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Pandas vs PyXLL: What are the differences?
What is Pandas? 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.
What is PyXLL? The Python Add-In for Microsoft Excel. Integrate Python into Microsoft Excel Use Excel as your user-facing front-end with calculations, business logic and data access powered by Python.
Works with all 3rd party and open source Python packages. No need to write any VBA!.
Pandas and PyXLL can be categorized as "Data Science" tools.
Some of the features offered by Pandas are:
- Easy handling of missing data (represented as NaN) in floating point as well as non-floating point data
- Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects
- Automatic and explicit data alignment: objects can be explicitly aligned to a set of labels, or the user can simply ignore the labels and let Series, DataFrame, etc. automatically align the data for you in computations
On the other hand, PyXLL provides the following key features:
- User Defined Functions: Write Excel worksheet functions in Python - no VBA required
- Ribbon Customization: Give your users a rich Excel native experience
- Macros: No need for VBA, access to the full Excel Object Model in Python
Pandas is an open source tool with 20.7K GitHub stars and 8.16K GitHub forks. Here's a link to Pandas's open source repository on GitHub.
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Learn MorePros of Pandas
Pros of PyXLL
Pros of Pandas
- Easy data frame management21
- Extensive file format compatibility1
Pros of PyXLL
- Fully replace VBA with Python5
- Excellent support2
- Very good performance1
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Cons of Pandas
Cons of PyXLL
Cons of Pandas
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Cons of PyXLL
- Cannot be deloyed to mac users1
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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 PyXLL?
Integrate Python into Microsoft Excel.
Use Excel as your user-facing front-end with calculations, business logic and data access powered by Python.
Works with all 3rd party and open source Python packages. No need to write any VBA!
Need advice about which tool to choose?Ask the StackShare community!
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What companies use PyXLL?
What companies use Pandas?
What companies use PyXLL?
See which teams inside your own company are using Pandas or PyXLL.
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What are some alternatives to Pandas and PyXLL?
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.
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.
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.