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    Overview
    ProductRatingMost Used ByProduct SummaryStarting Price

    pandas

    Score10 out of 10
    N/Apandas 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
    Open Source
    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.
    Incentivized
    Key User Insights
    Would buy again
    No answers on this topic
    Delivers good value for the price
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    Happy with the feature set
    No answers on this topic
    Lived up to sales and marketing promises
    No answers on this topic
    Implementation went as expected
    No answers on this topic
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    pandas
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    IBM Watson Studio
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    All AlternativesView all alternatives
    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.
    Incentivized
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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
    Incentivized
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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
    Incentivized
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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.
    Incentivized
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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.
    Incentivized
    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
    Incentivized
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