Dive into NumPy! Start organizing, rearranging, and sampling your data.

Python Data Analysis and Visualization

From Python to Numpy

**NumPy** is a *library for the Python programming language*, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

## Data type
---------
| **Type**   | **Name**  |**Bytes**   | **Description**  |
|---|---|---|---|---|
|`bool`|        `b`|    1 |    Boolean (True or False) stored as a byte 
|`int`|         `l` |   4-8  | Platform (long) integer (normally either int32 or int64)
|`intp`|        `p` |   4-8  | Integer used for indexing (normally either int32 or int64)
|`int8`|        `i1`|   1   |  Byte (-128 to 127)
|`int16`|       `i2`|   2  |   Integer (-32768 to 32767)|
|`int32`|       `i4`|   4 |    Integer (-2147483648 to 2147483647)
|`int64`|       `i8`|   8  |   Integer (-9223372036854775808 to 9223372036854775807)|
|`uint8`|       `u1`|   1   |  Unsigned integer (0 to 255)
|`uint16` |     `u2`|   2  |   Unsigned integer (0 to 65535)
|`uint32` |     `u4`|   4 |    Unsigned integer (0 to 4294967295)
|`uint64` |     `u8`|   8  |   Unsigned integer (0 to 18446744073709551615)
|`float` |      `f8`|   8 |    Shorthand for float64
|`float16`|     `f2`|   2 |    Half precision float: sign bit, 5 bits exponent, 10 bits mantissa
|`float32`|     `f` |   4 |   Single precision float: sign bit, 8 bits exponent, 23 bits mantissa
|`float64`|     `d` |   8 |    Double precision float: sign bit, 11 bits exponent, 52 bits mantissa
|`complex`|     `c16`|  16 |   Shorthand for complex128.
|`complex64`|   `c8` |  8  |   Complex number, represented by two 32-bit floats
|`complex128`|  `c16`|  16 |   Complex number, represented by two 64-bit floats

`NumPy` knows that `int` refers to `np.int_`, `bool` means `np.bool_`, that `float` is `np.float_` and `complex` is `np.complex_`. The other data-types do not have Python equivalents.

Additionally, the names such as `intc`,
`long`, or `double`  used in the C programming language are defined. 

Now that the concept of data types is clear,  let's move on to the next lesson "Creation in NumPy".

**NumPy** is a *library for the Python programming language*, adding support for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays.

# Data type
---------
| **Type**   | **Name**  |**Bytes**   | **Description**  |
|---|---|---|---|---|
|`bool`|        `b`|    1 |    Boolean (True or False) stored as a byte 
|`int`|         `l` |   4-8  | Platform (long) integer (normally either int32 or int64)
|`intp`|        `p` |   4-8  | Integer used for indexing (normally either int32 or int64)
|`int8`|        `i1`|   1   |  Byte (-128 to 127)
|`int16`|       `i2`|   2  |   Integer (-32768 to 32767)|
|`int32`|       `i4`|   4 |    Integer (-2147483648 to 2147483647)
|`int64`|       `i8`|   8  |   Integer (-9223372036854775808 to 9223372036854775807)|
|`uint8`|       `u1`|   1   |  Unsigned integer (0 to 255)
|`uint16` |     `u2`|   2  |   Unsigned integer (0 to 65535)
|`uint32` |     `u4`|   4 |    Unsigned integer (0 to 4294967295)
|`uint64` |     `u8`|   8  |   Unsigned integer (0 to 18446744073709551615)
|`float` |      `f8`|   8 |    Shorthand for float64
|`float16`|     `f2`|   2 |    Half precision float: sign bit, 5 bits exponent, 10 bits mantissa
|`float32`|     `f` |   4 |   Single precision float: sign bit, 8 bits exponent, 23 bits mantissa
|`float64`|     `d` |   8 |    Double precision float: sign bit, 11 bits exponent, 52 bits mantissa
|`complex`|     `c16`|  16 |   Shorthand for complex128.
|`complex64`|   `c8` |  8  |   Complex number, represented by two 32-bit floats
|`complex128`|  `c16`|  16 |   Complex number, represented by two 64-bit floats

`NumPy` knows that `int` refers to `np.int_`, `bool` means `np.bool_`, that `float` is `np.float_` and `complex` is `np.complex_`. The other data-types do not have Python equivalents.

Additionally, the names such as `intc`,
`long`, or `double`  used in the C programming language are defined. 

Now that the concept of data types is clear,  let's move on to the next lesson "Creation in NumPy".

This lesson gives a brief introduction to what is NumPy and explains data types in NumPy. 

Type	Name	Bytes	Description
`bool`	`b`	1	Boolean (True or False) stored as a byte
`int`	`l`	4-8

Introduction

Anatomy of an Array

Code Vectorization

Problem Vectorization

Custom Vectorization

Beyond NumPy

What is NumPy?

Data type