# Work with Scientific Data Using Numpy Arrays - Intro to earth data science textbook course module

Welcome to the first lesson in the Work with Scientific Data Using Numpy Arrays module. Numpy arrays are a commonly used scientific data structure in Python that store data as a grid, or a matrix. Learn how to import data into numpy arrays and how to run calculations, summarize, and select data from numpy arrays.

## Chapter Fourteen - Numpy Arrays

In this chapter, you will learn about a commonly used data structure in Python for scientific data: numpy arrays. You will write Python code to import text data (.txt and .csv) as numpy arrays and to run calculations and summarize data in numpy arrays.

After completing this chapter, you will be able to:

• Describe the key characteristics of numpy arrays.
• Import data from text files (.txt, .csv) into numpy arrays.
• Run calculations and summarize data in numpy arrays.
• Use indexing to slice (i.e. select) data from numpy arrays.

## What You Need

You should have Conda setup on your computer and the Earth Analytics Python Conda environment. Follow the Set up Git, Bash, and Conda on your computer to install these tools.

Be sure that you have completed the chapters on Jupyter Notebook, working with packages in Python, and working with paths and directories in Python.

## What are Numpy Arrays

Numpy arrays are a commonly used scientific data structure in Python that store data as a grid, or a matrix.

In Python, data structures are objects that provide the ability to organize and manipulate data by defining the relationships between data values stored within the data structure and by providing a set of functionality that can be executed on the data structure.

Recall that in the previous chapters, you used lists (another data structure in Python) to store values of monthly precipitation for Boulder, CO.

Like Python lists, numpy arrays are also composed of ordered values (called elements) and also use indexing to organize and manipulate the elements in the numpy arrays.

A key characteristic of numpy arrays is that all elements in the array must be the same type of data (i.e. all integers, floats, text strings, etc).

Unlike lists which do not require a specific Python package to be defined (or worked with), numpy arrays are defined using the array() function from the numpy package.

To this function, you can provide a list of values (i.e. the elements) as the input parameter:

array = numpy.array([0.7 , 0.75, 1.85])

The example above creates a numpy array with a simple grid structure along one dimension. However, the grid structure of numpy arrays allow them to store data along multiple dimensions (e.g. rows, columns) that are relative to each other. This dimensionality makes numpy arrays very efficient for storing large amounts of data of the same type and characteristic.

## Key Differences Between Python Lists and Numpy Arrays

While Python lists and numpy arrays have similarities in that they are both collections of values that use indexing to help you store and access data, there are a few key differences between these two data structures:

1. Unlike a Python list, all elements in a numpy arrays must be the same data type (i.e. all integers, decimals, text strings, etc).

2. Because of this requirement, numpy arrays support arithmetic and other mathematical operations that run on each element of the array (e.g. element-by-element multiplication). Recall that lists cannot have these numeric calculations applied directly to them.

3. Unlike a Python list, a numpy array is not edited by adding/removing/replacing elements in the array. Instead, each time that the numpy array is manipulated in some way, it is actually deleted and recreated each time.

4. Numpy arrays can store data along multiple dimensions (e.g. rows, columns) that are relative to each other. This makes numpy arrays a very efficient data structure for large datasets.

## Dimensionality of Numpy Arrays

Numpy arrays can be:

• one-dimensional composed of values along one dimension (resembling a Python list).
• two-dimensional composed of rows of individual arrays with one or more columns.
• multi-dimensional composed of nested arrays with one or more dimensions.

In this chapter, you will work with one-dimensional and two-dimensional numpy arrays.

For numpy arrays, brackets [] are used to assign and identify the dimensions of the numpy arrays.

This first example below shows how a single set of brackets [] are used to define a one-dimensional array.

# Import numpy with alias np
import numpy as np

# Monthly avg precip for Jan through Mar in Boulder, CO
avg_monthly_precip = np.array([0.70, 0.75, 1.85])

print(avg_monthly_precip)

[0.7  0.75 1.85]


Notice that the output of the one-dimensional numpy array is also contained within a single set of brackets [].

To create a two-dimensional array, you need to specify two sets of brackets [], the outer set that defines the entire array structure and inner sets that define the rows of the individual arrays.

# Monthly precip for Jan through Mar in 2002 and 2013
precip_2002_2013 = np.array([
[1.07, 0.44, 1.50],
[0.27, 1.13, 1.72]
])

print(precip_2002_2013)

[[1.07 0.44 1.5 ]
[0.27 1.13 1.72]]


Notice again that the output of the two-dimensional numpy array is contained with two sets of brackets [], which is an easy, visual way to identify whether the numpy array is two-dimensional.

Dimensionality will remain a key concept for working with numpy arrays, as you learn more throughout this chapter including how to use attributes of the numpy arrays to identify the number of dimensions and how to use indexing to slice (i.e. select) data from numpy arrays.

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