Using arrays for analysis
1. Using Arrays for Analyses
In the last lesson, you used indexing to slice elements from multidimensional arrays. A powerful characteristic of NumPy arrays is that they can also be indexed with other arrays.2. Indexing Arrays
Consider a 1-dimensional array, months_array. Now, let's create an "indexing array" that specifies specific indices, in this case 1, 3, and 5. You can now subset the months_array with this indexing array and store this solution as months_subset. In the months_array, indices 1, 3, and 5 correspond to months Feb Apr and Jun. And indeed, when we print out the result of months_subset, we see this result.3. More on indexing arrays
What do you think would be the output of using an indexing array with negative indices like the one shown here? The output returned is the -1 indexed element, Jun, and then the -2 indexed element, or May.4. Boolean arrays
Arrays can be comprised of numerics, strings or boolean values as shown here. A powerful characteristic of boolean arrays is that they can be used to manipulate other arrays. For example, consider the months_array and boolean_array arrays shown here. The boolean array would subset only the elements where the index is True. Thus, the output returned is Jan, Feb, and Mar.5. More on Boolean arrays
Boolean arrays can also be created with conditional tests. In this example, we create a boolean array using a conditional test which identifies the elements in prices_array greater than 238. We can now use this boolean_array as an index to subset our prices_array.6. Let's practice!
You can perform very powerful analyses combining boolean indexing and functions in numpy. Let's practice doing that!Create Your Free Account
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