Introduction to Python for Big data Analytics

Hi everyone, if you are a fresher or experienced having lot of queries for learning which programming language. Don’t worry… we are providing a Webinar on Python for Big data Analytics. The title of the webinar is ‘. We will be discussing the essential topics in detail and any queries can be rectified during the session.

Topics to be covered:

1. Introduction to Python
   2. Python and Big Data
   3. Python and Data Science
   4. Key features of Python and their usage in Business Analytics
   5. Business Analytics with Python – Real world Use Cases

Why Python?

Python is an interpreted, interactive, high level programming language similar to PERL with ease of accessibility, readability and having a simple syntax. Python is a preferred choice among professionals looking into Big Data Analytics and its feature of being a general-purpose, high level programming with a gradual learning curve makes it popular as compared to other programming languages.

Need for Python

Nowadays, most organizations would shift their emphasis to Big Data Analytics with an expected investment of over $50 billion. A simple and scalable tool is needed to analyze the big data, which has been answered by Python. In the today’s situation some of the most top organizations from search engine giants, like Google to NASA that are using Python for different purposes.

For more information also check out our on ‘Big Data Processing with Apache Storm’.

 Click here to obtain Source !   

If you have any queries?..Mention it in the comment section, we will clarify you!

 

Learn more about Python for Big Data Analytics

 

Introduction to strings in python

In Python, the strings should be created by simply enclosing the characters in quotes. Python does not support the character types. These are treated as length-one strings, and are also considered as substrings. Substrings are immutable and can’t be changed once created.

Strings are the ordered blocks of text that are enclosed in single or double quotations. Thus, whatever is written in quotes, is considered as string. Though it can be written in single or double quotations, double quotation marks allow the user to extend strings over multiple lines without backslashes, which is usually the signal of continuation of an expression, e.g., ‘abc’, “ABC”.

Concatenation and Repetition

• Strings are concatenated with the +sign:

>>> ‘abc’+‘def’

‘abcdef’

• Strings are repeated with the *sign:

>>> ‘abc’*3

‘abcabcabc’

Indexing and Slicing Operation

• Python starts the indexing at ‘0’
• A string s will have indexes running from 0 to len(s)-1 (where len(s) is the length of s) in integer quantities.
• S[i] fetches the ‘i’th element in the s.

Built-in String Methods

Following are the built-in String Methods that can be used in Python:

• capitalize() – This method is used to capitalize the first letter of string.
• count(str, beg= 0, end=len(string)) – Used to count how many times  the str occurs in string or in a substring of string, if beginning index ‘beg’ and ending index ‘end’ are given.
• encode(encoding=‘UTF-8’,errors=‘strict’) – This method is used to return the encoded string version of string; on error, default raises a ValueError, unless the error is given with ‘ignore’ or ‘replace’.
• decode (encoding=‘UTF-8’, errors=‘strict’) – This method is used to decode the string using the codec registered for Encoding. Encoding defaults to the default string function.
• index(str, beg=0, end=len(string))- Same as find(), but it raises an exception if str is not found.
• max(str)- Used to return the max alphabetical character from the string str.
• min(str)- This is used to return the min alphabetical character from the string str.
• replace(old, new [, max])- This method is used replace all the occurrences of ‘old’ in string with ‘new’ or maximum occurrences if max is given.
• upper()- This method is used to convert the lowercase letters in a string to uppercase.

If you have any queries? Mention them in the comments section and we will clarify you.