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

How Much Do We Earn

You may recognize the column 'money_spent' that corresponds to the amount of money the user spent and gained. In this chapter, we will find if there is any dependence between the day of the week and the amount of money we have!

But firstly, recall some functions:

Group Data:


              12
            
df = df[['columns which we group']]
         .groupby(['columns on which we group'])

Visualization:


              1234
            
sns.barplot(df = DataFrame,
                 x = 'column for x-axis',
                 y = 'column for y-axis')
plt.show()

Task

Group data:

Extract only columns 'day', 'money_spent' from the df DataFrame.
Group by the column 'day'.
Apply .mean() function to grouped df.
Apply .reset_index() function.

Create a barplot:

Use df as the first argument.
Use column 'day' for x-axis.
Use the column 'money_spent' for the y-axis.

Output barplot.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 4. Chapter 8

How Much Do We Earn

But firstly, recall some functions:

Group Data:


              12
            
df = df[['columns which we group']]
         .groupby(['columns on which we group'])

Visualization:


              1234
            
sns.barplot(df = DataFrame,
                 x = 'column for x-axis',
                 y = 'column for y-axis')
plt.show()

Task

Group data:

Extract only columns 'day', 'money_spent' from the df DataFrame.
Group by the column 'day'.
Apply .mean() function to grouped df.
Apply .reset_index() function.

Create a barplot:

Use df as the first argument.
Use column 'day' for x-axis.
Use the column 'money_spent' for the y-axis.

Output barplot.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

Section 4. Chapter 8

How Much Do We Earn

But firstly, recall some functions:

Group Data:


              12
            
df = df[['columns which we group']]
         .groupby(['columns on which we group'])

Visualization:


              1234
            
sns.barplot(df = DataFrame,
                 x = 'column for x-axis',
                 y = 'column for y-axis')
plt.show()

Task

Group data:

Extract only columns 'day', 'money_spent' from the df DataFrame.
Group by the column 'day'.
Apply .mean() function to grouped df.
Apply .reset_index() function.

Create a barplot:

Use df as the first argument.
Use column 'day' for x-axis.
Use the column 'money_spent' for the y-axis.

Output barplot.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Everything was clear?

Thanks for your feedback!

But firstly, recall some functions:

Group Data:


              12
            
df = df[['columns which we group']]
         .groupby(['columns on which we group'])

Visualization:


              1234
            
sns.barplot(df = DataFrame,
                 x = 'column for x-axis',
                 y = 'column for y-axis')
plt.show()

Task

Group data:

Extract only columns 'day', 'money_spent' from the df DataFrame.
Group by the column 'day'.
Apply .mean() function to grouped df.
Apply .reset_index() function.

Create a barplot:

Use df as the first argument.
Use column 'day' for x-axis.
Use the column 'money_spent' for the y-axis.

Output barplot.

Switch to desktop for real-world practiceContinue from where you are using one of the options below

Section 4. Chapter 8

Switch to desktop for real-world practiceContinue from where you are using one of the options below