Course Content

Introduction to Python for Data Analysis

1. Introduction to Python 1/2

First Step in Programming Deal with Variables Practice Math Skills Challenge Deal with Percents How to Attract One User?Challenge Learn Numbers Boolean Numbers Challenge Work with Strings Get Familiar with Lists How to Append New Element?

2. Introduction to Python 2/2

If/Else Challenge If/Elif/Else Challenge For Loop While Loop Challenge

3. Explore Dataset

Data Study Operations with the File How to Explore the Data Sorting Data Max, min, mean, median Challenge Group Data Dive Deeper into Grouping Data Group Data 2.0 Introduction to NumPy Pivot Tables Recall Loops .loc Function Deal with Several Conditions Challenge Visualization: First Steps Dive Deeper into Visualization

4. Becoming an Analyst

Data Study Examine Dataset Challenge СAC 1/2 CAC 2/2 Costly Customers Organic Traffic How Much Do We Earn Is Our Project Profitable? ROI 1/2 ROI 2/2 Visualize ROI

Costly 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

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

Print the number of the costly customers.
Find the percentage of costly customers using the formula.
Print the result of countings in the 0.00 format.

Task

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

Print the number of the costly customers.
Find the percentage of costly customers using the formula.
Print the result of countings in the 0.00 format.

Everything was clear?

Section 4. Chapter 6

Costly 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

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

Print the number of the costly customers.
Find the percentage of costly customers using the formula.
Print the result of countings in the 0.00 format.

Task

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

Print the number of the costly customers.
Find the percentage of costly customers using the formula.
Print the result of countings in the 0.00 format.

Everything was clear?

Section 4. Chapter 6

Costly 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

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

Print the number of the costly customers.
Find the percentage of costly customers using the formula.
Print the result of countings in the 0.00 format.

Task

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

Print the number of the costly customers.
Find the percentage of costly customers using the formula.
Print the result of countings in the 0.00 format.

Everything was clear?

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

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

Print the number of the costly customers.
Find the percentage of costly customers using the formula.
Print the result of countings in the 0.00 format.

Section 4. Chapter 6

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