Libraries and packages are a big part of Python and one of the reasons for its immense popularity. It doesn’t matter if you are a beginner or a well-versed programmer, Python libraries can always come in handy.
What Are Python Libraries?
Some of you might be wondering what Python libraries are. These libraries are a collection of pre-written code that you can effortlessly integrate into your Python project for various functionalities.
Why Are Python Libraries Important?
One of the main reasons for making use of Python libraries is that they can save you hours in time. Technically, you can write all the code yourself; however, it will take much more time and sometimes your own code won’t even be as optimized as these libraries.
Top 5 Python Libraries
Let’s look at the five most important Python libraries that will help you become a better Python programmer.
1. Pandas
Python is most popularly used for data science and the first part of that includes cleaning and analyzing your data. Pandas is an excellent library that can make your life easier and your data more organized. With Pandas, you can easily load your data into a data frame. You can then use these data frames to clean and prepare your data for further analysis.
2. NumPy
When working with large datasets, one of the biggest problems the programmers face is time complexity. NumPy is a super-efficient library used for storing data in an N-dimensional array. These NumPy arrays are extremely well-optimized and can exponentially reduce your computation time. Moreover, NumPy provides excellent built-in functions to manipulate your data.
3. TensorFlow
Almost everyone, nowadays, has heard about neural networks. TensorFlow is the leading Python library to implement neural network solutions. Like NumPy, TensorFlow has its own N-dimensional arrays called tensors. With its highly optimized parallel processing, TensorFlow can train neural networks extremely efficiently.
4. Scikit-Learn
The second step in data science after data cleaning is to build a machine learning model. This is where Scikit-learn comes in. Once you have used Pandas or any other library to clean your data, you can use Scikit-learn to train a machine learning model on it. This library offers both supervised and unsupervised models. Moreover, you can also cross-validate your model with the help of Scikit-learn.
5. Seaborn
Visualizations are an important part of displaying your work in Python and Seaborn is one of the best libraries for it. It is based on Matplotlib and offers a ton of options. There are multiple different types of charts available in Seaborn and everything on them can be customized. Seaborn offers a great experience and helps you display your work to others.
Conclusion
Now that we have looked at the five best libraries for Python, hopefully, you can take your programming to the next level. Moreover, these libraries are just the tip of the iceberg, and you can find libraries relevant to your work and streamline your workflow.
