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Dask
Dask

16
16
+ 1
0
Pandas
Pandas

618
485
+ 1
18
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Pandas vs Dask: What are the differences?

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; Dask: A flexible library for parallel computing in Python. It is a versatile tool that supports a variety of workloads. It is composed of two parts: Dynamic task scheduling optimized for computation. This is similar to Airflow, Luigi, Celery, or Make, but optimized for interactive computational workloads Big Data collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of dynamic task schedulers. .

Pandas and Dask belong to "Data Science Tools" category of the tech stack.

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, Dask provides the following key features:

  • Supports a variety of workloads
  • Dynamic task scheduling
  • Trivial to set up and run on a laptop in a single process

Pandas is an open source tool with 20.8K GitHub stars and 8.27K GitHub forks. Here's a link to Pandas's open source repository on GitHub.

- No public GitHub repository available -

What is Dask?

It is a versatile tool that supports a variety of workloads. It is composed of two parts: Dynamic task scheduling optimized for computation. This is similar to Airflow, Luigi, Celery, or Make, but optimized for interactive computational workloads. Big Data collections like parallel arrays, dataframes, and lists that extend common interfaces like NumPy, Pandas, or Python iterators to larger-than-memory or distributed environments. These parallel collections run on top of dynamic task schedulers.

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.
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        What are some alternatives to Dask and Pandas?
        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.
        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.
        Anaconda
        A free and open-source distribution of the Python and R programming languages for scientific computing, that aims to simplify package management and deployment. Package versions are managed by the package management system conda.
        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.
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        Decisions about Dask and Pandas
        Guillaume Simler
        Guillaume Simler
        at Velchanos.io | 4 upvotes 57.5K views
        Jupyter
        Jupyter
        Anaconda
        Anaconda
        Pandas
        Pandas
        IPython
        IPython

        Jupyter Anaconda Pandas IPython

        A great way to prototype your data analytic modules. The use of the package is simple and user-friendly and the migration from ipython to python is fairly simple: a lot of cleaning, but no more.

        The negative aspect comes when you want to streamline your productive system or does CI with your anaconda environment: - most tools don't accept conda environments (as smoothly as pip requirements) - the conda environments (even with miniconda) have quite an overhead

        See more
        Interest over time
        Reviews of Dask and Pandas
        No reviews found
        How developers use Dask and Pandas
        Avatar of Morris Clay
        Morris Clay uses PandasPandas

        Data wrangling, analysis and pre-processing

        Avatar of Eliana Abraham
        Eliana Abraham uses PandasPandas

        I used this a lot more than I used Jupyter.

        Avatar of GadgetSteve
        GadgetSteve uses PandasPandas

        Great data manipulation tool

        How much does Dask cost?
        How much does Pandas cost?
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