1. Data Manipulation and Analysis:

Pandas:
Overview: Pandas is the backbone for most data processing tasks in Python. It provides data structures for efficiently storing large datasets and tools for reshaping, aggregating, and filtering data.
Key Features:
- DataFrame object for spreadsheet-like data manipulation with labeled axes (rows and columns).
- High performance merging and joining of data.
- Time Series functionality.

NumPy:
Overview: Fundamental package for numerical computations in Python. It provides support for large multidimensional arrays and matrices, along with an assortment of mathematical functions to operate on these arrays.
Key Features:
- Mathematical functions for element-wise computation.
- Tools for integrating C/C++ code.
- Fourier transforms and random number capabilities.

2. Data Visualization:

Matplotlib:
Overview: A foundational plotting library for Python that offers a wide variety of static, animated, and interactive plots.
Key Features:
- Creating static, animated, and interactive visualizations.
- Versatile, allowing bar charts, error charts, scatter plots, and more.
- Can be used in Python scripts, the Python shell, web application servers, and more.

Seaborn:
Overview: Built on top of Matplotlib, Seaborn helps in creating visually appealing statistical graphics.
Key Features:
- Built-in themes for styling Matplotlib graphics.
- Functions to visualize statistical relationships in datasets.
- Tools for creating complex visualizations like pair plots, heatmaps, and facet grids.

3. Machine Learning:

Scikit-learn:
Overview: One of the most popular ML libraries for classical machine learning algorithms. It is built on NumPy, SciPy, and Matplotlib.
Key Features:
- Simple and efficient tools for data analysis and mining.
- Supports various supervised and unsupervised learning algorithms.
- Tools for model selection and evaluation.

TensorFlow and Keras:
Overview: TensorFlow is an open-source machine learning library developed by Google, and Keras is an interface for TensorFlow to make building neural networks easier.
Key Features:
- Perform high-performance numerical computations.
- Allows for deep learning model design and training.
- Keras provides simple and consistent APIs for deep learning models on top of TensorFlow.

4. Statistics:

SciPy:
Overview: Built on top of NumPy, SciPy is used for high-level computations. It provides modules for optimization, integration, interpolation, and other tasks common in science and engineering.
Key Features:
- Functions for numerical integration and optimization.
- Signal processing and statistical functions.
- Spatial data structures and algorithms.

5. Big Data & Distributed Computing:

PySpark:
Overview: PySpark is the Python API for Apache Spark, a fast and general-purpose cluster-computing system. It provides capabilities for distributed data processing and machine learning.
Key Features:
- Allows data processing in real-time.
- In-built modules for SQL, streaming, machine learning, and graph processing.
- Can interface with big data platforms and databases like Hadoop and Cassandra.

These libraries form the core toolkit for many data scientists using Python. Each has its own strengths, and the right tool typically depends on the specific task at hand. Being proficient in these libraries can provide a solid foundation for various data science endeavors.