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NumPy VS ZIP Code API

Compare NumPy VS ZIP Code API and see what are their differences

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Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

NumPy logo NumPy

NumPy is the fundamental package for scientific computing with Python

ZIP Code API logo ZIP Code API

REST API for US ZIP, ZIP+4, and Canadian postal codes. Single unified endpoint covers address validation and standardization, radius search (centroid haversine and true spatial polygon intersection), point-to-point distance, autocomplete/typeah
  • NumPy Landing page
    Landing page //
    2023-05-13
  • ZIP Code API Landing page
    Landing page //
    2026-05-21

API for US ZIP, ZIP+4, and Canadian postal code data. One unified endpoint set covers North America โ€” no separate APIs by country or data type.

What it does

  • Address validation and standardization โ€” production-grade parser with ZIP+4 append, refreshed monthly
  • Radius search โ€” centroid haversine and true spatial polygon intersection. Returns ZIPs/FSAs within range, with per-result pct_inside overlap percentage for spatial queries
  • Unified lookup endpoint โ€” accepts US ZIP, ZIP+4, Canadian FSA, full Canadian postal codes, or latitude/longitude inputs
  • Autocomplete/typeahead โ€” cities, counties, metros, states, FSAs, ZIPs
  • Point-to-point distance โ€” between any two postal points
  • Census ACS demographics โ€” 2011โ€“2024, 542 fields per ZIP across income, education, housing, social, and economic profiles
  • Boundary lookups โ€” Census tracts, congressional districts, state legislative areas, school districts โ€” with computed intersection percentages per ZIP

What makes it different

  • Licensed commercial data โ€” not commodity or scraped sources
  • Canadian postal coverage โ€” most peers in this space are US-only
  • True spatial radius โ€” not just centroid haversine
  • 14 years of historical ACS depth via API โ€” unusual outside of raw Census downloads
  • One endpoint for all of North America โ€” no country-detection or input-routing logic to maintain on the client side

Pricing

  • Free โ€” 2,500 lookups/day, no credit card required, no expiry
  • Developer โ€” $49/mo, 100K credits, 300/min
  • Professional โ€” $149/mo, 350K credits, 300/min
  • Business โ€” $499/mo, 1.5M credits, 600/min
  • Credit packs โ€” one-time, from 25K ($19) up to 2M ($799)

Resources

NumPy features and specs

  • Performance
    NumPy operations are executed with highly optimized C and Fortran libraries, making them significantly faster than standard Python arithmetic operations, especially for large datasets.
  • Versatility
    NumPy supports a vast range of mathematical, logical, shape manipulation, sorting, selecting, I/O, and basic linear algebra operations, making it a versatile tool for scientific and numeric computing.
  • Ease of Use
    NumPy provides an intuitive, easy-to-understand syntax that extends Python's ability to handle arrays and matrices, lowering the barrier to performing complex scientific computations.
  • Community Support
    With a large and active community, NumPy offers extensive documentation, tutorials, and support for troubleshooting issues, as well as continuous updates and enhancements.
  • Integrations
    NumPy integrates seamlessly with other libraries in Python's scientific stack like SciPy, Matplotlib, and Pandas, facilitating a streamlined workflow for data science and analysis tasks.

Possible disadvantages of NumPy

  • Memory Consumption
    NumPy arrays can consume large amounts of memory, especially when working with very large datasets, which can become a limitation on systems with limited memory capacity.
  • Learning Curve
    For users new to scientific computing or coming from different programming backgrounds, understanding the intricacies of NumPy's operations and efficient usage can take time and effort.
  • Limited GPU Support
    NumPy primarily runs on the CPU and doesn't natively support GPU acceleration, which can be a disadvantage for extremely compute-intensive tasks that could benefit from parallel processing.
  • Dependency on Python
    Since NumPy is a Python library, it depends on the Python runtime environment. This can be a limitation in environments where Python is not the primary language or isn't supported.
  • Indexing Complexity
    Although NumPy's slicing and indexing capabilities are powerful, they can sometimes be complex or unintuitive, especially for multi-dimensional arrays, leading to potential errors and confusion.

ZIP Code API features and specs

  • Comprehensive ZIP Code Data
    The ZIP Code API from zip-codes.com provides extensive data including ZIP code details, city information, state data, and geographic coordinates, making it a thorough resource for location-based lookups.
  • Multiple Lookup Options
    The API supports various types of lookups including ZIP code to city/state, city/state to ZIP code, distance calculations between ZIP codes, and radius searches, offering flexible querying capabilities.
  • Easy Integration
    The API uses standard REST-based HTTP requests and returns data in commonly used formats like JSON and XML, making it straightforward to integrate into most applications and programming languages.
  • Distance and Radius Calculations
    The API includes built-in functionality for calculating distances between ZIP codes and finding ZIP codes within a specified radius, which is valuable for store locators, shipping estimates, and proximity-based features.
  • Well-Documented Endpoints
    The API provides clear documentation for its various endpoints and parameters, helping developers understand available features and implement them correctly without extensive trial and error.

Analysis of NumPy

Overall verdict

  • Yes, NumPy is considered good. It is a foundational library in the Python ecosystem for numerical computing and is used globally by researchers, engineers, and data scientists.

Why this product is good

  • NumPy is widely regarded as a good library because it offers fast, flexible, and efficient array handling that is integral to scientific computing in Python. It provides tools for integrating C/C++ and Fortran code, useful linear algebra, random number capabilities, and a vast collection of mathematical functions. Its array broadcasting capabilities and versatility make complex mathematical computations straightforward.

Recommended for

  • Scientists and researchers working with large-scale scientific computations.
  • Data scientists engaged in data analysis and manipulation.
  • Engineers and developers needing performance-optimized mathematical computations.
  • Educators and students in STEM fields.

Analysis of ZIP Code API

Overall verdict

  • ZIP Code API from zip-codes.com is a solid, reliable choice for developers and businesses needing accurate US and Canadian postal code data, offering a straightforward RESTful interface with regularly updated databases.

Why this product is good

  • Provides accurate and frequently updated ZIP code, city, state, and geographic data
  • Offers a simple RESTful API that is easy to integrate into web and mobile applications
  • Supports features like ZIP code lookup, radius search, and distance calculations
  • Includes both US ZIP codes and Canadian postal codes for broader coverage
  • Backed by an established data provider with a long track record in postal data

Recommended for

  • Developers building address validation or autofill features
  • E-commerce platforms needing shipping and location-based calculations
  • Businesses performing geographic or radius-based store locators
  • Applications requiring reliable US and Canadian postal data
  • Marketing and logistics teams that need regional or demographic targeting

NumPy videos

+ Add

Learn NUMPY in 5 minutes - BEST Python Library!

More videos:

  • Review - Python for Data Analysis by Wes McKinney: Review | Learn python, numpy, pandas and jupyter notebooks
  • Review - Effective Computation in Physics: Review | Learn python, numpy, regular expressions, install python

ZIP Code API videos

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Category Popularity

0-100% (relative to NumPy and ZIP Code API)

NumPy

ZIP Code API

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Zip Lookup
0 0%
Zip Lookup
100% 100
Data Science Tools
100 100%
Data Science Tools
0% 0
APIs
0 0%
APIs
100% 100

User comments

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Reviews

These are some of the external sources and on-site user reviews we've used to compare NumPy and ZIP Code API

NumPy Reviews

25 Python Frameworks to Master
SciPy provides a collection of algorithms and functions built on top of the NumPy. It helps to perform common scientific and engineering tasks such as optimization, signal processing, integration, linear algebra, and more.
Source: kinsta.com
Top 8 Image-Processing Python Libraries Used in Machine Learning
Scipy is used for mathematical and scientific computations but can also perform multi-dimensional image processing using the submodule scipy.ndimage. It provides functions to operate on n-dimensional Numpy arrays and at the end of the day images are just that.
Source: neptune.ai
Top Python Libraries For Image Processing In 2021
Numpy It is an open-source python library that is used for numerical analysis. It contains a matrix and multi-dimensional arrays as data structures. But NumPy can also use for image processing tasks such as image cropping, manipulating pixels, and masking of pixel values.
Source: www.analyticsvidhya.com
4 open source alternatives to MATLAB
NumPy is the main package for scientific computing with Python (as its name suggests). It can process N-dimensional arrays, complex matrix transforms, linear algebra, Fourier transforms, and can act as a gateway for C and C++ integration. It's been used in the world of game and film visual effect development, and is the fundamental data-array structure for the SciPy Stack,...
Source: opensource.com

ZIP Code API Reviews

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Social recommendations and mentions

Based on our record, NumPy seems to be more popular. It has been mentiond 122 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

NumPy mentions (122)

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ZIP Code API mentions (0)

We have not tracked any mentions of ZIP Code API yet. Tracking of ZIP Code API recommendations started around May 2026.

What are some alternatives?

When comparing NumPy and ZIP Code API, you can also consider the following products

Pandas - Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.

Smarty - Smarty provides address validation, autocomplete, geocoding and reverse geocoding services covering addresses in over 240+ countries.

Scikit-learn - scikit-learn (formerly scikits.learn) is an open source machine learning library for the Python programming language.

Zipcodestack - Free Zip Code API - Free Postal Code Validation | Zipcodestack

OpenCV - OpenCV is the world's biggest computer vision library

PostalDataPI - The most affordable postal code API. 240+ countries, sub-5 ms responses. Simple, elegant, transparent.

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