Python Libraries
Explore important Python libraries used for numerical computing, data analysis, and visualization. This lesson introduces NumPy for arrays, pandas for tabular data, Matplotlib and Seaborn for plotting, plus SciPy and Imageio, helping you understand their applications in Python programming for scientific tasks.
NumPy
NumPy is a powerful library for numerical computing in Python. It provides a wide range of functions for working with arrays, including mathematical, logical, shape manipulation, sorting, selecting, I/O, discrete Fourier transforms, basic linear algebra, basic statistical operations, etc.
NumPy arrays are similar to Python lists but are more efficient for certain operations and take less memory. They are also more flexible, as they can be multidimensional (e.g., a 2D array is an array of arrays).
To use NumPy, we can import it into our Python code using the following import statement:
import numpy as np
NumPy is a core library for scientific computing in Python, and many other libraries, such as pandas and SciPy, are built on top of it.
pandas
pandas is a powerful library for working with data in Python. It provides many functions for loading, manipulating, and analyzing data, as well as for visualizing and reporting the results.
