Book Image

Mastering Python Scripting for System Administrators

By : Ganesh Sanjiv Naik
Book Image

Mastering Python Scripting for System Administrators

By: Ganesh Sanjiv Naik

Overview of this book

Python has evolved over time and extended its features in relation to every possible IT operation. Python is simple to learn, yet has powerful libraries that can be used to build powerful Python scripts for solving real-world problems and automating administrators' routine activities. The objective of this book is to walk through a series of projects that will teach readers Python scripting with each project. This book will initially cover Python installation and quickly revise basic to advanced programming fundamentals. The book will then focus on the development process as a whole, from setup to planning to building different tools. It will include IT administrators' routine activities (text processing, regular expressions, file archiving, and encryption), network administration (socket programming, email handling, the remote controlling of devices using telnet/ssh, and protocols such as SNMP/DHCP), building graphical user interface, working with websites (Apache log file processing, SOAP and REST APIs communication, and web scraping), and database administration (MySQL and similar database data administration, data analytics, and reporting). By the end of this book, you will be able to use the latest features of Python and be able to build powerful tools that will solve challenging, real-world tasks
Table of Contents (21 chapters)

Chapter 17, Statistics Gathering and Reporting

  1. NumPy's main object is the homogeneous multidimensional array. It is a table of elements (usually numbers), all of the same type, indexed by a tuple of positive integers. In NumPy, dimensions are called axes.
  2. Following is the output:
1st Input array : 
[[ 1 2 3]
[-1 -2 -3]]
2nd Input array :
[[ 4 5 6]
[-4 -5 -6]]
Output stacked array :
[[ 1 2 3 4 5 6]
[-1 -2 -3 -4 -5 -6]]
  1. Following is the answer:
Z = np.arange(10)
  1. Following is the answer:
# Delete the rows with labels 0,1,5
data = data.drop([0,1,2], axis=0)
# Delete the first five rows using iloc selector
data = data.iloc[5:,]
#to delete the column
del df.column_name
  1. Following is the answer:
df.to_csv(“file_name.csv”,index=False, sep=”,”)

  1. Not a number (NaN), such as a null value. Within pandas, a missing value is denoted by NaN...