- Python Data Science - Home
- Python Data Science - Getting Started
- Python Data Science - Environment Setup
- Python Data Science - Pandas
- Python Data Science - Numpy
- Python Data Science - SciPy
- Python Data Science - Matplotlib
- Python Data Operations
- Python Data cleansing
- Python Processing CSV Data
- Python Processing JSON Data
- Python Processing XLS Data
- Python Relational databases
- Python NoSQL Databases
- Python Date and Time
- Python Data Wrangling
- Python Data Aggregation
- Python Reading HTML Pages
- Python Processing Unstructured Data
- Python word tokenization
- Python Stemming and Lemmatization
- Python Data Visualization
- Python Chart Properties
- Python Chart Styling
- Python Box Plots
- Python Heat Maps
- Python Scatter Plots
- Python Bubble Charts
- Python 3D Charts
- Python Time Series
- Python Geographical Data
- Python Graph Data
Python Data Science - SciPy
What is SciPy?
The SciPy library of Python is built to work with NumPy arrays and provides many user-friendly and efficient numerical practices such as routines for numerical integration and optimization. Together, they run on all popular operating systems, are quick to install and are free of charge. NumPy and SciPy are easy to use, but powerful enough to depend on by some of the world's leading scientists and engineers.
SciPy Sub-packages
SciPy is organized into sub-packages covering different scientific computing domains. These are summarized in the following table −
| scipy.constants | Physical and mathematical constants |
| scipy.fftpack | Fourier transform |
| scipy.integrate | Integration routines |
| scipy.interpolate | Interpolation |
| scipy.io | Data input and output |
| scipy.linalg | Linear algebra routines |
| scipy.optimize | Optimization |
| scipy.signal | Signal processing |
| scipy.sparse | Sparse matrices |
| scipy.spatial | Spatial data structures and algorithms |
| scipy.special | Any special mathematical functions |
| scipy.stats | Statistics |
Data Structure
The basic data structure used by SciPy is a multidimensional array provided by the NumPy module. NumPy provides some functions for Linear Algebra, Fourier Transforms and Random Number Generation, but not with the generality of the equivalent functions in SciPy.
We will see lots of examples on using SciPy library of python in Data science work in the next chapters.