np.array() from Python lists in NumPy

Introduction

We use np.array() to turn regular Python lists into arrays that are easier to work with for math and data tasks.

You have a list of numbers and want to do math on all of them quickly.

You want to store data in a format that many data science tools understand.

You need to perform operations like addition or multiplication on whole lists at once.

You want to use functions that only work with arrays, not lists.

Syntax

NumPy

import numpy as np

# Create a numpy array from a Python list
array_name = np.array(python_list)

The input to np.array() is usually a Python list or a list of lists for multi-dimensional arrays.

The output is a numpy array, which supports fast math and many useful functions.

Examples

This creates a one-dimensional array from a simple list of numbers.

NumPy

import numpy as np

# Example 1: 1D array from a list
numbers_list = [1, 2, 3, 4]
numbers_array = np.array(numbers_list)
print(numbers_array)

This creates a two-dimensional array (like a table) from a list of lists.

NumPy

import numpy as np

# Example 2: 2D array from a list of lists
matrix_list = [[1, 2], [3, 4]]
matrix_array = np.array(matrix_list)
print(matrix_array)

Creating an array from an empty list results in an empty numpy array.

NumPy

import numpy as np

# Example 3: Empty list
empty_list = []
empty_array = np.array(empty_list)
print(empty_array)

This creates an array with just one element.

NumPy

import numpy as np

# Example 4: List with one element
single_element_list = [42]
single_element_array = np.array(single_element_list)
print(single_element_array)

Sample Program

This program shows how to convert a Python list to a numpy array and prints both to compare.

NumPy

import numpy as np

# Create a Python list
python_list = [10, 20, 30, 40]
print("Original Python list:", python_list)

# Convert the list to a numpy array
numpy_array = np.array(python_list)
print("Converted numpy array:", numpy_array)

# Show the type to confirm
print("Type of original list:", type(python_list))
print("Type of numpy array:", type(numpy_array))

Important Notes

Time complexity: Creating an array from a list takes O(n) time, where n is the number of elements.

Space complexity: The array uses space proportional to the number of elements, similar to the list.

A common mistake is to think numpy arrays behave exactly like lists; arrays support element-wise math, unlike lists.

Use np.array() when you want to do math or data operations efficiently on collections of numbers.

Summary

np.array() converts Python lists into numpy arrays for easier math and data work.

Arrays support fast, element-wise operations that lists do not.

Use this to prepare data for analysis or scientific computing.

Practice

1. What does np.array() do when given a Python list?

easy

A. It converts the list into a numpy array for easier math operations.

B. It changes the list into a Python dictionary.

C. It sorts the list in ascending order.

D. It removes duplicates from the list.

Solution

1. Step 1: Understand the purpose of np.array()

   np.array() is designed to convert Python lists into numpy arrays.

2. Step 2: Recognize the benefit of numpy arrays

   Numpy arrays allow fast, element-wise math operations that lists do not support.

3. Final Answer:

   It converts the list into a numpy array for easier math operations. -> Option A

4. Quick Check:

   np.array() converts lists to arrays [OK]

Hint: np.array() turns lists into arrays for math [OK]

Common Mistakes:

• Thinking np.array() sorts the list
• Confusing arrays with dictionaries
• Assuming it removes duplicates

2. Which of the following is the correct syntax to create a numpy array from a Python list [1, 2, 3]?

easy

A. np.array{1, 2, 3}

B. np.array[[1, 2, 3]]

C. np.array(1, 2, 3)

D. np.array([1, 2, 3])

Solution

1. Step 1: Recall correct function call syntax

   np.array() requires parentheses and a list inside, like np.array([1, 2, 3]).

2. Step 2: Identify syntax errors in other options

   np.array{1, 2, 3} uses curly braces which is invalid; C passes multiple arguments instead of one list; D uses double brackets incorrectly.

3. Final Answer:

   np.array([1, 2, 3]) -> Option D

4. Quick Check:

   np.array() needs a list inside parentheses [OK]

Hint: Use parentheses and a list inside for np.array() [OK]

Common Mistakes:

• Using curly braces instead of parentheses
• Passing multiple arguments instead of a single list
• Using double brackets incorrectly

3. What will be the output of this code?

import numpy as np
lst = [[1, 2], [3, 4]]
arr = np.array(lst)
print(arr.shape)

medium

A. (1, 4)

B. (4,)

C. (2, 2)

D. Error

Solution

1. Step 1: Understand the input list shape

   The list has 2 sublists, each with 2 elements, so shape is 2 rows and 2 columns.

2. Step 2: Check numpy array shape property

   np.array converts nested lists into 2D arrays with shape (2, 2).

3. Final Answer:

   (2, 2) -> Option C

4. Quick Check:

   Nested list shape = (2, 2) [OK]

Hint: Shape matches nested list dimensions [OK]

Common Mistakes:

• Thinking shape is (4,)
• Assuming shape is (1, 4)
• Expecting an error for nested lists

4. What is wrong with this code snippet?

import numpy as np
lst = [1, 2, 3]
arr = np.array(lst, 5)
print(arr)

medium

A. The list contains integers which are not allowed.

B. np.array() does not take a second positional argument like 5.

C. The print statement is missing parentheses.

D. The list must be nested to convert to an array.

Solution

1. Step 1: Check np.array() function signature

   np.array() takes the data as the first argument; a second positional argument like 5 is invalid.

2. Step 2: Identify the error caused by extra argument

   Passing 5 as a second positional argument causes a TypeError.

3. Final Answer:

   np.array() does not take a second positional argument like 5. -> Option B

4. Quick Check:

   Extra positional arguments cause error [OK]

Hint: np.array() takes one main argument: the list [OK]

Common Mistakes:

• Thinking list must be nested
• Forgetting print() needs parentheses (Python 3)
• Believing integers are not allowed in arrays

5. You have a list of lists representing daily temperatures for a week:

temps = [[20, 22, 19], [21, 23, 20], [19, 21, 18], [22, 24, 21], [20, 22, 19], [18, 20, 17], [21, 23, 20]]

How can you convert this into a numpy array and find the average temperature for each day?

hard

A. import numpy as np
temps_arr = np.array(temps)
daily_avg = temps_arr.mean(axis=1)
print(daily_avg)

B. import numpy as np
temps_arr = np.array(temps)
daily_avg = temps_arr.mean(axis=0)
print(daily_avg)

C. import numpy as np
daily_avg = np.array(temps).sum()
print(daily_avg)

D. import numpy as np
temps_arr = np.array(temps)
daily_avg = temps_arr.max(axis=1)
print(daily_avg)

Solution

1. Step 1: Convert list of lists to numpy array

   Use np.array(temps) to create a 2D array where each row is a day.

2. Step 2: Calculate average temperature per day

   Use mean(axis=1) to compute average across columns (hours) for each row (day).

3. Final Answer:

   import numpy as np temps_arr = np.array(temps) daily_avg = temps_arr.mean(axis=1) print(daily_avg) -> Option A

4. Quick Check:

   mean(axis=1) averages rows (days) [OK]

Hint: Use mean(axis=1) to average rows (days) [OK]

Common Mistakes:

• Using mean(axis=0) which averages columns (hours)
• Using sum() without axis for average
• Using max() instead of mean()