenient data structures — Series and DataFrame — which significantly simplify working with tabular data compared to regular Python lists and dictionaries. Main advantages:

• Speed and memory optimization: Pandas is implemented in C and uses NumPy, allowing efficient processing of large data volumes.
• Ease of data manipulation: built-in methods for filtering, grouping, aggregating, merging, and sorting data.
• Support for missing values: Pandas can handle NaN correctly, unlike regular lists.
• Indexing and labels: data is indexed, making access and manipulation easier.
• Integration with other libraries: easily used with matplotlib, scikit-learn, etc.

Example:

import pandas as pd

data = {'Name': ['Anna', 'Boris', 'Viktor'], 'Age': [25, 30, 22]}
df = pd.DataFrame(data)

# Filtering by age
young = df[df['Age'] < 30]
print(young)

This code is much shorter and clearer than an equivalent using lists and dictionaries.