## What is NumPy?

NumPy is a scientific library that provides multidimensional array object with fast routines on it. NumPy is short for Numerical Python.

When we talk about NumPy often we refer to the powerful **ndarray**, which is the multidimensional array (N-dimensional array).

A few comparisons between Python lists and ndarray.

ndarray | Python list |

Have fixed size at creation. | Is dynamic. You can add and remove elements. |

All elements have the same type. | Elements have type independent of each other. |

Can execute fast mathematical operations with simple syntax. | Need loops to make operations on each element. |

## Examples showing the difference: Fixed after creation

The Numpy is imported by default imported by **import numpy as np**. To create a ndarray, you can use the **array** call as defined below.

```
import numpy as np
data = np.array([[1, 2, 3], [1, 2, 3]])
print(data)
```

Which will create an 2 dimensional array object with 2 times 3 elements.

```
[[1 2 3]
[1 2 3]]
```

That will be a fixed sized **ndarray**. You cannot add new dimensions or elements to the the single arrays.

A Python list is a more flexible.

```
my_list = []
my_list.append(2)
my_list.append(4)
my_list.remove(2)
print(my_list)
```

Which demonstrates the flexibility and power of Python lists. It is simple to add and remove elements. The above code will result in the following output.

```
[4]
```

## Examples showing the difference: One type

The type of a **ndarray** is stored in **dtype**. Interesting thing is that each element must have the same type.

```
import numpy as np
data = np.random.randn(2, 3)
print(data)
print(data.dtype)
```

It will result in a random **ndarray** of type **float64**.

```
[[-0.85925182 -0.89247774 -2.40920842]
[ 0.84647869 0.27631307 -0.80772023]]
float64
```

An interesting way to demonstrate that only one type can be present in an ndarray, is by trying to create it with a mixture of **int**s and **float**s.

```
import numpy as np
data = np.array([[1.0, 2, 3], [1, 2, 3]])
print(data)
print(data.dtype)
```

As the first element is of type float they are all cast to float64.

```
[[1. 2. 3.]
[1. 2. 3.]]
float64
```

While the following list is valid.

```
my_list = [1.0, 2, 3]
print(my_list)
```

Where the first element will be **float** the second and third element are **int**s.

```
[1.0, 2, 3]
```

## Examples showing the difference: No loops needed

Many operations can be made directly on the **ndarray**.

```
import numpy as np
data = np.random.randn(2, 3)
print(data)
print(data*10)
print(data + data)
```

Which will result in the following output.

```
[[ 1.18303358 -2.20017954 0.46294824]
[-0.56508587 0.0990272 -1.8431866 ]]
[[ 11.83033584 -22.00179538 4.62948243]
[ -5.65085867 0.990272 -18.43186601]]
[[ 2.36606717 -4.40035908 0.92589649]
[-1.13017173 0.1980544 -3.6863732 ]]
```

Expected right? But easy to multiply and add out of the box.

Similar of the Python list would be.

```
my_list = [1, 2, 3]
for i in range(len(my_list)):
my_list[i] *= 10
for i in range(len(my_list)):
my_list[i] += my_list[i]
```

And it is not even the same, as you write it directly to the old elements.

## Another way to compare differences

It might at first glance seem like **ndarrays** are inflexible with all the restrictions comparing the Python lists. Yes, that is true, but the benefit is the speed.

```
import time
import numpy as np
my_arr = np.arange(1000000)
my_list = list(range(1000000))
start = time.time()
for _ in range(10): my_arr2 = my_arr * 2
end = time.time()
print(end - start)
start = time.time()
for _ in range(10): my_list2 = [x * 2 for x in my_list]
end = time.time()
print(end - start)
```

Which resulted in.

```
0.03456306457519531
0.9373760223388672
```

The advantage is that **ndarrays** are 10-100 times faster than Python lists, which makes a considerable impact on scientific calculations.

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