Libraries are a collection of related modules which carry bundles of code that can be used in various programs repeatedly. With this, the programming becomes:
- Complex and simple.
Libraries help out with a range of development needs like there is TensorFlow and Scikit for deep learning, Pandas for data analysis, NumPY for scientific computing and much more.
We don’t need to write the code again and again because of in-built modules. Why create the wheel again? They are written down in C language and gives access to functions of the system like file I/O which other shall not be accessible to programmers and modules written in Python which for many issues provide solutions. Libraries can also be used for:
- Object-oriented programming.
- GUI programming support.
- You can make GUI using modules like PyQt4, PyQt5, wxPython or TK in Python.
How Python Libraries are made
There are three simple steps in creating a Python library however what consists inside of it is the complex process.
- You need to create a python package and then give it a name if you want others to import it,
- Then move the class to its very own file, so far you have written down the code in the “main.py” or “app.py” file at the root of the project.
- Finally, call your library.
How many Python Libraries are currently available
For now it has more than 139,000 libraries and are helpful in the following ways:
- Create apps and models in a variety of fields.
- Machine learning.
- Data science.
- Ethical hacking.
- Malware analysis.
- Artificial intelligence.
- Data visualization.
- Image and data manipulation.
6 Best Python Libraries for Beginners to use in Data Science, AI and ML
Scikit-Learn – Machine learning on steroids
It has an association with Sci-PY and NumPY. It is popular due to working with complex data.
Many changes are made in this library one is:
- Cross-validation feature in which you can use more than one metric. Many training methods such as logistics and close neighbors have got seen some improvements.
3 reasons to use Scikit-Learn:
- Comes with cross-validation which helps to check the accuracy of supervised models on data that his not seem
- Unsupervised learning algorithms.
- Beneficial in the extraction of features from both images and text.
Sci-kit learn is being used in:
- Reducing dimensionality.
- Model selection.
Keras – Power neural networks
This is quite a cool one on the list. As you use this you shall get to know that it shall provide an easy mechanism to express out neutral networks.
Moreover, you shall have the best utilities for:
- Compiling models.
- Processing data-sets.
- Graph visualization.
Internally it either uses Theano or TensorFlow. When compared with other such libraries it is seemed to be quite slow, as it creates computational graphs using back-end infrastructure and then uses it to perform different functions. You shall find all models in this to be portable along with:
- Smoothly run on GPU and CPU.
- Supports neural network models; fully connected, convolutional, pooling, recurrent, embedding and much more.
- Apt for innovative research.
It also carries a many implementations of commonly used neural network building blocks like Layers, Objectives and optimizers.
Moreover, it shall provide you with many pre-processed data sets and pre-trained models including VGG and MNIST.
Pandas – Start manipulating any data set
Pandas is a machine learning library that shall provide you with:
- High-level data structures.
- Variety of tools for analysis.
It can translate complex operations with data by using one or two commands.
They come with built-in methods for:
- Combining data.
- Time-series functionality.
Why it is different than other ML libraries?
- With this manipulating data becomes easier.
- It provides support for operations like Re-indexing, Iteration, Sorting, Aggregation, Concatenations, and Visualizations.
- Group and sort data.
- Choose the best-suited output for apply method.
- Support for performing custom-kind operations.
When we talk about usage one thing which tops the list is data analysis, but when used with other tools and libraries they make sure high function and good flexibility.
SciPy – Technical Computing library
It is a machine learning library for app developers and engineers. But, there’s a difference between the SciPy library and the SciPy stack. The library carries modules for linear algebra and integration along with stats support.
4 features that set it apart:
- It has been developed using NumPy and its array makes the most use of NumPY.
- It shall provide you with efficient numerical routines such as:
- Numerical integration and sub-module functions are well-documented.
Which areas you can use it in:
- It helps to solve mathematical functions.
- It uses an array of data structures.
- Carries modules for commonly used tasks in scientific programming.
- Includes tasks like linear algebra, integration, ordinary differential equation solving and signal processing execute easily.
This is seemed to be a popular machine learning library. It is being used by Matplotlib and it is capable of performing multiple operations on Tensors. Mainly used to express images, sound waves, as well as other binary raw streams in N-dimensional.
One of the most important features is Array Interface along with:
- Open-source contribution on its codebase.
- To solve Intuitive and complex mathematical problems easily.
LightGBM (Light Gradient Boosting Machine)
LightGBM provides you with highly scalable, optimized and fast implementations of gradient boosting.
This one has a lot of popularity among the ML community and is one of the finest machine learning libraries. It helps in building algorithms through redefined elementary models and namely decision trees.
It consists of three built-in sub-modules that are:
2 reasons to consider using LightGBM
- Fast training of the algorithm and is user-friendly.
- No errors when considering NaN values and other canonical values.
Libraries are what make Python unique and allow the functionality to grow and integrate with other services. Python is famous in the AI and ML category because of some of the libraries we shared above that are great if you are a beginner and have maybe just come out of university. We also recommend you learn Python from books which may be PDF or hardcopy instead of courses if you are new to the programming scene.