Packages for Working with Data in Memory:
- NumPy: Provides powerful array functions and linear algebra capabilities.
- Matplotlib: Popular for 2D plotting, with some 3D functionality.
- Pandas: High-performance data-wrangling package, introducing dataframes for in-memory data manipulation.
- SymPy: Used for symbolic mathematics and computer algebra.
- StatsModels: Offers statistical methods and algorithms.
- SciPy: Integrates fundamental packages like NumPy, Matplotlib, Pandas, and SymPy, often used in scientific computing.
- Scikit-learn: A library filled with various machine learning algorithms.
- RPy2: Enables calling R functions from Python, leveraging the capabilities of R for statistical analysis.
- NLTK (Natural Language Toolkit): Focuses on text analytics, providing tools for natural language processing tasks.
Packages for Working with Big Data Technologies:
- PyDoop: Python package for Hadoop, facilitating interaction with Hadoop’s distributed file system (HDFS) and MapReduce framework.
- PySpark: Python API for Apache Spark, providing distributed computing capabilities for large-scale data processing.
- Hadoopy: A Python wrapper for Hadoop, simplifying interaction with Hadoop clusters.
- PP, Dispy, IPCluster: Packages for parallel computing, facilitating distributed processing of data.
Packages for Optimizing Code and Dealing with Memory or Speed Issues:
- Numba: Allows for optimization of Python code by compiling it to machine code, particularly useful for numerical computations.
- Cython: Translates Python code into C extensions, enhancing speed and performance.
- Cuda: Enables parallel computing on GPUs, useful for accelerating computations.
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