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  5. OpenRefine vs Pandas

OpenRefine vs Pandas

OverviewDecisionsComparisonAlternatives

Overview

Pandas
Pandas
Stacks2.1K
Followers1.3K
Votes23
OpenRefine
OpenRefine
Stacks33
Followers68
Votes0
GitHub Stars11.6K
Forks2.1K

OpenRefine vs Pandas: What are the differences?

Introduction

OpenRefine and Pandas are both widely used tools for data manipulation and analysis. While they serve similar purposes, there are several key differences between the two that set them apart. The following paragraphs highlight six of these key differences.

  1. Language and Environment: OpenRefine is a web-based tool that runs in a browser and primarily uses a spreadsheet-like interface. On the other hand, Pandas is a library in Python, one of the most popular programming languages for data analysis.

  2. Data Types: OpenRefine is designed to handle a wide range of data types, including text, numbers, dates, and more. It can easily recognize and manipulate different types of data. Pandas, being a Python library, supports various data types as well but provides more flexibility in handling complex data structures like multi-dimensional arrays.

  3. Scalability: OpenRefine is optimized for working with small to medium-sized datasets. It performs best when dealing with a few hundred thousand records or less. On the contrary, Pandas is highly scalable and can efficiently handle larger datasets with millions or even billions of records.

  4. Data Cleaning and Transformation: OpenRefine excels at data cleaning and transformation tasks. It provides intuitive functionalities for exploring and cleaning messy data, including advanced options for clustering, filtering, and merging. Pandas, being a powerful data manipulation tool, offers similar capabilities but provides a more extensive range of functions and operations for data cleaning and transformation.

  5. Integration with Programming: OpenRefine is primarily designed for non-programmers, providing a user-friendly interface for data manipulation tasks. It offers limited programming capabilities through its expression language, but the emphasis is on point-and-click operations. Pandas, being a Python library, seamlessly integrates with the broader Python ecosystem. It allows users to leverage the full power of Python programming for data analysis.

  6. Community and Support: OpenRefine has a dedicated user community and provides excellent support through forums, mailing lists, and extensive documentation. Pandas, being a widely adopted Python library, also has a large community and abundant resources available. As Python is one of the most popular programming languages for data analysis, Pandas users can benefit from the vast Python community and resources.

In summary, OpenRefine is a web-based tool primarily focused on data cleaning and transformation tasks, with a user-friendly interface for non-programmers. On the other hand, Pandas is a powerful Python library that provides extensive data manipulation capabilities, scalability, and integration with the broader Python ecosystem.

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Advice on Pandas, OpenRefine

Sarah
Sarah

Jun 25, 2020

Needs adviceonOpenRefineOpenRefine

I'm looking for an open-source/free/cheap tool to clean messy data coming from various travel APIs. We use many different APIs and save the info in our DB. However, many duplicates cannot be easily recognized as such.

We would either write an algorithm or use smart technology/tools with ML to help with product management.

While there are many things to be considered, this is one feature that it should have:

"To avoid confusion, we need to merge the suppliers & products accordingly. Products and suppliers must be able to be merged and assigned separately.

Reason: It may happen that one supplier offers different products. E.g., 1 tour operator offers 3 products via 1 API, but only 1 product with 3 (or a different amount of) variations via a different API. Also, the commission may differ for products, which we need to consider. Very often, products that are live (are bookable in real-time) on via 1 API, but are not live on the other. E.g., Supplier product 1 & 2 of API1 are live, product 3 not. For the same supplier, API2 provides live availability for products 1, 2, and 3.

Summing up, when merging the suppliers (tour operators) we need to consider:

  • Are the products the same for all APIs?
  • Which booking system API gives a better commission? Note: Some APIs charge us 1-5% depending on the monthly sale, which needs to be considered
  • Which booking system provides live availability
  • Is it the same supplier, or is the name only similar?

Most of the time, the supplier names differ even if they are the same (e.g., API1 often names them XX Pty Ltd, while API2 leaves "Pty Ltd" out). Additionally, the product title, description, etc. differ.

We need to write logic and create an algorithm to find the duplicates & to merge, assign, or (de)activate the respective supplier or product. My previous developer started a module to merge the suppliers, which does not seem to work correctly. Also, it is way too time taking considering the high amount of products that we have.

I would recommend merging, assigning etc. products and suppliers only if our algorithm says it's 90- 100% the matching supplier/product. Otherwise, admins need to be able to check & modify this. E.g. everything with a lower possibility of matching will be matched automatically, but can be undone or modified.

The next time the cron job runs, this needs to be considered to avoid recreating duplicates & creating a mess."

I am not sure in what way OpenRefine can help to achieve this and what ML tool can be connected to learn from the decisions the product management team makes. Maybe you have an idea of how other travel portals deal with messy data, duplicates, etc.?

I'm looking for the cheapest solution for a start-up, but it should do the work properly.

19.2k views19.2k
Comments

Detailed Comparison

Pandas
Pandas
OpenRefine
OpenRefine

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

It is a powerful tool for working with messy data: cleaning it; transforming it from one format into another; and extending it with web services and external data.

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;Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data;Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects;Intelligent label-based slicing, fancy indexing, and subsetting of large data sets;Intuitive merging and joining data sets;Flexible reshaping and pivoting of data sets;Hierarchical labeling of axes (possible to have multiple labels per tick);Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving/loading data from the ultrafast HDF5 format;Time series-specific functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging, etc.
Faceting; Clustering; Editing cells; Reconciling; Extending web services
Statistics
GitHub Stars
-
GitHub Stars
11.6K
GitHub Forks
-
GitHub Forks
2.1K
Stacks
2.1K
Stacks
33
Followers
1.3K
Followers
68
Votes
23
Votes
0
Pros & Cons
Pros
  • 21
    Easy data frame management
  • 2
    Extensive file format compatibility
No community feedback yet
Integrations
Python
Python
Python
Python
Dask
Dask
Ludwig
Ludwig
Vertica
Vertica

What are some alternatives to Pandas, OpenRefine?

Apache Spark

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.

Presto

Presto

Distributed SQL Query Engine for Big Data

Amazon Athena

Amazon Athena

Amazon Athena is an interactive query service that makes it easy to analyze data in Amazon S3 using standard SQL. Athena is serverless, so there is no infrastructure to manage, and you pay only for the queries that you run.

Apache Flink

Apache Flink

Apache Flink is an open source system for fast and versatile data analytics in clusters. Flink supports batch and streaming analytics, in one system. Analytical programs can be written in concise and elegant APIs in Java and Scala.

lakeFS

lakeFS

It is an open-source data version control system for data lakes. It provides a “Git for data” platform enabling you to implement best practices from software engineering on your data lake, including branching and merging, CI/CD, and production-like dev/test environments.

Druid

Druid

Druid is a distributed, column-oriented, real-time analytics data store that is commonly used to power exploratory dashboards in multi-tenant environments. Druid excels as a data warehousing solution for fast aggregate queries on petabyte sized data sets. Druid supports a variety of flexible filters, exact calculations, approximate algorithms, and other useful calculations.

Apache Kylin

Apache Kylin

Apache Kylin™ is an open source Distributed Analytics Engine designed to provide SQL interface and multi-dimensional analysis (OLAP) on Hadoop/Spark supporting extremely large datasets, originally contributed from eBay Inc.

Splunk

Splunk

It provides the leading platform for Operational Intelligence. Customers use it to search, monitor, analyze and visualize machine data.

Apache Impala

Apache Impala

Impala is a modern, open source, MPP SQL query engine for Apache Hadoop. Impala is shipped by Cloudera, MapR, and Amazon. With Impala, you can query data, whether stored in HDFS or Apache HBase – including SELECT, JOIN, and aggregate functions – in real time.

Vertica

Vertica

It provides a best-in-class, unified analytics platform that will forever be independent from underlying infrastructure.

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