SciPy (Science and Python) is a core library in computing by Python, which is used in scientific computing, mathematics, engineering, and technical computing.

**SciPy VS Numpy**

**SciPy:**

- SciPy is built on top of the NumPy
- SciPy is a fully-featured version of Linear Algebra while Numpy contains only a few features.
- Most new Data Science features are available in Scipy rather than Numpy.

**Numpy,**

- Numpy is written in C and use for mathematical or numeric calculation.
- It is faster than other Python Libraries
- Numpy is the most useful library for Data Science to perform basic calculations.
- Numpy contains nothing but an array data type which performs the most basic operation like sorting, shaping, indexing, etc.

**Sub-packages of SciPy,**

Every library like scipy for each goal or field has a series of featured **functions**. In the case of scipy:

- File input/output –
**scipy.io** - Special Function –
**scipy.special** - Linear Algebra Operation –
**scipy.linalg** - Interpolation –
**scipy.interpolate** - Optimization and fit –
**scipy.optimize** - Statistics and random numbers –
**scipy.stats** - Numerical Integration –
**scipy.integrate** - Fast Fourier transforms –
**scipy.fftpack** - Signal Processing –
**scipy.signal** - Image manipulation –
**scipy.ndimage**

**Fast Fourier transforms – scipy.fftpack by Python**

Know more: Numpy : The linear algebra library in Python for data science

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