You have a 2D NumPy array data with shape (6, 4). You want to split it into 3 equal parts along the first axis (rows) for separate analysis. Which code correctly does this using np.split()?

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NumPy - Array Manipulation

You have a 2D NumPy array data with shape (6, 4). You want to split it into 3 equal parts along the first axis (rows) for separate analysis. Which code correctly does this using np.split()?

Anp.split(data, [1, 3, 5], axis=1)

Bnp.split(data, 3, axis=1)

Cnp.split(data, [2, 4], axis=1)

Dnp.split(data, 3, axis=0)

Step-by-Step Solution

Solution:

Step 1: Understand array shape and axis
data has 6 rows and 4 columns. Splitting into 3 equal parts along rows means each part has 2 rows.
Step 2: Use np.split() with correct axis and parts
np.split(data, 3, axis=0) splits rows into 3 equal parts. axis=1 would split columns, which is not desired.
Final Answer:
np.split(data, 3, axis=0) -> Option D
Quick Check:
Split rows into 3 parts with axis=0 [OK]

Quick Trick: Use axis=0 to split rows, axis=1 for columns [OK]

Common Mistakes:

Splitting along wrong axis
Using indices instead of number for equal parts
Ignoring array shape

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You have a 2D NumPy array data with shape (6, 4). You want to split it into 3 equal parts along the first axis (rows) for separate analysis. Which code correctly does this using np.split()?

Step 1: Understand array shape and axis

Step 2: Use np.split() with correct axis and parts

Final Answer:

Quick Check:

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