np.genfromtxt() for handling missing data in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What is the purpose of np.genfromtxt() in NumPy?

np.genfromtxt() is used to load data from text files, especially when the data has missing or invalid entries. It helps handle missing data gracefully by filling in default values or using specified placeholders.

beginner

How does np.genfromtxt() handle missing data by default?

By default, np.genfromtxt() replaces missing values with nan (Not a Number) for floating-point data. This allows further processing without errors.

intermediate

Which parameter in np.genfromtxt() lets you specify what string represents missing data?

The missing_values parameter lets you define which strings in the file should be treated as missing data. For example, you can set missing_values='NA' to treat 'NA' as missing.

intermediate

What does the filling_values parameter do in np.genfromtxt()?

filling_values specifies what value to use to replace missing data. For example, you can fill missing numbers with 0 or any other number instead of nan.

beginner

How can you skip header lines when using np.genfromtxt()?

Use the skip_header parameter to tell np.genfromtxt() how many lines at the start of the file to ignore. This is useful when the file has column names or comments.

What does np.genfromtxt() return when it encounters missing numeric data by default?

A It replaces missing data with zero

B It throws an error

C It replaces missing data with nan

D It ignores the entire row

By default, np.genfromtxt() replaces missing numeric data with nan to allow further processing.

Which parameter lets you specify the string that marks missing data in np.genfromtxt()?

A filling_values

B missing_values

C delimiter

D skip_header

missing_values defines which strings in the file are treated as missing data.

If you want missing values to be replaced by 0 instead of nan, which parameter should you use?

A missing_values=0

B skip_header=0

C delimiter=0

D filling_values=0

filling_values sets the value used to replace missing data.

How do you tell np.genfromtxt() to ignore the first 2 lines of a file?

A skip_header=2

B missing_values=2

C delimiter=2

D skip_footer=2

skip_header=2 skips the first two lines of the file.

What type of data does np.genfromtxt() best handle?

A Text files with missing data

B Binary files

C Images

D Audio files

np.genfromtxt() is designed to read text files, especially those with missing or irregular data.

Explain how np.genfromtxt() helps when loading data files that have missing values.

Think about how missing data is identified and replaced.

Describe how you would use np.genfromtxt() to read a CSV file that has a header and some missing entries marked as 'NA'.

Consider how to skip the header and handle 'NA' as missing.