Here we call Lambda, where it will return the value of x only when the division of x by 2 is = 0, for each element of array1 NumPy - then apply the filter to print the numerical results in the list: list(filter(lambda x: x % 2 = 0, array1)) Therefore, when working with NumPy, remember that you can also work with List Comprehension. Translating: for (for) each value of x within (in) array1, if (if) the value of x divisible by two results in zero, return the value of x multiplied by itself - that is, the operation only with even numbers. Translating: for (for) each value of x within (in) array1, multiply the value of x by itself: Let’s use the NumPy array1 in the list comprehension the same way we would use any other type of array.
Once we have the array created with NumPy, we can call the mathematical methods or any methods with.
#NUMPY VSTACK VS CONCATENATION HOW TO#
?array1Ī complete help of that object is opened - the full description of what the object is specifically, how to use it, parameters that we can use, attributes and methods, other packages capable of generating that same type of object, etc.… Mathematical Methods directly in arrays When we have any questions about any object in Python, can we call help using? And the name of the object. Now we create an array with the arange function of numpy and not range built-in. This is for almost any package - always check the version. Let’s start by viewing the Python version and the NumPy package version. Note: Important functions, outputs, and terms are bold to facilitate understanding - at least mine. Go to Jupyter Notebook to see the concepts that will be covered about Operations with Arrays in NumPy.
Here we’ll do some operations with arrays through the NumPy library and take the opportunity to compare NumPy with List Comprehensions and Lambda Functions and even see the difference in performance between the different kinds of operations.
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