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Pandas

1.4K
1.1K
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22
R Language

2.4K
1.7K
+ 1
393
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Pandas vs R: What are the differences?

Developers describe Pandas as "High-performance, easy-to-use data structures and data analysis tools for the Python programming language". Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more. On the other hand, R is detailed as "A language and environment for statistical computing and graphics". R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible.

Pandas can be classified as a tool in the "Data Science Tools" category, while R is grouped under "Languages".

"Easy data frame management" is the top reason why over 16 developers like Pandas, while over 58 developers mention "Data analysis " as the leading cause for choosing R.

Pandas is an open source tool with 20.2K GitHub stars and 8K GitHub forks. Here's a link to Pandas's open source repository on GitHub.

According to the StackShare community, R has a broader approval, being mentioned in 128 company stacks & 97 developers stacks; compared to Pandas, which is listed in 73 company stacks and 49 developer stacks.

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Pros of Pandas
Pros of R Language
  • 21
    Easy data frame management
  • 1
    Extensive file format compatibility
  • 81
    Data analysis
  • 60
    Graphics and data visualization
  • 52
    Free
  • 43
    Great community
  • 37
    Flexible statistical analysis toolkit
  • 26
    Access to powerful, cutting-edge analytics
  • 25
    Easy packages setup
  • 18
    Interactive
  • 12
    R Studio IDE
  • 9
    Hacky
  • 7
    Shiny apps
  • 6
    Shiny interactive plots
  • 5
    Preferred Medium
  • 5
    Automated data reports
  • 4
    Cutting-edge machine learning straight from researchers
  • 2
    Machine Learning
  • 1
    Graphical visualization

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Cons of Pandas
Cons of R Language
    Be the first to leave a con
    • 5
      Very messy syntax
    • 4
      Tables must fit in RAM
    • 2
      Arrays indices start with 1
    • 2
      No push command for vectors/lists
    • 2
      Messy syntax for string concatenation
    • 1
      Messy character encoding
    • 0
      Poor syntax for classes
    • 0
      Messy syntax for array/vector combination

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    What is Pandas?

    Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more.

    What is R Language?

    R provides a wide variety of statistical (linear and nonlinear modelling, classical statistical tests, time-series analysis, classification, clustering, ...) and graphical techniques, and is highly extensible.

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    Jobs that mention Pandas and R Language as a desired skillset
    CBRE
    United Kingdom of Great Britain and Northern Ireland England London
    CBRE
    Philippines National Capital Region Makati City
    CBRE
    United States of America Texas Houston
    CBRE
    Philippines National Capital Region Makati City
    What companies use Pandas?
    What companies use R Language?
    See which teams inside your own company are using Pandas or R Language.
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    What tools integrate with Pandas?
    What tools integrate with R Language?

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    Blog Posts

    Aug 28 2019 at 3:10AM

    Segment

    PythonJavaAmazon S3+16
    6
    2291
    GitHubPythonReact+42
    48
    39894
    GitHubGitDocker+34
    29
    41578
    What are some alternatives to Pandas and R Language?
    Panda
    Panda is a cloud-based platform that provides video and audio encoding infrastructure. It features lightning fast encoding, and broad support for a huge number of video and audio codecs. You can upload to Panda either from your own web application using our REST API, or by utilizing our easy to use web interface.<br>
    NumPy
    Besides its obvious scientific uses, NumPy can also be used as an efficient multi-dimensional container of generic data. Arbitrary data-types can be defined. This allows NumPy to seamlessly and speedily integrate with a wide variety of databases.
    Apache Spark
    Spark is a fast and general processing engine compatible with Hadoop data. It can run in Hadoop clusters through YARN or Spark's standalone mode, and it can process data in HDFS, HBase, Cassandra, Hive, and any Hadoop InputFormat. It is designed to perform both batch processing (similar to MapReduce) and new workloads like streaming, interactive queries, and machine learning.
    PySpark
    It is the collaboration of Apache Spark and Python. it is a Python API for Spark that lets you harness the simplicity of Python and the power of Apache Spark in order to tame Big Data.
    SciPy
    Python-based ecosystem of open-source software for mathematics, science, and engineering. It contains modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.
    See all alternatives