{"id":54030,"date":"2024-04-16T00:03:14","date_gmt":"2024-04-16T00:03:14","guid":{"rendered":"https:\/\/exam.pscnotes.com\/mcq\/?p=54030"},"modified":"2024-04-16T00:03:14","modified_gmt":"2024-04-16T00:03:14","slug":"in-python-which-library-is-commonly-used-for-working-with-data-stored-in-the-apache-parquet-file-format","status":"publish","type":"post","link":"https:\/\/exam.pscnotes.com\/mcq\/in-python-which-library-is-commonly-used-for-working-with-data-stored-in-the-apache-parquet-file-format\/","title":{"rendered":"In Python, which library is commonly used for working with data stored in the Apache Parquet file format?"},"content":{"rendered":"

[amp_mcq option1=”Matplotlib” option2=”Seaborn” option3=”Pandas” option4=”pyarrow” correct=”option4″]<\/p>\n

The correct answer is D. pyarrow.<\/p>\n

pyarrow is a Python library that provides a unified interface for reading and writing data in a variety of formats, including Apache Parquet. It is designed to be efficient and scalable, and it supports a wide range of features, such as schema evolution and partitioning.<\/p>\n

Matplotlib is a Python library for plotting data. It is widely used for scientific and engineering applications, and it provides a variety of features for creating high-quality plots. However, it does not support reading or writing data in the Apache Parquet file format.<\/p>\n

Seaborn is a Python library for statistical visualization. It is built on top of Matplotlib, and it provides a number of features that make it easier to create effective statistical plots. However, it also does not support reading or writing data in the Apache Parquet file format.<\/p>\n

Pandas is a Python library for data analysis. It provides a variety of features for working with structured data, including reading and writing data in a variety of formats. However, it does not support the Apache Parquet file format natively. There are third-party libraries that can be used to read and write Parquet data with Pandas, but pyarrow is a more direct and efficient option.<\/p>\n

In conclusion, pyarrow is the best library for working with data stored in the Apache Parquet file format in Python. It is efficient, scalable, and supports a wide range of features.<\/p>\n","protected":false},"excerpt":{"rendered":"

[amp_mcq option1=”Matplotlib” option2=”Seaborn” option3=”Pandas” option4=”pyarrow” correct=”option4″]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[737],"tags":[],"class_list":["post-54030","post","type-post","status-publish","format-standard","hentry","category-data-analysis-with-python","no-featured-image-padding"],"yoast_head":"\nIn Python, which library is commonly used for working with data stored in the Apache Parquet file format?<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/exam.pscnotes.com\/mcq\/in-python-which-library-is-commonly-used-for-working-with-data-stored-in-the-apache-parquet-file-format\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta 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