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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

bookCostly Customers

Sometimes we don't receive as much as we spend. The same with some customers in our DataFrame. We do spend more money to 'buy' this customer, but we don't receive the the same amount of money.

Let's find the percentage of these customers!

We want to find costly customers, not payments. To do that, we need to add additional customer_id.nunique() that will find only unique customers with their costly (or not) payments.

To find the percentage, use the next formula:

Task

  1. Count the number of the costly customers:
  • Set the condition if the value in the 'cost' column is greater than the value in the 'money_spent' column
  • Use unique customers to find the metric
  1. Print the number of the costly customers.
  2. Find the percentage of costly customers using the formula.
  3. Print the result of countings in the 0.00 format.

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Section 4. Chapter 6
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bookCostly Customers

Sometimes we don't receive as much as we spend. The same with some customers in our DataFrame. We do spend more money to 'buy' this customer, but we don't receive the the same amount of money.

Let's find the percentage of these customers!

We want to find costly customers, not payments. To do that, we need to add additional customer_id.nunique() that will find only unique customers with their costly (or not) payments.

To find the percentage, use the next formula:

Task

  1. Count the number of the costly customers:
  • Set the condition if the value in the 'cost' column is greater than the value in the 'money_spent' column
  • Use unique customers to find the metric
  1. Print the number of the costly customers.
  2. Find the percentage of costly customers using the formula.
  3. Print the result of countings in the 0.00 format.

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 6
toggle bottom row

bookCostly Customers

Sometimes we don't receive as much as we spend. The same with some customers in our DataFrame. We do spend more money to 'buy' this customer, but we don't receive the the same amount of money.

Let's find the percentage of these customers!

We want to find costly customers, not payments. To do that, we need to add additional customer_id.nunique() that will find only unique customers with their costly (or not) payments.

To find the percentage, use the next formula:

Task

  1. Count the number of the costly customers:
  • Set the condition if the value in the 'cost' column is greater than the value in the 'money_spent' column
  • Use unique customers to find the metric
  1. Print the number of the costly customers.
  2. Find the percentage of costly customers using the formula.
  3. Print the result of countings in the 0.00 format.

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!

Sometimes we don't receive as much as we spend. The same with some customers in our DataFrame. We do spend more money to 'buy' this customer, but we don't receive the the same amount of money.

Let's find the percentage of these customers!

We want to find costly customers, not payments. To do that, we need to add additional customer_id.nunique() that will find only unique customers with their costly (or not) payments.

To find the percentage, use the next formula:

Task

  1. Count the number of the costly customers:
  • Set the condition if the value in the 'cost' column is greater than the value in the 'money_spent' column
  • Use unique customers to find the metric
  1. Print the number of the costly customers.
  2. Find the percentage of costly customers using the formula.
  3. Print the result of countings in the 0.00 format.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 4. Chapter 6
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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