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MySQL VS Pandas

Compare MySQL VS Pandas and see what are their differences

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LibHunt tracks mentions of software libraries on relevant social networks. Based on that data, you can find the most popular projects and their alternatives. featured
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MySQL logo MySQL

The world's most popular open source database

Pandas logo Pandas

Pandas is an open source library providing high-performance, easy-to-use data structures and data analysis tools for the Python.
  • MySQL Landing page
    Landing page //
    2022-06-17
  • Pandas Landing page
    Landing page //
    2023-05-12

MySQL features and specs

  • Reliability
    MySQL is known for its reliability and durability, making it a solid choice for many businesses' database management needs.
  • Performance
    It offers robust performance, handling large databases and complex queries efficiently.
  • Open Source
    MySQL is an open-source database, making it freely available under the GNU General Public License (GPL).
  • Scalability
    MySQL supports large-scale applications and can handle high volumes of transactions.
  • Community Support
    There is a large, active MySQL community that offers extensive resources, documentation, and support.
  • Cross-Platform
    MySQL is compatible with various operating systems like Windows, Linux, and macOS.
  • Integrations
    MySQL integrates well with numerous development frameworks, including LAMP (Linux, Apache, MySQL, PHP/Python/Perl).
  • Security
    MySQL offers various security features, such as user account management, password policies, and encrypted connections.
  • Cost
    The open-source nature of MySQL means that it can be very cost-effective, especially for small to medium-sized businesses.

Possible disadvantages of MySQL

  • Support
    While community support is plentiful, official support from Oracle can be quite expensive.
  • Complexity
    More advanced features and configurations can be complex and may require a steep learning curve for new users.
  • Scalability Limitations
    While MySQL is scalable, very high-scale applications may run into limitations compared to some newer database technologies.
  • Plug-in Storage Engines
    The use of plug-in storage engines like InnoDB or MyISAM can cause inconsistencies and complicate backups and recovery processes.
  • ACID Compliance
    Although MySQL supports ACID compliance, certain configurations or storage engines may not fully adhere to ACID properties, affecting transaction reliability.
  • Concurrent Writes
    Handling a high number of concurrent writes can be less efficient compared to some other database systems designed specifically for high concurrency.
  • Feature Set
    Some advanced features found in other SQL databases (e.g., full-text indexing, rich analytics) may be less robust or absent.
  • Vendor Dependency
    With Oracle now owning MySQL, there can be concerns about licensing changes or other forms of vendor lock-in.
  • Replication Complexities
    Setting up replication and ensuring data consistency across distributed systems can be complex and error-prone.

Pandas features and specs

  • Data Wrangling
    Pandas offers robust tools for manipulating, cleaning, and transforming data, making it easier to prepare data for analysis.
  • Flexible Data Structures
    Pandas provides two primary data structures: Series and DataFrame, which are flexible and offer powerful capabilities for handling various types of datasets.
  • Integration with Other Libraries
    Pandas integrates seamlessly with other Python libraries such as NumPy, Matplotlib, and SciPy, facilitating comprehensive data analysis workflows.
  • Performance with Data Size
    For data sizes that fit into memory, Pandas performs excellently with operations and computations being highly optimized.
  • Rich Feature Set
    Pandas provides a wide array of functionalities, including but not limited to group-by operations, merging and joining data sets, time-series functionality, and input/output tools.
  • Community and Documentation
    Pandas has a strong community and extensive documentation, offering a wealth of tutorials, examples, and support for new and experienced users alike.

Possible disadvantages of Pandas

  • Memory Consumption
    Pandas can become memory inefficient with very large datasets because it relies heavily on in-memory operations.
  • Single-threaded
    Many Pandas operations are single-threaded, which can lead to performance bottlenecks when handling very large datasets.
  • Steep Learning Curve
    For users who are new to data analysis or Pandas, there can be a steep learning curve due to its extensive capabilities and complex syntax at times.
  • Less Suitable for Real-time Analytics
    Pandas is not designed for real-time analytics and is better suited for batch processing due to its in-memory operations and single-threaded nature.
  • Error Handling
    Error messages in Pandas can sometimes be cryptic and hard to interpret, making debugging a challenge for users.

Analysis of MySQL

Overall verdict

  • Yes, MySQL is generally considered to be a good choice for many applications, especially those requiring a relational database management system. Its performance, ease of integration, and support for various storage engines make it a versatile option.

Why this product is good

  • MySQL is a popular open-source relational database management system known for its reliability, ease of use, and strong community support. It has a proven track record and is widely used for web applications, data warehousing, and logging applications. Additionally, MySQL offers robust security features, scalability, and cross-platform support.

Recommended for

  • Small to medium-sized web applications
  • LAMP stack environments
  • E-commerce platforms
  • Logging and data warehousing applications
  • Projects requiring a mature, well-supported RDBMS

Analysis of Pandas

Overall verdict

  • Pandas is highly recommended for tasks involving data manipulation and analysis, especially for those working with tabular data. Its efficiency and ease of use make it a staple in the data science toolkit.

Why this product is good

  • Pandas is widely considered a good library for data manipulation and analysis due to its powerful data structures, like DataFrames and Series, which make it easy to work with structured data. It provides a wide array of functions for data cleaning, transformation, and aggregation, which are essential tasks in data analysis. Furthermore, Pandas seamlessly integrates with other libraries in the Python ecosystem, making it a versatile tool for data scientists and analysts. Its extensive documentation and strong community support also contribute to its reputation as a reliable tool for data analysis tasks.

Recommended for

    Pandas is particularly recommended for data scientists, analysts, and engineers who need to perform data cleaning, transformation, and analysis as part of their work. It is also suitable for academics and researchers dealing with data in various formats and needing powerful tools for their data-driven research.

MySQL videos

+ Add

MySQL IN 10 MINUTES (2020) | Introduction to Databases, SQL, & MySQL

More videos:

  • Review - A Review of MySQL Open Source Software

Pandas videos

+ Add

Ozzy Man Reviews: Pandas

More videos:

  • Review - Ozzy Man Reviews: PANDAS Part 2
  • Review - Trash Pandas Review with Sam Healey

Category Popularity

0-100% (relative to MySQL and Pandas)

MySQL

Pandas

Databases
100 100%
Databases
0% 0
Data Science And Machine Learning
0 0%
Data Science And Machine Learning
100% 100
Relational Databases
100 100%
Relational Databases
0% 0
Data Science Tools
0 0%
Data Science Tools
100% 100

User comments

Share your experience with using MySQL and Pandas. For example, how are they different and which one is better?
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Reviews

These are some of the external sources and on-site user reviews we've used to compare MySQL and Pandas

MySQL Reviews

MariaDB Vs MySQL In 2019: Compatibility, Performance, And Syntax
MySQL: MySQL is an open-source relational database management system (RDBMS). Just like all other relational databases, MySQL uses tables, constraints, triggers, roles, stored procedures and views as the core components that you work with. A table consists of rows, and each row contains a same set of columns. MySQL uses primary keys to uniquely identify each row (a.k.a...
Source: blog.panoply.io
20+ MongoDB Alternatives You Should Know About
MySQL® is another feasible replacement. MySQL 5.7 and MySQL 8 have great support for JSON, and it continues to get better with every maintenance release. You can also consider MySQL Cluster for medium size sharded environments. You can also consider MariaDB and Percona Server for MySQL
Source: www.percona.com

Pandas Reviews

25 Python Frameworks to Master
Pandas is a powerful and flexible open-source library used to perform data analysis in Python. It provides high-performance data structures (i.e., the famous DataFrame) and data analysis tools that make it easy to work with structured data.
Source: kinsta.com
Python & ETL 2020: A List and Comparison of the Top Python ETL Tools
When it comes to ETL, you can do almost anything with Pandas if you're willing to put in the time. Plus, pandas is extraordinarily easy to run. You can set up a simple script to load data from a Postgre table, transform and clean that data, and then write that data to another Postgre table.
Source: www.xplenty.com

Social recommendations and mentions

Based on our record, Pandas seems to be a lot more popular than MySQL. While we know about 220 links to Pandas, we've tracked only 4 mentions of MySQL. 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.

MySQL mentions (4)

  • I have a recurring issue with a MySQL DB where I continually run out of disk space due to logs being filled. I've tried everything I can think of. Can anyone think of anything else I should try?
    So, I did a quick read through the mysql reference and found a bunch of flush related commands. I tried:. Source: over 2 years ago
  • MMORPG design resources
    MySQL: Any SQL or DB knock-off, really... mysql.com - mariadb.org - sqlite.org. Source: almost 3 years ago
  • Probably a syntax error
    15 years and five strokes ago. I was a Unix sysadmin. ALthough I was never an actual programmer, I did maintenance/light enhancement for the organization's website, in php. Now, as self-administered cognative therapy, I'm going back to it. This is an evil HR application that uses the mysql.com employees sample database. The module below enables the evil HR end user to generate a list of the oldest workers so... Source: about 4 years ago
  • An absolute nightmare with mysql 8.0.25
    I always use the packages from mysql.com, that way I don't have to deal with strange configuration stuff along those lines, but anyway, I'm afraid I'm out of ideas. Surely someone else would have run in to the same issue here though. Source: over 4 years ago

Pandas mentions (220)

  • 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, A…. - Source: dev.to / 14 days ago
  • Top Programming Languages for AI Development in 2025
    Libraries for data science and deep learning that are always changing. - Source: dev.to / 5 months ago
  • How to import sample data into a Python notebook on watsonx.ai and other questions…
    # Read the content of nda.txt Try: Import os, types Import pandas as pd From botocore.client import Config Import ibm_boto3 Def __iter__(self): return 0 # @hidden_cell # The following code accesses a file in your IBM Cloud Object Storage. It includes your credentials. # You might want to remove those credentials before you share the notebook. Cos_client = ibm_boto3.client(service_name='s3', ... - Source: dev.to / 6 months ago
  • How I Hacked Uber’s Hidden API to Download 4379 Rides
    As with any web scraping or data processing project, I had to write a fair amount of code to clean this up and shape it into a format I needed for further analysis. I used a combination of Pandas and regular expressions to clean it up (full code here). - Source: dev.to / 6 months ago
  • Must-Know 2025 Developer’s Roadmap and Key Programming Trends
    Python’s Growth in Data Work and AI: Python continues to lead because of its easy-to-read style and the huge number of libraries available for tasks from data work to artificial intelligence. Tools like TensorFlow and PyTorch make it a must-have. Whether you’re experienced or just starting, Python’s clear style makes it a good choice for diving into machine learning. Actionable Tip: If you’re new to Python,... - Source: dev.to / 8 months ago
View more

What are some alternatives?

When comparing MySQL and Pandas, you can also consider the following products

PostgreSQL - PostgreSQL is a powerful, open source object-relational database system.

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

Microsoft SQL - Microsoft SQL is a best in class relational database management software that facilitates the database server to provide you a primary function to store and retrieve data.

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

SQLite - SQLite Home Page

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

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