np.concatenate() for joining arrays in NumPy - Mini Project: Build & Apply

Joining Arrays Using np.concatenate()

📖 Scenario: Imagine you have two lists of daily temperatures from two different cities. You want to combine these lists into one to analyze the overall temperature trend.

🎯 Goal: Learn how to use np.concatenate() to join two numpy arrays into one array.

📋 What You'll Learn

Create two numpy arrays with exact values

Create a variable to hold the combined array

Use np.concatenate() to join the arrays

Print the combined array

💡 Why This Matters

🌍 Real World

Joining arrays is useful when combining data from multiple sources, like merging temperature readings from different cities to analyze overall weather patterns.

💼 Career

Data scientists often need to combine datasets before analysis. Knowing how to join arrays efficiently is a key skill in data preparation.

1

Create two numpy arrays

Import numpy as np and create two numpy arrays called city1_temps and city2_temps with these exact values: [20, 22, 19] and [25, 24, 23] respectively.

NumPy

import numpy as np
# Create city1_temps and city2_temps arrays
# Your code here

Hint

Use np.array() to create numpy arrays from lists.

2

Create a variable for the combined array

Create a variable called combined_temps to hold the joined array.

NumPy

import numpy as np
city1_temps = np.array([20, 22, 19])
city2_temps = np.array([25, 24, 23])
# Create combined_temps variable
# Your code here

Hint

Just create the variable first. You will assign the joined array in the next step.

3

Join the arrays using np.concatenate()

Use np.concatenate() with a list containing city1_temps and city2_temps to assign the joined array to combined_temps.

NumPy

import numpy as np
city1_temps = np.array([20, 22, 19])
city2_temps = np.array([25, 24, 23])
combined_temps = None
# Use np.concatenate() to join city1_temps and city2_temps
# Your code here

Hint

Pass a list of arrays to np.concatenate() to join them.

4

Print the combined array

Print the variable combined_temps to display the joined array.

NumPy

import numpy as np
city1_temps = np.array([20, 22, 19])
city2_temps = np.array([25, 24, 23])
combined_temps = np.concatenate([city1_temps, city2_temps])
# Print combined_temps
# Your code here

Hint

Use print(combined_temps) to show the result.

Practice

1. What does the function np.concatenate() do in NumPy?

easy

A. Splits an array into multiple smaller arrays

B. Joins two or more arrays along an existing axis

C. Sorts the elements of an array in ascending order

D. Creates a new array filled with zeros

Solution

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

   It is used to join arrays along a specified axis, combining them into one larger array.

2. Step 2: Compare with other array functions

   Splitting, sorting, or creating zero arrays are different functions like np.split(), np.sort(), and np.zeros().

3. Final Answer:

   Joins two or more arrays along an existing axis -> Option B

4. Quick Check:

   np.concatenate() = join arrays [OK]

Hint: Remember: concatenate means join arrays along axis [OK]

Common Mistakes:

• Confusing concatenate with split or sort functions
• Thinking it modifies original arrays
• Assuming it creates arrays from scratch

2. Which of the following is the correct syntax to join two 1D arrays a and b using np.concatenate()?

easy

A. np.concatenate([a, b])

B. np.concatenate(a, axis=1)

C. np.concatenate(a + b)

D. np.concatenate(a, b)

Solution

1. Step 1: Check the function signature

   np.concatenate() expects a single argument: a sequence (like a list or tuple) of arrays to join.

2. Step 2: Analyze each option

   np.concatenate([a, b]) correctly passes a list of arrays [a, b]. np.concatenate(a, b) passes two separate arguments, which is invalid. np.concatenate(a + b) tries to add arrays before concatenation, which is not the function's purpose. np.concatenate(a, axis=1) passes axis=1 for 1D arrays, which is invalid.

3. Final Answer:

   np.concatenate([a, b]) -> Option A

4. Quick Check:

   Pass arrays as a list to concatenate() [OK]

Hint: Always pass arrays inside a list or tuple to concatenate [OK]

Common Mistakes:

• Passing arrays as separate arguments instead of a list
• Using plus (+) operator instead of concatenate
• Setting wrong axis for 1D arrays

3. What is the output of the following code?

import numpy as np
x = np.array([[1, 2], [3, 4]])
y = np.array([[5, 6]])
result = np.concatenate((x, y), axis=0)
print(result)

medium

A. [[1 2]
[3 4]
[5 6]]

B. [[1 2 5 6]
[3 4 5 6]]

C. [[1 2]
[3 4]]

D. Error: shapes do not match

Solution

1. Step 1: Check shapes of arrays

   x has shape (2, 2), y has shape (1, 2). They match in columns (axis=1) but differ in rows (axis=0).

2. Step 2: Concatenate along axis=0 (rows)

   Joining along rows stacks y below x, resulting in a (3, 2) array with rows from x then y.

3. Final Answer:

   [[1 2] [3 4] [5 6]] -> Option A

4. Quick Check:

   Concatenate rows stacks arrays vertically [OK]

Hint: Concatenate axis=0 stacks rows; axis=1 stacks columns [OK]

Common Mistakes:

• Confusing axis=0 and axis=1
• Expecting horizontal stacking with axis=0
• Ignoring shape compatibility

4. What is wrong with this code snippet?

import numpy as np
x = np.array([[1, 2], [3, 4]])
y = np.array([5, 6, 7])
result = np.concatenate((x, y), axis=0)
print(result)

medium

A. Error because y must be reshaped to 3D

B. No error, output is concatenated array

C. Error because axis=0 is invalid for 2D arrays

D. Error because arrays have incompatible shapes along axis=0

Solution

1. Step 1: Check shapes of arrays

   x shape is (2, 2), y shape is (3,). They differ in both dimensions.

2. Step 2: Understand concatenate axis rules

   Arrays must match in all dimensions except the concatenation axis. Here, axis=0 means rows, but y has no matching columns.

3. Final Answer:

   Error because arrays have incompatible shapes along axis=0 -> Option D

4. Quick Check:

   Shapes must match except on concat axis [OK]

Hint: Check array shapes match except on concat axis [OK]

Common Mistakes:

• Ignoring shape mismatch errors
• Assuming 1D array can concatenate with 2D without reshape
• Misunderstanding axis parameter

5. You have three arrays:

a = np.array([[1, 2], [3, 4]])
b = np.array([[5, 6]])
c = np.array([[7, 8]])

You want to join them into one array with shape (4, 2). Which code correctly does this?

hard

A. np.concatenate((a, b), axis=0) + c

B. np.concatenate([a, b, c], axis=1)

C. np.concatenate((a, b, c), axis=0)

D. np.concatenate((a, b, c), axis=2)

Solution

1. Step 1: Check shapes and desired output

   a shape is (2, 2), b and c are (1, 2). Joining along rows (axis=0) stacks them vertically to get (4, 2).

2. Step 2: Analyze options

   np.concatenate((a, b, c), axis=0) correctly concatenates all three arrays along axis=0. np.concatenate([a, b, c], axis=1) tries axis=1 which would require matching rows, which is false. np.concatenate((a, b), axis=0) + c adds arrays after concatenation, which is invalid. np.concatenate((a, b, c), axis=2) uses axis=2 which is invalid for 2D arrays.

3. Final Answer:

   np.concatenate((a, b, c), axis=0) -> Option C

4. Quick Check:

   Stack arrays vertically with axis=0 [OK]

Hint: Use axis=0 to stack arrays vertically when shapes match columns [OK]

Common Mistakes:

• Using wrong axis for concatenation
• Trying to add arrays instead of concatenate
• Using invalid axis number for 2D arrays