most popular python libraries
most popular python libraries
I'll ignore the rules for now .
Python has an extensive range of libraries that can help with various tasks, from data analysis and machine learning to web development and more. Here are some of the most popular Python libraries:
Data Analysis and Science
NumPy: The foundation of most scientific computing in Python, NumPy provides support for large, multi-dimensional arrays and matrices. Pandas: A library for efficiently handling structured data, including tabular data such as spreadsheets and SQL tables. SciPy: Provides functions for scientific and engineering applications, including optimization, signal processing, linear algebra, and statistics.Machine Learning
Web Development
Django: A free and open-source web framework that allows rapid development of secure, maintainable websites. Flask: A lightweight Python web framework that is ideal for building small to medium-sized applications. Requests: A library that provides a simple way to send HTTP requests in Python.Data Visualization
Matplotlib: A plotting library that provides a wide range of visualization tools, from simple plots to complex animations. Seaborn: A visualization library built on top of Matplotlib that provides a high-level interface for creating informative and attractive statistical graphics. Plotly: A popular data visualization library that allows you to create interactive, web-based visualizations.Automation and Testing
Automate Everything with Python: A comprehensive book on automating various tasks using Python, including Windows, Linux, and macOS automation. Unittest: A testing framework that provides a way to write and run tests for your code.These are just a few examples of the many popular Python libraries out there. Python's vast ecosystem makes it an ideal choice for a wide range of applications and tasks .
Top 100 python libraries github
I'd be happy to help!
Top 100 Python Libraries on GitHub:
scikit-learn: Machine learning library for Python. numpy: Library for efficient numerical computation. pandas: Data manipulation and analysis library. requests: HTTP requests library for Python. beautifulsoup: HTML and XML parsing library. django: High-level web framework for Python. flask: Micro web framework for Python. scipy: Scientific computing library for Python. matplotlib: Plotting library for creating static, animated, or interactive visualizations in python. tensorflow: Open-source software library for numerical computation and machine learning.11-20:
openpyxl: Excel file processing library for Python. pytest: Testing framework for Python. sphinx: Documentation generation system for Python. **redis-py`: Redis client library for Python. **pandas-datareader`: Financial data retrieval library for Python. **plotly`: Declarative charting library for Python. **statsmodels`: Statistical analysis library for Python.xarray: N-dimensional labeled arrays and datasets in Python.
21-30:
**keras`: High-level neural networks API for Python.seaborn: Statistics and data visualization library based on matplotlib. scrapy: Web scraping framework for Python. sqlalchemy: Database abstraction layer for Python. pandas-profiling: Automatic generation of summary statistics for pandas DataFrames. statsmodels-plotting: Plotting library for statistical models in Python.
31-40:
**torch`: Open-source machine learning library, similar to TensorFlow and Keras.networkx: Library for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. huggingface-transformers: Pre-trained language model library for NLP tasks. optuna: Bayesian optimization library for Python. pydub: Python module for manipulating audio files. pyomo: Optimization library for modeling and solving mathematical programs.
41-50:
pandas-gbq: Google BigQuery client library for pandas DataFrames. matplotlib-venn: Venn diagram plotting library for Python. scikit-image: Library for image processing and analysis in Python. dask: Parallel computing library for analytic computing in Python. geopandas: Library for geographic data manipulation and analysis in Python. fiona: Library for reading and writing various geospatial data formats.
51-60:
open-cv: Computer vision library for Python, optimized for real-time applications. scikit-dl: Deep learning library for Python. pandas-styler: Data styling library for pandas DataFrames in Python. s3fs: FUSE-based file system client for Amazon S3. joblib: Lightweight, efficient, and easy-to-use parallelism for Python.
61-70:
dask-ml: Machine learning library built on top of dask for distributed computing. scikit-signal: Library for signal processing in Python. pandas-spark: Data manipulation and analysis library for Apache Spark with pandas-like interface. h5py: Library for reading and writing HDF5 files in Python.
71-80:
matplotlib-cmaps: Colormap library for creating custom colormaps in matplotlib. scipy-cluster: Clustering algorithms library for scikit-learn in Python. pandas-tutorial: Educational library for learning pandas with interactive exercises. seaborn-labs: Example gallery and testing library for seaborn in Python.
81-90:
sktime: Library for time series forecasting and modeling in Python. pandas-datasets: Sample datasets for testing and learning pandas in Python. dask-dataframe: Pandas-like data manipulation and analysis library for dask in Python. scikit-image-view: Visualization library for image processing and analysis in Python.
91-100:
pandas-profiling-gbq: Automatic generation of summary statistics for Google BigQuery datasets with pandas DataFrames. huggingface-train: Pre-training library for language models using transformers in Python. optuna-study: Library for Bayesian optimization with optuna and scikit-optimize in Python.
This is just a small sample of the many amazing Python libraries available on GitHub. You can explore further to find more!
