Numpy vs Pandas: Comparing Two Top Python Libraries
Amidst the plethora of programming languages, one popular name that needs no introduction is Python – a favorite of developers in modern-day areas like AI, ML, and scientific calculations. Huge conglomerates like Google, Dropbox, Instagram, Spotify, Netflix, and many more are leveraging the power of Python. It is considered one of the most user-friendly language with multiple libraries to support its key features. Two significant libraries of Python are Numpy and Pandas, which are often compared with each other, due to their high-level user acceptance.
Both are open-source tools that have been favorites of data scientists and hence are often called data science tools. These essential libraries have made Python coding much simpler and easily accessible. They are best needed for any kind of scientific computation or machine learning jobs, thanks to their good performance competencies, intuitive syntax, and matrix computation capacities. Data science applications can make the most of these libraries. Both have their set of similarities and differences, because of which organizations prefer them over the other peers.
Before we compare NumPy and Pandas based on certain parameters, let us understand the basic concept of each, along with its key features:
What Is Numpy?
NumPy is a library for the Python programming language, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. – Wikipedia
Numpy, also called Numeric Python, is an important basic package for scientific computing in Python. It is used as an effective multi-dimensional container of generalized data and is a Python library that offers different derived objects and routines. It caters to arrays, matrices, arbitrary data types, statistical operations, I/O, sorting, selecting, random simulation, etc. Numpy can easily integrate with a variety of databases with speed.
Developed in 2005, Numpy is built on C language and its main aim is to support the Python language in terms of performing different numerical computations and single/multi-dimensional array (ndarray) elements. Because of these elements, it can work with a lot of speed and accuracy. It also takes good care of matrix multiplication and data reshaping. It is most useful for almost all areas of data science and engineering.
Be it novices or experienced developers, Numpy can be used extensively by all, and its API can be used in scikit-learn, Pandas, SciPy, and other Python packages. As an open-source third-party Python library, it is powerful, fast, effective, and high-performant. It is not a part of the standard installation of Python but is easy to install. Its sophisticated and broadcasting functions act as limelight and it has efficient tools for integration of C/C++ and Fortran code.
Numpy Features:
- Powerful, multi-dimensional arrays
- Facilitates writing of fast programs
- Effective linear algebra computations, matrix manipulation methods, mathematical functions, Fourier transform, random number competencies
- Support for a wide variety of hardware and computing platforms
- Accessible and easy to use high-level syntax
- Fast and easy framework to operate on homogenous datasets
- Vectorization or broadcasting of applied operations
- Broadcasting (sophisticated) functions
- Works with varied databases
- Easy integration from C and C++ code
Companies Using Numpy:
Facebook, Instacart, Intuit, trivago, Harris Corporation, SweepSouth, SendGrid, Ruangguru, Capital One, JPMorgan Chase, Engage3, Sendcloud, Walmart, Avito, etc.
What Is Pandas?
Pandas is a software library written for the Python programming language for data manipulation and analysis. It offers data structures and operations for manipulating numerical tables and time series. – Wikipedia
Pandas is a flexible, powerful, and fast tool that is built on top of Python. The Pandas library acts as a strong Python library support that is easy to use, open-source, and widely used for data manipulation and analysis. Created in 2008, Pandas today has heavy community support because of which it is highly well-known among developers. It acts as a basic block for real-time data analysis, for data scientists.
Pandas offer an effective framework to extract data from disparate sources like CSV, Excel, JSON, SQL, etc and it utilizes data from multidimensional arrays. It is built on top of Numpy, and it has enhanced the competencies of data analytics for Python. It is capable of loading, manipulating, preparing, modeling, and analyzing a variety of data components, regardless of their origin.
It easily handles missing data in floating/non-floating points of data. It is easy to insert/delete columns from high-dimensional objects. It, in combination with Numpy, can garner the best results since the Numpy libraries have most things that are needed for the effective operation of Pandas objects. Pandas offer two types of data objects – Pandas Series and Pandas DataFrame. As a powerful data manipulation/analysis library, it offers labeled data structures like R data, frames objects, statistical functions, etc.
Features Of Python Pandas:
- Facilitates flexible reshaping, merging, joining, and pivoting of datasets
- Effective for data manipulation and transformation
- Possesses tools for reading/writing data between data structures and file formats
- Fancy indexing, label-based slicing, subsets of large data sets
- Hierarchical axis indexing for collation of high dimensional data in low dimensional data
- Data representation in tabular format
- Support for inbuilt data visualization and group-by clause
- Integrated handling of missing data and intelligent data alignment
- Effective and quick DataFrame object with customizable indexing
- Aligning data and integrated management of data that is missing
Companies Using Pandas:
Capital One, Instacart, PNC, Tokopedia, trivago, Facebook, Sighten, JPMorgan Chase, Intuit, SendGrid, Engage3, Delivery Hero, Abeja Inc., Avito, platform, etc.
Numpy vs Pandas: A Detailed Comparison
| Parameters | Numpy | Pandas |
| Core Language | Written in C language and hence utilizes many functionalities from it | Uses R language as a reference and hence offers many similar functions from it |
| Data Compatibility | Works mostly with numerical and mathematical data | Works mostly with tabular data |
| Tools | It Includes several different arrays | It includes powerful tools like Series and DataFrame |
| Memory Usage | It is memory efficient | It consumes more memory |
| Performance | Better performance for up to 50K rows and less. Complex operations are faster on ndarrays. | Better performance for 500K rows and higher. Complex operations make the overall process slow. |
| Objects | Supports multidimensional arrays | Supports a 2D table object named DataFrame |
| Indexing | Indexing of Numpy arrays is very fast. There is no default indexing of data rows in Numpy arrays. | Indexing of Pandas series is comparatively slow. Data rows are by default indexed in series and data frames. |
| Types of Data Objects | Creates homogenous types of objects | Creates heterogeneous types of objects |
| Industry Segments | Used in creating arrays or matrices for ML models in industry segments like banking, aerospace, defense, information collection & delivery, software manufacturers, etc. | Used in manipulating or wrangling data in industry segments like finance, economics, neuroscience, stock prediction, advertising, statistics, NLP, marketing, Big Data, etc. |
| Access of Data | Data is accessible only using index positions | Data is accessible using index positions or index labels |
| Built By | Travis Oliphant in 2005 | Wes McKinney in 2008 |
| External Data | Uses user-created data or inbuilt functions | Uses data from external sources like Excel, SQL, CSV, etc. |
| Storage | Consumes less storage and hence better in storage management | Consumes more storage and hence not as efficient as Numpy |
| Data Objects | The main data object is ndarray (n-dimensional) | The main data object is a series (one dimensional) |
| Main Industry Utilization | Mainly used for numerical calculations | Mainly used for data analysis and visualization |
| Utilization in Deep Learning and ML | Tools like scikit etc. can be fed using Numpy arrays | Pandas cannot be input directly in these toolkits |
| Integration with Tools | Python, Streamlit, Dask, Ludwig, etc. | Python, Jupyter, Streamlit, Dask, etc. |
| Application Areas | Quantum computing, statistical computing, image/signal processing, graphs and networks, bioinformatics, mathematical analysis, geoscience, architecture, astronomy processes, etc. | Academic and commercial domains like Finance, Neuroscience, Economics, Statistics, Advertising, Web Analytics, Natural Language Processing, Recommendation systems, etc. |
Wrapping It Up
In the world of Python development, Numpy and Pandas are effective Python libraries and hence have a lot in common. Yet, both are designed in such a way that they yield benefits to each other if they function together.
Pandas depend upon Numpy for their functionalities and Numpy depends upon Pandas for expansion and extension. Pandas depend upon Numpy for implementing many data objects like data frames or series. Pandas make use of Numpy for data analysis.
Numpy Vs Pandas is a competitive comparison and together, they make a great pair! Both are complementary technologies to each other!
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