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# Special functions

Lesson 4 Chapter 2

We can create Numpy arrays using some special Numpy functions. I used a couple of them below for your reference:

```# Import Numpy library
import numpy as np

### Defining arrays using special functions ###
# Arguments:
#   shape: The shape of the numpy array
#   dtype: Specifying the data type (not required)

# Defining an all-zero array
zeroArray = np.zeros(shape=(3,5), dtype=np.int16 )
print("zeroArray: ", zeroArray)

# Defining an all-one array
onesArray = np.ones(shape=(3,5), dtype=np.float32 )
print("onesArray: ", onesArray)

# Defining an array filled with one specific elements
fullArray = np.full(shape=(3,5), fill_value=4.2, dtype=np.float64 )
print("fullArray: ", fullArray)```

```zeroArray:  [[0 0 0 0 0]
[0 0 0 0 0]
[0 0 0 0 0]]
onesArray:  [[1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1.]]
fullArray:  [[4.2 4.2 4.2 4.2 4.2]
[4.2 4.2 4.2 4.2 4.2]
[4.2 4.2 4.2 4.2 4.2]]```

One of the most important special functions is np.arange which is similar to Python range built-in function. An example of using np.arrage is as below:

```# Import Numpy library
import numpy as np

# Define a Numpy array using np.arrage
# np.arrage defines an interval of numbers
# Arguments:
#   start: Starting of the interval.
#   stop: Ending of the interval.
#   step: Step size.
# NOTE: The interval includes "start" number but does NOT include "stop" number.
arr = np.arange(start=3,stop=10,step=2)
print("Array: ", arr)```

`Array:  [3 5 7 9]` Numpy indexing starts from 0 to infinity! By defining np.arange(start=3,stop=10,step=2), start index is 3. The stop index is 10 BUT it is NOT included in the range. Step size is 2 means every other index is picked, i.e., we only pick green indexes and jump over orange ones!!
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