The libraries i'd list below provide tools and functions for various tasks in data analysis such as data manipulation, visualization, machine learning, and distributed computing:
pandas: A powerful library for data manipulation and analysis. It provides data structures like DataFrame for handling large datasets, along with a wide range of functions for filtering, aggregating, and transforming data.
NumPy: The fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with mathematical functions to operate on these arrays.
Dask: Dask is a library that enables parallel computing and scalable data processing. It allows you to work with larger-than-memory datasets by providing parallel algorithms and tools that closely resemble the syntax and functions of pandas.
matplotlib and Seaborn: These libraries are used for data visualization. matplotlib provides a flexible framework for creating various types of plots and graphs, while Seaborn builds on top of matplotlib to provide a higher-level interface for creating visually appealing statistical graphics.
scikit-learn: If you're interested in machine learning, scikit-learn is a popular library that provides a wide range of machine learning algorithms, tools for model selection, evaluation, and preprocessing of data.
PySpark: PySpark is the Python library for Apache Spark, a powerful open-source data processing engine. Spark is designed for big data processing and can handle large datasets efficiently through distributed computing.
Vaex: Vaex is a library for lazy, out-of-core dataframes that enables high-performance analytics even on very large datasets. It's particularly useful when working with datasets that are too large to fit in memory.
Bokeh: Bokeh is a library for interactive data visualization. It's particularly well-suited for creating interactive, web-ready visualizations directly from Python code.
Holoviews: Holoviews is another library for interactive visualization, focusing on making complex visualizations simple and declarative, and enabling you to build a wide variety of interactive plots with minimal code.
Koalas: Koalas is a library that provides a pandas-like API on top of Apache Spark, allowing you to use familiar pandas syntax while taking advantage of Spark's distributed computing capabilities.
Cudf: If you have access to NVIDIA GPUs, cudf is a library that provides a GPU-accelerated DataFrame for working with large datasets. It's particularly beneficial for speeding up data processing tasks.
TensorFlow : To build, train, and evaluate neural networks, from computer vision to natural language processing.
NLTK: If you're working with natural language processing, NLTK essential library, for text processing, grammar analysis, information extraction, and many other NLP-related tasks.
pandas: A powerful library for data manipulation and analysis. It provides data structures like DataFrame for handling large datasets, along with a wide range of functions for filtering, aggregating, and transforming data.
NumPy: The fundamental package for scientific computing in Python. It provides support for large, multi-dimensional arrays and matrices, along with mathematical functions to operate on these arrays.
Dask: Dask is a library that enables parallel computing and scalable data processing. It allows you to work with larger-than-memory datasets by providing parallel algorithms and tools that closely resemble the syntax and functions of pandas.
matplotlib and Seaborn: These libraries are used for data visualization. matplotlib provides a flexible framework for creating various types of plots and graphs, while Seaborn builds on top of matplotlib to provide a higher-level interface for creating visually appealing statistical graphics.
scikit-learn: If you're interested in machine learning, scikit-learn is a popular library that provides a wide range of machine learning algorithms, tools for model selection, evaluation, and preprocessing of data.
PySpark: PySpark is the Python library for Apache Spark, a powerful open-source data processing engine. Spark is designed for big data processing and can handle large datasets efficiently through distributed computing.
Vaex: Vaex is a library for lazy, out-of-core dataframes that enables high-performance analytics even on very large datasets. It's particularly useful when working with datasets that are too large to fit in memory.
Bokeh: Bokeh is a library for interactive data visualization. It's particularly well-suited for creating interactive, web-ready visualizations directly from Python code.
Holoviews: Holoviews is another library for interactive visualization, focusing on making complex visualizations simple and declarative, and enabling you to build a wide variety of interactive plots with minimal code.
Koalas: Koalas is a library that provides a pandas-like API on top of Apache Spark, allowing you to use familiar pandas syntax while taking advantage of Spark's distributed computing capabilities.
Cudf: If you have access to NVIDIA GPUs, cudf is a library that provides a GPU-accelerated DataFrame for working with large datasets. It's particularly beneficial for speeding up data processing tasks.
TensorFlow : To build, train, and evaluate neural networks, from computer vision to natural language processing.
NLTK: If you're working with natural language processing, NLTK essential library, for text processing, grammar analysis, information extraction, and many other NLP-related tasks.