[Solved] Why does np.array() sometimes create arrays with dtype=object when given nested Python lists? — Ans: Because inner lists have different lengths or types | NumPy

NumPy - Creating Arrays

Why does np.array() sometimes create arrays with dtype=object when given nested Python lists?

ABecause numpy always uses object dtype for nested lists

BBecause inner lists have different lengths or types

CBecause Python lists are immutable

DBecause np.array() cannot handle nested lists

Step-by-Step Solution

Solution:

Step 1: Understand numpy array dtype assignment
Numpy tries to create arrays with uniform shape and type.
Step 2: Effect of uneven inner lists
If inner lists differ in length or type, numpy cannot create uniform array, so uses dtype=object to store elements as generic Python objects.
Final Answer:
Because inner lists have different lengths or types -> Option B
Quick Check:
Uneven nested lists cause dtype=object arrays [OK]

Quick Trick: Uneven inner lists cause dtype=object arrays [OK]

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Why does np.array() sometimes create arrays with dtype=object when given nested Python lists?