After installing NumPy and other key Python-programming libraries and getting some code to work, it's time to pass over NumPy arrays. This chapter acquaints you with the fundamentals of NumPy and arrays. At the end of this chapter, you will have a basic understanding of NumPy arrays and their related functions.
The topics we will address in this chapter are as follows:
- Data types
- Array types
- Type conversions
- Creating arrays
- Indexing
- Fancy indexing
- Slicing
- Manipulating shapes
NumPy has a multidimensional array object called ndarray. It consists of two parts, which are as follows:
- The actual data
- Some metadata describing the data
The bulk of array procedures leaves the raw information unaffected; the sole facet that varies is the metadata.
We have already discovered in the preceding chapter how to produce an array by applying the arange() function. Actually, we made a one-dimensional array that held a set of numbers. The ndarray can have more than a single dimension.
The NumPy array is, in general, homogeneous (there is a particular record array type that is heterogeneous)—the items in the array have to be of the same type. The advantage is that if we know that the items in an array are of the same type, it is easy to ascertain the storage size needed for the array. NumPy arrays can execute vectorized operations, processing a complete array, in contrast to Python lists, where you usually have to loop through the list and execute the operation on each element. Also, NumPy utilizes an optimized C API to make them particularly quick.
NumPy arrays are indexed just like in Python, commencing from 0. Data types are represented by special objects. These objects will be discussed comprehensively further in this chapter.
We will make an array with the arange() subroutine again (examine arrayattributes.py from this book's code). In this chapter, you will see snippets from IPython sessions where NumPy is already imported. Here's how to get the data type of an array:
In: a = arange(5)
In: a.dtype
Out: dtype('int64')The data type of the array a is int64 (at least on my computer), but you may get int32 as the output if you are using 32-bit Python. In both the cases, we are dealing with integers (64 bit or 32 bit). Besides the data type of an array, it is crucial to know its shape. The example in Chapter 1, Getting Started with Python Libraries, demonstrated how to create a vector (actually, a one-dimensional NumPy array). A vector is commonly used in mathematics but most of the time we need higher-dimensional objects. Let's find out the shape of the vector we produced a few minutes ago:
In: a Out: array([0, 1, 2, 3, 4]) In: a.shape Out: (5,)
As you can see, the vector has five components with values ranging from 0 to 4. The shape property of the array is a tuple; in this instance, a tuple of 1 element, which holds the length in each dimension.