Type casting with astype() in NumPy - Mini Project: Build & Apply

Type Casting with astype() in NumPy

📖 Scenario: You are working with a small dataset of product prices stored as floating-point numbers. You want to convert these prices to integers to simplify further calculations.

🎯 Goal: Learn how to use the astype() method in NumPy to change the data type of an array.

📋 What You'll Learn

Create a NumPy array with specific float values

Create a variable to hold the target data type

Use astype() to convert the array to integers

Print the converted array

💡 Why This Matters

🌍 Real World

In data science, data often comes in different formats. Converting data types helps prepare data for analysis or machine learning.

💼 Career

Knowing how to change data types with NumPy is essential for data cleaning and preprocessing tasks in data science roles.

1

Create a NumPy array with float values

Import NumPy as np and create a NumPy array called prices with these float values: 19.99, 5.49, 3.50, 12.75, 7.30.

NumPy

import numpy as np
# Create the NumPy array called prices with the given float values
# Your code here

Hint

Use np.array([...]) to create the array with the exact float values.

2

Create a variable for the target data type

Create a variable called target_type and set it to int to specify the desired data type for conversion.

NumPy

import numpy as np
prices = np.array([19.99, 5.49, 3.50, 12.75, 7.30])
# Create the variable target_type and set it to int
# Your code here

Hint

Just assign int to the variable target_type.

3

Convert the array to integers using astype()

Create a new variable called prices_int by converting the prices array to integers using the astype() method with the target_type variable.

NumPy

import numpy as np
prices = np.array([19.99, 5.49, 3.50, 12.75, 7.30])
target_type = int
# Convert prices to integers using astype() and store in prices_int
# Your code here

Hint

Use prices.astype(target_type) to convert the array.

4

Print the converted integer array

Print the prices_int array to see the integer values after conversion.

NumPy

import numpy as np
prices = np.array([19.99, 5.49, 3.50, 12.75, 7.30])
target_type = int
prices_int = prices.astype(target_type)
# Print the prices_int array
# Your code here

Hint

Use print(prices_int) to display the converted array.

Practice

1. What does the astype() method do in NumPy?

easy

A. It changes the data type of a NumPy array and returns a new array.

B. It sorts the elements of a NumPy array in ascending order.

C. It removes duplicate elements from a NumPy array.

D. It reshapes the NumPy array into a new shape.

Solution

1. Step 1: Understand the purpose of astype()

   The astype() method is used to convert the data type of elements in a NumPy array.

2. Step 2: Recall the behavior of astype()

   It returns a new array with the changed data type and does not modify the original array.

3. Final Answer:

   It changes the data type of a NumPy array and returns a new array. -> Option A

4. Quick Check:

   astype() changes type and returns new array [OK]

Hint: Remember: astype() changes type and returns a new array [OK]

Common Mistakes:

• Thinking astype() modifies the original array in place
• Confusing astype() with sorting or reshaping functions
• Assuming astype() removes duplicates

2. Which of the following is the correct syntax to convert a NumPy array arr to float type using astype()?

easy

A. arr.convert(float)

B. astype(arr, float)

C. arr.to_float()

D. arr.astype(float)

Solution

1. Step 1: Recall the method call syntax for astype()

   The astype() method is called on the array object like arr.astype().

2. Step 2: Check the argument for data type

   The argument should be the desired data type, here float, passed inside parentheses.

3. Final Answer:

   arr.astype(float) -> Option D

4. Quick Check:

   Correct syntax is arr.astype(type) [OK]

Hint: Call astype() on array with desired type inside parentheses [OK]

Common Mistakes:

• Using astype as a standalone function instead of a method
• Using incorrect method names like convert or to_float
• Passing data type outside parentheses

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

import numpy as np
arr = np.array([1.7, 2.3, 3.9])
new_arr = arr.astype(int)
print(new_arr)

medium

A. [2 3 4]

B. [1.7 2.3 3.9]

C. [1 2 3]

D. Error: Cannot convert float to int

Solution

1. Step 1: Understand astype(int) on float array

   Converting floats to int using astype(int) truncates the decimal part (does not round).

2. Step 2: Apply truncation to each element

   1.7 becomes 1, 2.3 becomes 2, 3.9 becomes 3.

3. Final Answer:

   [1 2 3] -> Option C

4. Quick Check:

   astype(int) truncates floats to integers [OK]

Hint: astype(int) truncates decimals, does not round [OK]

Common Mistakes:

• Assuming astype(int) rounds instead of truncates
• Expecting original array to change
• Thinking conversion causes error

4. Identify the error in the following code snippet:

import numpy as np
arr = np.array([1, 2, 3])
new_arr = arr.astype('int32')
print(new_arr.astype('float64'))

medium

A. Error because 'int32' is not a valid dtype string

B. No error; code runs and prints float64 array

C. Error because astype() cannot be chained

D. Error because original array is modified

Solution

1. Step 1: Check dtype string validity

   'int32' and 'float64' are valid NumPy dtype strings for 32-bit int and 64-bit float.

2. Step 2: Verify chaining of astype()

   Chaining astype() calls is allowed because each returns a new array.

3. Final Answer:

   No error; code runs and prints float64 array -> Option B

4. Quick Check:

   Valid dtype strings and chaining astype() work [OK]

Hint: astype() returns new array; chaining is allowed [OK]

Common Mistakes:

• Thinking dtype strings like 'int32' are invalid
• Believing astype() modifies original array
• Assuming astype() cannot be chained

5. You have a NumPy array arr = np.array([10, 20, 30]) of integers. You want to save memory by converting it to the smallest unsigned integer type that can hold these values. Which astype() call is best?

hard

A. arr.astype('uint8')

B. arr.astype('int8')

C. arr.astype('float16')

D. arr.astype('uint16')

Solution

1. Step 1: Check the range of values in arr

   Values are 10, 20, 30, all positive and less than 255.

2. Step 2: Choose smallest unsigned integer type

   uint8 holds values 0 to 255, so it fits all values and saves memory.

3. Step 3: Compare with other options

   int8 supports -128 to 127 but is signed; float16 uses more memory; uint16 is larger than needed.

4. Final Answer:

   arr.astype('uint8') -> Option A

5. Quick Check:

   Use smallest unsigned int type fitting data [OK]

Hint: Pick smallest unsigned int type covering max value [OK]

Common Mistakes:

• Choosing signed int type that wastes range or causes errors
• Using float type which uses more memory
• Ignoring value ranges when selecting dtype