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Hands-On Data Analysis with NumPy and pandas
Hands-On Data Analysis with NumPy and pandas

Hands-On Data Analysis with NumPy and pandas: Implement Python packages from data manipulation to processing

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Hands-On Data Analysis with NumPy and pandas

Diving into NumPY

By now you should have installed everything you need to use Python for data analysis. Let's now begin discussing NumPy, an important package for managing data and performing calculations. Without NumPy, there would not be any data analysis using Python, so understanding NumPy is critical. Our key objective in this chapter is learning to use the tools provided in NumPy.

In this chapter, the following topics will be covered:

  • NumPy data types
  • Creating arrays
  • Slicing arrays
  • Mathematics
  • Methods and functions

We begin by discussing data types, which are conceptually important when handling NumPy arrays. In this chapter, we will discuss NumPy data types controlled by dtype objects, which are the way NumPy stores and manages data. We'll also briefly introduce NumPy arrays called ndarray and discuss what they do.

...

NumPy arrays

Let's now talk about NumPy arrays, which are called ndarray. These are not the arrays you may encounter in C or C++. A better analog is matrices in MATLAB or R; that is, they behave like a mathematical object resembling a mathematical vector, matrix, or tensor. While they can store non-mathematical information such as strings, they exist mainly to manage and facilitate operations with data that is numeric in nature. ndarray are assigned a particular data type or dtype upon creation, and all current and future data in the array must be of that dtype. They also have more than one-dimension, referred to as axes.

A one-dimensional ndarray is a line of data; this would be a vector. A two-dimensional ndarray would be a square of data, effectively a matrix. A three-dimensional ndarray would be key book data, like a tensor. Any number of dimensions is permitted...

Special numeric values

In addition to dtype objects, NumPy introduces special numeric values: nan and inf. These can arise in mathematical computations. Not A Number (nan). It indicates that a value that should be numeric is, in fact, not defined mathematically. For example, 0/0 yields nan. Sometimes, nan is also used to signify missing information; for example, pandas uses this. inf indicates a quantity that is arbitrarily large, so in practice, it means larger than any number the computer can conceive of. -inf is also defined and it means arbitrarily small. This could occur if a numeric operation blows up, that is, grows rapidly without bounds.

Nothing is ever equal to nan; it makes no sense for something undefined to be equal to something else. You need to use the NumPy function isnan to identify nan. While the == sign does not work for nan, it does work for inf....

Creating NumPy arrays

Now that we have discussed NumPy data types and have been briefly introduced to NumPy arrays, let's talk about how we can create NumPy arrays. In this section, we will create NumPy arrays using various functions. There are functions that create what are known as empty ndarray; functions for creating ndarray filled with 0s, 1s, or random numbers; and functions for creating ndarray using data. We will discuss all of these, along with saving and loading NumPy arrays from disk. There are a few ways to create arrays. One way is to use the array function, where we give an iterable object or a list of iterable objects, from which an array will be generated.

We will do this using lists of lists, but these could be lists of tuples, tuples of tuples, or even other arrays. There are ways to automatically create arrays filled with data as well. For example...

Summary

In this chapter, we started by introducing NumPy data types. We then quickly moved on to discuss NumPy arrays, called ndarray objects, which are the main objects of interest in NumPy. We discussed how to create these arrays from programmer input, from other Python objects, from files, and even from functions. We proceeded to discuss how mathematical operations are performed on ndarray objects, from basic arithmetic to full-blown linear algebra.

In the next chapter, we will discuss some important topics: slicing ndarray objects arithmetic and linear algebra with arrays, and employing array methods and functions.

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Key benefits

  • Explore the tools you need to become a data analyst
  • Discover practical examples to help you grasp data processing concepts
  • Walk through hierarchical indexing and grouping for data analysis

Description

Python, a multi-paradigm programming language, has become the language of choice for data scientists for visualization, data analysis, and machine learning. Hands-On Data Analysis with NumPy and Pandas starts by guiding you in setting up the right environment for data analysis with Python, along with helping you install the correct Python distribution. In addition to this, you will work with the Jupyter notebook and set up a database. Once you have covered Jupyter, you will dig deep into Python’s NumPy package, a powerful extension with advanced mathematical functions. You will then move on to creating NumPy arrays and employing different array methods and functions. You will explore Python’s pandas extension which will help you get to grips with data mining and learn to subset your data. Last but not the least you will grasp how to manage your datasets by sorting and ranking them. By the end of this book, you will have learned to index and group your data for sophisticated data analysis and manipulation.

Who is this book for?

Hands-On Data Analysis with NumPy and Pandas is for you if you are a Python developer and want to take your first steps into the world of data analysis. No previous experience of data analysis is required to enjoy this book.

What you will learn

  • Understand how to install and manage Anaconda
  • Read, sort, and map data using NumPy and pandas
  • Find out how to create and slice data arrays using NumPy
  • Discover how to subset your DataFrames using pandas
  • Handle missing data in a pandas DataFrame
  • Explore hierarchical indexing and plotting with pandas
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Length: 168 pages
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Table of Contents

7 Chapters
Setting Up a Python Data Analysis Environment Chevron down icon Chevron up icon
Diving into NumPY Chevron down icon Chevron up icon
Operations on NumPy Arrays Chevron down icon Chevron up icon
pandas are Fun! What is pandas? Chevron down icon Chevron up icon
Arithmetic, Function Application, and Mapping with pandas Chevron down icon Chevron up icon
Managing, Indexing, and Plotting Chevron down icon Chevron up icon
Other Books You May Enjoy Chevron down icon Chevron up icon

Customer reviews

Top Reviews
Rating distribution
Full star icon Full star icon Half star icon Empty star icon Empty star icon 2.9
(7 Ratings)
5 star 28.6%
4 star 14.3%
3 star 0%
2 star 28.6%
1 star 28.6%
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Akshay Jan 02, 2024
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Excellent book that gets down to the basics!
Subscriber review Packt
S. Sankara Subramanian Sep 10, 2018
Full star icon Full star icon Full star icon Full star icon Full star icon 5
no specific comments
Amazon Verified review Amazon
Amazon Customer Aug 27, 2018
Full star icon Full star icon Full star icon Full star icon Empty star icon 4
Would recommend this book to those with a background in data analysis and are untrained in using Python.Pros - This book delivers exactly what is written in the title, no more, no less. The writing style is introductory and there are plenty of examples. The book addresses how to clean data using Python which is mandatory when performing data analysis. Examples discussed in this book could be used to supplement references which are less practical.Cons - The editing uses incorrect fonts on words that refer to technical terms. For example, some Python functions in this book are type-font, but the editor frequently omits this formatting. Many screenshots include cursors. Some sections, such as the linear algebra section, explain how to implement code but do not explain the context or give references.
Amazon Verified review Amazon
BBCReview Sep 30, 2021
Full star icon Full star icon Empty star icon Empty star icon Empty star icon 2
The books has good content on Numpy and Pandas, but you can't read the code snippets without a magnifying glass, or worse yet, zooming each one. Not the fault of the author, but it's darn hard to follow when it take 10 seconds to read each each snippet.
Amazon Verified review Amazon
Philip H Sep 15, 2018
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The explanations are reasonable although the book could have been written much more concisely. The examples are written in tiny fonts
Amazon Verified review Amazon
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