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

ROI 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

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.

Create column 'ROI':

Subtract df['cost'] from df['money_spent']
Divide the result by df['cost'].

Task

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.

Create column 'ROI':

Subtract df['cost'] from df['money_spent']
Divide the result by df['cost'].

Everything was clear?

Section 4. Chapter 10

ROI 2/2

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

Task

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.

Create column 'ROI':

Subtract df['cost'] from df['money_spent']
Divide the result by df['cost'].

Task

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.

Create column 'ROI':

Subtract df['cost'] from df['money_spent']
Divide the result by df['cost'].

Everything was clear?

Section 4. Chapter 10

ROI 2/2

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

Task

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.

Create column 'ROI':

Subtract df['cost'] from df['money_spent']
Divide the result by df['cost'].

Task

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.

Create column 'ROI':

Subtract df['cost'] from df['money_spent']
Divide the result by df['cost'].

Everything was clear?

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

Task

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.

Create column 'ROI':

Subtract df['cost'] from df['money_spent']
Divide the result by df['cost'].

Section 4. Chapter 10

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