Python Array Operations
Python array operations involve manipulating arrays, which are data structures that store elements of the same type. Common array operations in Python include accessing elements by index, slicing, adding or removing elements, and performing mathematical operations on arrays. These operations are essential for working with large sets of data and implementing algorithms in Python.
3 Key excerpts on "Python Array Operations"
eBook - ePubData Analysis for Corporate Finance
Building financial models using SQL, Python, and MS PowerBI
- Mariano F. Scandizzo CFA CQF(Author)
- 2021(Publication Date)
- Fulton Books, Inc.(Publisher)
...Chapter 4 Numpy Introduction “Numerical precision is the very soul of science” D’Arcy Wentworth Thomson In this chapter we will deep dive into Python and data science. I have discovered that probably 90 if not 100 percent of the analytical work can be conducted using premade tools or formulas in the NumPy and Pandas modules. Even if that’s not the case, you will probably create your own by using the tools available on those modules. Let’s begin by formally introducing NumPy. NumPy is a library for the Python programming language, adding support for large multidimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays. Features NumPy targets the CPython reference implementation of Python, which is a non-optimizing bytecode interpreter. Mathematical algorithms written for this version of Python often run much slower than compiled equivalents. NumPy addresses the slowness problem partly by providing multidimensional arrays, functions, and operators that operate efficiently on arrays, requiring rewriting some code, mostly inner loops using NumPy. Using NumPy in Python provides functionality comparable to MATLAB since they are both interpreted and allow the user to write fast programs as long as operations work on arrays or matrices rather than scalars. Moreover, complementary Python packages are available; SciPy is a library that adds more MATLAB-like functionality, and Matplotlib is a plotting package that provides MATLAB-like plotting functionality. Internally, both MATLAB and NumPy rely on BLAS and LAPACK for efficient linear algebra computations. Although the previous description sounds more tedious than what it should be, it is important to have some basic references about what happens behind the scenes as it will help you decide whether to go into more complex, fewer ready-made data manipulations. Our next step is to learn to use the NumPy library...
- Yasha Hasija, Rajkumar Chakraborty(Authors)
- 2021(Publication Date)
- CRC Press(Publisher)
...4 Python for Data Analysis Introduction In data analysis pipelines, data is commonly structured in table formats - where columns represent features, properties, or characteristics of data, and rows serve as the instances or observations of those characteristics. The management of these rows and columns is one of the necessary skills of a data scientist. These rows and columns constitute a matrix with arrays of data. In chapter 2 we have learned about the concept of “nested lists” (recall Figure 2.4), where we defined a nested list as a 2D table or matrix. We have also learned about and discussed a standard table format file “.csv”, where columns in the data are separated by commas, and the data could be loaded as a nested list using Python’s built-in library “CSV”. Loading data in the “list” format is always preferable, but processing data in this format is not always advisable in data analysis due to a variety of reasons, such as: 1. Manipulating the data in a list requires “for” loops, and these are considered slow for large lists. 2. Pieces of data are mostly in numerical form (i.e. number type), and we know that lists are designed to be used for any datatype. The list is, therefore, not optimized for number operations. 3. Finally, the “list” format is not memory efficient. To overcome these lacunae in Python’s built-in data structure, a package called “NumPy”, an abbreviation for Numerical Python, has been developed. This library is very effective in handling numbers and matrices. NumPy also contains various functions for matrix operations, linear algebra, statistics, and many more. Data in row and column formats can also be represented in spreadsheets for analysis. Spreadsheets are important for storing and organizing data or information. These are used for preliminary data analysis such as rearranging data, applying the formula or functions to data, and modifying data, among others...
eBook - ePub- Silas Toms, Bill Parker, Dr. Christopher Tucker, Rene Rubalcava(Authors)
- 2022(Publication Date)
- Packt Publishing(Publisher)
...NumPy was originally written by Travis Oliphant, who also went on to develop the Anaconda project. It is an open-source Python library based on two competing numeric structure libraries known as Numeric and Numarray. NumPy was written to be able to handle large arrays of data and also to extend the functionality of Python for mathematical and scientific processing. This makes it a very useful library for writing code to read, analyze, and write raster data. For ArcGIS Pro users and code writers, using NumPy directly allows you to create custom functions and tools that are not available in the basic tools included in ArcGIS Pro. NumPy’s speed and mathematical capabilities open up new ways to perform analyses and create data workflows. These custom data workflows will often use Pandas, and NumPy is at the heart of Pandas. Pandas is built on NumPy and its array structure, known as ndarray (or N-dimensional array), is what is used for the Pandas Series data structure. As we learned in Chapter 8, a Pandas Series is akin to a row of data and will often be called a row in this chapter. Advantages of NumPy arrays NumPy ndarrays are homogenous (all data is of the same data type) and multidimensional. This makes ndarrays very useful for many data types used in GIS, including continuous geospatial data such as rasters, which often have multiple dimensions, known as bands. The core code of NumPy is written in the C programming language. This allows for better memory management of large arrays of data and quicker processing of data. Python, as an interpreted language, must be converted into byte-code to be executed, which can make the code run more slowly for some applications. Owing to that C code core, NumPy is more efficient and faster. NumPy arrays versus Python lists NumPy arrays are often compared to the built-in Python list data type. Both lists and ndarrays are ordered data structures that are mutable and enclosed in square brackets...
