#### Overview of this book

Python, one of the world's most popular programming languages, has a number of powerful packages to help you tackle complex mathematical problems in a simple and efficient way. These core capabilities help programmers pave the way for building exciting applications in various domains, such as machine learning and data science, using knowledge in the computational mathematics domain. The book teaches you how to solve problems faced in a wide variety of mathematical fields, including calculus, probability, statistics and data science, graph theory, optimization, and geometry. You'll start by developing core skills and learning about packages covered in Python’s scientific stack, including NumPy, SciPy, and Matplotlib. As you advance, you'll get to grips with more advanced topics of calculus, probability, and networks (graph theory). After you gain a solid understanding of these topics, you'll discover Python's applications in data science and statistics, forecasting, geometry, and optimization. The final chapters will take you through a collection of miscellaneous problems, including working with specific data formats and accelerating code. By the end of this book, you'll have an arsenal of practical coding solutions that can be used and modified to solve a wide range of practical problems in computational mathematics and data science.
Preface
Basic Packages, Functions, and Concepts
Free Chapter
Mathematical Plotting with Matplotlib
Working with Randomness and Probability
Geometric Problems
Finding Optimal Solutions
Miscellaneous Topics
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# Creating Series and DataFrame objects

Most data handling in Python is done using the pandas library, which builds on NumPy to provide R-like structures for holding data. These structures allow the easy indexing of rows and columns, using strings or other Python objects besides just integers. Once data is loaded into a pandas DataFrame or Series, it can be easily manipulated, just as if it were in a spreadsheet. This makes Python when combined with pandas a powerful tool for processing and analyzing data.

In this recipe, we will see how to create new pandas Series and DataFrame objects and access items from Series or DataFrame.

`import pandas as pd`
`from numpy.random import default_rngrng = default_rng(12345)`