Array creation - use Technical Quotient

Reading arrays from disk, either from standard or custom formats

1. Understand

What is an example of a situation where you would use h5py instead of PIL for reading data into NumPy arrays?

Manipulating tabular data for data analysis.
Reading image data from formats like jpg or png.
Generating plots for data visualization.
Working with scientific data stored in HDF5 format.

2. Remember

Which NumPy function is used to load data from a CSV file, specifying a delimiter and skipping the first row?

np.loadtxt('simple.csv', delimiter=',', skiprows=1)

Correct; np.loadtxt() is used to load data from a CSV, with options to specify the delimiter and skip initial rows.

np.array('simple.csv')

Incorrect; np.array() does not directly read files; it converts sequence-like inputs into arrays.

np.fromfile('simple.csv', delimiter=',')

Incorrect; np.fromfile() reads binary data and is not typically used for CSV files.

np.genfromtxt('simple.csv', delimiter=',')

Incorrect; np.genfromtxt() also reads delimited data but is not specified with skiprows=1 in this context.

3. Understand

What is an advantage of using np.loadtxt over np.genfromtxt for loading simple.csv?

np.genfromtxt does not support specifying delimiters.

Incorrect; np.genfromtxt supports delimiters, among other features for handling more complex file types.

np.genfromtxt cannot skip rows.

Incorrect; np.genfromtxt can skip rows and is actually designed to handle missing data more flexibly.

np.loadtxt can read files of any size.
np.loadtxt is generally faster for files without missing values.

Correct; np.loadtxt is optimized and faster for simpler, well-formatted files without missing data.

4. Apply

You need to load a CSV file data.csv that contains header information and missing values in some rows. Which command correctly loads the data while ignoring headers and handling missing values as NaN?

np.loadtxt('data.csv', delimiter=',', skiprows=1)

Incorrect; np.loadtxt can skip rows but does not handle missing values as flexibly as np.genfromtxt.

np.array('data.csv')

Incorrect; np.array does not read files; it converts sequence-like inputs into arrays.

np.fromfile('data.csv', delimiter=',')

Incorrect; np.fromfile reads binary data and is not suitable for CSV files with missing values and headers.

np.genfromtxt('data.csv', delimiter=',', skip_header=1, missing_values="", filling_values=np.nan)

Correct; np.genfromtxt is designed for more complex data structures, including handling missing values and skipping headers.

1. Understand

2. Remember

3. Understand

4. Apply

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