# Printing NumPy array values

Printing NumPy array values is a fundamental operation that allows you to inspect the contents of an array and verify the results of your computations. NumPy provides several ways to print array values, such as using the print() function or the NumPy-specific functions. This is useful for debugging purposes, or for visualizing the contents of an array for analysis. In this thread, we will discuss how to print all values of the array.

#### 1. By "print" statement:

In Python, the `print` statement is used to display output on the screen or console. It takes one or more arguments, which are separated by commas, and prints them to the standard output device. By default, the “print” statement adds a newline character at the end of the output, but this behavior can be modified using the “end” argument.

• The above code imports the NumPy library and assigns it an alias `np`.

• It then creates a NumPy array named `arr`. The array contains the values [1, 2, 3, 4, 5]. NumPy arrays are similar to lists in Python but are optimized for numerical operations.

• It calls the `print` function and passes the `arr` array as an argument. This will display the contents of the `arr` array on the console or screen.

• When the `print` function is executed, it will display the following output:[1 2 3 4 5].

It is a simple and convenient way to communicate with the user and provide feedback about the program’s execution. The `print` statement can be used to display text, variables, or the results of computations.

#### 2. By "ndarray.tolist()" method:

In NumPy, the `ndarray.tolist()` method is used to convert a NumPy array to a Python list. The method returns a new list object that contains the elements of the NumPy array in the same order. The `ndarray.tolist()` method takes no arguments and can be called on any NumPy array object.
You can convert the NumPy array to a Python list using the `numpy.ndarray.tolist()` method, which can then be printed using the “print” statement.

• The above code imports the NumPy library and assigns it an alias `np`.

• It then creates a NumPy array named `arr`. The array contains the values [1, 2, 3, 4, 5].

• It calls the `tolist()` method on the `arr` array. This method is used to convert the NumPy array to a Python list.

• It calls the `print` function and passes the result of the `tolist()` method as an argument. This will display the contents of the `arr` array as a Python list on the console or screen.

• When the `print` function is executed, it will display the following output:[1, 2, 3, 4, 5].

The `tolist()` method converts the NumPy array to a list, which can be useful when working with other Python libraries or when you need to pass the array data to a function that only accepts Python lists.

#### 3. By "ndarray.flat" attribute:

In NumPy, the `ndarray.flat` attribute is used to get a 1-dimensional iterator over the elements of a multi-dimensional NumPy array. The `flat` attribute returns a `numpy.flatiter` object that allows you to iterate over the elements of the array in a linear fashion, regardless of the shape of the array.
You can use the `numpy.ndarray.flat` attribute, which returns a 1-dimensional iterator over the array elements. The elements can then be printed using a `loop`.

• The above code imports the NumPy library and assigns it an alias `np`.

• It then creates a NumPy array named `arr`. The array contains two rows and two columns with values [[1, 2], [3, 4]].

• The third line of the code starts a for loop that iterates over the values of the `arr.flat` attribute. This attribute returns a 1-dimensional iterator over the elements of the “arr” array.

• It calls the `print` function and passes the current value of the iterator as an argument. This will display the current value of the iterator on the console or screen.

• When the code is executed, it will iterate over all elements of the `arr` array in a linear fashion, regardless of its shape, and print each element to the console.

As you can see, the `arr.flat` attribute allows us to iterate over the elements of a multi-dimensional NumPy array as if it were a 1-dimensional array. This can be useful when we need to perform certain operations on each element of the array, such as applying a mathematical function or performing a statistical analysis.

#### 4. By "nditer()" function:

In NumPy, the `nditer()` function is used to iterate over the elements of a multi-dimensional NumPy array in a specified order. The function returns an iterator object that can be used to loop through the array elements in a specific order.

You can use the `numpy.nditer()` function, which returns a multi-dimensional iterator over the array elements. The elements can then be printed using a loop.

• The above code imports the NumPy library and assigns it an alias `np`.

• It then creates a 2-dimensional NumPy array named `arr` with values [[1, 2], [3, 4]].

• It starts a for loop that iterates over the elements of the `arr` array using the `nditer()` function.

• It calls the `print` function and passes the current value of the iterator as an argument. This will display the current value of the iterator on the console or screen.

• When the code is executed, it will iterate over all elements of the `arr` array and print each element to the console.

As we can see, the `nditer()` function allows us to iterate over the elements of a multi-dimensional NumPy array in a specific order. In this case, the order of iteration is row-wise (C order) by default.