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# Data Type Conversion and Copying Arrays

NumPy arrays have a fixed *data type*, such as `int`, `float`, or `bool`.

You can change the type with `.astype()`.

When copying arrays, it's also important to know the difference between creating a true copy and just referencing the same data.

<br/>

## Changing Data Type with `.astype()`

Convert an array from one type to another:

```python title="Changing Data Type with .astype()"
arr = np.array([1.5, 2.8, 3.0])
int_arr = arr.astype(int)

print(int_arr)  # [1 2 3]
```

This converts float values into integers.

<br/>

## Copying Arrays

Assigning one array to another does *not* create a real copy. Both variables point to the same data.

```python title="Copying Arrays"
a = np.array([1, 2, 3])
b = a  # Not a copy!
b[0] = 99

print(a)  # [99 2 3]  original was modified
```

Use `.copy()` to create a true copy:

```python title="Copying Arrays"
c = a.copy()
c[0] = 0

print(a)  # Still [99 2 3]
```

<br/>

## Summary

* Use `.astype()` to change data types (e.g., float to int).
* Use `.copy()` to create a real copy of an array.
* Without `.copy()`, both variables refer to the same array in memory.

The `.copy()` method is specifically used to create a true copy of a NumPy array, meaning the new array is a separate object in memory. Without using `.copy()`, any changes to the new variable will affect the original array since they would both refer to the same memory location.

Data Type Conversion and Copying Arrays

Changing Data Type with .astype()

Copying Arrays

Summary

What is the correct method to create a true copy of a NumPy array?

Changing Data Type with `.astype()`