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Python for Data Science

Data Manipulation Using Python

4 Modules
1 Hour


Data manipulation refers to the process of transforming raw data into a format that is more suitable for analysis and modeling. The goal of data manipulation is to clean, organize, and transform data into a usable form that can be easily consumed and analyzed. It involves a range of techniques and methods used to preprocess and organize data, making it easier to work with and analyze. This section will provide an overview of the key techniques involved in data manipulation, including indexing, slicing, filtering, and sorting. It is an important part of any data analysis or data science project due to several reasons:

In this course, you will:

  • Understand how to locate and extract specific data from a DataFrame.
  • Analyze the process of filtering a DataFrame based on conditions.
  • Apply the methods of sorting data in a DataFrame.
  • Understand how to manipulate and analyze data in a DataFrame.

Course Contents

1. Indexing

  • What is indexing?
  • Why is it important?
  • Indexing based on columns
  • Indexing by labels
  • Indexing by position
  • Indexing by values
  • Knowledge check

2. Slicing

  • What is slicing?
  • Why is it important?
  • Slicing based on columns
  • Slicing by labels
  • Slicing by position
  • Slicing with slice object
  • Knowledge check

3. Filtering

  • What is filtering?
  • Why is it important?
  • Filtering using booleans
  • Filtering with ‘query()’ method
  • Filtering with ‘where()’ method
  • Filtering with ‘filter()’ method
  • Filtering with ‘isna()’ method
  • Filtering with ‘between()’ method
  • Filtering with ‘nsmallest()’ method
  • Filtering with ‘nlargest()’ method
  • Knowledge check

4. Sorting

  • What is sorting?
  • Why is it important?
  • Sorting by index
  • Sorting by value
  • Sorting by multiple columns
  • Sorting based on a custom order
  • Sorting by rank
  • Knowledge check