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Questions and Answers
NumPy supports csv format and can handle both numeric and non-numeric values.
False
Pandas requires the use of third-party packages such as OpenPyXl for handling XLSX format.
False
JSON is not commonly used in web and mobile applications.
False
Pandas can handle mixed data types including strings, date, time, and numeric values in csv format.
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Excel File (XLSX) is one of the most popular formats for data.
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NumPy can handle mixed data types including strings, date, time, and numeric values in csv format.
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Pandas can handle mixed data types including strings, date, time, and numeric values in csv format.
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Pandas requires the use of third-party packages such as OpenPyXl for handling XLSX format.
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JSON is the most commonly used data format in web and mobile applications.
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Excel File (XLSX) is not one of the most popular formats for data.
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Study Notes
Data Access for IoT
- IoT systems are composed of numerous sensors and actuators that create and process data.
- Proper data exchange formats are crucial for devices and applications to efficiently produce and consume data.
- High throughput data sharing among producers may require the implementation of distributed file systems.
Text Format
- Text format is the most basic and straightforward format for data representation.
- All programming languages can natively read and write to text files without the need for additional libraries or modules.
CSV Format
- Comma Separated Values (CSV) is a simple method for storing records in files.
- Despite its name, CSV can utilize any character as a separator, not just commas.
- If records contain the separator character, an escape character must be used to prevent conflicts.
- Many Python packages are available to facilitate the reading and writing of CSV files.
Python CSV Module
- The Python ‘csv’ module is specifically designed to handle CSV file operations.
- It provides functionality for working with CSV formats, but has limitations when it comes to handling date and time data.
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Description
Test your knowledge on data access formats for IoT devices like text, CSV, XLSX, and JSON, as well as the application of supervised and unsupervised learning in IoT.
