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.
Pandas vs. NumPy: What are they?
Pandas is a robust data analysis and manipulation library constructed on top of NumPy. It offers high-performance, user-friendly data structures like Series and DataFrames that are built for effective data management. Pandas provides a wide range of data manipulation functionalities and excels at processing structured data, including spreadsheets and SQL tables.
Pandas’ elegant handling of missing data is one of its main merits. It offers several approaches, including interpolation, imputation, and deletion, to deal with missing variables. Pandas is a great option for data cleaning and preprocessing activities because it also has strong data filtering, grouping, and merging features.
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.
NumPy is an essential Python package for scientific computing. NumPy is an acronym for Numerical Python. Large, multi-dimensional arrays and matrices are supported, and a wide range of mathematical operations are available for effective use on these arrays. NumPy is renowned for its lightning-fast performance and numerical operations that are optimised.
The homogeneous, vectorized operations that NumPy’s arrays, known as arrays, support greatly increase processing efficiency. The library offers a wide variety of mathematical operations, such as Fourier transformations, linear algebra, and random number generation. For numerical calculations, statistical analysis, and machine learning techniques, it is widely employed.
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.
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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.
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When doing a Pandas vs NumPy comparison, remember that, although NumPy and Pandas have features in common, they are used for different things. Intuitive data structures and strong data handling capabilities are provided by Pandas, which focuses on data manipulation and analysis. It is ideal for working with structured data since it makes it simple to clean, filter, merge, and convert data.
NumPy, on the other hand, focuses more on numerical calculations and offers effective array operations. It enables complex linear algebra and statistical calculations and provides strong mathematical functions. The homogenous and performance-optimized arrays in NumPy make it a superior option for numerical calculations and scientific computing tasks.
Though there is a difference between NumPy and Pandas, they are frequently used in conjunction to carry out complicated data processing tasks. Under the hood, Pandas makes use of NumPy’s array operations, enabling smooth interaction between the two libraries. NumPy arrays may be quickly created from Pandas DataFrames, allowing for effective numerical computations utilising NumPy’s mathematical functions.
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In conclusion, the Python ecosystem’s essential libraries for data manipulation and analysis are NumPy and Pandas. While NumPy is optimised for quick numerical computations and provides a wide range of mathematical functions, Pandas excels at managing structured data and has strong data manipulation functionalities.
For data scientists and analysts, it is essential to comprehend the NumPy and Pandas difference as well as how these two programming languages work together. Professionals can effectively handle and analyse data, carry out statistical operations, and create complex models like generalised linear mixed models by combining the advantages of both libraries.
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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