Software Alternatives, Accelerators & Startups

Pandas VS Grafana

Compare Pandas VS Grafana and see what are their differences

Note: These products don't have any matching categories. If you think this is a mistake, please edit the details of one of the products and suggest appropriate categories.

Pandas logo Pandas

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

Grafana logo Grafana

Data visualization & Monitoring with support for Graphite, InfluxDB, Prometheus, Elasticsearch and many more databases
  • Pandas Landing page
    Landing page //
    2023-05-12
  • Grafana Landing page
    Landing page //
    2023-10-21

Grafana

$ Details
Release Date
2014 January
Startup details
Country
United States
State
New York
City
New York
Founder(s)
Anthony Woods
Employees
100 - 249

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.

Grafana features and specs

  • Customizable Dashboards
    Grafana provides highly customizable and flexible dashboards, allowing users to create and arrange panels in a way that best represents their metrics and data.
  • Wide Range of Data Sources
    Grafana supports numerous data sources including Prometheus, Elasticsearch, Graphite, AWS CloudWatch, and more, making it versatile and adaptable to various data environments.
  • Rich Plugin Ecosystem
    The platform offers a rich ecosystem of plugins for data visualization, data sources, and apps, enabling users to extend its functionality to suit specific needs.
  • Open Source
    As an open-source tool, Grafana is free to use and customize, allowing organizations to tailor it to their specific requirements without licensing costs.
  • Alerting System
    Grafana comes with a powerful alerting system that can notify users about important events through various channels like email, Slack, and PagerDuty.
  • Community and Support
    Grafana has a large and active community, providing extensive documentation, forums, and tutorials to help users solve issues and improve their dashboards.

Possible disadvantages of Grafana

  • Learning Curve
    The extensive customization features and numerous data sources can be overwhelming for new users, leading to a steep learning curve.
  • Performance Issues with Large Datasets
    When dealing with very large datasets or high-cardinality data, performance issues can arise, requiring additional tuning or more powerful infrastructure.
  • Limited Built-in Data Storage
    Grafana itself does not store data; it relies on external data sources. This could necessitate using additional services or infrastructure for data storage.
  • Complex Setup for Alerting
    Setting up and managing the alerting system can be complicated, especially for users who are not familiar with monitoring and alerting concepts.
  • Dependence on External Data Sources
    The effectiveness of Grafana depends heavily on the quality and stability of the external data sources it connects to, which can be a point of failure.
  • Cost for Enterprise Features
    While the open-source version is free, advanced features and support are available only in the paid enterprise version, which could be costly for some organizations.

Pandas videos

Ozzy Man Reviews: Pandas

More videos:

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

Grafana videos

Grafana vs Kibana | Beautiful data graphs and log analysis systems

More videos:

  • Review - Business Dashboards with Grafana and MySQL
  • Review - Grafana Labs 2019 Year in Review

Category Popularity

0-100% (relative to Pandas and Grafana)
Data Science And Machine Learning
Monitoring Tools
0 0%
100% 100
Data Science Tools
100 100%
0% 0
Data Dashboard
16 16%
84% 84

User comments

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Reviews

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

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

Grafana Reviews

Self Hosting Like Its 2025
If you’re looking for straightforward monitoring and the thought of setting up a full Zabbix or Grafana stack seems daunting, this software is a real lifesaver. With just one deployment, you can monitor your services and receive notifications through a wide variety of channels including…
Source: kiranet.org
Top 10 Grafana Alternatives in 2024
Middleware is one such Grafana alternative that offers robust data monitoring and visualization capabilities at affordable prices. Though it’s commercial, unlike Grafana, its rich feature set ensures accommodating your present and future business needs.
Source: middleware.io
Top 11 Grafana Alternatives & Competitors [2024]
Are you looking for Grafana alternatives? Then you have come to the right place. Grafana started as a data visualization tool. It slowly evolved into a tool that can take data from multiple data sources for visualization. For observability, Grafana offers the LGTM stack (Loki for logs, Grafana for visualization, Tempo for traces, and Mimir for metrics). You need to configure...
Source: signoz.io
10 Best Grafana Alternatives [2023 Comparison]
For this reason, many have set out in search of Grafana alternatives. Since you’ve landed yourself here, I’m guessing that you’re one of those people. Fear not! We’ve put together a comprehensive list of the 10 best Grafana alternatives out there today.
Source: sematext.com
Top 10 Tableau Open Source Alternatives: A Comprehensive List
When it comes to visualization, Grafana is a great tool for visualizing time series data with support for various databases including Prometheus, InfluxDB, and Graphite. It is also compatible with relational databases such as MySQL and Microsoft SQL Server. While Tableau can do the same thing, Grafana’s open-source status allows the users to add additional data sources and...
Source: hevodata.com

Social recommendations and mentions

Grafana might be a bit more popular than Pandas. We know about 238 links to it since March 2021 and only 219 links to Pandas. 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.

Pandas mentions (219)

  • Top Programming Languages for AI Development in 2025
    Libraries for data science and deep learning that are always changing. - Source: dev.to / 6 days 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 / 22 days 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 / 25 days 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 / 3 months ago
  • Sample Super Store Analysis Using Python & Pandas
    This tutorial provides a concise and foundational guide to exploring a dataset, specifically the Sample SuperStore dataset. This dataset, which appears to originate from a fictional e-commerce or online marketplace company's annual sales data, serves as an excellent example for learning and how to work with real-world data. The dataset includes a variety of data types, which demonstrate the full range of... - Source: dev.to / 8 months ago
View more

Grafana mentions (238)

  • Send Node.js logs from Docker to Grafana Cloud with Alloy
    Navigate to Grafana Cloud and sign up or log in. In the sidebar, select Connections → Add new connection, select Loki. This is the place that prompts you to set up your Loki connection and allows you to generate an access token for Alloy. - Source: dev.to / 3 days ago
  • Monitoring API Requests and Responses for System Health
    Prometheus + Grafana: Open-source tools that offer maximum flexibility without ongoing licensing costs—ideal for teams willing to manage their own infrastructure and configuration. - Source: dev.to / 6 days ago
  • How to Optimize Your Fintech API in 2025: A Guide
    Prometheus: This open-source monitoring solution pairs with Grafana for powerful custom visualization of exactly what matters to your business. - Source: dev.to / 6 days ago
  • Monitoring Docker Hub limits with Prometheus
    Grafana Is used to visualize metrics, logs, traces, and by the time you read this probably other things 😄. - Source: dev.to / 8 days ago
  • 3 Types of Chaos Experiments and How To Run Them
    Utilize monitoring solutions like Prometheus, Grafana, or Datadog to monitor how services communicate under normal and failure conditions. Service meshes like Istio or Linkerd can provide detailed insights without changing your application code. - Source: dev.to / 11 days ago
View more

What are some alternatives?

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

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

Prometheus - An open-source systems monitoring and alerting toolkit.

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

Datadog - See metrics from all of your apps, tools & services in one place with Datadog's cloud monitoring as a service solution. Try it for free.

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

Zabbix - Track, record, alert and visualize performance and availability of IT resources