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Data Science  with Python

You're reading from   Data Science with Python Combine Python with machine learning principles to discover hidden patterns in raw data

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Product type Paperback
Published in Jul 2019
Publisher Packt
ISBN-13 9781838552862
Length 426 pages
Edition 1st Edition
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Authors (3):
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Rohan Chopra Rohan Chopra
Author Profile Icon Rohan Chopra
Rohan Chopra
Mohamed Noordeen Alaudeen Mohamed Noordeen Alaudeen
Author Profile Icon Mohamed Noordeen Alaudeen
Mohamed Noordeen Alaudeen
Aaron England Aaron England
Author Profile Icon Aaron England
Aaron England
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Toc

Table of Contents (10) Chapters Close

About the Book 1. Introduction to Data Science and Data Pre-Processing FREE CHAPTER 2. Data Visualization 3. Introduction to Machine Learning via Scikit-Learn 4. Dimensionality Reduction and Unsupervised Learning 5. Mastering Structured Data 6. Decoding Images 7. Processing Human Language 8. Tips and Tricks of the Trade 1. Appendix

Python Libraries

Throughout this book, we'll be using various Python libraries, including pandas, Matplotlib, Seaborn, and scikit-learn.

pandas

pandas is an open source package that has many functions for loading and processing data in order to prepare it for machine learning tasks. It also has tools that can be used to analyze and manipulate data. Data can be read from many formats using pandas. We will mainly be using CSV data throughout this book. To read CSV data, you can use the read_csv() function by passing filename.csv as an argument. An example of this is shown here:

>>> import pandas as pd

>>> pd.read_csv("data.csv")

In the preceding code, pd is an alias name given to pandas. It is not mandatory to give an alias. To visualize a pandas DataFrame, you can use the head() function to list the top five rows. This will be demonstrated in one of the following exercises.

Note

Please visit the following link to learn more about pandas: https://pandas.pydata.org/pandas-docs/stable/.

NumPy

NumPy is one of the main packages that Python has to offer. It is mainly used in practices related to scientific computing and when working on mathematical operations. It comprises of tools that enable us to work with arrays and array objects.

Matplotlib

Matplotlib is a data visualization package. It is useful for plotting data points in a 2D space with the help of NumPy.

Seaborn

Seaborn is also a data visualization library that is based on matplotlib. Visualizations created using Seaborn are far more attractive than ones created using matplotlib in terms of graphics.

scikit-learn

scikit-learn is a Python package used for machine learning. It is designed in such a way that it interoperates with other numeric and scientific libraries in Python to achieve the implementation of algorithms.

These ready-to-use libraries have gained interest and attention from developers, especially in the data science space. Now that we have covered the various libraries in Python, in the next section we'll explore the roadmap for building machine learning models.

You have been reading a chapter from
Data Science with Python
Published in: Jul 2019
Publisher: Packt
ISBN-13: 9781838552862
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