np.savetxt() and np.loadtxt() for text in NumPy - Practice Problems & Coding Challenges

Challenge - 5 Problems

❓ Predict Output

intermediate

Output of np.savetxt() and np.loadtxt() with default settings

What will be the output of the following code snippet?

import numpy as np
arr = np.array([[1.5, 2.5], [3.5, 4.5]])
np.savetxt('test.txt', arr)
loaded = np.loadtxt('test.txt')
print(loaded)

NumPy

import numpy as np
arr = np.array([[1.5, 2.5], [3.5, 4.5]])
np.savetxt('test.txt', arr)
loaded = np.loadtxt('test.txt')
print(loaded)

A

[[1.5 2.5]
 [3.5 4.5]]

B

[[1 2]
 [3 4]]

C [1.5 2.5 3.5 4.5]

D SyntaxError

💡 Hint

Remember that np.savetxt saves the array in text format and np.loadtxt loads it back as a numpy array with floats by default.

The np.savetxt function saves the 2D array in a text file with default float formatting. When loaded back with np.loadtxt, it returns the same 2D array with float values.

❓ data_output

intermediate

Number of rows loaded by np.loadtxt()

Given a text file data.txt containing the following lines:

10 20 30
40 50 60
70 80 90

What is the number of rows in the numpy array returned by np.loadtxt('data.txt')?

NumPy

import numpy as np
arr = np.loadtxt('data.txt')
print(arr.shape[0])

A 3

B 9

C 1

D 0

💡 Hint

Each line in the text file corresponds to one row in the loaded array.

The file has 3 lines, so np.loadtxt loads 3 rows in the array.

🔧 Debug

advanced

Error raised by np.loadtxt() with mixed data types

What error will the following code raise?

import numpy as np
np.savetxt('mixed.txt', [[1, 2], ['a', 4]], fmt='%s')
np.loadtxt('mixed.txt')

NumPy

import numpy as np
np.savetxt('mixed.txt', [[1, 2], ['a', 4]], fmt='%s')
np.loadtxt('mixed.txt')

A TypeError

B ValueError

C OSError

D No error, returns array

💡 Hint

Consider what happens when np.loadtxt tries to convert non-numeric strings to floats.

np.loadtxt tries to convert all data to floats by default. The string 'a' cannot be converted, so it raises a ValueError.

🚀 Application

advanced

Saving and loading with custom delimiter

You want to save a 2D numpy array with values separated by semicolons and then load it back correctly. Which code snippet will achieve this?

A

np.savetxt('file.csv', arr, delimiter=';')
loaded = np.loadtxt('file.csv')

B

np.savetxt('file.csv', arr)
loaded = np.loadtxt('file.csv', delimiter=';')

C

np.savetxt('file.csv', arr, delimiter=',')
loaded = np.loadtxt('file.csv', delimiter=';')

D

np.savetxt('file.csv', arr, delimiter=';')
loaded = np.loadtxt('file.csv', delimiter=';')

💡 Hint

Both saving and loading must use the same delimiter to match the data format.

Using delimiter=';' in both np.savetxt and np.loadtxt ensures the data is saved and read correctly with semicolon separators.

🧠 Conceptual

expert

Handling missing values with np.loadtxt()

You have a text file with numeric data but some values are missing and represented as empty strings. Which np.loadtxt parameter helps to handle missing values by replacing them with a specified number?

A missing_values

B usecols

C filling_values

D default_value

💡 Hint

Look for the parameter that fills missing data with a default number.

The filling_values parameter in np.loadtxt replaces missing values with a specified number during loading.

Practice

1. What is the main purpose of np.savetxt() in NumPy?

easy

A. To convert a NumPy array into a Python list

B. To load a NumPy array from a binary file

C. To save a NumPy array to a text file in a readable format

D. To display a NumPy array on the screen

Solution

1. Step 1: Understand the function purpose

   np.savetxt() is designed to save arrays to text files, making the data readable and shareable.

2. Step 2: Compare with other options

   Options B, C, and D describe different functions or actions unrelated to saving arrays as text files.

3. Final Answer:

   To save a NumPy array to a text file in a readable format -> Option C

4. Quick Check:

   np.savetxt() saves arrays to text files [OK]

Hint: Remember: savetxt saves arrays as readable text files [OK]

Common Mistakes:

• Confusing savetxt with loadtxt
• Thinking it saves to binary files
• Assuming it converts arrays to lists

2. Which of the following is the correct syntax to save a 2D NumPy array arr to a file named data.txt using np.savetxt()?

easy

A. np.savetxt('data.txt', arr)

B. np.savetxt(arr, 'data.txt')

C. np.save('data.txt', arr)

D. np.loadtxt('data.txt', arr)

Solution

1. Step 1: Recall the correct parameter order for np.savetxt()

   The first argument is the filename (string), the second is the array to save.

2. Step 2: Check each option

   np.savetxt('data.txt', arr) matches the correct order. np.savetxt(arr, 'data.txt') reverses the order. np.save('data.txt', arr) uses np.save which saves binary files. np.loadtxt('data.txt', arr) uses np.loadtxt which reads files, not saves.

3. Final Answer:

   np.savetxt('data.txt', arr) -> Option A

4. Quick Check:

   Filename first, array second in np.savetxt() [OK]

Hint: Filename goes first, array second in np.savetxt() [OK]

Common Mistakes:

• Swapping filename and array arguments
• Using np.save instead of np.savetxt
• Confusing np.loadtxt with np.savetxt

3. What will be the output of the following code?

import numpy as np
arr = np.array([[1, 2], [3, 4]])
np.savetxt('temp.txt', arr, fmt='%d', delimiter=',')
loaded = np.loadtxt('temp.txt', delimiter=',', dtype=int)
print(loaded)

medium

A. SyntaxError

B. [[1, 2], [3, 4]]

C. [1 2 3 4]

D. [[1 2]
[3 4]]

Solution

1. Step 1: Understand saving with delimiter and format

   The array is saved as text with comma delimiter and integer format, so the file lines look like '1,2' and '3,4'.

2. Step 2: Loading with matching delimiter and dtype

   Using np.loadtxt with delimiter=',' and dtype=int reads the file back into a 2D integer array.

3. Step 3: Print output format

   Printing a NumPy array shows it with spaces between elements and new lines for rows, so output is [[1 2] [3 4]].

4. Final Answer:

   [[1 2] [3 4]] -> Option D

5. Quick Check:

   Loadtxt reads saved text back as array [OK]

Hint: Match delimiter and dtype when loading saved text [OK]

Common Mistakes:

• Expecting list output instead of array
• Missing delimiter in loadtxt causing errors
• Using wrong dtype causing float instead of int

4. Identify the error in this code snippet:

import numpy as np
arr = np.array([1.5, 2.5, 3.5])
np.savetxt('file.txt', arr, fmt='%d')
loaded = np.loadtxt('file.txt', dtype=float)
print(loaded)

medium

A. Using '%d' format truncates floats to integers when saving

B. np.loadtxt cannot read float data

C. Missing delimiter argument causes error

D. Array must be 2D to use np.savetxt

Solution

1. Step 1: Check format string in np.savetxt()

   The format '%d' saves numbers as integers, so 1.5, 2.5, 3.5 become 1, 2, 3 in the file.

2. Step 2: Loading with dtype=float

   Loading back as float converts these integers to floats 1.0, 2.0, 3.0, losing original decimal parts.

3. Step 3: Identify the error

   The error is the format string truncates data, causing loss of precision.

4. Final Answer:

   Using '%d' format truncates floats to integers when saving -> Option A

5. Quick Check:

   Format string controls saved data type [OK]

Hint: Use '%f' to save floats, not '%d' [OK]

Common Mistakes:

• Assuming loadtxt can't read floats
• Thinking delimiter is required for 1D arrays
• Believing np.savetxt requires 2D arrays only

5. You have a 2D NumPy array with mixed integer and float values. You want to save it to a text file with comma separation and load it back preserving the exact values. Which code snippet correctly achieves this?

hard

A. np.savetxt('data.csv', arr, delimiter=',', fmt='%d')
loaded = np.loadtxt('data.csv', delimiter=',', dtype=float)

B. np.savetxt('data.csv', arr, delimiter=',', fmt='%.2f')
loaded = np.loadtxt('data.csv', delimiter=',')

C. np.savetxt('data.csv', arr, delimiter=';')
loaded = np.loadtxt('data.csv', delimiter=',')

D. np.savetxt('data.csv', arr)
loaded = np.loadtxt('data.csv', delimiter=',')

Solution

1. Step 1: Choose correct format for mixed data

   Using fmt='%.2f' saves all numbers as floats with 2 decimals, preserving float and integer values.

2. Step 2: Match delimiter in save and load

   Both saving and loading use delimiter=',' ensuring data is correctly split on commas.

3. Step 3: Check other options for errors

   np.savetxt('data.csv', arr, delimiter=',', fmt='%d') loaded = np.loadtxt('data.csv', delimiter=',', dtype=float) uses '%d' which truncates floats. np.savetxt('data.csv', arr, delimiter=';') loaded = np.loadtxt('data.csv', delimiter=',') mismatches delimiters. np.savetxt('data.csv', arr) loaded = np.loadtxt('data.csv', delimiter=',') saves without delimiter but loads with comma, causing errors.

4. Final Answer:

   np.savetxt('data.csv', arr, delimiter=',', fmt='%.2f') loaded = np.loadtxt('data.csv', delimiter=',') -> Option B

5. Quick Check:

   Match format and delimiter to preserve data [OK]

Hint: Use float format and matching delimiter both ways [OK]

Common Mistakes:

• Using integer format for float data
• Mismatching delimiters between save and load
• Not specifying format causing precision loss