Contenido del Curso

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

Dive Deeper into Visualization

Let's imagine that it is essential for you to sort out the user continued subscription after the trial period. Let's move to the dataset that we use, for example:

Look at the code:

As you can see, we just add the hue parameter, which helps you sort out data by categories. For instance, here, hue = 'trial', the column 'trial' has two categories: True and False.

And here is the output:

barplot

Tarea

Visualize the sum of money you receive from users depending on their subscription plan. Take into account if the user continued the subscription after the trial period.

Import the seaborn with the sns alias.
Import the matplotlib.pyplot with the plt alias.
Prepare data for visualization using the .groupby() function:

Extract columns 'plan', 'price', 'trial' for grouping
Group by column 'plan' and then by 'trial'.
Calculate the sum of all prices for each plan.
Reset indices.

Create the barplot using the seaborn:

Use df as the data argument.
Use the 'plan' column for x-axis.
Use the 'price' column for y-axis.
Use the 'trial' column for hue variable.

Display the plot.

Tarea

Visualize the sum of money you receive from users depending on their subscription plan. Take into account if the user continued the subscription after the trial period.

Import the seaborn with the sns alias.
Import the matplotlib.pyplot with the plt alias.
Prepare data for visualization using the .groupby() function:

Extract columns 'plan', 'price', 'trial' for grouping
Group by column 'plan' and then by 'trial'.
Calculate the sum of all prices for each plan.
Reset indices.

Create the barplot using the seaborn:

Use df as the data argument.
Use the 'plan' column for x-axis.
Use the 'price' column for y-axis.
Use the 'trial' column for hue variable.

Display the plot.

¿Todo estuvo claro?

Sección 3. Capítulo 17

Dive Deeper into Visualization

Let's imagine that it is essential for you to sort out the user continued subscription after the trial period. Let's move to the dataset that we use, for example:

Look at the code:

As you can see, we just add the hue parameter, which helps you sort out data by categories. For instance, here, hue = 'trial', the column 'trial' has two categories: True and False.

And here is the output:

barplot

Tarea

Visualize the sum of money you receive from users depending on their subscription plan. Take into account if the user continued the subscription after the trial period.

Import the seaborn with the sns alias.
Import the matplotlib.pyplot with the plt alias.
Prepare data for visualization using the .groupby() function:

Extract columns 'plan', 'price', 'trial' for grouping
Group by column 'plan' and then by 'trial'.
Calculate the sum of all prices for each plan.
Reset indices.

Create the barplot using the seaborn:

Use df as the data argument.
Use the 'plan' column for x-axis.
Use the 'price' column for y-axis.
Use the 'trial' column for hue variable.

Display the plot.

Tarea

Visualize the sum of money you receive from users depending on their subscription plan. Take into account if the user continued the subscription after the trial period.

Import the seaborn with the sns alias.
Import the matplotlib.pyplot with the plt alias.
Prepare data for visualization using the .groupby() function:

Extract columns 'plan', 'price', 'trial' for grouping
Group by column 'plan' and then by 'trial'.
Calculate the sum of all prices for each plan.
Reset indices.

Create the barplot using the seaborn:

Use df as the data argument.
Use the 'plan' column for x-axis.
Use the 'price' column for y-axis.
Use the 'trial' column for hue variable.

Display the plot.

¿Todo estuvo claro?

Sección 3. Capítulo 17

Dive Deeper into Visualization

Let's imagine that it is essential for you to sort out the user continued subscription after the trial period. Let's move to the dataset that we use, for example:

Look at the code:

As you can see, we just add the hue parameter, which helps you sort out data by categories. For instance, here, hue = 'trial', the column 'trial' has two categories: True and False.

And here is the output:

barplot

Tarea

Visualize the sum of money you receive from users depending on their subscription plan. Take into account if the user continued the subscription after the trial period.

Import the seaborn with the sns alias.
Import the matplotlib.pyplot with the plt alias.
Prepare data for visualization using the .groupby() function:

Extract columns 'plan', 'price', 'trial' for grouping
Group by column 'plan' and then by 'trial'.
Calculate the sum of all prices for each plan.
Reset indices.

Create the barplot using the seaborn:

Use df as the data argument.
Use the 'plan' column for x-axis.
Use the 'price' column for y-axis.
Use the 'trial' column for hue variable.

Display the plot.

Tarea

Visualize the sum of money you receive from users depending on their subscription plan. Take into account if the user continued the subscription after the trial period.

Import the seaborn with the sns alias.
Import the matplotlib.pyplot with the plt alias.
Prepare data for visualization using the .groupby() function:

Extract columns 'plan', 'price', 'trial' for grouping
Group by column 'plan' and then by 'trial'.
Calculate the sum of all prices for each plan.
Reset indices.

Create the barplot using the seaborn:

Use df as the data argument.
Use the 'plan' column for x-axis.
Use the 'price' column for y-axis.
Use the 'trial' column for hue variable.

Display the plot.

¿Todo estuvo claro?

Let's imagine that it is essential for you to sort out the user continued subscription after the trial period. Let's move to the dataset that we use, for example:

Look at the code:

As you can see, we just add the hue parameter, which helps you sort out data by categories. For instance, here, hue = 'trial', the column 'trial' has two categories: True and False.

And here is the output:

barplot

Tarea

Visualize the sum of money you receive from users depending on their subscription plan. Take into account if the user continued the subscription after the trial period.

Import the seaborn with the sns alias.
Import the matplotlib.pyplot with the plt alias.
Prepare data for visualization using the .groupby() function:

Extract columns 'plan', 'price', 'trial' for grouping
Group by column 'plan' and then by 'trial'.
Calculate the sum of all prices for each plan.
Reset indices.

Create the barplot using the seaborn:

Use df as the data argument.
Use the 'plan' column for x-axis.
Use the 'price' column for y-axis.
Use the 'trial' column for hue variable.

Display the plot.

Sección 3. Capítulo 17

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