I will explain how to use random seed in Numpy, a popular Python library for scientific computing. Random seed is a way of controlling the randomness of Numpy’s random number generators, which are used for various purposes such as generating random data, shuffling arrays, sampling from distributions, and more.

# Category: numpy

Enter here to see how can you do with Numpy! More than 100 tricks for you!

## How to resolve AttributeError: ‘numpy.ndarray’ object has no attribute ‘function_name

If you are working with Python and numpy, you may encounter an error like this:

AttributeError: ‘numpy.ndarray’ object has no attribute ‘function_name’

This error means that you are trying to call a function that does not exist for numpy arrays. Numpy arrays are objects that store multiple values of the same data type in a fixed-size grid. They have many methods and attributes that allow you to manipulate and analyze them, but they do not have every function that you may want to use.

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## How to resolve TypeError: Cannot perform reduce with flexible type

I will explain how to resolve the error `TypeError: Cannot perform reduce with flexible type` that may occur when using NumPy functions on arrays with different data types.

NumPy is a popular Python library for scientific computing that provides fast and efficient operations on multidimensional arrays. One of the features of NumPy is that it allows you to apply reduction functions (such as sum, mean, max, min, etc.) to an array along a given axis or over the whole array. For example, you can use `np.sum(arr)` to get the sum of all the elements in `arr`, or `np.sum(arr, axis=0)` to get the sum of each column in `arr`.

However, sometimes you may encounter the error `TypeError: Cannot perform reduce with flexible type` when you try to use a reduction function on an array that contains elements of different data types. For example, if you have an array like this:

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## How to resolve ValueError: operands could not be broadcast together with shapes (X,) (Y,)

I will explain how to resolve the error: ValueError: operands could not be broadcast together with shapes (X,) (Y,) when performing operations on NumPy arrays in Python.

## How to resolve ValueError: operands could not be broadcast together with shapes

If you have ever worked with NumPy arrays, you might have encountered the ValueError: operands could not be broadcast together with shapes. This error occurs when you try to perform an operation on two arrays that have incompatible shapes. We will explain what broadcasting is, how NumPy determines the shapes of the operands, and how to resolve this error.

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## How to fix ValueError: The truth value of an array with zero elements is ambiguous?

This error typically occurs when you are trying to use an empty array as a Boolean condition in an if-statement or a while-loop. The error message is telling you that the truth value of an empty array is ambiguous because there is no value to evaluate.

Here is an example code that can produce this error:

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## How to solve NameError: name ‘numpy’ is not defined

The “NameError: name ‘numpy’ is not defined” error message is typically encountered when trying to use the NumPy library in Python but the library has not been imported or has not been imported correctly.

To fix this error, you need to ensure that you have installed the NumPy library in your environment and that you have imported it correctly in your code. Here are some steps you can follow:

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## How to solve TypeError: ‘numpy.int64’ object is not callable

The “TypeError: ‘numpy.int64’ object is not callable” error is usually caused by attempting to call a variable or object that is not a function or method.

Here are a few steps you can follow to try and solve the error:

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## How to solve AttributeError: module ‘numpy’ has no attribute ‘random’

The error “AttributeError: module ‘numpy’ has no attribute ‘random'” can occur when you try to use the “random” submodule of the NumPy library, but it cannot be found. Here are some possible solutions:

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## How to solve TypeError: ‘numpy.float64’ object is not iterable

The error message “TypeError: ‘numpy.float64’ object is not iterable” usually occurs when you try to iterate over a numpy float64 object directly.

To solve this error, you need to ensure that you are not trying to iterate over a single numpy float64 object. Instead, you should iterate over a numpy array or a Python list.

Here is an example of how to fix this error:

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## How to solve ValueError: setting an array element with a sequence

The ValueError: setting an array element with a sequence error typically occurs when you try to assign a sequence (e.g., list, tuple) to an element of a numpy array that expects a scalar value.

To solve this error, you need to make sure that you are assigning a scalar value to the array element, rather than a sequence.

Here are some steps you can take to solve this error:

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## Swap Numpy row vector to column vector

This time we teach ourselves how to swap Numpy row vector to column vector.

## How to calculate exponential of complex number in Numpy?

In NumPy, you can calculate the exponential of a complex number using numpy.exp. The exponential of a complex number z can be represented as exp(z) = exp(x) * (cos(y) + 1j * sin(y)), where x and y are the real and imaginary parts of the complex number z, respectively.

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## How to reverse a vector in numpy

You can reverse a NumPy array using the [::-1] slicing syntax. This creates a new reversed array, leaving the original array unchanged.

Here’s an example of how to reverse a 1-D NumPy array:

## How to calculate the factorial of an array in Numpy?

In this post, you will learn how to calculate the factorial of an array in Numpy.

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