Question

Which one of the following syntaxes is used to import a csv file by considering special characters as NaN?
1) pandas.read_csv(file_name.csv, true_values = [ ])
2) pandas.read_csv(file_name.csv, na_values = [ ])
3) pandas.read_csv(file_name.csv, skiprows = [ ]) 4)pandas.read_csv(file_name.csv, na_filter = [ ])

Answer 1

Answer:

2) pandas.read_csv(file_name.csv, na_values = [ ])

Explanation:

Pandas is "Python Data Analysis Library", it allows to take data from a csv i.e. comma separated file.

read_csv() allows multiple parameters and one of them is na_values which allows values like str, scalar, list-like, dict. It allows additional strings to be recognized as NA/NaN. If dict passed, specific per-column NA values or by default the following values are interpreted as NaN: ‘’, ‘#N/A’, ‘#N/A N/A’, ‘#NA’, ‘-1.#IND’, ‘-1.#QNAN’, ‘-NaN’, ‘-nan’, ‘1.#IND’, ‘1.#QNAN’, ‘<NA>’, ‘N/A’, ‘NA’, ‘NULL’, ‘NaN’, ‘n/a’, ‘nan’, ‘null’.

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Answer 2

2) pandas.read_csv(file_name.csv, na_values = [ ])

• Using CSV (comma-separated values) files is an easy way to store large amounts of data.
• Plain text is present in CSV files, which are a common format that anybody can read, including Pandas.
• The most often used open-source Python library for data science, data analysis, and machine learning activities is called Pandas.
• It is constructed on top of Numpy, a different package that supports multi-dimensional arrays.
• The syntax is used to import a CSV file by considering special characters as NaN is pandas.read_csv(file_name.csv, na_values = [ ])

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