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

Visualization: First Steps

An essential tool for Data Analysts is visualization. The first one here is .barplot(). To use the tools you need to import the libraries, look at the syntax:

import matplotlib.pyplot as plt
import seaborn as sns

We will use the second one, Seaborn, but it is based on Matplotlib, so we need to import two of them. Look at the dataset that we used to use for examples:

Our task is to visualize experience_level and the mean salary for each of them. Look at the code:

Here, is the output

barplot

Look at the sixth line of code:

df = df[['experience_level', 'salary']].groupby(['experience_level']).median().reset_index()

Here you can recognize the new function .reset_index(). It is easy and just transforms the result of .groupby() function into the regular dataset. Look at the pictures (the first one is before and the second one is after):

Then we will move to the seventh line of code.

sns.barplot(data = df, x = 'experience_level', y = 'salary')

sns - referring to seaborn library.
barplot the type of plot.
data = df the DataFrame.
x = 'experience_level' the column for x-axis.
y = 'salary' the column for y-axis.

Move to the eighth line of code:

plt.show()

Function from the matplotlib library to output the plot.

Tarea

Visualize the sum of money you receive from users depending on their subscription plan.

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'.
Group by column plan.
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 the x-axis
Use the 'price' column for the y-axis.

Output the plot.

Tarea

Visualize the sum of money you receive from users depending on their subscription plan.

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'.
Group by column plan.
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 the x-axis
Use the 'price' column for the y-axis.

Output the plot.

¿Todo estuvo claro?

Sección 3. Capítulo 16

Visualization: First Steps

An essential tool for Data Analysts is visualization. The first one here is .barplot(). To use the tools you need to import the libraries, look at the syntax:

import matplotlib.pyplot as plt
import seaborn as sns

We will use the second one, Seaborn, but it is based on Matplotlib, so we need to import two of them. Look at the dataset that we used to use for examples:

Our task is to visualize experience_level and the mean salary for each of them. Look at the code:

Here, is the output

barplot

Look at the sixth line of code:

df = df[['experience_level', 'salary']].groupby(['experience_level']).median().reset_index()

Then we will move to the seventh line of code.

sns.barplot(data = df, x = 'experience_level', y = 'salary')

sns - referring to seaborn library.
barplot the type of plot.
data = df the DataFrame.
x = 'experience_level' the column for x-axis.
y = 'salary' the column for y-axis.

Move to the eighth line of code:

plt.show()

Function from the matplotlib library to output the plot.

Tarea

Visualize the sum of money you receive from users depending on their subscription plan.

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'.
Group by column plan.
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 the x-axis
Use the 'price' column for the y-axis.

Output the plot.

Tarea

Visualize the sum of money you receive from users depending on their subscription plan.

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'.
Group by column plan.
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 the x-axis
Use the 'price' column for the y-axis.

Output the plot.

¿Todo estuvo claro?

Sección 3. Capítulo 16

Visualization: First Steps

An essential tool for Data Analysts is visualization. The first one here is .barplot(). To use the tools you need to import the libraries, look at the syntax:

import matplotlib.pyplot as plt
import seaborn as sns

We will use the second one, Seaborn, but it is based on Matplotlib, so we need to import two of them. Look at the dataset that we used to use for examples:

Our task is to visualize experience_level and the mean salary for each of them. Look at the code:

Here, is the output

barplot

Look at the sixth line of code:

df = df[['experience_level', 'salary']].groupby(['experience_level']).median().reset_index()

Then we will move to the seventh line of code.

sns.barplot(data = df, x = 'experience_level', y = 'salary')

sns - referring to seaborn library.
barplot the type of plot.
data = df the DataFrame.
x = 'experience_level' the column for x-axis.
y = 'salary' the column for y-axis.

Move to the eighth line of code:

plt.show()

Function from the matplotlib library to output the plot.

Tarea

Visualize the sum of money you receive from users depending on their subscription plan.

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'.
Group by column plan.
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 the x-axis
Use the 'price' column for the y-axis.

Output the plot.

Tarea

Visualize the sum of money you receive from users depending on their subscription plan.

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'.
Group by column plan.
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 the x-axis
Use the 'price' column for the y-axis.

Output the plot.

¿Todo estuvo claro?

An essential tool for Data Analysts is visualization. The first one here is .barplot(). To use the tools you need to import the libraries, look at the syntax:

import matplotlib.pyplot as plt
import seaborn as sns

We will use the second one, Seaborn, but it is based on Matplotlib, so we need to import two of them. Look at the dataset that we used to use for examples:

Our task is to visualize experience_level and the mean salary for each of them. Look at the code:

Here, is the output

barplot

Look at the sixth line of code:

df = df[['experience_level', 'salary']].groupby(['experience_level']).median().reset_index()

Then we will move to the seventh line of code.

sns.barplot(data = df, x = 'experience_level', y = 'salary')

sns - referring to seaborn library.
barplot the type of plot.
data = df the DataFrame.
x = 'experience_level' the column for x-axis.
y = 'salary' the column for y-axis.

Move to the eighth line of code:

plt.show()

Function from the matplotlib library to output the plot.

Tarea

Visualize the sum of money you receive from users depending on their subscription plan.

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'.
Group by column plan.
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 the x-axis
Use the 'price' column for the y-axis.

Output the plot.

Sección 3. Capítulo 16

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