np.savetxt() and np.loadtxt() for text in NumPy - Cheat Sheet & Quick Revision

Recall & Review

beginner

What does np.savetxt() do in NumPy?

np.savetxt() saves a NumPy array to a text file. It writes the array data in a readable text format, like CSV or space-separated values.

beginner

How do you load data from a text file into a NumPy array?

You use np.loadtxt(). It reads numbers from a text file and returns them as a NumPy array.

intermediate

What parameter in np.savetxt() controls the format of saved numbers?

The fmt parameter controls how numbers are saved, for example, fmt='%.2f' saves numbers with two decimal places.

intermediate

Can np.loadtxt() read files with comments or headers?

Yes, you can skip lines with skiprows and ignore comments with the comments parameter.

beginner

What happens if you save a 2D array with np.savetxt() and then load it back with np.loadtxt()?

You get the same 2D array back, as np.savetxt() writes rows line by line and np.loadtxt() reads them back into a 2D array.

Which function saves a NumPy array to a text file?

A np.savetxt()

B np.loadtxt()

C np.save()

D np.load()

np.savetxt() saves arrays to text files. np.loadtxt() reads from text files.

How do you skip the first 3 lines when loading a text file with np.loadtxt()?

A skiprows=3

B skip=3

C rows=3

D ignore=3

The parameter skiprows=3 tells np.loadtxt() to skip the first 3 lines.

What does the fmt parameter in np.savetxt() control?

A Delimiter character

B Data type

C File name

D Number format in the file

fmt controls how numbers are formatted when saved, like decimal places.

If a text file has lines starting with '#', how can np.loadtxt() ignore them?

A ignore='#'

B skiprows='#'

C comments='#'

D header='#'

The comments='#' parameter tells np.loadtxt() to ignore lines starting with '#'.

What type of data does np.loadtxt() return?

A List

B NumPy array

C Dictionary

D String

np.loadtxt() returns a NumPy array with the data read from the text file.

Explain how to save a 2D NumPy array to a text file and then load it back.

Think about writing and reading files with NumPy functions.

Describe how to handle text files with headers or comments when loading data using NumPy.

Consider parameters that help ignore unwanted lines.