numpy assignment by reference

Nick McCullum

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NumPy Indexing and Assignment

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In this lesson, we will explore indexing and assignment in NumPy arrays.

The Array I'll Be Using In This Lesson

As before, I will be using a specific array through this lesson. This time it will be generated using the np.random.rand method. Here's how I generated the array:

Here is the actual array:

To make this array easier to look at, I will round every element of the array to 2 decimal places using NumPy's round method:

Here's the new array:

How To Return A Specific Element From A NumPy Array

We can select (and return) a specific element from a NumPy array in the same way that we could using a normal Python list: using square brackets.

An example is below:

We can also reference multiple elements of a NumPy array using the colon operator. For example, the index [2:] selects every element from index 2 onwards. The index [:3] selects every element up to and excluding index 3. The index [2:4] returns every element from index 2 to index 4, excluding index 4. The higher endpoint is always excluded.

A few example of indexing using the colon operator are below.

Element Assignment in NumPy Arrays

We can assign new values to an element of a NumPy array using the = operator, just like regular python lists. A few examples are below (note that this is all one code block, which means that the element assignments are carried forward from step to step).

arr[2:5] = 0.5

Returns array([0. , 0. , 0.5, 0.5, 0.5])

As you can see, modifying second_new_array also changed the value of new_array .

Why is this?

By default, NumPy does not create a copy of an array when you reference the original array variable using the = assignment operator. Instead, it simply points the new variable to the old variable, which allows the second variable to make modification to the original variable - even if this is not your intention.

This may seem bizarre, but it does have a logical explanation. The purpose of array referencing is to conserve computing power. When working with large data sets, you would quickly run out of RAM if you created a new array every time you wanted to work with a slice of the array.

Fortunately, there is a workaround to array referencing. You can use the copy method to explicitly copy a NumPy array.

An example of this is below.

As you can see below, making modifications to the copied array does not alter the original.

So far in the lesson, we have only explored how to reference one-dimensional NumPy arrays. We will now explore the indexing of two-dimensional arrays.

Indexing Two-Dimensional NumPy Arrays

To start, let's create a two-dimensional NumPy array named mat :

There are two ways to index a two-dimensional NumPy array:

• mat[row, col]
• mat[row][col]

I personally prefer to index using the mat[row][col] nomenclature because it is easier to visualize in a step-by-step fashion. For example:

You can also generate sub-matrices from a two-dimensional NumPy array using this notation:

Array referencing also applies to two-dimensional arrays in NumPy, so be sure to use the copy method if you want to avoid inadvertently modifying an original array after saving a slice of it into a new variable name.

Conditional Selection Using NumPy Arrays

NumPy arrays support a feature called conditional selection , which allows you to generate a new array of boolean values that state whether each element within the array satisfies a particular if statement.

An example of this is below (I also re-created our original arr variable since its been awhile since we've seen it):

You can also generate a new array of values that satisfy this condition by passing the condition into the square brackets (just like we do for indexing).

An example of this is below:

Conditional selection can become significantly more complex than this. We will explore more examples in this section's associated practice problems.

In this lesson, we explored NumPy array indexing and assignment in thorough detail. We will solidify your knowledge of these concepts further by working through a batch of practice problems in the next section.

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Home » Python » Python Programs

Are numpy arrays passed by reference?

In this tutorial, we will learn are numpy arrays passed by reference or how can I pass numpy arrays as reference? By Pranit Sharma Last updated : September 16, 2023

NumPy is an abbreviated form of Numerical Python. It is used for different types of scientific operations in python. Numpy is a vast library in python which is used for almost every kind of scientific or mathematical operation. It is itself an array which is a collection of various methods and functions for processing the arrays.

Problem statement

Basically, in python, all the variable names are passed by reference. Suppose that we are passing an array into a function where we are subtracting some values from the original array.

Passing numpy arrays by reference

When Python evaluates an assignment, the right-hand side is evaluated before the left-hand side. Any operation on an array creates a new array; it does not modify the arr in-place.

Any operation makes the local variable a reference to the new array. It does not modify the value originally referenced by arr which was passed to function.

Let us understand with the help of an example,

Python code to pass numpy arrays by reference

The output of the above program is:

Python NumPy Programs »

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101 NumPy Exercises for Data Analysis (Python)

• February 26, 2018
• Selva Prabhakaran

The goal of the numpy exercises is to serve as a reference as well as to get you to apply numpy beyond the basics. The questions are of 4 levels of difficulties with L1 being the easiest to L4 being the hardest.

If you want a quick refresher on numpy, the following tutorial is best: Numpy Tutorial Part 1: Introduction Numpy Tutorial Part 2: Advanced numpy tutorials .

Related Post: 101 Practice exercises with pandas .

1. Import numpy as np and see the version

Difficulty Level: L1

Q. Import numpy as np and print the version number.

You must import numpy as np for the rest of the codes in this exercise to work.

To install numpy its recommended to use the installation provided by anaconda.

2. How to create a 1D array?

Q. Create a 1D array of numbers from 0 to 9

Desired output:

3. How to create a boolean array?

Q. Create a 3×3 numpy array of all True’s

4. How to extract items that satisfy a given condition from 1D array?

Q. Extract all odd numbers from arr

5. How to replace items that satisfy a condition with another value in numpy array?

Q. Replace all odd numbers in arr with -1

Desired Output:

6. How to replace items that satisfy a condition without affecting the original array?

Difficulty Level: L2

Q. Replace all odd numbers in arr with -1 without changing arr

7. How to reshape an array?

Q. Convert a 1D array to a 2D array with 2 rows

8. How to stack two arrays vertically?

Q. Stack arrays a and b vertically

9. How to stack two arrays horizontally?

Q. Stack the arrays a and b horizontally.

10. How to generate custom sequences in numpy without hardcoding?

Q. Create the following pattern without hardcoding. Use only numpy functions and the below input array a .

11. How to get the common items between two python numpy arrays?

Q. Get the common items between a and b

12. How to remove from one array those items that exist in another?

Q. From array a remove all items present in array b

13. How to get the positions where elements of two arrays match?

Q. Get the positions where elements of a and b match

14. How to extract all numbers between a given range from a numpy array?

Q. Get all items between 5 and 10 from a .

15. How to make a python function that handles scalars to work on numpy arrays?

Q. Convert the function maxx that works on two scalars, to work on two arrays.

16. How to swap two columns in a 2d numpy array?

Q. Swap columns 1 and 2 in the array arr .

17. How to swap two rows in a 2d numpy array?

Q. Swap rows 1 and 2 in the array arr :

18. How to reverse the rows of a 2D array?

Q. Reverse the rows of a 2D array arr .

19. How to reverse the columns of a 2D array?

Q. Reverse the columns of a 2D array arr .

20. How to create a 2D array containing random floats between 5 and 10?

Q. Create a 2D array of shape 5x3 to contain random decimal numbers between 5 and 10.

21. How to print only 3 decimal places in python numpy array?

Q. Print or show only 3 decimal places of the numpy array rand_arr .

22. How to pretty print a numpy array by suppressing the scientific notation (like 1e10)?

Q. Pretty print rand_arr by suppressing the scientific notation (like 1e10)

23. How to limit the number of items printed in output of numpy array?

Q. Limit the number of items printed in python numpy array a to a maximum of 6 elements.

24. How to print the full numpy array without truncating

Q. Print the full numpy array a without truncating.

25. How to import a dataset with numbers and texts keeping the text intact in python numpy?

Q. Import the iris dataset keeping the text intact.

Since we want to retain the species, a text field, I have set the dtype to object . Had I set dtype=None , a 1d array of tuples would have been returned.

26. How to extract a particular column from 1D array of tuples?

Q. Extract the text column species from the 1D iris imported in previous question.

27. How to convert a 1d array of tuples to a 2d numpy array?

Q. Convert the 1D iris to 2D array iris_2d by omitting the species text field.

28. How to compute the mean, median, standard deviation of a numpy array?

Difficulty: L1

Q. Find the mean, median, standard deviation of iris's sepallength (1st column)

29. How to normalize an array so the values range exactly between 0 and 1?

Difficulty: L2

Q. Create a normalized form of iris 's sepallength whose values range exactly between 0 and 1 so that the minimum has value 0 and maximum has value 1.

30. How to compute the softmax score?

Difficulty Level: L3

Q. Compute the softmax score of sepallength .

31. How to find the percentile scores of a numpy array?

Q. Find the 5th and 95th percentile of iris's sepallength

32. How to insert values at random positions in an array?

Q. Insert np.nan values at 20 random positions in iris_2d dataset

33. How to find the position of missing values in numpy array?

Q. Find the number and position of missing values in iris_2d 's sepallength (1st column)

34. How to filter a numpy array based on two or more conditions?

Q. Filter the rows of iris_2d that has petallength (3rd column) > 1.5 and sepallength (1st column) < 5.0

35. How to drop rows that contain a missing value from a numpy array?

Difficulty Level: L3:

Q. Select the rows of iris_2d that does not have any nan value.

36. How to find the correlation between two columns of a numpy array?

Q. Find the correlation between SepalLength(1st column) and PetalLength(3rd column) in iris_2d

37. How to find if a given array has any null values?

Q. Find out if iris_2d has any missing values.

38. How to replace all missing values with 0 in a numpy array?

Q. Replace all ccurrences of nan with 0 in numpy array

39. How to find the count of unique values in a numpy array?

Q. Find the unique values and the count of unique values in iris's species

40. How to convert a numeric to a categorical (text) array?

Q. Bin the petal length (3rd) column of iris_2d to form a text array, such that if petal length is:

• Less than 3 --> 'small'
• 3-5 --> 'medium'
• '>=5 --> 'large'

41. How to create a new column from existing columns of a numpy array?

Q. Create a new column for volume in iris_2d, where volume is (pi x petallength x sepal_length^2)/3

42. How to do probabilistic sampling in numpy?

Q. Randomly sample iris 's species such that setose is twice the number of versicolor and virginica

Approach 2 is preferred because it creates an index variable that can be used to sample 2d tabular data.

43. How to get the second largest value of an array when grouped by another array?

Q. What is the value of second longest petallength of species setosa

44. How to sort a 2D array by a column

Q. Sort the iris dataset based on sepallength column.

45. How to find the most frequent value in a numpy array?

Q. Find the most frequent value of petal length (3rd column) in iris dataset.

46. How to find the position of the first occurrence of a value greater than a given value?

Q. Find the position of the first occurrence of a value greater than 1.0 in petalwidth 4th column of iris dataset.

47. How to replace all values greater than a given value to a given cutoff?

Q. From the array a , replace all values greater than 30 to 30 and less than 10 to 10.

48. How to get the positions of top n values from a numpy array?

Q. Get the positions of top 5 maximum values in a given array a .

49. How to compute the row wise counts of all possible values in an array?

Difficulty Level: L4

Q. Compute the counts of unique values row-wise.

Output contains 10 columns representing numbers from 1 to 10. The values are the counts of the numbers in the respective rows. For example, Cell(0,2) has the value 2, which means, the number 3 occurs exactly 2 times in the 1st row.

50. How to convert an array of arrays into a flat 1d array?

Difficulty Level: 2

Q. Convert array_of_arrays into a flat linear 1d array.

51. How to generate one-hot encodings for an array in numpy?

Difficulty Level L4

Q. Compute the one-hot encodings (dummy binary variables for each unique value in the array)

52. How to create row numbers grouped by a categorical variable?

Q. Create row numbers grouped by a categorical variable. Use the following sample from iris species as input.

53. How to create groud ids based on a given categorical variable?

Q. Create group ids based on a given categorical variable. Use the following sample from iris species as input.

54. How to rank items in an array using numpy?

Q. Create the ranks for the given numeric array a .

55. How to rank items in a multidimensional array using numpy?

Q. Create a rank array of the same shape as a given numeric array a .

56. How to find the maximum value in each row of a numpy array 2d?

DifficultyLevel: L2

Q. Compute the maximum for each row in the given array.

57. How to compute the min-by-max for each row for a numpy array 2d?

DifficultyLevel: L3

Q. Compute the min-by-max for each row for given 2d numpy array.

58. How to find the duplicate records in a numpy array?

Q. Find the duplicate entries (2nd occurrence onwards) in the given numpy array and mark them as True . First time occurrences should be False .

59. How to find the grouped mean in numpy?

Difficulty Level L3

Q. Find the mean of a numeric column grouped by a categorical column in a 2D numpy array

Desired Solution:

60. How to convert a PIL image to numpy array?

Q. Import the image from the following URL and convert it to a numpy array.

URL = 'https://upload.wikimedia.org/wikipedia/commons/8/8b/Denali_Mt_McKinley.jpg'

61. How to drop all missing values from a numpy array?

Q. Drop all nan values from a 1D numpy array

np.array([1,2,3,np.nan,5,6,7,np.nan])

62. How to compute the euclidean distance between two arrays?

Q. Compute the euclidean distance between two arrays a and b .

63. How to find all the local maxima (or peaks) in a 1d array?

Q. Find all the peaks in a 1D numpy array a . Peaks are points surrounded by smaller values on both sides.

a = np.array([1, 3, 7, 1, 2, 6, 0, 1])

where, 2 and 5 are the positions of peak values 7 and 6.

64. How to subtract a 1d array from a 2d array, where each item of 1d array subtracts from respective row?

Q. Subtract the 1d array b_1d from the 2d array a_2d , such that each item of b_1d subtracts from respective row of a_2d .

65. How to find the index of n'th repetition of an item in an array

Difficulty Level L2

Q. Find the index of 5th repetition of number 1 in x .

66. How to convert numpy's datetime64 object to datetime's datetime object?

Q. Convert numpy's datetime64 object to datetime's datetime object

67. How to compute the moving average of a numpy array?

Q. Compute the moving average of window size 3, for the given 1D array.

68. How to create a numpy array sequence given only the starting point, length and the step?

Q. Create a numpy array of length 10, starting from 5 and has a step of 3 between consecutive numbers

69. How to fill in missing dates in an irregular series of numpy dates?

Q. Given an array of a non-continuous sequence of dates. Make it a continuous sequence of dates, by filling in the missing dates.

70. How to create strides from a given 1D array?

Q. From the given 1d array arr , generate a 2d matrix using strides, with a window length of 4 and strides of 2, like [[0,1,2,3], [2,3,4,5], [4,5,6,7]..]

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NumPy Copies and Views

When working with NumPy arrays, understanding the difference between copies and views is essential to avoid confusion and bugs. Let's look at both concepts in more detail:

1. No Copy at All

Simple assignments do not make the copy of array object. Instead, it uses the same id() of the original array to access it. The id() returns a universal identifier of Python object, similar to the pointer in C.

Moreover, any changes in either gets reflected in the other. For example, the changing shape of one will change the shape of the other too.

2. View or Shallow Copy

NumPy has a function called view() which is a new array object that looks at the same data of the original array. Unlike the earlier case, change in dimensions of the new array doesn��t change dimensions of the original.

3. Deep Copy

The copy() function creates a deep copy. It is a complete copy of the array and its data, and doesn��t share with the original array.

In the above program, when b is copied from a (using copy() method), a new array is created with new memory allocated to b. That is the reason that when we change b, it does not affect a.

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scipy.optimize.linear_sum_assignment ¶

Solve the linear sum assignment problem.

The linear sum assignment problem is also known as minimum weight matching in bipartite graphs. A problem instance is described by a matrix C, where each C[i,j] is the cost of matching vertex i of the first partite set (a “worker”) and vertex j of the second set (a “job”). The goal is to find a complete assignment of workers to jobs of minimal cost.

Formally, let X be a boolean matrix where \(X[i,j] = 1\) iff row i is assigned to column j. Then the optimal assignment has cost

s.t. each row is assignment to at most one column, and each column to at most one row.

This function can also solve a generalization of the classic assignment problem where the cost matrix is rectangular. If it has more rows than columns, then not every row needs to be assigned to a column, and vice versa.

The method used is the Hungarian algorithm, also known as the Munkres or Kuhn-Munkres algorithm.

New in version 0.17.0.

• http://csclab.murraystate.edu/bob.pilgrim/445/munkres.html
• Harold W. Kuhn. The Hungarian Method for the assignment problem. Naval Research Logistics Quarterly , 2:83-97, 1955.
• Harold W. Kuhn. Variants of the Hungarian method for assignment problems. Naval Research Logistics Quarterly , 3: 253-258, 1956.
• Munkres, J. Algorithms for the Assignment and Transportation Problems. J. SIAM , 5(1):32-38, March, 1957.
• https://en.wikipedia.org/wiki/Hungarian_algorithm

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