Array population notice that we wrote item0 not item1

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array population . Notice that we wrote .item(0) , not .item(1) , to get the first element. This is a weird convention in computer science. 0 is called the index of the first item. It's the number of elements that appear before that item. So 3 is the index of the 4th item. Here are some more examples. In the examples, we've given names to the things we get out of population . Read and run each cell. In [52]: # The third element in the array is the population # in 1952. population_1952 = population.item(2) population_1952 In [53]: # The thirteenth element in the array is the population # in 1962 (which is 1950 + 12). population_1962 = population.item(12) population_1962 In [54]: # The 66th element is the population in 2015. population_2015 = population.item(65) population_2015 In [55]: # The array has only 66 elements, so this doesn't work. # (There's no element with 66 other elements before it.) population_2016 = population.item(66) population_2016 In [ ]: # Since make_array returns an array, we can call .item(3) # on its output to get its 4th element, just like we # "chained" together calls to the method "replace" earlier. make_array(-1, -3, 4, -2).item(3) Out[52]: 2636772306 Out[53]: 3140093217 Out[54]: 7256490011 --------------------------------------------------------------------------- IndexError Traceback (most recent call last) <ipython-input-55-7cf1bbebe489> in <module>() 1 # The array has only 66 elements, so this doesn't work. 2 # (There's no element with 66 other elements before it.) ----> 3 population_2016 = population.item(66) 4 population_2016 IndexError: index 66 is out of bounds for axis 0 with size 66
Question 4.2.1. Set population_1973 to the world population in 1973, by getting the appropriate element from population using item . In [56]: population_1973 = population.item(23) #SOLUTION population_1973 In [57]: _ = ok.grade('q421') 4.3. Doing something to every element of an array Arrays are primarily useful for doing the same operation many times, so we don't often have to use .item and work with single elements. Logarithms Here is one simple question we might ask about world population: How big was the population in orders of magnitude in each year? The logarithm function is one way of measuring how big a number is. The logarithm (base 10) of a number increases by 1 every time we multiply the number by 10. It's like a measure of how many decimal digits the number has, or how big it is in orders of magnitude. We could try to answer our question like this, using the log10 function from the math module and the item method you just saw: In [58]: population_1950_magnitude = math.log10(population.item(0)) population_1951_magnitude = math.log10(population.item(1)) population_1952_magnitude = math.log10(population.item(2)) population_1953_magnitude = math.log10(population.item(3)) ... Out[56]: 3942096442 ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Running tests --------------------------------------------------------------------- Test summary Passed: 1 Failed: 0 [ooooooooook] 100.0% passed Out[58]: Ellipsis
But this is tedious and doesn't really take advantage of the fact that we are using a computer. Instead, NumPy provides its own version of log10 that takes the logarithm of each element of an array. It takes a single array of numbers as its argument. It returns an array of the same length, where the first element of the result is the logarithm of the first element of the argument, and so on.

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