Python Libraries For Data Science
This is the part where the actual power of Python with Data Science comes into the picture. Python comes with numerous libraries for scientific computing, analysis, visualization, etc. Some of them are listed below:
NumPy is a core library of Python for Data Science which stands for ‘Numerical Python’. It is used for scientific computing, which contains a powerful n-dimensional array object and provides tools for integrating C, C++, etc. It can also be used as a multi-dimensional container for generic data where you can perform various Numpy Operations and special functions.
Pandas is an important library in Python for Data Science. It is used for data manipulation and analysis. It is well suited for different data such as tabular, ordered and unordered time series, matrix data, etc.
Matplotlib is a powerful library for visualization in Python. It can be used in Python scripts, shell, web application servers, and other GUI toolkits. You can use different types of plots and how multiple plots work using Matplotlib.
Seaborn is a statistical plotting library in Python. So whenever you’re using Python for Data Science, you will be using matplotlib (for 2D visualizations) and Seaborn, which has its beautiful default styles and a high-level interface to draw statistical graphics.
Scikit learn is one of the main attractions, wherein you can implement machine learning using Python. It is a free library that contains simple and efficient tools for data analysis and mining purposes. You can implement the various algorithms, such as logistic regression, time series algorithm using scikit-learn. It is suggested that you should go through this tutorial video on Scikit-learn to understand machine learning and various techniques before proceeding ahead.
www.csdt.co.in