IO with NumPy - use Technical Quotient

1. Understand

What is the effect of specifying names=True when using np.genfromtxt() to read data from a file?

It ignores the first line of the file.
It generates default names for the columns.
It reads column names from the first line of the file.

Correct; specifying names=True instructs np.genfromtxt() to read column names from the first line of the data, assuming it contains headers.

It sets all column names to True.

Incorrect; it does not set column names to True but uses the first line as headers.

2. Apply

You have a CSV file with the first row as headers name,age,grade and subsequent rows containing data. How would you load this file into a NumPy array with the headers used as column names?

Use np.genfromtxt('file.csv', delimiter=',', names='name, age, grade')
Use np.genfromtxt('file.csv', delimiter=',')

Incorrect; without names=True, the function will not treat the first row as headers.

Use np.loadtxt('file.csv', delimiter=',')

Incorrect; np.loadtxt does not support reading column names from the file.

Use np.genfromtxt('file.csv', delimiter=',', names=True)

Correct; using names=True reads the first line as column names, assuming it contains headers.

3. Analyze

Given a structured array created from a file using np.genfromtxt() with names=True, if the file's first row (header) includes a name that conflicts with a field name defined in dtype, what will happen?

The field name from dtype will be overwritten by the name from the file's header.

Correct; if names=True, the names read from the file's first row will overwrite any field names defined in the dtype, aligning the array structure with the file's actual headers.

The function will raise an error due to conflicting names.

Incorrect; np.genfromtxt() will not raise an error due to conflicting names but will overwrite them.

Names from the file's header will be ignored in favor of dtype definitions.

Incorrect; names from the file's header will not be ignored; they take precedence when names=True is specified.

Duplicate fields will be created for each conflicting name.

Incorrect; duplicate fields will not be created; the names from the file's header overwrite those specified in dtype.

4. Understand

What happens when you use np.genfromtxt() to load data without explicitly naming all fields in a structured dtype?

Missing names are automatically generated using a default template like "f%i".
Explicit names are required; otherwise, data loading is not possible.
The operation fails due to missing names.
All fields are named using a single default name.

5. Apply

You're loading data with three columns from a text file but only provide one name in the names argument. You want the unnamed fields to be named with a prefix "data_" followed by a number, starting from 0. How would you achieve this using np.genfromtxt()?

np.genfromtxt('filepath', dtype=(int, float, int), names="Time", defaultfmt="data_%i")

np.genfromtxt('filepath', dtype=(int, float, int), names="Time", defaultfmt="data_%02i")

Correct; using defaultfmt="data_%02i" ensures that the unnamed fields are named with a prefix "data_" and a zero-padded number, starting from 0, according to the requirement.

np.genfromtxt('filepath', dtype=(int, float, int), names="Time")

Incorrect; providing only one name without specifying defaultfmt would result in the remaining names being automatically generated using the default "f%i" template, not the desired "data_" prefix.

np.genfromtxt('filepath', dtype=(int, float, int))

Incorrect; without specifying names or defaultfmt, the default naming convention "f%i" would be used.

6. Understand

If genfromtxt is used to read data with column names that include spaces and special characters, which parameter ensures these characters are removed from the column names?

excludelist

Incorrect; excludelist is used to specify names to exclude by appending an underscore, not for removing characters.

deletechars

Correct; deletechars specifies characters to be removed from column names, ensuring valid attribute access in a recarray.

replacechars

Incorrect; replacechars does not exist as a parameter in genfromtxt for handling column names.

case_sensitive

Incorrect; case_sensitive controls whether names are treated as case-sensitive or converted to upper/lower case, not for removing characters.

7. Apply

You are loading a CSV file using np.genfromtxt with column names "Return Value, Size#Shape, Print". You want to ensure these names are converted to lowercase without spaces or special characters and "Return" does not conflict with Python keywords. Which set of parameters correctly achieves this?

deletechars="!@#$%^&*()", excludelist=["size", "shape"], case_sensitive=True

Incorrect; does not remove all invalid characters, and case_sensitive=True does not convert names to lowercase.

deletechars=" ,#\"", excludelist=["return", "file", "print"], case_sensitive='lower'

Correct; deletechars=" ,#\"", excludelist=["return", "file", "print"], and case_sensitive='lower' will remove spaces, special characters, avoid keyword conflicts, and ensure all names are in lowercase.

deletechars=" -", excludelist=["print", "return"], case_sensitive=False

Incorrect; deletechars=" -" misses other special characters, and case_sensitive=False converts to uppercase, not lowercase.

deletechars="~!@#$%^&*()-=+~\|]}[{';:/?.>,<", excludelist=["return", "file", "print"], case_sensitive='lower'

Incorrect; overly specific in the deletechars pattern; however, it reflects the default behavior and accurately addresses the criteria for the question's requirements, focusing on making names lowercase, removing problematic characters, and avoiding keyword conflicts.