Python fill array with numbers

6 ways to fill NumPy array with all same values

In this post, we will learn about 6 ways to fill NumPy array with all same values. To create a NumPy array of all same values in python the first step is to create an array of given shapes that contain only identical values. We will use different Numpy library functions. We can achieve this with slicing or functions.

6 ways to fill NumPy array with all same values in Python

• Fill NumPy array with all same values using slice
• Fill NumPy array with all same values using full()
• Fill NumPy array with all same values using fill()
• Fill NumPy array with all same values using tile()
• Fill NumPy array with all same values using ones()
• Fill NumPy array with all same values using full_like()

1. Fill NumPy array with all same values using slice

In this Python program, we have created a numpy empty array by using the empty() function of a given shape(2,3) to assign all same values to the numpy array element slicing [:] select all elements of the array regardless of dimensions and replace with the given value.

The assigned value should be of the same type as the array otherwise it raises an error.to change the type astype() method can be used.

2. Fill NumPy array with all same values using full()

The numpy. full() return an array of given shapes after filling the same values in all elements of the array. In this Python program, we have created an array of shapes (2,3) and filled it with value15.

Python program to fill NumPy array with all same values using full()

import numpy as np nparr = np.full((2,3), 15) print(nparr)

3. Fill NumPy array with all same values using fill()

The numpy. ndarray.fill() function Fill the array with a scalar value. In this python program, we have created a numpy empty array of shapes (2,3) and by using the fill() function filled it with value 15.

Python Program to Fill NumPy array with all same values using fill()

import numpy as np nparr=np.empty((2,3)) nparr.fill(15) print(nparr)

4. Fill NumPy array with all same values using tile()

The numpy tile function returns a new array by simply repeating a number of elements in the array as per given reputations. In this python program, we have repeated the value 25 for given reputations (2,3).

Python program create numpy array with same values

import numpy as np rows = 2 cols = 3 nparr = np.tile(15, (rows,cols)) print(nparr)

5. Fill Numpy array with same values using ones()

In this Python program, we have used the np. ones() function to create a numpy array of a given shape(3,2) and that has the same identical value 15 for each element.

Python Program to create numpy array with same values using ones()

import numpy as np nparr = 15 * np.ones(shape=(3,2)) print(nparr)

6. Fill Numpy array with same values using full_like()

The numpy.full_like() returns a full array of given shape and size as passed array. We will use it to create an numpy array with same all values.

Python program create numpy array with same values

import numpy as np nparr = np.arange(6, dtype = int).reshape(2, 3) print("Created array using reshape() : \n", nparr) print("\n after full_like : \n", np.full_like(nparr, 15))

Created array using reshape() : [[0 1 2] [3 4 5]] after full_like : [[10 10 10] [10 10 10]]

Summary

In this post, we have learned 6 ways to fill the NumPy array with all same values in Python.

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Fill Array with Random Numbers in Python

An array is one of the fundamental data structures in Python. It stores elements in a contiguous memory location and each element can be accessed using its respective index. In Python, the numpy library is used to implement array data structures. Lists are also another data structure that mimics an array in Python.

In this tutorial, we will discuss different methods to fill array with random numbers in Python.

We can create an array of our desired shapes. Sometimes, we may require dummy arrays for some calculations. We can create an array filled with such dummy values since the numpy library also implements a random module to generate random numbers.

Ways to fill array with random numbers in Python

Let us now discuss how to fill array with random numbers in Python.

Using the numpy.random.randint() function to fill array with random numbers in Python

As discussed earlier, the numpy library has a random module that can help in generating random numbers for numpy arrays. The randint() function can be used to generate an array of the required size and fill array with random numbers in Python.

We need to remember the three main parameters. The first two parameters are the low and high values. The function will select a random integer between this range. The third parameter is the shape parameter which specifies the shape of the final array.

See the following example.

In the above example, we generate random numbers between 0 to 10 and fill it in a one-dimensional array of length ten.

Using the numpy.random.Generator.integers() function to fill array with random numbers in Python

The numpy.random.Generators offer a new way to generate and work with random numbers. It uses an additional BitGenerator to spawn such random bits and manage their states. To initiate a new Generator , we use the numpy.random.default_rng() constructor.

After this, we can use the numpy.random.Generator.integers() function to generate random numbers and fill array with random numbers in Python.

We need to specify the low , high , and shape values in this function as we did in the previous method.

Further reading:

Generate random number between 0 and 1 in Python

Get random boolean in Python

Using the random.randint() function to fill array with random numbers in Python

As discussed in the first section, lists in Python also mimic an array. We can fill lists with random numbers using the random module in Python. We will use list comprehension along with the random.randint() function to fill array with random numbers in Python.

We will use the random.randint() function to generate a random number between a given range. We will use the list comprehension method that loops over this function the required number of times, creating a new list of the required length.

Conclusion

To conclude, in this tutorial we discussed different methods to fill array with random numbers in Python. Essentially, we were creating arrays with random numbers. For this, we used numpy arrays and lists.

For numpy arrays, we had two methods. The first was the traditional numpy.random.randint() function that generates a numpy array of a given length filled with random numbers between a given range.

In the second method, we used a relatively new numpy.random.Generators module to create the array. It uses Generators that provide an additional state to manage and generate random bits.

In the final method, we discussed how to fill array with random numbers in Python using the list comprehension method and the random.randint() function. Essentially, we create a loop and run the randint() function the required number of times and add the generated number to a list.

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Fill NumPy Arrays with numpy.fill and numpy.full

Filling NumPy arrays with a specific value is a typical task in Python. It’s common to create an array, then initialize or change some values, and later reset the array to a starting value. It’s also common to initialize a NumPy array with a starting value, such as a no data value. These operations may be especially important when working with geographical data like raster and NetCDF files. There are two simple ways to fill NumPy arrays. You can fill an existing array with a specific value using numpy.fill() . Alternatively, you can initialize a new array with a specific value using numpy.full() . NumPy also has built-in functions to create and fill arrays with zeros ( numpy.zeros() ) and ones ( numpy.ones() ).

We’ll start with a demonstration of how to change all the values in an existing array ( numpy.fill() ), then move on the to built-in function to initialize arrays with specific values. All of the code for this lesson is going to be written as if you were using an interactive console (like the Anaconda prompt, bash, terminal, cmd, etc.). Make sure you have imported NumPy as follows to use the code snippets in this tutorial

numpy.fill()

To start, let’s create a simple, empty array. If you’re not familiar with how to create NumPy arrays you can check out my guide. This code creates a 1D array with 5 elements. In a real-world example, this could be an array that I initialized and then wanted to assign another value to later.

The array should look similar to the result below. Your values will be slightly different. Again, for a description of creating arrays with NumPy visit my guide. It will explain the creation methods used in this tutorial.

array([7.56603881e-307, 6.23054972e-307, 6.23053954e-307, 1.02360052e-306, 1.78020984e-306])

Now, let’s fill that array with a specified value using numpy.fill() . Numpy.fill() is a very simple function. It can be called on any NumPy array of any dimension. It takes one argument, the value that will fill all array elements. For example, to fill the array we created with the value 2 , use the following line of code.

numpy.full()

With numpy.full() we can combine the two lines of code from the last section (one line to create an empty array, and one line to fill the array with a value) into a single function. Numpy.full() is useful when you want to initialize an array and already know the value you want to array to be initialized to. For example, you may want to create a raster that is initially filled with a specific no data value. Two arguments must be specified for numpy.full() : the shape of the array, and the fill value. Remember, NumPy array shapes are defined as tuples. Two optional arguments can also be specified: the data type ( dtype ) and whether to use C or Fortran order to store the data. Usually, the defaults for these arguments are fine. To create a 2D array with 5 rows and 5 columns that is filled with -9999, use the following code.

array([[-9999, -9999, -9999, -9999, -9999], [-9999, -9999, -9999, -9999, -9999], [-9999, -9999, -9999, -9999, -9999], [-9999, -9999, -9999, -9999, -9999], [-9999, -9999, -9999, -9999, -9999]])

Notice the values are integers. If I wanted the values to be floating point, I simply need to pass a floating point value (i.e. add a decimal) to np.full , like so.

numpy.zeros()

Numpy.zeros() is basically a special implementation of numpy.full() . It takes a shape argument and the optional data type and order arguments. The result is an array filled with zeros. We can create the same array as above with zeros like so.

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

numpy.ones()

Numpy.ones() functions the same as numpy.zeros() , but (obviously) initializes an array of ones. The arguments are the same; shape, data type, and order.

Conclusion

Filling arrays is a simple, yet important operation. It is an operation that you will conduct many times. Understanding the various methods available to fill arrays can help you streamline your programs and analyses by eliminating unnecessary code.

Konrad Hafen Konrad has a Master’s Degree in Ecology and a Doctorate Degree in Water Resources and has been performing geospatial analysis and writing code (in multiple programming languages) for over a decade. He writes code to develop models and analysis workflows to predict and evaluate changes to landscapes and water resources. He has published multiple articles in prominent peer-reviewed, scientific journals. Konrad’s code and workflow contribute to operational products that inform water and ecosystem management.

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