6 Best Beginner Friendly Python Libraries For Data Science and AI (2024)

Libraries are a collection of related modules that carry bundles of code that can be used in various programs repeatedly. With this, the programming becomes:

• Easy.
• Convenient.
• Complex and simple.

Libraries help out with a range of development needs like such 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 repeatedly because of in-built modules. Why create the wheel again? They are written down in C language and give access to functions of the system like file I/O which shall not be accessible to programmers and modules written in Python which for many issues provide solutions. Libraries can also be used for:

1. Object-oriented programming.
2. GUI programming support.
3. 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.

1. You need to create a python package and then give it a name if you want others to import it,
2. 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 project’s root.
3. 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:

1. Create apps and models in a variety of fields.
2. Machine learning.
3. Data science.
4. Ethical hacking.
5. Malware analysis.
6. Artificial intelligence.
7. Data visualization.
8. Image and data manipulation.

6 Best Python Libraries for Beginners to use in Data Science, AI and ML

Before you continue make sure to download Python and have it properly installed on your PC.

Scikit-Learn – Machine learning on steroids

It is associated with Sci-PY and NumPY. It is popular due to working with complex data.

Many changes have been 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 seen some improvements.

3 reasons to use Scikit-Learn:

1. It comes with cross-validation which helps to check the accuracy of supervised models on data that his not seem
2. Unsupervised learning algorithms.
3. Beneficial in the extraction of features from both images and text.

Sci-kit learn is being used in:

• Reducing dimensionality.
• Classification.
• Regression.
• Clustering.
• Model selection.

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ALSO SEE: Python Hacking Scripts and Tools Download (With Source Code).

Keras – Power neural networks

This is quite a cool one on the list. As you use this, you shall get to know that it provides an easy mechanism to express neutral networks.

Moreover, you shall have the best utilities for:

1. Compiling models.
2. Processing data-sets.
3. Graph visualization.

Internally it either uses Theano or TensorFlow. When compared with other such libraries it seems 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:

1. Smoothly run on GPU and CPU.
2. Supports neural network models; fully connected, convolutional, pooling, recurrent, embedding and much more.
3. Apt for innovative research.

It also carries 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.

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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:

• Grouping.
• Combining data.
• Filtering.
• Time-series functionality.

Why it is different than other ML libraries?

• With this manipulating data becomes easier.
• It supports 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 ensure high function and good flexibility.

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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:

1. It has been developed using NumPY and its array makes the most use of NumPY.
2. It shall provide you with efficient numerical routines such as:
3. Optimization.
4. Numerical integration and sub-module functions are well-documented.

Which areas you can use it in:

1. It helps to solve mathematical functions.
2. It uses an array of data structures.
3. Carries modules for commonly used tasks in scientific programming.
4. Includes tasks like linear algebra, integration, ordinary differential equation solving and signal processing execute easily.

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NumPy

This is seemed to be a popular machine learning library. It is being used by Matplotlib and is capable of performing multiple operations on Tensors. Mainly used to express images, sound waves, and other binary raw streams in N-dimensional.

One of the most important features is Array Interface along with:

1. Open-source contribution on its codebase.
2. To solve Intuitive and complex mathematical problems easily.

ALSO SEE: How To Install NumPy Library in Python 2/3 on Windows and Mac.

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:

• LightGBM.
• XGBoost.
• CatBoost.

2 reasons to consider using LightGBM

1. Fast training of the algorithm and is user-friendly.
2. No errors when considering NaN values and other canonical values.

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Conclusion

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.