#!/usr/bin/env python
# coding: utf-8

# ---   
#  <img align="left" width="75" height="75"  src="https://upload.wikimedia.org/wikipedia/en/c/c8/University_of_the_Punjab_logo.png"> 
# 
# <h1 align="center">Department of Data Science</h1>
# <h1 align="center">Course: Tools and Techniques for Data Science</h1>
# 
# ---
# <h3><div align="right">Instructor: Muhammad Arif Butt, Ph.D.</div></h3>    

# <h1 align="center">Lecture 3.6 (NumPy-06)</h1>

# <a href="https://colab.research.google.com/github/arifpucit/data-science/blob/master/Section-3-Python-for-Data-Scientists/Lec-3.06(NumPy-06-ArrayManipulation).ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>

# # _Manipulating Array Elements.ipynb_

# <img align="right" width="400" height="400"  src="images/numpy123D.png" > 
# 
# # Learning agenda of this notebook
# 
# 1. Updating existing values of NumPy array elements
# 2. Append new elements to a NumPy array using np.append()
# 3. Insert new elements in a NumPy array using np.insert()
# 4. Delete elements of a NumPy array using np.delete()
# 5. Alias vs Shallow Copy vs Deep Copy

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# To install this library in Jupyter notebook
#import sys
#!{sys.executable} -m pip install numpy


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import numpy as np
np.__version__ , np.__path__


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# ## 1. Updating Existing Values of Numpy Array Elements

# ### a. 1-D Arrays

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arr = np.random.randint(low = 1, high = 100, size = 10)
print("Original Array \n", arr)
arr[2] = 333
arr[-1] = 777
print("Updated Array \n", arr)


# ### b. 2-D Arrays

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# Creating 2-D array of size 4x4 of int type b/w interval 1 to 9
arr = np.random.randint(low = 1, high = 10, size = (4, 4))
print("Original Array \n", arr)
arr[0][1] = 77
arr[1][2] = 66
arr[2][-1] = 22
print("Updated Array \n", arr)


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# ## 2. Append New Elements to Numpy Arrays
# - The `np.append()` method allows us to insert new values at the end of a NumPy array.
# - The method always returns a copy of the existing numpy array with the values appended to the given axis.
# ```
# np.append(arr, values, axis=None)
# ```
# - Where,
#     - `arr` is the array in which we want to append
#     - `values` must be of the correct shape (the same shape as `arr` excluding `axis`)
#     - If `axis` is not specified, both `arr` and `values` are flattened before use.
#     - If `axis` is specified, then `values` must be of the correct shape (the same shape as `arr` excluding `axis`)
# - The original array remains as such, as it does not occur in-place.

# ### a. Appending Elements in 1-D Arrays

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arr1 = np.random.randint(low = 1, high = 100, size = 10)
print("arr1 = ", arr1)


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# You can add a scalar value or a list of values at the end of a 1-D array
arr2 = np.append(arr1, [101, 202,303])
print("After append:")
print("arr1 = ", arr1)
print("arr2 = ", arr2)


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print(id(arr1))
print(id(arr2))


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# ### b. Appending Elements in 2-D Arrays

# **Example:** In case of 2-D Arrays if `axis` is not mentioned both `arr` and `values` are flattened before use

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arr1 = np.random.randint(low = 1, high = 10, size = (3,3))
print("arr1 = \n", arr1)


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# If the axis is not mentioned, values can be of any shape and both `arr` and `values` are flattened before use.
arr2 = np.append(arr1, [101, 202,303, 404, 505])
print("After append:")
print("arr1 = \n", arr1)
print("arr2 = ", arr2)


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# **Example:** Appending a Row to a 2-D array (`axis=0`)

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arr1 = np.random.randint(low = 1, high = 10, size = (4,3))
print("arr1 = \n", arr1)
print("shape: ", arr1.shape)


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# For appending at axis 0, the values argument must the same shape as `arr` excluding `axis`
# so the values should be a row vector, and in this case of shape (1,3), having 1 row and 3 columns
arr2 = np.append(arr1, [[101, 202,303]], axis=0)
print("After append:")
print("arr1 = \n", arr1)
print("arr2 = \n", arr2)


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# **Example:** Appending a Column to a 2-D array (`axis=1`)

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arr1 = np.random.randint(low = 1, high = 10, size = (4,3))
print("arr1 = \n", arr1)
print("shape: ", arr1.shape)


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# For appending at axis 1, the values argument must the same shape as `arr` excluding `axis`
# so the values should be a column vector of shape (4,1), having 4 rows and 1 column
arr2 = np.append(arr1, [[101], [202], [303], [404]], axis=1)
print("After append:")
print("arr1 = \n", arr1)
print("arr2 = \n", arr2)


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# ## 3. Inserting New Elements in Numpy Arrays
# - The `np.insert()` method allows us to insert new values along the given axis before the given index.
# - The method always returns a copy of the existing numpy array with the values inserted to the given axis.
# ```
# np.insert(arr, index, values, axis=None)
# ```
# - Where,
#     - `arr` is the array in which we want to insert
#     - `index` is the index before which we want to insert
#     - `values` [array_like] values to be added in the `arr`
#     - If `axis` is not specified, both `arr` and `values` are flattened before use.
#     - If `axis` is zero, a row is inserted (For 2-D arrays)
#     - If `axis` is one, a column is inserted (For 2-D arrays)
# - The original array remains as such, as it does not occur in-place.

# ### a. Inserting Elements in 1-D Arrays

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arr1 = np.random.randint(low = 1, high = 100, size = 5)
print("arr1 = ", arr1)


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# You can insert a scalar value or a list of values in between array elements before the mentioned index
arr2 = np.insert(arr1, 3, [55, 66,77])
print("After insert:")
print("arr1 = ", arr1)
print("arr2 = ", arr2)


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# ### b. Inserting Elements in 2-D Arrays

# **Example:** In case of 2-D array, if `axis` is not mentioned the array is flattened first

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arr1 = np.random.randint(low = 1, high = 10, size = (3,4))
print("arr1 = \n", arr1)


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# Inserting a single value
arr2 = np.insert(arr1, 4, 55)
print("After insert:")
print("arr1 = \n", arr1)
print("arr2 = ", arr2)


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# Inserting a multiple values
arr2 = np.insert(arr1, 4, [55, 66])
print("After insert:")
print("arr1 = \n", arr1)
print("arr2 = ", arr2)


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# **Example:** If axis=0, value(s) are added as a row before mentioned index

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arr1 = np.random.randint(low = 1, high = 10, size = (3,4))
print("arr1 = \n", arr1)


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# For axis = 0, note how the scalar value is replicated before insertion
arr2 = np.insert(arr1, 2, 55, axis=0)
print("After insert:")
print("arr1 = \n", arr1)
print("arr2 = \n", arr2)


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# For axis=0, note the size of values has to be 4 in this case (equal to number of columns)
arr2 = np.insert(arr1, 2, [55, 66, 77, 88], axis=0)
print("After insert:")
print("arr1 = \n", arr1)
print("arr2 = \n", arr2)


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# **Example:** If axis=1, value(s) are added as a column at mentioned index

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arr1 = np.random.randint(low = 1, high = 10, size = (3,4))
print("arr1 = \n", arr1)


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# For axis = 1, note how the scalar value is replicated before insertion
arr2 = np.insert(arr1, 2, 55, axis=1)
print("After insert:")
print("arr1 = \n", arr1)
print("arr2 = \n", arr2)


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# For axis=1, note the size of values has to be 3 in this case (equal to number of rows)
arr2 = np.insert(arr1, 2, [55, 66, 77], axis=1)
print("After insert:")
print("arr1 = \n", arr1)
print("arr2 = \n", arr2)


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# ## 4. Deleting  Elements of Numpy Arrays
# - The `np.delete()` method allows us to delete value(s) from an array at the given index
# - This function always returns a copy of the existing numpy array with the values deleted from the given axis.
# - If axis is not specified, values can be of any shape and will be flattened before use
# ```
# np.delete(arr, index, axis=None)
# ```
# - The original array remains as such, as it does not occur in-place.

# ### a. Deleting Elements from a 1-D Arrays

# **Example:**

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arr1 = np.random.randint(low = 1, high = 10, size = 5)
print("arr1 = ", arr1)


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# You can delete a scalar value from a specific index
arr2 = np.delete(arr1, 3)
print("After delete:")
print("arr1 = ", arr1)
print("arr2 = ", arr2)


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# **Example:**

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arr1 = np.random.randint(low = 1, high = 100, size = 10)
print("arr1 = ", arr1)


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# You can delete a list of values in between array elements from specific indices
arr2 = np.delete(arr1, [2,5])
print("After delete:")
print("arr1 = ", arr1)
print("arr2 = ", arr2)


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# ### b. Deleting Elements from a 2-D Arrays

# **Example:** Delete a specific element from a 2-D array, don't mention the axis. The resulting array is flattened before use

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arr1 = np.random.randint(low = 1, high = 10, size = (3,3))
print("arr1 = \n", arr1)


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arr2 = np.delete(arr1, 5)
print("After delete:")
print("arr1 = \n", arr1)
print("arr2 = ", arr2)


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# **Example:**  Delete a specific row from an existing 2-D array

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arr1 = np.random.randint(low = 1, high = 10, size = (4,4))
print("arr1 = \n", arr1)


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arr2 = np.delete(arr1, 2, axis=0)
print("After delete:")
print("arr1 = \n", arr1)
print("arr2 = \n", arr2)


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# **Example:**  Delete a specific column from an existing 2-D array

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arr1 = np.random.randint(low = 1, high = 10, size = (4,4))
print("arr1 = \n", arr1)


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arr2 = np.delete(arr1, 2, axis=1)
print("After delete:")
print("arr1 = \n", arr1)
print("arr2 = \n", arr2)


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# ## 5. Assigning vs Coping NumPy Arrays

# ### a. Assigning two NumPy Arrays (Create an alias)

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arr1 = np.random.randint(low = 1, high = 10, size = 10)

# Creating a copy using assignment operator, both variables point at the same array
arr2 = arr1

print("arr1 = ", arr1)
print("arr2 = ", arr2)
print(id(arr1))
print(id(arr2))


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# Change value in arr1 will also occur in arr2
arr2[2] = 55
print("arr1 = ", arr1)
print("arr2 = ", arr2)


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# ### b. View/Shallow Copy
# Arrays that share some data. The view method creates an object looking at the same data. Slicing an array returns a view of that array.

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import numpy as np
arr1 = np.random.randint(low = 1, high = 10, size = 10)

# Creating a shallow copy (view) using slice operator
arr2 = arr1[:]

print("arr1 = ", arr1)
print("arr2 = ", arr2)
print(id(arr1))
print(id(arr2))


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# Change value in arr1 will occur in arr2
arr2[2] = 55

print("arr1 = ", arr1)
print("arr2 = ", arr2)


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# ### c. Deep Copy

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arr1 = np.random.randint(low = 1, high = 10, size = 10)

# Create a Deep copy using copy() method, which will create a new copy of the array
arr2 = arr1.copy()
print("arr1 = ", arr1)
print("arr2 = ", arr2)
print(id(arr1))
print(id(arr2))


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# Change value in array 1 will NOT occur in array 2
arr2[2] = 55

print("arr1 = ", arr1)
print("arr2 = ", arr2)


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