IO with NumPy - use Technical Quotient

1. Understand

What is the purpose of using skip_header and skip_footer in np.genfromtxt() when reading data from a file?

To exclude specific rows from the beginning and end of the file during loading.

Correct; skip_header and skip_footer are used to exclude header rows at the beginning and footer rows at the end of the file, which might not contain relevant data.

To automatically detect and skip non-numeric headers and footers.
To improve the speed of reading large files by skipping unnecessary data.
To count the number of lines in the file before reading.

2. Apply

You are loading a dataset from a CSV file data.csv that includes a three-line header and a two-line footer with summaries. How would you load this data into a NumPy array, excluding these header and footer lines, using np.genfromtxt()?

np.fromfile('data.csv', skip_header=3, skip_footer=2)

Incorrect; np.fromfile() is for reading binary data and does not support skip_header or skip_footer.

np.loadtxt('data.csv', delimiter=',', skiprows=3, skipfooter=2)

Incorrect; np.loadtxt() does not have a skipfooter parameter and is not as flexible as np.genfromtxt() for handling both headers and footers.

np.array('data.csv', skip_header=3, skip_footer=2)

Incorrect; np.array() does not directly read files and does not have skip_header or skip_footer parameters.

np.genfromtxt('data.csv', delimiter=',', skip_header=3, skip_footer=2)

Correct; np.genfromtxt() is the appropriate function for reading CSV files and supports both skip_header and skip_footer for excluding header and footer lines.

3. Understand

What is the effect of using the usecols argument with np.genfromtxt() when importing data from a file?

Selectively imports columns to enhance memory and speed.

Correct; usecols enables selective import of columns, which can save memory and improve processing speed by only importing necessary data.

Converts all imported data to floating-point numbers.

Incorrect; does not automatically convert all data to floats, but follows the dtype specification or inference.

Increases the number of columns imported by default.
Randomly selects columns to import from the file.

4. Apply

You have a CSV file data.csv with six columns of data, but you are only interested in the second and fifth columns for analysis. How would you import these columns using np.genfromtxt()?

np.genfromtxt('data.csv', delimiter=',', usecols='1, 4')

np.genfromtxt('data.csv', usecols=(2, 5))

np.genfromtxt('data.csv', delimiter=',', usecols=(1, 4))

np.genfromtxt('data.csv', usecols=('second', 'fifth'))

Incorrect; column names 'second', 'fifth' would only work if the file's header explicitly used these names, and it's not specified here that these are the actual column names.

1. Understand

2. Apply

3. Understand

4. Apply

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