# Numpy Tutorial - NumPy Data Type and Conversion

Data type - `dtype`

in NumPy is different from the primitive data types in Python, for example, `dtype`

has the type with higher resolution that is useful in the data calculation.

## NumPy Data Type

Data Type | Description |
---|---|

`bool` |
Boolean |

`int8` |
8-bit signed integer |

`int16` |
16-bit signed integer |

`int32` |
32-bit signed integer |

`int64` |
64-bit signed integer |

`uint8` |
8-bit unsigned integer |

`uint16` |
16-bit unsigned integer |

`uint32` |
32-bit unsigned integer |

`uint64` |
64-bit unsigned integer |

`float16` |
16-bit floating point number |

`float32` |
32-bit floating point number |

`float64` |
64-bit floating point number |

`complex64` |
64-bit complex number |

`complex128` |
128-bit complex number |

When creating a new `ndarray`

data, you can define the data type of the element by string or or data type constants in the `NumPy`

library.

```
import numpy as np
# by string
test = np.array([4, 5, 6], dtype='int64')
# by data type constant in numpy
test = np.array([7, 8, 8], dtype=np.int64)
```

## Data Type Conversion

After the data instance is created, you can change the type of the element to another type with `astype()`

method, such as from integer to floating and so on.

```
>>> import numpy as np
>>> test = np.array([11, 12, 13, 14], dtype="int32")
>>> x = test.astype('float32')
>>> x
array([11., 12., 13., 14.], dtype=float32)
>>> test, test.dtype
(array([11, 12, 13, 14]), dtype('int32'))
```

Attention

The data type conversion method will only return a new array instance, and the data and information of the original array instance has not changed.

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