Top 6 Python Libraries for Data Scientists
The more time you spend looking up data science job roles and interact with the people in and around the data science industry, the more you will realize the importance of being able to conduct data science with Python. As a general purpose, object oriented language Python has stayed at the top of many popularity indexes in terms of web development and application development. In terms of applications in data science Python has had to compete with age old industry leaders like SAS and R but it has come out on top. Now, with a number of libraries which are curated specifically for use in data science, machine learning, and deep learning, Python is a force to reckon with, and those with Python skills are a revered class.
What is a Python library?
A library generally contains certain data types which are considered to reside in the core of a language. It also contains modules which can be used in a code to perform certain preset functions or can be built upon to perform more complex tasks. In simpler terms, a library provides you with written code that you can apply to your programme. The modules in Python libraries are either written in C and then imported into Python or built into Python. We will look into some such libraries that have been playing a significant role in the lives of data science professionals.
It stands for Python Data Analysis and that is what it does. This has easy to use data structures that can be used to rename, merge, index, or manipulate data. It is a great tool for data wrangling and munging.
Pandas take data from databases and put them into data frames that resemble excel sheets. This then allows you to perform a wide range of functions – aggregation, reading, and visualization.
Built on the NumPy array object SciPy is a formidable tool for performing a wide range of mathematical operations. It belongs in the same stack with Matplotlib, SymPy, and NumPy.
This library contains modules that allow you to perform tasks involving linear algebra, statistics, integration, and interpolation.
This plays a crucial role in building predictive models and machine learning algorithms.
MatplotLib and Seaborn
These two can be discussed together as the latter is sort of an improvement on the earlier. matplotlib allows you to embed plots into applications. You can create line plots, scatter plots, and area plots among others with matplotlib.
Seaborn is an extension of MatplotLib which allows you to achieve better visualization with reduced effort and less code. It is a great tool for data visualization as well as the form of data analysis that is allowed by good visualization.
This is a powerhouse for machine learning. This features modules for machine learning algorithms like the random forest, k-means clustering, mean shift, cross-validation. It supports a wide range of practical purposes like spam detection, image recognition, customer segmentation, etc.
TensorFlow helps in building deep neural networks. This library along with Keras, which has a more advanced API, creates a formidable package for deep learning experts. TensorFlow is used in processes like voice recognition, facial recognition, text analysis, video detection, and sentiment analysis.
This is not an exhaustive list nor an attempt to rank these libraries. Nevertheless, it should provide you with some sort of a template as you plan your Python training.