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Introduction to Data Science with Python: Basics of Numpy and Pandas
| AUTHOR | Smart, Mark |
| PUBLISHER | Independently Published (11/09/2018) |
| PRODUCT TYPE | Paperback (Paperback) |
Description
This book is a guide for you on how to use Pandas and Numpy in Python programming language for data analysis. The author begins by helping you familiarize yourself with the basics of data science, Numpy and Pandas. You are guided on how to work with Numpy arrays and how to manipulate them. The various operations that you can perform on your data via the Pandas library have been discussed. You will also know how to create various data structures in Pandas for data storage. Data from the environment is dirty. The process of cleaning such data has been discussed. This involves handling outliers, missing values etc. The author guides you on how to work with data in various types of storage formats. Examples include MS Excel, CSV files, JSON, etc. You are also guided on how to calculate various measures for your data. The process of visualizing data has been explored in detail.
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Product Format
Product Details
ISBN-13:
9781731036841
ISBN-10:
1731036841
Binding:
Paperback or Softback (Trade Paperback (Us))
Content Language:
English
More Product Details
Page Count:
102
Carton Quantity:
80
Product Dimensions:
6.00 x 0.21 x 9.00 inches
Weight:
0.32 pound(s)
Country of Origin:
US
Subject Information
BISAC Categories
Computers | Languages - Python
Descriptions, Reviews, Etc.
publisher marketing
This book is a guide for you on how to use Pandas and Numpy in Python programming language for data analysis. The author begins by helping you familiarize yourself with the basics of data science, Numpy and Pandas. You are guided on how to work with Numpy arrays and how to manipulate them. The various operations that you can perform on your data via the Pandas library have been discussed. You will also know how to create various data structures in Pandas for data storage. Data from the environment is dirty. The process of cleaning such data has been discussed. This involves handling outliers, missing values etc. The author guides you on how to work with data in various types of storage formats. Examples include MS Excel, CSV files, JSON, etc. You are also guided on how to calculate various measures for your data. The process of visualizing data has been explored in detail.
Show More