IO with NumPy - use Technical Quotient

1. Remember

What argument is used in np.genfromtxt to apply conversion functions to data columns when reading from a file?

converters

Correct; converters is used to apply custom conversion functions to data columns.

dtype

Incorrect; dtype specifies the data type of the array to be returned, not conversion functions.

formatters

Incorrect; formatters is not related to reading data but formatting output in other contexts.

parsers

Incorrect; parsers is not a valid argument in np.genfromtxt.

2. Understand

Why would you use a converter function when reading data with np.genfromtxt?

To transform string data into a specified format or data type.
To specify the delimiter used in the data file.

Incorrect; the delimiter is specified with the delimiter argument, not with converters.

To increase the speed of data reading.
To reduce the size of the resulting array.

3. Apply

You have a CSV file data.csv containing dates in the format YYYY-MM-DD that you want to convert to datetime objects. Which np.genfromtxt call correctly applies the conversion?

np.genfromtxt('data.csv', delimiter=',', converters={0: lambda x: datetime.strptime(x.decode('utf-8'), '%Y-%m-%d')})

Correct; uses a lambda function to convert date strings to datetime objects.

np.genfromtxt('data.csv', delimiter=',', converters={0: lambda x: x.replace('-', '/')})

Incorrect; replaces dashes with slashes in the date string without converting to datetime.

np.genfromtxt('data.csv', delimiter=',', converters={0: lambda x: x.split('-')})

Incorrect; splits the date string at dashes but doesn't convert to datetime.

np.genfromtxt('data.csv', delimiter=',', converters={0: lambda x: list(map(int, x.split('-')))})

Incorrect; splits the date and converts each part to an integer, not creating datetime objects.

4. Analyze

You are analyzing a dataset with temperature readings stored as strings in Celsius degrees, with some missing values represented as empty strings. Why is using a converter with np.genfromtxt to handle missing temperature readings as -999.0 beneficial for data analysis?

It converts all temperatures to Fahrenheit.

Incorrect; the converter described converts missing values to -999.0 and does not involve temperature unit conversion.

It automatically corrects errors in temperature readings.
It reduces the file size for faster processing.
It explicitly marks missing data, aiding in data cleaning and imputation.

Correct; explicitly marking missing data with -999.0 aids in identifying, cleaning, or imputing missing values during data analysis.

5. Understand

What is the purpose of using the missing_values and filling_values arguments together in np.genfromtxt()?

To increase the speed of data import.
To filter out rows with missing data completely.
To identify missing data and fill them with specified values during import.

Correct; missing_values identifies the missing data, and filling_values specifies what value to replace them with, facilitating cleaner data import.

To convert all data to strings before processing.

6. Understand

What is a use case for specifying missing_values as a dictionary when loading data with np.genfromtxt()?

To convert all data to a uniform missing value marker.
To specify unique missing markers per column for diverse data types.

Correct; specifying missing_values as a dictionary allows for different missing data markers for each column, accommodating diverse data types or content within the same dataset.

To increase the speed of data loading by specifying missing values.
To ignore missing value markers and treat all data as valid.

Incorrect; the specification of missing_values is intended to accurately identify and handle missing data, not to ignore it.

7. Understand

What does the filling_values argument do in np.genfromtxt() when reading data with missing values?

Ignores any rows with missing data.
Replaces missing values with specified values based on column index or name.

Correct; filling_values allows specifying replacement values for missing data, tailored by column.

Converts all data to strings.
Counts the number of missing values in each column.

8. Apply

You are loading data with missing values flagged as "NA" for all columns. You need to replace these with zero for numerical columns and "None" for a string column. How would you structure the np.genfromtxt() call?

kwargs = {\n\t'missing_values': "NA",\n\t'filling_values': 0,\n\t'dtype': "int",\n}\nnp.genfromtxt(StringIO(data), **kwargs)

kwargs = {\n\t'missing_values': "NA",\n\t'filling_values': 0,\n\t'dtype': None,\n}\nnp.genfromtxt(StringIO(data), **kwargs)

kwargs = {\n\t'missing_values': "NA",\n\t'filling_values': {"None": 0, 'all': "None"},\n\t'dtype': "object",\n}\nnp.genfromtxt(StringIO(data), **kwargs)

kwargs = {\n\t'missing_values': "NA",\n\t'filling_values': {"None": "None", 'all': 0},\n\t'dtype': None,\n}\nnp.genfromtxt(StringIO(data), **kwargs)

Correct; it specifies a default filling value and attempts to handle a string column, but the syntax for filling_values is not correctly applied as per NumPy documentation.

9. Analyze

You're processing a dataset with mixed data types (integers for the first two columns and strings for the third column) and missing values indicated as "NA" for integers and "missing" for strings. You decide to replace missing integers with -1 and missing strings with "unknown". Considering the structure of np.genfromtxt() for handling such specific missing value replacements, analyze the appropriateness of specifying filling_values as {0: -1, 1: -1, 2: "unknown"} in this context.

Yes, it enables correct missing value replacements, preserving data integrity.
No, because dictionaries cannot specify filling values for different data types.
No, because np.genfromtxt() cannot handle mixed data types.

Incorrect; np.genfromtxt() can handle mixed data types, especially when dtype=None or appropriately specified.

Yes, but only if all columns are of the same data type.

Incorrect; np.genfromtxt() and its filling_values parameter are designed to handle datasets with columns of different data types, making it unnecessary for all columns to have the same type.

10. Understand

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

Converts all missing data to zeros.
Generates an error for any missing data encountered.
Creates a MaskedArray indicating where data was missing.

Correct; setting usemask=True creates a MaskedArray, with True for missing data and False for present data.

Prevents any missing data from being read into the array.