np.genfromtxt() for handling missing data in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of np.genfromtxt() with missing values

What is the output of the following code snippet that uses np.genfromtxt() to read data with missing values?

NumPy

import numpy as np
from io import StringIO

data = """
1,2,3
4,,6
7,8,
"""

arr = np.genfromtxt(StringIO(data), delimiter=',', filling_values=-1)
print(arr)

A ValueError: could not convert string to float: ''

B

[[ 1.  2.  3.]
 [ 4. -1.  6.]
 [ 7.  8. -1.]]

C

[[1. 2. 3.]
 [4. nan 6.]
 [7. 8. nan]]

D

[[1 2 3]
 [4 0 6]
 [7 8 0]]

💡 Hint

Look at how filling_values replaces missing entries.

The filling_values=-1 argument replaces missing data with -1. So missing entries become -1 instead of NaN or errors.

🧠 Conceptual

intermediate

Understanding the role of filling_values in np.genfromtxt()

What does the filling_values parameter do in np.genfromtxt() when reading a file with missing data?

A It specifies the value to replace missing data entries during loading.

B It skips rows that contain any missing data.

C It converts all data to strings instead of numbers.

D It raises an error if any missing data is found.

💡 Hint

Think about how missing data can be handled automatically.

The filling_values parameter tells np.genfromtxt() what value to use instead of missing entries, so the array remains numeric and complete.

❓ data_output

advanced

Resulting array shape and content with missing data

Given this CSV data with missing values, what is the shape and content of the numpy array after loading with np.genfromtxt() using delimiter=',' and default parameters?

NumPy

import numpy as np
from io import StringIO

data = """
10,20,30
40,,60
,80,90
"""

arr = np.genfromtxt(StringIO(data), delimiter=',')
print(arr)
print(arr.shape)

A

[[10. 20. 30.]
 [40. 0. 60.]
 [0. 80. 90.]]
(3, 3)

B

[[10 20 30]
 [40 0 60]
 [0 80 90]]
(3, 3)

C ValueError: could not convert string to float: ''

D

[[10. 20. 30.]
 [40. nan 60.]
 [nan 80. 90.]]
(3, 3)

💡 Hint

By default, missing values become NaN in float arrays.

Without specifying filling_values, missing entries become nan and the array is float type with shape (3,3).

🔧 Debug

advanced

Identify the error when loading CSV with missing data

What error will this code raise when trying to load CSV data with missing values using np.genfromtxt() without specifying filling_values?

NumPy

import numpy as np
from io import StringIO

data = """
1,2,3
4,,6
7,8,9
"""

arr = np.genfromtxt(StringIO(data), delimiter=',', dtype=int)
print(arr)

A No error, prints array with zeros for missing values

B TypeError: unsupported operand type(s) for +: 'int' and 'str'

C ValueError: invalid literal for int() with base 10: ''

D SyntaxError: invalid syntax

💡 Hint

Missing values cannot be converted to int without filling.

Missing entries are empty strings which cannot convert to int, causing a ValueError.

🚀 Application

expert

Handling mixed missing data types with np.genfromtxt()

You have a CSV file with numeric and string columns, some missing values in both. Which np.genfromtxt() call correctly loads the data, replacing missing numeric values with -999 and missing strings with 'missing'?

NumPy

import numpy as np
from io import StringIO

data = """
1,apple,3.5
2,,
,banana,4.1
"""

A np.genfromtxt(StringIO(data), delimiter=',', dtype=None, encoding=None, filling_values={0: -999, 1: 'missing', 2: -999})

B np.genfromtxt(StringIO(data), delimiter=',', dtype=None, encoding=None, filling_values=-999)

C np.genfromtxt(StringIO(data), delimiter=',', dtype='U10,f8,i4', filling_values=['missing', -999, -999])

D np.genfromtxt(StringIO(data), delimiter=',', dtype=None, encoding=None, missing_values='', filling_values='missing')

💡 Hint

Use a dictionary for filling_values to specify per-column replacements.

Using a dictionary for filling_values allows specifying different fill values per column, matching their data types.

Practice

1. What is the main purpose of using np.genfromtxt() in data loading?

easy

A. To load data files while handling missing values automatically

B. To save data files with missing values

C. To visualize data with missing values

D. To delete rows with missing values from a file

Solution

1. Step 1: Understand the function's purpose

   np.genfromtxt() is designed to read text files and handle missing data gracefully.

2. Step 2: Compare options with function role

   Only To load data files while handling missing values automatically correctly states it loads data files and manages missing values automatically.

3. Final Answer:

   To load data files while handling missing values automatically -> Option A

4. Quick Check:

   Purpose of np.genfromtxt() = Load with missing data handled [OK]

Hint: Remember: genfromtxt reads files and fills missing data [OK]

Common Mistakes:

• Confusing loading with saving data
• Thinking it visualizes data
• Assuming it deletes missing data rows automatically

2. Which of the following is the correct way to specify missing values as empty strings when using np.genfromtxt()?

easy

A. np.genfromtxt('data.csv', missing_values='')

B. np.genfromtxt('data.csv', missing_values=null)

C. np.genfromtxt('data.csv', missing_values=[''])

D. np.genfromtxt('data.csv', missing_values='NA')

Solution

1. Step 1: Check the parameter type for missing_values

   The missing_values parameter expects a list or set of strings representing missing data markers.

2. Step 2: Identify correct syntax for empty string

   Empty string must be inside a list: [''] to be recognized as missing.

3. Final Answer:

   np.genfromtxt('data.csv', missing_values=['']) -> Option C

4. Quick Check:

   missing_values needs list for empty string [OK]

Hint: Use a list for missing_values even if one item [OK]

Common Mistakes:

• Passing empty string directly without list
• Using null which disables missing value detection
• Confusing 'NA' with empty string

3. What will be the output of this code snippet?

import numpy as np
from io import StringIO
text = '1,2,\n4,,6'
data = np.genfromtxt(StringIO(text), delimiter=',', filling_values=-1)
print(data)

medium

A. [[1 2 nan]
[4 nan 6]]

B. [1. 2. -1. 4. -1. 6.]

C. [1 2 nan 4 nan 6]

D. [[1. 2. -1.]
[4. -1. 6.]]

Solution

1. Step 1: Understand input and parameters

   The input text has two rows with missing values (empty fields). The delimiter is ',', and missing values are replaced by -1.

2. Step 2: Predict output array shape and values

   Output is a 2D array with missing values replaced by -1, so first row: [1, 2, -1], second row: [4, -1, 6].

3. Final Answer:

   [[1. 2. -1.] [4. -1. 6.]] -> Option D

4. Quick Check:

   Missing replaced by -1 in 2D array [OK]

Hint: Missing values become filling_values in 2D arrays [OK]

Common Mistakes:

• Expecting 1D array instead of 2D
• Confusing nan with filling_values
• Ignoring delimiter effect on shape

4. Identify the error in this code snippet that tries to load data with missing values:

import numpy as np
np.genfromtxt('data.csv', delimiter=',', missing_values='NA', filling_values=0)

medium

A. filling_values must be a string, not an integer

B. missing_values should be a list, not a string

C. delimiter cannot be a comma

D. np.genfromtxt cannot handle missing values

Solution

1. Step 1: Check parameter types

   missing_values expects a list or set of strings, not a single string.

2. Step 2: Validate other parameters

   filling_values=0 is valid, and delimiter=',' is correct for CSV files.

3. Final Answer:

   missing_values should be a list, not a string -> Option B

4. Quick Check:

   missing_values needs list/set [OK]

Hint: Always wrap missing_values in a list or set [OK]

Common Mistakes:

• Passing string directly instead of list
• Thinking filling_values must be string
• Misunderstanding delimiter usage

5. You have a CSV file with numeric data and missing values marked as 'NA' and empty strings. You want to load it using np.genfromtxt() so that all missing values become -999. Which is the correct way to do this?

hard

A. np.genfromtxt('file.csv', delimiter=',', missing_values=['NA', ''], filling_values=-999)

B. np.genfromtxt('file.csv', delimiter=',', missing_values='NA', filling_values='-999')

C. np.genfromtxt('file.csv', delimiter=',', missing_values=['NA'], filling_values=null)

D. np.genfromtxt('file.csv', delimiter=',', missing_values=[''], filling_values=0)

Solution

1. Step 1: Specify all missing value markers

   Both 'NA' and empty strings '' must be included in a list for missing_values.

2. Step 2: Set filling_values to -999

   Use filling_values=-999 to replace all missing entries with -999.

3. Final Answer:

   np.genfromtxt('file.csv', delimiter=',', missing_values=['NA', ''], filling_values=-999) -> Option A

4. Quick Check:

   List all missing markers and set filling_values [OK]

Hint: List all missing markers, set filling_values to desired number [OK]

Common Mistakes:

• Passing missing_values as string instead of list
• Using string '-999' instead of integer -999
• Not including all missing markers