Data Cleaning with Numpy and Pandas

Selenium is an open-source and a portable automated software testing tool for testing web applications. It has capabilities to operate across different browsers and operating systems. Selenium is not just a single tool, but a set of tools that helps testers to automate web-based applications more efficiently.

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Data Cleaning with Numpy and Pandas
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Includes
  • 4 Hrs of lecture Videos
  • 36 hands-on practice exercises
  • 6 Assessment exercises
  • 119 knowledge based questions
  • Lifetime access
About the Course

In this course, you will learn how to test the web application, and how that knowledge helps you avoid common pitfalls and drastically improve your ability to debug problems. This course follows a hands-on, example-driven approach to show and explain all the important features added in selenium. This course is perfect for anyone who wants to learn more about automation testing.

Course Objectives

Upon successful completion of the course, the learner will be able to

  • Dropping unnecessary columns in a DataFrame

  • Changing the index of a DataFrame

  • Using the function to clean the entire dataset, element-wise and to clean columns

  • Renaming columns to a more recognizable set of labels

  • Skipping unnecessary rows in a CSV file

Course Content
Basic Operations on Data using NumPy

This module aims to deliver a brief introduction to the operations of data cleaning using Numpy.

  • 2 Videos
  • 3 Hrs
  • 27 Problems
Basic Operations on Data using Pandas

This module aims to deliver a brief introduction to the operations of data cleaning using Pandas.

  • 2 Videos
  • 3 Hrs
  • 27 Problems
Dropping Columns and Changing the index of a dataframe

This module aims to deliver some of the functionalities used to drop the undesired columns and making the desired columns as the index.

  • 2 Videos
  • 3 Hrs
  • 27 Problems
Tidying up the fields and cleaning the columns

This module aims to enhance the cleaning of specific columns and get them to a uniform format to get a better understanding of the dataset and enforce consistency.

  • 2 Videos
  • 3 Hrs
  • 27 Problems
Renaming the columns and skipping rows

In this module we will work with the data that has either column names that are not easy to understand or unimportant information in the first few and/or last rows so that you will learn about renaming the columns and skipping the data to drill down to necessary information with correct and sensible labels.

  • 2 Videos
  • 3 Hrs 30 Mins
  • 26 Problems
Combining data for analysis

This module aims to deliver the various facilities that pandas provide for easily combining together Series, DataFrame, and Panel objects with various kinds of set logic for the indexes and relational algebra functionality in the case of join / merge-type operations.

  • 2 Videos
  • 3 Hrs
  • 27 Problems
You will Learn through our Scientifically Designed & Proven 5 Step Practice Oriented Learning Process
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In this session, you will find interesting set of activities which will make you explore more on the specific topic.
iAssess
In this session, you will have activities to self assess your knowledge and skills on the specific topic.
iAnalyse
In this session, you will find varieties of exercises to improve your code analysis, testing and debugging skills.
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Harihara Budra P, XII std

Harihara Budra P, XII std
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