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Numpy arrays are great alternatives to Python Lists. Some of the key advantages of Numpy arrays are that they are fast, easy to work with, and give users the opportunity to perform calculations across entire arrays.
In the following example, you will first create two Python lists. Then, you will import the numpy package and create numpy arrays out of the newly created lists.
# Create 2 new lists height and weight height = [1.87, 1.87, 1.82, 1.91, 1.90, 1.85] weight = [81.65, 97.52, 95.25, 92.98, 86.18, 88.45] # Import the numpy package as np import numpy as np # Create 2 numpy arrays from height and weight np_height = np.array(height) np_weight = np.array(weight)
Print out the type of np_height
Now we can perform element-wise calculations on height and weight. For example, you could take all 6 of the height and weight observations above, and calculate the BMI for each observation with a single equation. These operations are very fast and computationally efficient. They are particularly helpful when you have 1000s of observations in your data.
Another great feature of Numpy arrays is the ability to subset. For instance, if you wanted to know which observations in our BMI array are above 23, we could quickly subset it to find out.
First, convert the list of weights from a list to a Numpy array. Then, convert all of the weights from kilograms to pounds. Use the scalar conversion of 2.2 lbs per kilogram to make your conversion. Lastly, print the resulting array of weights in pounds.
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