# Converting numpy dtypes to native python types

For a **fast conversion** of **numpy types to Python types**, leverage the ** .item()** method for single values and

**method for entire arrays. A native Python integer can be obtained from a single value as**

`.tolist()`

`np_int64_var.item()`

. To convert whole arrays, simply use `numpy_arr.tolist()`

, ensuring every element is transformed into its plain Python equivalent.Example for a scalar:

Example for an array:

## Overcoming quirky cases

While working with conversions, you might encounter certain edge cases or nuances, like when the element is already a native Python format or when the puzzle is more complex. No worries. There's always a way out!

- Reality check before conversion: To know if a value is a numpy scalar or a native Python type, use
`isinstance(val, np.generic)`

. - Auto-magic with
`asscalar()`

:`numpy.asscalar()`

is like your fairy godmother! It safely converts a size-one array into its corresponding native Python type. - For the conversion buffs - Dictionary mapping: You can construct conversion maps using dictionary comprehension. It's like having a personalized guidebook!

Example of pre-conversion check:

## Polish your skills with best practices

The `.tolist()`

and `.item()`

methods are pretty convenient, but let's talk about optimization for bonus points, especially when dealing with large data sets or performance-critical scenarios.

- The wise usage of
`tolist()`

:`tolist()`

is a handy tool for converting both scalars and arrays. Just be careful with huge arrays - you don't want a performance cliffhanger! - Lambda to the rescue: Create lambda functions with
`tolist`

to handle either scalars or arrays like a pro.

Example of a lambda function simplifying conversion:

## The method maze: Which to use and when?

The best method to apply for conversion wholly depends on the context. Gain insight through these analyzed scenarios:

- Single Scalar Values: Use
`.item()`

when you have a lone numpy scalar begging to be converted to a Python type. - Full Arrays: When you need a list representing your entire array, go with
`.tolist()`

. - Large Arrays: For large arrays, consider chunk conversions or streaming methods to avoid memory collapse.

Here's a code snippet for converting large arrays in chunks:

## Grasping the mappings

Understanding how NumPy dtypes map to Python types is crucial to prevent hiccups during conversion.

- Precision loss: Beware of precision loss during conversion, as some NumPy types hold more precision than Python types.
- Alien types: Certain NumPy types do not have actual Python equivalents. Choose the closest analog we have on Earth!

Here's how you can create your mapping table from NumPy dtypes to Python types:

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