Python is undoubtedly one of the most popular programming languages in the software development and Data Science communities. The best part about this beginner-friendly language is that along with English-like syntax. It comes with a wide range of libraries. Pandas and NumPy are two of the most popular Python libraries.
Today’s post is all about exploring the differences between Pandas and NumPy to understand their features and aspects that make them unique.
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Pandas vs. NumPy: What are they?
Pandas is an open-source library exclusively designed for data analysis and data manipulation. It is built on top of Python’s NumPy package, meaning that Pandas relies on NumPy for functioning. Essentially, Pandas includes data structures and operations for manipulating time series and numerical tables. Before the inception of Pandas, Python programming language could offer only limited support for data analysis.
Pandas can perform five core operations for data processing and analysis – load, manipulate, prepare, model, and analyze. For data manipulation, Pandas allows for functions like data wrangling, cleaning, selecting, merging, and reshaping.
Wes McKinney designed Pandas in 2008. Pandas’ name is derived from “Panel Data,” an econometrics term for datasets including multidimensional data.
- It allows you to reshape and pivot datasets.
- It allows you to merge and join datasets.
- It enables data alignment and integrated handling of missing data.
- It supports the DataFrame object for data manipulation with integrated indexing.
- It includes tools for reading and writing data between in-memory data structures and multiple file formats.
- It offers features like label-based slicing, fancy indexing, and subsetting of large data sets.
- It supports hierarchical axis indexing for collating high-dimensional data in lower-dimensional data structures.
As the official site states, NumPy is “the fundamental package for scientific computing with Python.” It is a Python library designed for supporting large, multidimensional arrays and matrices. NumPy features an extensive collection of high-level mathematical functions to perform complex numerical computations on both single-dimensional and multidimensional arrays.
Travis Oliphant developed the NumPy package in 2005 by incorporating the Numeric module’s functionalities into the Numarray module. This amalgamation led to creating a Python package that can efficiently handle colossal volumes of data along with support with matrix multiplication and data reshaping.
- The “ndarray” forms the core functionality of NumPy for n-dimensional array and data structures.
- It allows for writing fast programs, provided that most operations work on arrays or matrices and not on scalars.
- It relies on BLAS and LAPACK for efficient linear algebra computations.
- It does not support for easy insertion or appending of entries to arrays as quickly as Python lists.
- It functions as a universal data structure in OpenCV for images, filter kernels, and extracted feature points.
Pandas and NumPy are two vital tools in the Python SciPy stack that can be used for any scientific computation, from performing high-performance matrix computations to Machine Learning functions. since Pandas is based on NumPy, it relies on NumPy array for the implementation of data objects and is often used in collaboration with NumPy. If you are a beginner in Python, data science and would like to gain more expertise, check out our data science courses online from top universities.
Pandas vs. NumPy: The core difference between Pandas and NumPy
Here are some of the most compelling points of difference between Pandas and NumPy:
While Pandas primarily works with tabular data, the NumPy module works with numerical data.
Pandas include powerful data analysis tools like DataFrame and Series, whereas the NumPy module offers Arrays.
While the performance of Pandas is better than NumPy for 500K rows and higher, NumPy performs better than Pandas up to 50K rows and less. The performance between 50K to 500K rows depends mostly on the type of operation Pandas, and NumPy have to perform.
While Pandas offers a 2D table object called DataFrame, NumPy supports multidimensional arrays.
As far as memory utilization is concerned, Pandas requires a much higher memory capacity than NumPy.
Pandas is used by companies like Trivago, Kaidee, Abeja Inc., etc., whereas NumPy is used by companies like Instacart, SendGrid, Walmart, and Tokopedia.
Pandas boast of higher industry application as mentioned in 73 company stacks and 46 developer stacks, while NumPy mentions 62 company stacks and 32 developer stacks.
To wrap up, even though Pandas is based on NumPy, there are significant differences between them. However, since both Pandas and NumPy simplify matrix manipulation, they are immensely useful for ML model development.
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