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.

Difference between NumPy SciPy and Pandas
Difference between NumPy SciPy and Pandas

NumPy vs SciPy vs Pandas

Installationpip install numpypip install scipypip install pandas
Written inC, PythonC, C++, Python, FortranC, Python, Cython
Used forMathematical or numeric calculation Scientific computing and technical computing Data analysis and manipulation
Work withNumerical dataNumerical dataTabular Data
Data Structures ArrayArray, Sparse matrices, and k-dimensional treesSeries and DataFrame
SpeedFaster than other librariesFast computational power Slower than NumPy
PerformanceHigh Performance when rows are lessSimilar as NumPyHigh Performance when a large number of rows
UsageEasy to useNot easy as NumPyEasy to use
Modules/ FunctionsLinear 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 onNumPy doesn’t use SciPy or PandasTop of the NumPyPandas also use NumPy for their tasks.
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.
NumPy vs SciPy vs Pandas

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