Numpy arrange how to#

The syntax for NumPy arange is pretty straightforward. Once we look at the syntax, I’ll show you more complicated examples which will make everything more clear.

Having said that, what’s actually going on here is a little more complicated, so to fully understand the np.arange function, we need to examine the syntax. The np.arange function produced a sequence of 5 evenly spaced values from 0 to 4, stored as an ndarray object (i.e., a NumPy array). Which will produce a NumPy array like this: We can call the arange() function like this: That might sound a little complicated, so let’s look at a quick example. The NumPy arange function returns evenly spaced numeric values within an interval, stored as a NumPy array (i.e., an ndarray object). Numpy arange creates sequences of evenly spaced values It will also show you some working examples of the np.arange function, so you can play with it and see how it operates.

Numpy arrange how to#

Having said that, this tutorial will show you how to use the NumPy arange function in Python. The NumPy arange function is particularly important because it’s very common you’ll see the np.arange function in a lot of data science code. If you’re learning data science in Python, the Numpy toolkit is important. The Numpy arange function (sometimes called np.arange) is a tool for creating numeric sequences in Python.