# SciPy : Scientific Python

SciPy, pronounced as **"Sigh Pie,"** is an open-source library comprising a collection of mathematical algorithms, including minimization, Fourier transformation, regression, and other essential mathematical and scientific techniques. A significant portion of SciPy's functionalities are Python "wrappers," meaning they serve as interfaces for **numerical and scientific libraries** that were initially implemented in Fortran, C, or C++, seamlessly integrating these powerful functionalities into Python for efficient scientific computing.

## SciPy

SciPy complements the Python NumPy extension and is an integral part of the NumPy stack. It enhances NumPy's capabilities by providing optimized and additional functions commonly used in N-dimensional array manipulation and Data Science tasks. **SciPy** further empowers the interactive Python session by offering high-level commands and classes for data manipulation and visualization, making it a popular choice among researchers in academia and industry. Notably, SciPy has played a crucial role in significant scientific achievements, such as the detection of gravitational waves by LIGO and the imaging of a black hole in galaxy M87 by the Event Horizon Telescope. Importantly, as SciPy builds on NumPy, importing SciPy in Python eliminates the need to separately import NumPy.

## SciPy - Installation

You can install **SciPy** in Windows via pip.

## Subpackages in SciPy

Package Name | Description |
---|---|

constants | Physical constants and conversion factors |

cluster | Clustering algorithms |

fft | Discrete Fourier Transform algorithms |

fftpack | Fast Fourier Transforms algorithms |

integrate | Numerical integration routines |

interpolate | Interpolation tools |

io | Data input and output |

lib | Python wrappers to external libraries |

linalg | linear algebra routines |

misc | Miscellaneous utilities (e.g. image reading/writing) |

ndimage | N-dimensional image processing |

optimize | Optimization algorithms including linear programming |

signal | Signal processing tools |

sparse | Sparse matrix and associated algorithms |

spatial | Spatial data structure and algorithms |

special | Special functions |

stats | Statistical functions |

weave | Tool for writing C/C++ code as Python multiline strings |

These **SciPy packages** need to be imported before to using them.

**example**

Above code output how many **cubic meters** are in one liter.

## SciPy Constants

You can find a large collection of **mathematical and physical constants** in scipy.constants. These **SciPy constants** can be helpful when you are working with Data Science projects.

## Spatial Data

Spatial data pertains to information that is represented in a geometric space, employing various techniques like Triangulation, **Voronoi Diagram,** and Convex Hulls of a set of points. These spatial computations are facilitated by utilizing the Qhull library, which enables efficient handling and processing of geometric data.

## Triangulation example

In spatial data, a Triangulation of a polygon involves dividing the polygon into multiple triangles, allowing for the calculation of the polygon's area. The **Delaunay() Triangulation** is a method used to generate triangulations based on a set of points, creating a network of non-overlapping triangles that cover the space efficiently and are suitable for various spatial data analysis tasks.

### Text

**output**

### Conclusion

SciPy, short for **Scientific Python,** is an essential library that builds upon NumPy to provide a rich collection of mathematical algorithms and scientific tools. With its powerful functions for numerical integration, optimization, and data manipulation, SciPy serves as a crucial component of the Python ecosystem for scientific computing and data analysis.

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