Difference between NumPy SciPy and Pandas

In this article, I will explain the difference between NumPy, SciPy, and Pandas. NumPy is the abbreviation of numerical Python. SciPy is the abbreviation of scientific Python. These two libraries are used for scientific computations. These libraries provide a wide range of functionalities. Most of the visualization libraries in Python use NumPy for their mathematical tasks. SciPy also uses NumPy for some of its tasks. On the other hand, Pandas is the most famous library for data analysis and manipulation. All of these are the most popular libraries of Python. If you want to learn Python, I highly recommend reading This Book.

NumPy vs SciPy vs Pandas

	NumPy	SciPy	Pandas
Installation	pip install numpy	pip install scipy	pip install pandas
Written in	C, Python	C, C++, Python, Fortran	C, Python, Cython
Used for	Mathematical or numeric calculation	Scientific computing and technical computing	Data analysis and manipulation
Work with	Numerical data	Numerical data	Tabular Data
Data Structures	Array	Array, Sparse matrices, and k-dimensional trees	Series and DataFrame
Speed	Faster than other libraries	Fast computational power	Slower than NumPy
Performance	High Performance when rows are less	Similar as NumPy	High Performance when a large number of rows
Usage	Easy to use	Not easy as NumPy	Easy to use
Modules/ Functions	Linear Algebra, Mathematical functions, Statistics, Sorting, searching, and counting	Linear Algebra, Integration, Signal Processing, Image Processing, Statistics, Fourier Transforms, Interpolation, and optimization.	Read Data, Indexing and Selecting Data, Reshape, Visualization, Time Series Data, Handle Missing, Categorical, and Text Data
Built on	NumPy doesn’t use SciPy or Pandas	Top of the NumPy	Pandas also use NumPy for their tasks.
My Recommendation	When you want to perform basic mathematics/ statistics operations. For Example, Mean, Mode, Median	When you want to perform advanced mathematics/statistics tasks. For Example, Integration, linear algebra, signal processing, interpolation, and optimization.	When you want to handle tabular data i.e. rows and columns. If your data has more than 100k rows then it provides high performance relative to Microsoft Excel. For Example, Read the data, Analyze the data, and manipulate the data.