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

Group Data 2.0

Let's imagine the situation where you want to group by job_title, but then you want to group by experience_level , for example.

Here, everything is so simple you need just put several columns in needed order to groupby function:

Look at the output.

output

By the way, if you don't want to group the whole table, you can specify the name of columns for which we should apply grouping. For instance, look at the previous code. If we want to calculate the mean value only for the 'salary' column, we specify needed columns, but do not forget about columns that should be grouped:

Be careful; if you want to work with several columns, you have to put them into [[]] (look at the example).

Look at the result:

result

Task

Your task here is to work with the known dataset and count amount of users for each plan depending on the status of their trial. To do it, follow the algorithm:

Group by 'plan' column, then by 'trial' column, using only three columns: 'user_id', 'plan', 'trial'. Apply the count() function.
Print the df using only print() function.

Task

Your task here is to work with the known dataset and count amount of users for each plan depending on the status of their trial. To do it, follow the algorithm:

Group by 'plan' column, then by 'trial' column, using only three columns: 'user_id', 'plan', 'trial'. Apply the count() function.
Print the df using only print() function.

Everything was clear?

Section 3. Chapter 9

Group Data 2.0

Let's imagine the situation where you want to group by job_title, but then you want to group by experience_level , for example.

Here, everything is so simple you need just put several columns in needed order to groupby function:

Look at the output.

output

Be careful; if you want to work with several columns, you have to put them into [[]] (look at the example).

Look at the result:

result

Task

Your task here is to work with the known dataset and count amount of users for each plan depending on the status of their trial. To do it, follow the algorithm:

Group by 'plan' column, then by 'trial' column, using only three columns: 'user_id', 'plan', 'trial'. Apply the count() function.
Print the df using only print() function.

Task

Your task here is to work with the known dataset and count amount of users for each plan depending on the status of their trial. To do it, follow the algorithm:

Group by 'plan' column, then by 'trial' column, using only three columns: 'user_id', 'plan', 'trial'. Apply the count() function.
Print the df using only print() function.

Everything was clear?

Section 3. Chapter 9

Group Data 2.0

Let's imagine the situation where you want to group by job_title, but then you want to group by experience_level , for example.

Here, everything is so simple you need just put several columns in needed order to groupby function:

Look at the output.

output

Be careful; if you want to work with several columns, you have to put them into [[]] (look at the example).

Look at the result:

result

Task

Your task here is to work with the known dataset and count amount of users for each plan depending on the status of their trial. To do it, follow the algorithm:

Group by 'plan' column, then by 'trial' column, using only three columns: 'user_id', 'plan', 'trial'. Apply the count() function.
Print the df using only print() function.

Task

Your task here is to work with the known dataset and count amount of users for each plan depending on the status of their trial. To do it, follow the algorithm:

Group by 'plan' column, then by 'trial' column, using only three columns: 'user_id', 'plan', 'trial'. Apply the count() function.
Print the df using only print() function.

Everything was clear?

Let's imagine the situation where you want to group by job_title, but then you want to group by experience_level , for example.

Here, everything is so simple you need just put several columns in needed order to groupby function:

Look at the output.

output

Be careful; if you want to work with several columns, you have to put them into [[]] (look at the example).

Look at the result:

result

Task

Your task here is to work with the known dataset and count amount of users for each plan depending on the status of their trial. To do it, follow the algorithm:

Group by 'plan' column, then by 'trial' column, using only three columns: 'user_id', 'plan', 'trial'. Apply the count() function.
Print the df using only print() function.

Section 3. Chapter 9

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