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17track VS Matplotlib

Compare 17track VS Matplotlib and see what are their differences

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

17track logo 17track

All-in-one package tracking

Matplotlib logo Matplotlib

matplotlib is a python 2D plotting library which produces publication quality figures in a variety...
  • 17track Landing page
    Landing page //
    2023-04-04
  • Matplotlib Landing page
    Landing page //
    2023-06-14

17track features and specs

  • Comprehensive Tracking
    17track supports tracking for over 500 carriers worldwide, making it a versatile tool for monitoring packages from various shipping services.
  • User-Friendly Interface
    The platform features a clean and intuitive interface, allowing users to easily input tracking numbers and view the status of their shipments.
  • Real-Time Updates
    Users get real-time updates on the status and location of their packages, which helps in tracking the exact progress of deliveries.
  • Multi-Language Support
    17track is available in multiple languages, making it accessible to a global audience.
  • Mobile App Availability
    The service offers mobile apps for both iOS and Android, enabling users to track packages on-the-go.

Possible disadvantages of 17track

  • Advertisements
    The free version of 17track includes advertisements, which may be distracting for users.
  • Data Privacy Concerns
    Users may be concerned about sharing their tracking numbers and personal data with a third-party platform.
  • Occasional Inaccuracies
    Some users report occasional inaccuracies in tracking information, which can lead to confusion about the actual status of their shipments.
  • Limited Advanced Features
    While great for basic tracking, 17track may lack some advanced features found in specialized logistics and supply chain management tools.
  • Dependency on Carrier Updates
    The accuracy and timeliness of tracking information depend on the data provided by the carriers, which can vary in quality.

Matplotlib features and specs

  • Versatility
    Matplotlib can generate a wide variety of plots, ranging from simple line plots to complex 3D plots. This versatility makes it a go-to library for many scientific and technical visualizations.
  • Customization
    It offers extensive customization options for virtually every element of a plot, including colors, labels, line styles, and more, allowing users to tailor plots to meet specific needs.
  • Integrations
    Matplotlib integrates well with other Python libraries such as NumPy, Pandas, and SciPy, making it easier to plot data directly from these sources.
  • Community and Documentation
    It has a large, active community and comprehensive documentation that includes tutorials, examples, and detailed references, which can help users solve problems and improve their plot-making skills.
  • Interactivity
    Matplotlib supports interactive plots, which can be embedded in Jupyter notebooks and GUIs, allowing for dynamic data exploration and presentation.
  • Publication-Quality
    The library is capable of producing high-quality, publication-ready graphics that meet the stringent requirements of academic journals and professional presentations.

Possible disadvantages of Matplotlib

  • Complexity
    While Matplotlib offers extensive customization, it can be complex and sometimes unintuitive for beginners, requiring a steep learning curve to master all its functionality.
  • Performance
    Rendering a large number of plots or handling very large datasets can be slow, making Matplotlib less suitable for real-time data visualization.
  • Modern Aesthetics
    Out-of-the-box plots from Matplotlib can look somewhat dated compared to those from newer plotting libraries like Seaborn or Plotly, requiring additional customization to achieve a modern look.
  • 3D Plots
    Although Matplotlib supports 3D plotting, its capabilities are relatively limited and less sophisticated compared to specialized 3D plotting libraries.
  • Size and Structure
    The package is relatively large and can be slow to import. Its extensive structure can make finding specific functions and understanding the overall architecture challenging.

Analysis of 17track

Overall verdict

  • Yes, 17track is considered to be a good and reliable tool for tracking packages from multiple carriers.

Why this product is good

  • 17track is widely regarded as a good package tracking platform because it allows users to track shipments from a vast number of international and domestic carriers, making it convenient for those who shop online from different sources. The platform also offers a user-friendly interface, mobile app support, and detailed tracking information.

Recommended for

    17track is recommended for individuals who frequently shop online from various international retailers and need a centralized tool to track parcels from numerous carriers in one place. It's also useful for small businesses that want to provide customers with an easy way to track their orders.

Analysis of Matplotlib

Overall verdict

  • Yes, Matplotlib is a good library for data visualization, particularly for users who require a versatile and powerful plotting solution in Python.

Why this product is good

  • Matplotlib is highly regarded due to its extensive customization options, versatility in creating a wide range of static, animated, and interactive plots, and its large user community and support. It integrates well with other scientific libraries in Python, making it a staple for data visualization. The library is also open-source and frequently updated, ensuring it remains a reliable choice for users.

Recommended for

  • Data scientists and analysts needing to create detailed, customized visual representations of their data.
  • Researchers and engineers looking for a comprehensive plotting library that supports scientific and engineering formats.
  • Python developers who require integration with other scientific computing libraries like NumPy and Pandas.

17track videos

+ Add

17track.net Website Review How To Use and Track Packages

Matplotlib videos

+ Add

Learn Matplotlib in 6 minutes | Matplotlib Python Tutorial

Category Popularity

0-100% (relative to 17track and Matplotlib)

17track

Matplotlib

Shipping and Tracking
100 100%
Shipping and Tracking
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Web Application Bundler
100 100%
Web Application Bundler
0% 0
Technical Computing
0 0%
Technical Computing
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 17track and Matplotlib

17track Reviews

  1. Stivan
    ยท Working at Ordertracker ยท

Matplotlib Reviews

25 Python Frameworks to Master
Matplotlib is a widely used tool for data visualization in Python. It provides an object-oriented API for embedding plots into applications.
Source: kinsta.com
5 Best Python Libraries For Data Visualization in 2023
You can use this library for multiple purposes such as generating plots, bar charts, histograms, power spectra, stemplots, pie charts, and more. The best thing about Matplotlib is you just have to write a few lines of code and it handles the rest by itself. Metaplotilib focuses on static images for publication along with interactive figures using toolkits like Qt and GTK.
Source: www.technotification.com
15 data science tools to consider using in 2021
Matplotlib is an open source Python plotting library that's used to read, import and visualize data in analytics applications. Data scientists and other users can create static, animated and interactive data visualizations with Matplotlib, using it in Python scripts, the Python and IPython shells, Jupyter Notebook, web application servers and various GUI toolkits.
Source: searchbusinessanalytics.techtarget.com
Top Python Libraries For Image Processing In 2021
Matplotlib is primarily used for 2D visualizations such as scatter plots, bar graphs, histograms, and many more, but we can also use it for image processing. It is effective to get information out of an image. It doesnโ€™t support all file formats.
Source: www.analyticsvidhya.com
Top 8 Python Libraries for Data Visualization
Matplotlib is a data visualization library and 2-D plotting library of Python It was initially released in 2003 and it is the most popular and widely-used plotting library in the Python community. It comes with an interactive environment across multiple platforms. Matplotlib can be used in Python scripts, the Python and IPython shells, the Jupyter notebook, web application...
Source: www.geeksforgeeks.org

Social recommendations and mentions

Based on our record, Matplotlib seems to be a lot more popular than 17track. While we know about 114 links to Matplotlib, we've tracked only 2 mentions of 17track. 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.

17track mentions (2)

  • ETA to North America?
    14-21 days for me. 17track.net/enallows you to follow the progress if you know your tracking number. Source: about 3 years ago
  • ๐ŸŽ‰Special offer
    โœ…Package timely tracked on:https://17track.net/en. Source: almost 4 years ago

Matplotlib mentions (114)

  • The soul file
    In February, an AI agent named MJ Rathbun submitted a pull request to matplotlib โ€” the Python plotting library used by half the scientific computing world. Scott Shambaugh, a volunteer maintainer, rejected it. Standard code review. Nothing unusual. - Source: dev.to / 4 months ago
  • How to Analyze CSV Files with Python and Pandas
    Numbers are useful, but sometimes itโ€™s easier to spot patterns when you can actually see your data. Pandas works seamlessly with Matplotlib, a popular Python library for creating visualizations. Together, they make it easy to turn raw numbers into clear charts. - Source: dev.to / 7 months ago
  • libmalloc, jemalloc, tcmalloc, mimalloc - Exploring Different Memory Allocators
    We are storing the results in JSON files, which we combine, analyze and visualize using matplotlib in Python. Here's the structure of a benchmark result file:. - Source: dev.to / 8 months ago
  • Building an AI Scoring Agent: Step-By-Step
    NetworkX and Matplotlib were used to visualize the graph structure of the agent. - Source: dev.to / 9 months ago
  • Top 5 GitHub Repositories for Data Science in 2026
    The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, Aโ€ฆ. - Source: dev.to / 10 months ago
View more

What are some alternatives?

When comparing 17track and Matplotlib, you can also consider the following products

AfterShip - AfterShip is the shipment tracking API for ecommerce businesses and marketplaces.

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

OneTracker - OneTracker โ€“ Package Tracker is an app by OneTracker Team that helps users track all their delivery vehicles and staff to increase the productivity of their delivery business.

NumPy - NumPy is the fundamental package for scientific computing with Python

TrackChecker Mobile - TrackChecker Mobile app features tracking of parcels and online orders, so users donโ€™t have to worry about losing a package by tracking the parcel right on their mobile phone screen.

Seaborn - Seaborn is a Python data visualization library that uses Matplotlib to make statistical graphics.

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