np.savetxt() and np.loadtxt() for text in NumPy - Mini Project: Build & Apply

Saving and Loading Data with np.savetxt() and np.loadtxt()

📖 Scenario: Imagine you are working with temperature data collected from sensors every hour. You want to save this data to a text file so you can share it or use it later. Then, you want to load the data back into your program to analyze it.

🎯 Goal: You will create a small array of temperature data, save it to a text file using np.savetxt(), then load it back using np.loadtxt(). Finally, you will print the loaded data to check it matches the original.

📋 What You'll Learn

Create a numpy array called temperatures with the exact values: 22.5, 23.0, 21.8, 22.1, 23.3

Create a string variable called filename with the value 'temps.txt'

Use np.savetxt() to save temperatures to the file filename

Use np.loadtxt() to load the data from filename into a variable called loaded_temps

Print the variable loaded_temps to display the loaded data

💡 Why This Matters

🌍 Real World

Saving sensor or experiment data to text files is common in data science to share or archive data in a simple format.

💼 Career

Data scientists often need to save processed data and reload it later for analysis or reporting. Knowing how to use <code>np.savetxt()</code> and <code>np.loadtxt()</code> is a basic but important skill.

1

Create the temperature data array

Create a numpy array called temperatures with these exact values: 22.5, 23.0, 21.8, 22.1, 23.3

NumPy

import numpy as np
# Create the numpy array called temperatures with the given values
# Your code here

Hint

Use np.array() and put the values inside a list.

2

Create the filename variable

Create a string variable called filename and set it to 'temps.txt'

NumPy

import numpy as np

temperatures = np.array([22.5, 23.0, 21.8, 22.1, 23.3])
# Create the filename variable with value 'temps.txt'
# Your code here

Hint

Just assign the string 'temps.txt' to the variable filename.

3

Save the array to a text file

Use np.savetxt() to save the temperatures array to the file named filename

NumPy

import numpy as np

temperatures = np.array([22.5, 23.0, 21.8, 22.1, 23.3])

filename = 'temps.txt'
# Save the temperatures array to the file using np.savetxt
# Your code here

Hint

Call np.savetxt() with the filename and the array as arguments.

4

Load the data and print it

Use np.loadtxt() to load the data from the file filename into a variable called loaded_temps. Then print loaded_temps to display the loaded data.

NumPy

import numpy as np

temperatures = np.array([22.5, 23.0, 21.8, 22.1, 23.3])

filename = 'temps.txt'

np.savetxt(filename, temperatures)
# Load the data from the file into loaded_temps
# Print loaded_temps
# Your code here

Hint

Use loaded_temps = np.loadtxt(filename) to load, then print(loaded_temps).

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