10 Questions
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.
True
Excel File (XLSX) is one of the most popular formats for data.
True
NumPy can handle mixed data types including strings, date, time, and numeric values in csv format.
False
Pandas can handle mixed data types including strings, date, time, and numeric values in csv format.
True
Pandas requires the use of third-party packages such as OpenPyXl for handling XLSX format.
True
JSON is the most commonly used data format in web and mobile applications.
True
Excel File (XLSX) is not one of the most popular formats for data.
False
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.
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