1.TensorFlow
TensorFlow is the first Python library for data science on the list. TensorFlow is a high-performance numerical computation framework with over 35,000 comments and a thriving community of over 1,500 contributors. It is employed in a variety of scientific domains. TensorFlow is a framework for building and conducting tensor-based computations. Tensors are partially defined computational objects that finally yield a value.Features:
- Better representations of computational graphs
- In neural machine learning, it reduces error by 50 to 60%.
- Complex models can be run in parallel.
- Google-backed seamless library management
- More frequent updates and new releases to keep you up to date with the latest features
The following applications benefit greatly from TensorFlow:
- Recognition of speech and images
- Applications that are text-based
- Analyzing time series
- detection of video
2. SciPy
Another free and open-source Python library for data science that is widely used for high-level computations is SciPy (Scientific Python). On GitHub, SciPy has over 19,000 comments and a community of about 600 contributors. Because it extends NumPy and provides many user-friendly and efficient routines for scientific calculations, it is widely used for scientific and technical computations.Features:
- NumPy is a Python extension that contains a collection of algorithms and routines.
- High-level data manipulation and visualization commands
- SciPy and image submodule for multidimensional image processing
- Functions for solving differential equations are built-in.
Applications:
- Operations on multi-dimensional images
- The Fourier transform and solving differential equations
- Algorithms for optimization
- Algebraic equations
3. NumPy
NumPy (Numerical Python) is the most important Python library for numerical calculation; it includes a powerful N-dimensional array object. On GitHub, it has over 18,000 comments and a community of 700 contributors. It's a general-purpose array-processing package that includes high-performance multidimensional objects known as arrays as well as tools for working with them. NumPy tackles the slowness issue in part by providing these multidimensional arrays, as well as methods and operators that efficiently operate on them.Features:
- For numerical routines, it provides quick, precompiled functions.
- Better efficiency with array-oriented computing
- Supports object-oriented thinking.
- Vectorization allows for more compact and faster computations.
Applications:
- Used extensively in data analysis
- This function generates a powerful N-dimensional array.
- Other libraries, such as SciPy and sci-kit-learn, are built on top of it.
4. Pandas
In the data science life cycle, Pandas (Python data analysis) is a requirement. Along with NumPy in matplotlib, it is the most popular and commonly used Python package for data research. It is frequently used for data analysis and cleansing, with about 17,00 comments on GitHub and an active community of 1,200 contributors. Pandas deliver quick, versatile data structures like data frame CDs that make working with structured data simple and natural.Features:
- Rich features and eloquent syntax provide you the ability to cope with missing data.
- Allows you to write your own function and apply it to a set of data.
- Abstraction at a high level
- High-level data structures and manipulation tools are included.
Applications:
- Data cleansing and wrangling in general
- Because it has great support for loading CSV files into its data frame format, it is ideal for ETL (extract, transform, load) processes for data transformation and storage.
- Statistics, finance, and neuroscience are only a few examples of academic and commercial applications.
- Date range creation, moving window, linear regression, and date shifting are examples of time-series-specific capabilities.
5. Matplotlib
Matplotlib's visualizations are both powerful and elegant. It's a Python charting package with over 26,000 GitHub comments and a thriving community of roughly 700 developers. It's widely used for data visualization because of the graphs and charts it generates. It also has an object-oriented API for integrating the charts into applications.Features:
- It can be used as a MATLAB substitute and has the benefit of being free and open source.
- Supports a wide range of backends and output formats, allowing you to utilize it independently of your operating system or desired output format.
- Pandas can be used to wrap the MATLAB API and make it work as a cleaner.
- Low memory use and improved runtime behavior.
Applications:
- Variable correlation analysis
- Visualize the models' 95 percent confidence intervals.
- Detecting outliers with a scatter plot, for example.
- Visualize data distribution to acquire immediate insights.
6. Keras
Keras, like TensorFlow, is a popular library for deep learning and neural network modules. Keras offers both TensorFlow and Theano backends, making it an excellent choice for those who don't want to get too deep into TensorFlow.Features:
- Keras provides a large number of prelabeled datasets that may be immediately imported and loaded.
- It has a number of implemented layers and parameters that can be used to build, configure, train, and evaluate neural networks.
Applications:
The deep learning models that are offered with their pre-trained weights are one of Keras' most important applications. You can use these models to make predictions or extract characteristics without having to create or train your own.Which Python libraries are used for data science?
Pandas. In the data science life cycle, Pandas (Python data analysis) is a requirement. Along with NumPy in matplotlib, it is the most popular and commonly used Python package for data research.Which Python version is best for data science?
Python 3.x for data science has been developed since Python 2's development period has ended, and all future updates will be for Python 3. Tensorflow and other popular and new frameworks and modules are supported in Python 3.
Which Python library is in demand?
Numpy is one of the most widely used machine learning libraries in Python. Numpy is used internally by TensorFlow and other libraries to execute numerous operations on Tensors.
Is TensorFlow a Python library?
Google designed and distributed TensorFlow, a Python library for fast numerical computing. It is a foundation library that can be used to develop Deep Learning models directly or through wrapper libraries created on top of TensorFlow to make the process easier.
Is Jupyter good for Python?
The Jupyter Notebook is great not just for studying and teaching Python, but also for exchanging data. You may make a slideshow out of your Notebook or post it on GitHub.
