Python is a popular programming language for data science, and there are many libraries and frameworks available that can make it easier to work with data and build machine learning models. Here are 20 top Python libraries for data science:
- NumPy
- Pandas
- SciPy
- Matplotlib
- Seaborn
- Scikit-learn
- TensorFlow
- Keras
- PyTorch
- XGBoost
- CatBoost
- scikit-image
- OpenCV
- NLTK
- spaCy
- Gensim
NUMPY:
NumPy is a library for numerical computing that provides support for large, multi-dimensional arrays and matrices of numerical data. It is a fundamental library for scientific computing with Python.
PANDAS:
Pandas is a library for data manipulation and analysis that provides support for data structures and operations for working with structured data. It is particularly useful for working with tabular data, such as data stored in spreadsheets or dataframes.
SCIPY:
SciPy is a library for scientific computing that provides a range of algorithms and functions for tasks such as optimization, linear algebra, and signal processing.
MATPLOTLIB:
Matplotlib is a library for data visualization that provides a wide range of plotting functions for creating static, animated, and interactive plots.
SEABORN:
Seaborn is a library for statistical data visualization that is built on top of Matplotlib. It provides a high-level interface for creating attractive and informative statistical plots.
SCIKIT-LEARN:
Scikit-learn is a library for machine learning that provides a range of algorithms and tools for tasks such as classification, regression, clustering, and dimensionality reduction.
TENSORFLOW:
TensorFlow is a library for machine learning and deep learning that provides support for building and training neural networks.
KERAS:
Keras is a high-level library for building and training neural networks that sits on top of TensorFlow. It provides an easy-to-use interface for building and training complex models.
PYTORCH:
PyTorch is a library for machine learning and deep learning that provides support for building and training neural networks. It is popular for its flexible, dynamic computation graph model and efficient memory management.
XGBOOST:
XGBoost is a library for gradient boosting that provides fast and accurate implementations of gradient boosting for a range of machine learning tasks.
LIGHTGBM:
LightGBM is a library for gradient boosting that is designed to be faster and more memory-efficient than other boosting libraries, such as XGBoost.
CATBOOST:
CatBoost is a library for gradient boosting that is specifically designed for working with categorical data. It provides support for handling missing values and automatically selecting the best features for a given task.
SCIKIT-IMAGE:
scikit-image is a library for image processing and computer vision that provides a range of algorithms and functions for tasks such as image filtering, segmentation, and feature extraction.
OPENCV:
OpenCV is a library for image processing and computer vision that provides a range of algorithms and functions for tasks such as image filtering, feature detection, and object recognition.
NLTK:
NLTK is a library for natural language processing that provides support for tasks such as tokenization, stemming, and part-of-speech tagging.
SPACY:
spaCy is a library for natural language processing that provides support for tasks such as tokenization, part-of-speech tagging, and entity recognition.
GENSIM:
Gensim is a library for natural language processing and information retrieval that provides support for tasks such as topic modeling and word embedding.
I hope you like this article and become really good at these libraries. And, you never do this.
enjoy, and best of luck!
————————–
