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# Master the NumPy Basics

## What will we cover in this Tutorial

If you are starting from scratch with NumPy and do not know what ndarray is, then you should read this tutorial first.

• How to make arithmetics with ndarray.
• Sliding and indexing of ndarray with 1-dimension.
• Sliding and indexing of ndarray with 2-dimensions.

## Arithmetics with NumPy

An amazing feature with ndarrays is that you do not need to make forloops for simple operations.

```import numpy as np
a1 = np.array([[1., 2., 3.], [3., 2., 1.]])
a2 = np.array([[4., 5., 6.], [6., 5., 4.]])
print(a1)
print(a2)
print(a2 - a1)
print(a1*a2)
print(1/a1)
print(a2**0.5)
```

This looks too good to be true. Right?

The output is as you would expect.

```[[1. 2. 3.]
[3. 2. 1.]]
[[4. 5. 6.]
[6. 5. 4.]]
[[3. 3. 3.]
[3. 3. 3.]]
[[ 4. 10. 18.]
[18. 10.  4.]]
[[1.         0.5        0.33333333]
[0.33333333 0.5        1.        ]]
[[2.         2.23606798 2.44948974]
[2.44948974 2.23606798 2.        ]]
```

Then you understand why all are so madly in love with NumPy.

This type of “batch” operation is called vectorization.

You can also make comparisons.

```import numpy as np
a1 = np.array([[1., 2., 3.], [6., 5., 4.]])
a2 = np.array([[4., 5., 6.], [3., 4., 5.]])
print(a1 < a2)
```

Which gives what you expect.

```[[ True  True  True]
[False False  True]]
```

At least I hope you would expect the above.

## Slicing and basic indexing

If you are familiar with Python lists, then this should not surprise you.

```import numpy as np
a = np.arange(10)
print(a)
print(a)
print(a[2:5])
```

You guessed it.

```[0 1 2 3 4 5 6 7 8 9]
5
[2 3 4]
```

But this might surprise you a bit.

```import numpy as np
a = np.arange(10)
print(a)
a[4:7] = 10
print(a)
```

Resulting in.

```[0 1 2 3 4 5 6 7 8 9]
[ 0  1  2  3 10 10 10  7  8  9]
```

That is quite a surprise.

You can take a “view” from it (also called a slice) like the following example shows.

```import numpy as np
a = np.arange(10)
print(a)
a_slice = a[4:7]
print(a_slice)
a_slice[0:1] = 30
print(a_slice)
print(a)
```

Resulting in.

```[0 1 2 3 4 5 6 7 8 9]
[4 5 6]
[30  5  6]
[ 0  1  2  3 30  5  6  7  8  9]
```

## Slicing and indexing of 2-dimensions

First of, this seems similar to Python lists.

```import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(a)
print(a)
print(a[2, 2])
```

Maybe the last statement is surprising, but it does the same as the above. That is, the effect of a is the same as of a[2, 2].

```[4 5 6]
9
9
```

Slicing the above ndarray will be done by rows.

```import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(a[:2])
```

Which results in the following.

```[[1 2 3]
[4 5 6]]
```

A bit more advanced to slice it as.

```import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(a[:2, 1:])
```

Resulting in.

```[[2 3]
[5 6]]
```

It might not be clear the the second slice does fully vertical slices, which is illustrated by the following example.

```import numpy as np
a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print(a[:, :1])
```

This will most likely surprise you. Right?

```[

]
```

Makes sense, right?

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