np.concatenate() for joining arrays in NumPy - Step-by-Step Execution

Concept Flow - np.concatenate() for joining arrays

Start with arrays

↓

Choose axis to join

↓

Check arrays shape compatibility

Yes↓

Join arrays along axis

↓

Return combined array

↓

End

We start with arrays, pick an axis to join them on, check if their shapes fit, then join and return the combined array.

Execution Sample

NumPy

import numpy as np
arr1 = np.array([1, 2])
arr2 = np.array([3, 4])
result = np.concatenate((arr1, arr2))
print(result)

This code joins two 1D arrays into one longer array.

Execution Table

Step	Action	Input Arrays	Axis	Check Shapes	Resulting Array
1	Start with arr1 and arr2	[1, 2], [3, 4]	N/A	N/A	N/A
2	Choose axis=0 (default)	[1, 2], [3, 4]	0	Shapes compatible	N/A
3	Concatenate arrays	[1, 2], [3, 4]	0	Shapes compatible	[1, 2, 3, 4]
4	Print result	N/A	N/A	N/A	[1 2 3 4]

💡 Arrays joined along axis 0, resulting in a longer 1D array.

Variable Tracker

Variable	Start	After Step 2	After Step 3	Final
arr1	[1 2]	[1 2]	[1 2]	[1 2]
arr2	[3 4]	[3 4]	[3 4]	[3 4]
result	None	None	[1 2 3 4]	[1 2 3 4]

Key Moments - 3 Insights

Why do the arrays need compatible shapes except on the concatenation axis?

Because np.concatenate joins arrays along one axis, the other axes must match in size to stack them properly. See Step 2 in execution_table where shape compatibility is checked.

What happens if axis is not specified?

The default axis is 0, meaning arrays are joined along the first dimension. This is shown in Step 2 where axis=0 is chosen by default.

Why does the resulting array length equal the sum of input arrays lengths?

Because concatenation stacks arrays end to end along the chosen axis, so lengths add up. Step 3 shows the combined array length is 4, sum of 2 and 2.

Visual Quiz - 3 Questions

Look at the execution_table at Step 3, what is the resulting array?

A [1 2 3 4]

B [[1 2] [3 4]]

C [1 2]

D [3 4]

💡 Hint

Check the 'Resulting Array' column at Step 3 in execution_table.

At which step is the axis chosen for concatenation?

A Step 3

B Step 1

C Step 2

D Step 4

💡 Hint

Look at the 'Action' and 'Axis' columns in execution_table.

If arr1 was shape (2,1) and arr2 shape (2,2), what would happen at Step 2?

A Shapes compatible, concatenation proceeds

B Shapes incompatible, error raised

C Arrays concatenated along axis 1

D Resulting array shape is (4,3)

💡 Hint

Recall the 'Check Shapes' column in execution_table and the need for shape compatibility.

Concept Snapshot

np.concatenate((arr1, arr2, ...), axis=0)
Joins arrays along specified axis.
Arrays must have same shape except on axis.
Default axis=0 stacks arrays vertically.
Result is a combined array with length sum along axis.

Full Transcript

The np.concatenate() function joins multiple arrays along a chosen axis. We start with input arrays, pick an axis (default 0), check if their shapes match except on that axis, then join them. For example, joining [1, 2] and [3, 4] along axis 0 produces [1, 2, 3, 4]. The arrays must be compatible in shape to stack properly. If shapes differ on other axes, an error occurs. This process creates a longer or bigger array combining the inputs.