If you are a data scientist, these Python libraries are for you! Master them to perform well in your job, Explore now:
This library mainly provides data manipulation and analysis tools,which are used for analyzing data using its powerful data structures for manipulating numerical tables and time series.
The NumPy library is popular for array and matrix processing using a set of mathematical functions. This library is mostly used in machine learning computations.
For rigorous Statsmodels is a fantastic library. This multipurpose library is a mix of multiple Python libraries, drawing on Matplotlib for its graphical functionalities.
Some of the associations that aren’t immediately visible can be represented in a visual context, which helps data scientists better comprehend the models.
This is another different library module in Python used for sending HTTP requests and supports functionalities like adding headers, the formation of data and accessing.
In Python, the scipy library is one of the open-source libraries mainly used in mathematical and scientific computations, technical and engineering computations.
Python programming language provides a library for database operations. This library is mainly used for database operations using sql queries.
Francois Chollet created it, and it was initially launched in 2015. Keras provides tools for constructing models, visualizing graphs, and analyzing datasets.
TensorFlow is an open-source library for deep learning applications built by the Google Brain Team. Initially conceived for numeric computations, flexible and wide range of tools.
DBSCAN, gradient boosting, support vector machines, and random forests are among the classification, regression, and clustering methods included in SciKit-Learn.