#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
ENGG1811. 

Illustrating numpy dtype 
"""
import numpy as np

# numpy array: 
# data types of elements
array1 = np.array([1, 2, 3, -3, -5])

# An attribute of a numpy array is its dtype 
# 
# A requirement is that all the elements in
# the array must have the same dtype
# 
# Since all the elements in array1 are integers, 
# the dtype of array 1 is 'int64'
# 
# You can find out about the dtype by using
# the command 
array1.dtype # dtype('int64')

# Let us look at another array 
array2 = np.array([1.1, 2.2, 3, -3, -5])

# The elements of array2 are either float or integer 
# The dtype will be the more general of the two types
array2.dtype # dtype('float64')

# Since the dtype of array1 is dtype('int64')
# Let us try the following
array1[1] = 5.7  
print(array1[1])     # 5 # Fractional part is dropped 
#
# The lesson is that it is important to know the
# dtype of a numpy array 

# A good practice is to specify the dtype when
# initialising an array, so that you know  
array3 = np.array([1, 2, 3, -3, -5], dtype = int)
array4 = np.array([1, 2, 3, -3, -5], dtype = float)

# You can change the dtype by using .astype 
# 
array3.dtype  # dtype('int64')
array3 = array3.astype(float) 
array3.dtype  # dtype('float64')