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Introduction to Python for Data Analysis
course content

Course Content

Introduction to Python for Data Analysis

Introduction to Python for Data Analysis

1. Introduction to Python 1/2
First Step in ProgrammingDeal with VariablesPractice Math SkillsChallengeDeal with PercentsHow to Attract One User?ChallengeLearn NumbersBoolean NumbersChallengeWork with StringsGet Familiar with ListsHow to Append New Element?
2. Introduction to Python 2/2
If/ElseChallengeIf/Elif/ElseChallengeFor LoopWhile LoopChallenge
3. Explore Dataset
Data StudyOperations with the FileHow to Explore the DataSorting DataMax, min, mean, medianChallengeGroup DataDive Deeper into Grouping DataGroup Data 2.0Introduction to NumPyPivot TablesRecall Loops.loc FunctionDeal with Several ConditionsChallengeVisualization: First StepsDive Deeper into Visualization
4. Becoming an Analyst
Data StudyExamine DatasetChallengeСAC 1/2CAC 2/2Costly CustomersOrganic TrafficHow Much Do We EarnIs Our Project Profitable? ROI 1/2ROI 2/2Visualize ROI

bookROI 2/2

As you can recognize from the previous chapter, we have some problems with ROI in general; we received -19.644756245106056%. Unfortunately, we suffer losses. In this case, we must dive deeper and find the reason.

The next step of our research is to check if we have unprofitable days.

Task

  1. Group data:
  • Extract only columns 'day', 'cost', 'money_spent' from the df DataFrame.
  • Group by the column 'day'.
  • Apply sum() function to grouped data.
  • Apply reset_index() function.
  1. Create column 'ROI':
  • Subtract df['cost'] from df['money_spent']
  • Divide the result by df['cost'].

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 4. Chapter 10
toggle bottom row

bookROI 2/2

As you can recognize from the previous chapter, we have some problems with ROI in general; we received -19.644756245106056%. Unfortunately, we suffer losses. In this case, we must dive deeper and find the reason.

The next step of our research is to check if we have unprofitable days.

Task

  1. Group data:
  • Extract only columns 'day', 'cost', 'money_spent' from the df DataFrame.
  • Group by the column 'day'.
  • Apply sum() function to grouped data.
  • Apply reset_index() function.
  1. Create column 'ROI':
  • Subtract df['cost'] from df['money_spent']
  • Divide the result by df['cost'].

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 4. Chapter 10
toggle bottom row

bookROI 2/2

As you can recognize from the previous chapter, we have some problems with ROI in general; we received -19.644756245106056%. Unfortunately, we suffer losses. In this case, we must dive deeper and find the reason.

The next step of our research is to check if we have unprofitable days.

Task

  1. Group data:
  • Extract only columns 'day', 'cost', 'money_spent' from the df DataFrame.
  • Group by the column 'day'.
  • Apply sum() function to grouped data.
  • Apply reset_index() function.
  1. Create column 'ROI':
  • Subtract df['cost'] from df['money_spent']
  • Divide the result by df['cost'].

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

As you can recognize from the previous chapter, we have some problems with ROI in general; we received -19.644756245106056%. Unfortunately, we suffer losses. In this case, we must dive deeper and find the reason.

The next step of our research is to check if we have unprofitable days.

Task

  1. Group data:
  • Extract only columns 'day', 'cost', 'money_spent' from the df DataFrame.
  • Group by the column 'day'.
  • Apply sum() function to grouped data.
  • Apply reset_index() function.
  1. Create column 'ROI':
  • Subtract df['cost'] from df['money_spent']
  • Divide the result by df['cost'].

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 4. Chapter 10
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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