TrustRadius

pandas

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
pandas
Score 10.0 out of 10
N/A
pandas is an open source, BSD-licensed library providing high-performance data structures and data analysis tools for the Python programming language. pandas is a Python package providing expressive data structures designed to make working with “relational” or “labeled” data both easier. It aims to be the fundamental high-level building block for doing practical, real-world data analysis in Python.N/A
Pricing
pandas
Editions & Modules
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Offerings
Pricing Offerings
pandas
Free Trial
No
Free/Freemium Version
No
Premium Consulting/Integration Services
No
Entry-level Setup FeeNo setup fee
Additional Details—
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Community Pulse
pandas
Considered Both Products
pandas
Verified User
Employee
Chose pandas
All these frameworks are great for gathering data and providing some initial analysis. But for real performance debugging work one needs more than tools provided by this tools. That's where the pandas excel.
Features
pandas
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
pandas
8.5
1 Ratings
2% above category average
Connect to Multiple Data Sources8.01 Ratings
Extend Existing Data Sources8.01 Ratings
Automatic Data Format Detection10.01 Ratings
MDM Integration8.01 Ratings
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Score 10.0 out of 10
Enterprises
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Score 10.0 out of 10
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User Ratings
pandas
Likelihood to Recommend
10.0
(1 ratings)
Usability
10.0
(1 ratings)
User Testimonials
pandas
Likelihood to Recommend
Open Source
Pandas are great for quick and relatively simple analytics and visualizations
Pandas work well for exploratory ad-hoc analytic work
But , We had little success in implementing complicated predictive analytics. And large data sizes can be a problem.
Verified User
Anonymous
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Pros
Open Source
  • It is easy to do statistical analysis
  • It is easy to clean the data
  • It is easy to produce graphs and charts to visualize
Verified User
Anonymous
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Cons
Open Source
  • There are a lot of libraries and ways to do visualization. Sometimes it is very confusing.
  • Error handling can be a challenge. Sometimes the error messages do not provide valuable clues for the debugging.
  • In our case, there are a bunch of different frameworks and libraries working together. I would rather work with one framework, well tuned for my use case
Verified User
Anonymous
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Usability
Open Source
Over the years, we tried a lot of different frameworks and tools, homegrown and commercial. Pandas provide the best results.
It is lightweight, flexible and easy to implement.
Verified User
Anonymous
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Alternatives Considered
Open Source
All these frameworks are great for gathering data and providing some initial analysis. But for real performance debugging work one needs more than tools provided by this tools. That's where the pandas excel.
Verified User
Anonymous
Read full review
Return on Investment
Open Source
  • Performance debugging was time consuming and mostly poorly automated exploratory process. Once we started use pandas for these tasks, it really moved the needle. Pandas are instrumental to provide actionable insights. As a result we were able to improve notably cloud software resource utilization and performance
  • Analytics implemented with pandas allow us to detect and. address problems in our APIs before they are notable to our customers
Verified User
Anonymous
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