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Python is a highly popular coding language in the programming industry. When looking for Python libraries for data science, you can easily experience a sense of overwhelm by the mass amount available. Having the correct information for Python functions is one thing, but having access to extensive Python libraries for data science is another.
This list provides you with a full roundup of the top Python libraries for data science that you can use to program your next project accurately. With this database of resources, you can easily find the information you are looking for to accurately and efficiently program for your exact need. With Amphy, you can learn Python and access hundreds of other courses to expand your machine learning knowledge.
Continue reading below to review the available options and some of the most common questions about these libraries.
Which Python Libraries Are Used for Data Science?
There are many Python libraries that you can use for data science. While we will input an extensive list below, some of the most popular include the following:
- TensorFlow
- NumPy
- SciPy
- Pandas
How Many Libraries Are There in Python?
When you are using Python for programming, you will have access to over 137,000 different libraries. These libraries focus on many different subjects, including data science. Others that you will find include machine learning, data visualization, data manipulation, and image manipulation.
Is Python Enough for Data Science?
Although many programming languages are available, not all of them gear toward data science. When it comes to Python, the inner workings adapt well to data science’s needs in programming. Therefore, Python is one of the best options for those in that specific industry.
When you are using Python for programming, you will have access to over 137,000 different libraries
Our List of Python Libraries for Data Science
Below we list our top choices when it comes to Python libraries for data science.
1. TensorFlow
This Python library has an expanded arsenal for deep learning, with a focus on data science and machine learning.
2. Numphy
This library provides precise assistance in Python for numerical operations. Numphy is a top choice if you have a need involving multi-dimensional arrays and matrices.
3. SciPy
SciPy is an open-source library that offers a comprehensive collection of algorithms and functions for scientific and technical computing. SciPy works alongside Numphy to reach a wide variety of industries, including data science.
4. Pandas
Pandas has a strong focus on data manipulation. Through its data analysis tools, you can expand your horizons when it comes to data science, merging, data cleaning, visualization, and reshaping.
5. Matplotlib
Another Python library option is Matplotlib, which creates various visualizations through scatter plots, line plots, bar plots, and histograms.
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6. Seaborn
When you use the Seaborn library, you receive access to a Matplotlib expansion that includes creating statistical graphics to display the data science elements you are working with properly.
7. Scikit-learn
A library for machine learning in Python, Scikit-learn provides support for a wide range of learning algorithms, including regression, classification, clustering, and dimensionality reduction in data science.
8. Keras
The Python library Keras is a high-level API for building and training deep learning models. It provides a user-friendly interface for building complex neural networks for data science needs.
9. PyTorch
While it is a primary resource for data science, PyTorch is also a library for building and training machine learning models. It provides support for both deep learning and traditional machine learning algorithms.
10. Statsmodels
Those who frequently use Stasmodels appreciate the database as a library for statistical modeling and testing in Python. It provides tools for regression analysis, time series analysis, and more.
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11. NLTK
If you are looking for a library that has natural language processing in Python, turn to NLTK. This library provides tools for tokenization, part-of-speech tagging, sentiment analysis, and more for data science.
12. Dask
Dask is a popular data science library for parallel computing in Python. It provides a way to scale computations on larger-than-memory datasets using parallel algorithms and task scheduling.
13. XGBoost
When you need a library for data science, XGBoost is an excellent option. As a library for gradient boosting algorithms, it is designed to be efficient, scalable, and portable and is widely used for machine learning tasks, such as classification and regression.
14. PySpark
PySpark is an extensive library that programmers use for distributed computing in conjunction with Apache Spark. It provides a Python interface to Spark, which allows data scientists to scale up their machine learning and data processing tasks to large clusters.
15. Gensim
Many people love using Gensim, which is a library for natural language processing and topic modeling in Python. This data science library provides tools for working with large text datasets, including algorithms for document similarity, topic modeling, and word embeddings.
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Final Thoughts
Python is a top programming asset that individuals use in many industries, including data science. Therefore, being able to utilize Python libraries for data science is imperative to operating at your best.
This guide delivers 15 Python libraries for data science that will help you navigate your next programming project. The vast amount of information available at your fingertips will make your work go smoother and more efficient.
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