# NumPy Array Operations

November 24, 2020 2021-02-26 21:51## NumPy Array Operations

Learn to perform arithmetic, statistical and transformation operations on NumPy arrays in this lesson.

## Arithmetic Operations on NumPy Arrays

Various arithmetic operations such as addition, subtraction, multiplication, division, etc can be performed on NumPy arrays.

Such operations can be either performed between NumPy arrays of similar shape or between a NumPy array and a number. Following are some of the examples of arithmetic operations on NumPy arrays:

# Importing the NumPy library as np import numpy as np # Creating a NumPy array from a Python List arr1 = np.array([1, 2, 3, 4]) # Creating a NumPy array from a Python List arr2 = np.array([2, 4, 6, 8]) # Printing the arrays print("Carr1: ", arr1) print("arr2: ", arr2) # Arithmetic operations between an array and a number print("arr1 + 2: ", arr1 + 2) print("arr1 - 2: ", arr1 - 2) print("arr1 * 2: ", arr1 * 2) print("arr1 / 2: ", arr1 / 2) # Arithmetic operations between NumPy arrays print("arr1 + arr2: ", arr1 + arr2) print("arr1 - arr2: ", arr1 - arr2) print("arr1 * arr2: ", arr1 * arr2) print("arr1 / arr2: ", arr1 / arr2)

arr1: [6 7 8 9] arr2: [1 2 3 4] arr1 + 2: [ 8 9 10 11] arr1 - 2: [4 5 6 7] arr1 * 2: [12 14 16 18] arr1 / 2: [3. 3.5 4. 4.5] arr1 + arr2: [ 7 9 11 13] arr1 - arr2: [5 5 5 5] arr1 * arr2: [ 6 14 24 36] arr1 / arr2: [6. 3.5 2.66666667 2.25 ]

## Statistical Operations on NumPy arrays

NumPy contains various in-built functions to get statistical information regarding the array such as the maximum or minimum value in the array, the mean or median of the array, etc. Below is a list of built-in functions that can perform such operations:

Statistics | Built-In NumPy Functions |

Minimum Value | numpy.min() |

Maximum Value | numpy.max() |

Mean Value | numpy.mean() |

Median Value | numpy.median() |

Standard deviation | numpy.std() |

Get count of unique values | numpy.unique() |

# Importing the NumPy library as np import numpy as np # Creating a NumPy array from a Python List arr1 = np.array([1, 2, 3, 4]) # Printing the array print("arr1: ", arr1) # Minimum value print("Min: ", np.min(arr1)) # Maximum Value print("Max: ", np.max(arr1)) # Mean print("Mean: ", np.mean(arr1)) # Median print("Median: ", np.median(arr1)) # Standard Deviation print("Standard Deviation: ", np.std(arr1)) # Get unique values and their counts uniqs, counts = np.unique(arr1, return_counts=True) print("Unique values: ", uniqs) print("Count of respective unique values: ", counts)

arr1: [1 2 3 4] Min: 1 Max: 4 Mean: 2.5 Median: 2.5 Standard Deviation: 1.118033988749895 Unique values: [1 2 3 4] Count of respective unique values: [1 1 1 1]

## Transformation Operations on NumPy arrays

Transformation Operations on NumPy arrays can help transform the shape and order of elements in a NumPy array. Below is a built-in functions that can perform such operations:

Operations | Built-In NumPy Functions |

Change the shape of an array | numpy.array.reshape() |

Sort the elements of an array | numpy.sort() |

Change n-d array to 1-D array | numpy.array.flatten() |

Transpose an array | numpy.array.transpose() |

# Importing the NumPy library as np import numpy as np # Creating a NumPy array from a Python List arr = np.array([1, 2, 3, 4, 5, 6]) # Print the array print("arr:", arr) print("Shape of arr: ", arr.shape) # Sort the array in ascending order print("Sorted array: ", np.sort(arr)) # Change the array shape to (2, 3) reshaped_arr = arr.reshape(2, 3) print("Reshaped array: ", reshaped_arr) print("Shape of reshaped array: ", reshaped_arr.shape) # Transpose the reshaped array transposed_arr = reshaped_arr.transpose() print("Transopose array: ", transposed_arr) print("Shape of transposed array: ", transposed_arr.shape) # Change the reshaped 2-D array to 1-D flattened_arr = reshaped_arr.flatten() print("Flattened array: ", flattened_arr)

arr: [1 2 3 4 5 6] Shape of arr: (6,) Sorted array: [1 2 3 4 5 6] Reshaped array: [[1 2 3] [4 5 6]] Shape of reshaped array: (2, 3) Transopose array: [[1 4] [2 5] [3 6]] Shape of transposed array: (3, 2) Flattened array: [1 2 3 4 5 6]

This is it for operations on NumPy arrays. Feel free to construct your own NumPy arrays and try out different operations.