#!/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 3.7 (NumPy-07)</h1>

# <a href="https://colab.research.google.com/github/arifpucit/data-science/blob/master/Section-3-Python-for-Data-Scientists/Lec-3.07(NumPy-07-StackingandSplitting).ipynb" target="_parent"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/></a>

# <img align="left" width="500" height="500"  src="images/concatfunctions.png" >

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# # Learning agenda of this notebook

# ## 1. Concatenating of NumPy Arrays

# <img align="right" width="500" height="200"  src="images/concataxis1.png" >
# <img align="left" width="400" height="400"  src="images/concataxis0.png" > 

# ## 2. Stacking of NumPy Arrays

# <img align="left" width="350" height="350"  src="images/hs1.png" > 
# <img align="right" width="350" height="350"  src="images/vs1.png" > 

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# ## 3. Splitting NumPy Arrays

# <img align="center" width="400" height="300"  src="images/splitting.png" > 

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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. Concatenating NumPy Arrays
# <img align="right" width="400" height="200"  src="images/concataxis1.png" >
# <img align="left" width="500" height="400"  src="images/concataxis0.png" > 
# 
# <br><br><br><br><br><br><br><br><br><br>
# 
# The `np.concatenate()` method is used to join arrays with respect to given axis.
# 
# ```
# np.concatenate(tup, axis=0)
# ```
# 
# - Where `tup` is comma separated ndarrays
# - If axis is 0, it will join the arrays by row-wise (vertically). For 2-D arrays, the number of columns must match.
# - If axis is 1, it will join the arrays by column-wise (horizontally). For 2-D arrays, the number of rows must match.
# - For 1-D arrays, the arrays can be of any size/length.
# - The original arrays remains as such, as it does not occur in-place.

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# **Example:** Concatenate two 1-D Arrays along axis = 0 (row wise). The 1-D arrays can be of any size/length.

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import numpy as np
arr1 = np.random.randint(low = 1, high = 100, size = 5)
arr2 = np.random.randint(low = 1, high = 100, size = 3)
print("arr1 = ", arr1)
print("arr2 = ", arr2)

arr3 = np.concatenate((arr1, arr2))
arr3 = np.concatenate((arr1, arr2), axis=0)
print("\nnp.concatenate((arr1,arr2)) = ", arr3)


# You cannot concatenate 1-D arrays on `axis=1`, as it do not exist :)

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# **Example:** Concatenate two 2-D Arrays along `axis=0` (vertically/row-wise). The number of columns of two arrays must match.

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arr1 = np.random.randint(low = 1, high = 10, size = (2,3))
arr2 = np.random.randint(low = 1, high = 10, size = (3,3))
print("arr1 = \n", arr1)
print("arr2 = \n", arr2)

arr3 = np.concatenate((arr1, arr2), axis=0)
print("\nnp.concatenate((arr1,arr2)) = \n", arr3)


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# **Example:** Concatenate two 2-D Arrays along `axis=1` (horizontally/column-wise). The number of rows of two arrays must match.

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arr1 = np.random.randint(low = 1, high = 10, size = (2,2))
arr2 = np.random.randint(low = 1, high = 10, size = (2,3))
print("arr1 = \n", arr1)
print("arr2 = \n", arr2)

arr3 = np.concatenate((arr1, arr2), axis=1)
print("\nnp.concatenate((arr1,arr2)) = \n", arr3)


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# ## 2. Stacking NumPy Arrays
# - Concatenating joins a sequence of arrays along an existing axis, and stacking joins a sequence of arrays along existing as well as along a new axis.
# - We can perform stacking along three dimensions:
#     - `np.vstack()` : it performs vertical stacking along the rows.
#     - `np.hstack()` : it performs horizontal stacking along with the columns.
#     - `np.dstack()` : it performs in-depth stacking along a new third axis (depth).
# 
# **Note:** 
# - `numpy.stack()` is the most general of the three methods, offering an axis parameter for specifying which way to put the arrays together.
# - `np.column_stack()` is used to stack 1-D arrays as columns into 2-D array.
# - `np.row_stack()` is used to stack 1-D arrays as rows into 2-D array.

# **Example: `np.row_stack()`**

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#ROW STACK
import numpy as np
arr1 = np.array([2,5,1])
arr2 = np.array([3,6,2])

print("arr1 = ", arr1)
print("arr2 = ", arr2)
  
arr3 = np.row_stack((arr1, arr2))

print ("\nnp.row_stack((arr1, arr2)):\n ", arr3)


# **Example: `np.column_stack()`**

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#COLUMN STACK
import numpy as np
arr1 = np.array([2,5,1])
arr2 = np.array([3,6,2])

print("arr1 = ", arr1)
print("arr2 = ", arr2)
  
arr3 = np.column_stack((arr1, arr2))

print ("\nnp.column_stack((arr1, arr2)):\n ", arr3)


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# <img align="right" width="250" height="100"  src="images/vstackfinal.png" > 
# 
# ### a. Use `np.vstack()` for Row-Wise Concatenation
# The `np.vstack()` method is used to stack arrays vertically or row-wise.
# 
# ```
# np.vstack(tup)
# ```
# 
# - Where `tup` is comma separated ndarrays
# - 1-D arrays must have the same size/length, while for 2-D arrays, the number of columns must match.
# - It returns an ndarray formed by stacking the given arrays, will be at least 2-D.
# - The original arrays remains as such, as it does not occur in-place.

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# **Example:** Perform vertical stacking of two 1-D Arrays, which must have the same size/length.

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import numpy as np
arr1 = np.random.randint(low = 1, high = 10, size = 4)
arr2 = np.random.randint(low = 1, high = 10, size = 4)

print("arr1 = ", arr1)
print("arr2 = ", arr2)
  
arr3 = np.vstack((arr1, arr2))

print ("\nnp.vstack((arr1, arr2)):\n ", arr3)


# Note: The output array is a 2-D array

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# **Example:** Perform vertical stacking of two 2-D Arrays. The number of columns of two arrays must match

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arr1 = np.random.randint(low = 1, high = 10, size = (2,3))
arr2 = np.random.randint(low = 1, high = 10, size = (3,3))

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

arr3 = np.vstack((arr1, arr2))
print ("\n np.vstack((arr1, arr2)):\n ", arr3)


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# <img align="right" width="250" height="100"  src="images/hstackfinal.png" > 
# 
# ### b. Using `np.hstack()` for Column-Wise Concatenation
# The `np.hstack()` method is used to stack arrays horizontally or column-wise.
# 
# ```
# np.hstack(tup)
# ```
# 
# - Where `tup` is comma separated ndarrays
# - 1-D arrays can have any size/length, while for 2-D arrays, the number of rows must match.
# - It returns an ndarray formed by stacking the given arrays.
# - The original arrays remains as such, as it does not occur in-place.

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# **Example:** Perform horizontal stacking of two 1-D Arrays, which can be of different  size/length

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arr1 = np.random.randint(low = 1, high = 10, size = 5)
arr2 = np.random.randint(low = 1, high = 10, size = 4)
print("arr1 = ", arr1)
print("arr2 = ", arr2)
  
arr3 = np.hstack((arr1, arr2))
print ("\n np.hstack((arr1, arr2)):\n ", arr3)


# Note: The output array is a 1-D array

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# **Example:** Perform horizontal stacking of two 2-D Arrays. The number of rows of two arrays must match

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arr1 = np.random.randint(low = 1, high = 10, size = (2,2))
arr2 = np.random.randint(low = 1, high = 10, size = (2,3))
print("arr1 = \n", arr1)
print("arr2 = \n", arr2)

arr3 = np.hstack((arr1, arr2))
print ("\n np.hstack((arr1, arr2)):\n ", arr3)


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# ### e. Using `np.stack()`
# 
# - The `np.stack()` method is used to join a sequence of same dimension arrays along a new axis.
# - The axis parameter specifies the index of the new axis in the dimensions of the result. 
# - For example, if axis=0 it will be the first dimension and if axis=-1 it will be the last dimension.
# ```
# np.stack(a1, a2, a3, ..., axis=0)
# ```
# 
# - Where `tup` is comma separated ndarrays
# - 1-D or 2-D arrays must have the same shape, while n-D arrays must have the same shape along all but the third axis.
# - It returns the array formed by stacking the given arrays, which has one more dimension than the input arrays.
# - This function makes most sense for arrays with up to 3 dimensions. For instance, for pixel-data with a height (first axis), width (second axis), and r/g/b channels (third axis).
# 
# 
# 
# **Note:** Concatenating joins a sequence of tensors along an existing axis, and stacking joins a sequence of tensors along a new axis

# **Example:** Perform stacking of two 1-D Arrays, which must have the same size/shape.

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import numpy as np
arr1 = np.random.randint(low = 1, high = 10, size = 4)
arr2 = np.random.randint(low = 1, high = 10, size = 4)
print("arr1 = ", arr1)
print("arr2 = ", arr2)
  
# Stacking two 1-D arrays along axis 0 using stack()
arr3 = np.stack((arr1, arr2), axis = 0)
print ("\n np.stack(arr1, arr2, axis=0):\n ", arr3)
# Stacking the two 1-D arrays using row_stack()
arr4 = np.row_stack((arr1, arr2))
print ("\n np.row_stack(arr1, arr2)):\n ", arr4)
  

# Stacking two 1-D arrays along axis 1 using stack()
arr5 = np.stack((arr1, arr2), axis = 1)
print ("\n np.stack((arr1, arr2), axis=1):\n ", arr5)
# Stacking the two 1-D arrays using column_stack()
arr6 = np.column_stack((arr1, arr2))
print ("\n np.column_stack(arr1, arr2):\n ", arr6)


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# **Example:** Perform stacking of two 2-D Arrays, which must have the same size/shape.

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import numpy as np
arr1 = np.array([[4,3,1],[5,6,2]])
arr2 = np.array([[5,1,8],[3,9,1]])

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

# Stacking the two arrays along axis 0
arr3 = np.stack((arr1, arr2), axis = 0)
print ("\n np.stack((arr1, arr2), axis=0): \n", arr3)

print("shape of arr3:", arr3.shape)


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import numpy as np
arr1 = np.array([[4,3,1],[5,6,2]])
arr2 = np.array([[5,1,8],[3,9,1]])

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

# Stacking the two arrays along axis 1
arr3 = np.stack((arr1, arr2), axis = 1)
print ("\n np.stack((arr1, arr2), axis=1): \n", arr3)

print("shape of arr3:", arr3.shape)


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import numpy as np
arr1 = np.array([[4,3,1],[5,6,2]])
arr2 = np.array([[5,1,8],[3,9,1]])

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

  
# Stacking the two arrays along last axis
arr3 = np.stack((arr1, arr2), axis = -1)
print ("\n np.stack((arr1, arr2), axis=-1):\n ", arr3)
print("shape of arr3:", arr3.shape)


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# ## 3. Splitting NumPy Arrays
# - Splitting is reverse operation of Joining, and is used to split one array into multiple arrays....
# - We can perform splitting along three dimensions:
#     - `np.split()` : Split array into a list of multiple sub-arrays of equal size.
#     - `np.hsplit()` : Split array into multiple sub-arrays horizontally (column wise).
#     - `np.vsplit()` : Split array into multiple sub-arrays vertically (row wise).

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# ### a. The `np.split()` and `np.array_split()` Methods
# 
# <img align="left" width="400" height="200"  src="images/split.png" > 
# <img align="right" width="400" height="200"  src="images/array_split.png" > 
# 
# <br><br><br><br><br><br><br><br><br><br>
# - The `np.split()` method splits an array into multiple sub-arrays of equal sizes.
# 
# ```
# np.split(arr, size, axis=0)
# ```
# - Where,
#     - `arr` is the array to be divided into sub-arrays.
#     - `size` is an size of the sub-arrays, into which `arr` will be divided along the axis. 
#     - `axis` is the axis along which to split, default is 0.
# - If such split is not possible, an error is raised. To avoid error you can use `np.array_split()`
# - It returns a list of sub-arrays as views into `arr`

# **Example:** Use of `split()`

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arr1 = np.random.randint(low = 1, high = 10, size = 20)
print("arr1:\n",arr1)

# The split size must be a factor of array size (can be 1, 2, 4, 5, 10) 
print("\nSub-arrays: \n", np.split(arr1, 4))


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# **Example:** Use of `array_split()`

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# create an array of float type
arr1 = np.random.randint(low = 1, high = 10, size = 13)
print("arr1:\n",arr1)

# The array_split() will not flag an error if size is not a factor of array size (will manage)
print("\nSub-arrays: \n", np.array_split(arr1, 4))


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# <img align="right" width="150" height="50"  src="images/hsplit.png" > 
# 
# ### b. The `np.hsplit()` Method
# - The `np.hsplit()` method is used to split an array into multiple sub-arrays horizontally (column-wise). 
# - The `np.hsplit()` is equivalent to split with axis=1, the array is always split along the second axis regardless of the array dimension.
# 
# ```
# np.hsplit(arr, size)
# ```
# - Where,
#     - `arr` is the array to be divided into sub-arrays.
#     - `size` is the size of the sub-arrays, into which `arr` will be divided along the axis. For `hsplit()`, size argument should be a factor of number of columns, else it flags an error
# - It returns a list of sub-arrays as views into `arr`

# **Example:**

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# create an array of float type with 4 rows and 4 columns with sequential values from 0 to 15
arr1 = np.arange(16.0).reshape(4,4)
# print array
print("arr1:\n",arr1)
print("shape: ", arr1.shape)


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# horizontally split array into 2 subarrays
print("\nSub-arrays: \n", np.hsplit(arr1, 2))


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# <img align="right" width="200" height="50"  src="images/vsplit.png" > 
# 
# ### c. The `np.vsplit()` Method
# - The `np.vsplit()` method is used to split an array into multiple sub-arrays vertically (row-wise). Not applicable for 1-D array.
# - The `np.vsplit()` is equivalent to split with axis=0, the array is always split along the first axis regardless of the array dimension.
# 
# ```
# np.vsplit(arr, size)
# ```
# -Where,
#    - `arr` is the array to be divided into sub-arrays.
#    - `size` is the size of the sub-arrays, into which `arr` will be divided along the axis. For `vsplit()`, size argument should be a factor of number of rows, else it flags an error
# - It returns a list of sub-arrays as views into `arr`

# **Example:**

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# create an array of float type with 4 rows and 5 columns with sequential values from 0 to 19
arr1 = np.arange(16.0).reshape(4,4)
print("arr1:\n",arr1)
print("shape: ", arr1.shape)


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# vertically split array into 2 subarrays (remember size argument must be a factor of number of rows )
print("\nSub-arrays: \n", np.vsplit(arr1, 2))


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import numpy as np
arr1 = np.array([2,5,1])
arr2 = np.array([3,6,2])
arr3 = np.column_stack((arr1, arr2))
arr4 = np.vstack((arr1, arr2))
arr5 = np.hstack((arr1, arr2))
arr6 = np.stack((arr1, arr2), axis = 1)
print ("arr3: ", arr3)
print ("arr4: ", arr4)
print ("arr5: ", arr5)
print ("arr6: ", arr6)


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