# 15 What are the Benefits of Using Numpy Arrays? The sum method calculates the sum of numbers in an array. The * operator performs an element-wise multiplication of two arrays if they have the same size. We can achieve the same result with low-level operations supported by Numpy arrays: performing an element-wise multiplication and calculating the resulting numbers' sum. We can now compute the dot product of the two vectors using the np.dot function. Just like lists, Numpy arrays support the indexing notation. We can now use the np.array function to create Numpy arrays. It's common practice to import numpy with the alias np. Let's install the Numpy library using the pip package manager. However, we must first convert the lists into Numpy arrays. The Numpy library provides a built-in function to compute the dot product of two vectors. The calculation performed by the crop_yield (element-wise multiplication of two vectors and taking a sum of the results) is also called the dot product. # 74.9 How to Turn Python Lists into Numpy Arrays We can now write a function crop_yield to calculate the yield of apples (or any other crop) given the climate data and the respective weights. We can also represent the set of weights used in the formula as a vector. The three numbers in each vector represent the temperature, rainfall, and humidity data, respectively. To make it slightly easier to perform the above computation for multiple regions, we can represent the climate data for each region as a vector, that is a list of numbers. # The expected yield of apples in Kanto region is 56.8 tons per hectare. Print("The expected yield of apples in Kanto region is tons per hectare.".format(kanto_yield_apples)) kanto_yield_apples = kanto_temp * w1 + kanto_rainfall * w2 + kanto_humidity * w3 We can now substitute these variables into the linear equation to predict the yield of apples. To begin, we can define some variables to record climate data for a region. Given some climate data for a region, we can now predict the yield of apples. But a simple linear model like this often works well in practice.īased on some statistical analysis of historical data, we might come up with reasonable values for the weights w1, w2, and w3. This equation is an approximation, since the actual relationship may not necessarily be linear, and there may be other factors involved. We're expressing the yield of apples as a weighted sum of the temperature, rainfall, and humidity. Yield_of_apples = w1 * temperature + w2 * rainfall + w3 * humidity Suppose we want to use climate data like the temperature, rainfall, and humidity to determine if a region is well suited for growing apples.Ī simple approach to do this would be to formulate the relationship between the annual yield of apples (tons per hectare) and the climatic conditions like the average temperature (in degrees Fahrenheit), rainfall (in millimeters), and average relative humidity (in percentage) as a linear equation. Let's work through an example to see why and how to use Numpy to work with numerical data. The Numpy library provides specialized data structures, functions, and other tools for numerical computing in Python. The "data" in Data Analysis typically refers to numerical data, like stock prices, sales figures, sensor measurements, sports scores, database tables, and so on. How to Work with Numerical Data in Python How to work with CSV data files using Numpy.Array operations, broadcasting, indexing, and slicing.Multi-dimensional Numpy arrays and their benefits.How to turn Python lists into Numpy arrays.How to work with numerical data in Python.This section covers the following topics: You can follow along with the tutorial and run the code here: What is Numerical Computation? Python and Numpy for Beginners Source: Elegant Scipy
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