Course Content

Ultimate Visualization with Python

1. Matplotlib Introduction

Plotting Applications Why Use Matplotlib?Creating a Canvas Basic Plotting

2. Creating Commonly Used Plots

Line Plot Multiple Line Plots Scatter Plot Bar Chart Stacked Bar Charts Grouped Bar Charts

3. Plots Customization

Adding Title to the Plot Adding Legend Axes Customization Colors and Transparency Customizing Grid Subplots

4. More Statistical Plots

Histogram Box Plot Pie Chart

5. Plotting with Seaborn

Seaborn Introduction Countplot Plots Decoration KDE Plot Joint Plot Pair Plot Heatmap

Pie Chart

Pie chart is a graph which uses a circle divided into slices (segments) to represent the numerical proportion (percentage distribution) of nominal data. Here is an example of a pie chart:

This chart represents the percentage distribution of the population by region. Looks pretty neat, doesn’t it?

Note

Despite being neat, pie charts should mostly be avoided, since they distort the view of the data. A category with a lot of instances will seem even bigger, a category with few instances will seem even smaller.

Now we’ll discuss how to create such a chart in matplotlib.

Pie Chart with Labels

The function we will use is pie() from the pyplot module, and its first and the only required parameter is our data (called x).

Another important parameter is labels which specifies the labels of the segments (it should be a sequence of strings).

Let’s first have a look at the data for our example:


              1234
            
import pandas as pd
url = 'https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/population.csv'
population_df = pd.read_csv(url)
print(population_df)

This DataFrame contains the population of each region. Now to our example:


              123456
            
import matplotlib.pyplot as plt
import pandas as pd
population_df = pd.read_csv('https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/population.csv')
# Creating a pie chart and setting the labels for each region 
plt.pie(population_df['Population'], labels=population_df['Region'])
plt.show()

We called the pie() function passing the Series with population data as x parameter and the Series with region names as the labels for the segments.

Adding Percents

The chart looks alright, but there is still something missing. We don’t know the exact percentage of each individual region. Fortunately, there is an autopct parameter which specifies the format of the labeling of the wedges (the labels are placed inside).

You can either pass a format string or a function, however, we’ll focus here on the format string. Let’s now modify our example:


              123456
            
import matplotlib.pyplot as plt
import pandas as pd
population_df = pd.read_csv('https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/population.csv')
# Setting the label for each region and its percentage
plt.pie(population_df['Population'], labels=population_df['Region'], autopct='%1.1f%%')
plt.show()

Format String

Here we passed the following string: %1.1f%%. f indicates that the value should be treated as a floating-point number (d indicates an integer), and .1 that there should be exactly one digit after the decimal point.

The percent signs specify that it is a format string and that the number should be followed by the percent sign (%).

If you want to explore more parameters of the .pie() method, here is its documentation for you.

Task

Swipe to show code editor

Use the correct function to create a pie chart.
Use incomes as the data for the pie chart (the first argument).
Set the labels to names via the second argument.
Set the format of the percentage to a floating number with one digit after the decimal point via the third argument.

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 3

Pie Chart

Pie chart is a graph which uses a circle divided into slices (segments) to represent the numerical proportion (percentage distribution) of nominal data. Here is an example of a pie chart:

This chart represents the percentage distribution of the population by region. Looks pretty neat, doesn’t it?

Note

Despite being neat, pie charts should mostly be avoided, since they distort the view of the data. A category with a lot of instances will seem even bigger, a category with few instances will seem even smaller.

Now we’ll discuss how to create such a chart in matplotlib.

Pie Chart with Labels

The function we will use is pie() from the pyplot module, and its first and the only required parameter is our data (called x).

Another important parameter is labels which specifies the labels of the segments (it should be a sequence of strings).

Let’s first have a look at the data for our example:


              1234
            
import pandas as pd
url = 'https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/population.csv'
population_df = pd.read_csv(url)
print(population_df)

This DataFrame contains the population of each region. Now to our example:


              123456
            
import matplotlib.pyplot as plt
import pandas as pd
population_df = pd.read_csv('https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/population.csv')
# Creating a pie chart and setting the labels for each region 
plt.pie(population_df['Population'], labels=population_df['Region'])
plt.show()

We called the pie() function passing the Series with population data as x parameter and the Series with region names as the labels for the segments.

Adding Percents

You can either pass a format string or a function, however, we’ll focus here on the format string. Let’s now modify our example:


              123456
            
import matplotlib.pyplot as plt
import pandas as pd
population_df = pd.read_csv('https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/population.csv')
# Setting the label for each region and its percentage
plt.pie(population_df['Population'], labels=population_df['Region'], autopct='%1.1f%%')
plt.show()

Format String

The percent signs specify that it is a format string and that the number should be followed by the percent sign (%).

If you want to explore more parameters of the .pie() method, here is its documentation for you.

Task

Swipe to show code editor

Use the correct function to create a pie chart.
Use incomes as the data for the pie chart (the first argument).
Set the labels to names via the second argument.
Set the format of the percentage to a floating number with one digit after the decimal point via the third argument.

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 3

Pie Chart

Pie chart is a graph which uses a circle divided into slices (segments) to represent the numerical proportion (percentage distribution) of nominal data. Here is an example of a pie chart:

This chart represents the percentage distribution of the population by region. Looks pretty neat, doesn’t it?

Note

Despite being neat, pie charts should mostly be avoided, since they distort the view of the data. A category with a lot of instances will seem even bigger, a category with few instances will seem even smaller.

Now we’ll discuss how to create such a chart in matplotlib.

Pie Chart with Labels

The function we will use is pie() from the pyplot module, and its first and the only required parameter is our data (called x).

Another important parameter is labels which specifies the labels of the segments (it should be a sequence of strings).

Let’s first have a look at the data for our example:


              1234
            
import pandas as pd
url = 'https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/population.csv'
population_df = pd.read_csv(url)
print(population_df)

This DataFrame contains the population of each region. Now to our example:


              123456
            
import matplotlib.pyplot as plt
import pandas as pd
population_df = pd.read_csv('https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/population.csv')
# Creating a pie chart and setting the labels for each region 
plt.pie(population_df['Population'], labels=population_df['Region'])
plt.show()

We called the pie() function passing the Series with population data as x parameter and the Series with region names as the labels for the segments.

Adding Percents

You can either pass a format string or a function, however, we’ll focus here on the format string. Let’s now modify our example:


              123456
            
import matplotlib.pyplot as plt
import pandas as pd
population_df = pd.read_csv('https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/population.csv')
# Setting the label for each region and its percentage
plt.pie(population_df['Population'], labels=population_df['Region'], autopct='%1.1f%%')
plt.show()

Format String

The percent signs specify that it is a format string and that the number should be followed by the percent sign (%).

If you want to explore more parameters of the .pie() method, here is its documentation for you.

Task

Swipe to show code editor

Use the correct function to create a pie chart.
Use incomes as the data for the pie chart (the first argument).
Set the labels to names via the second argument.
Set the format of the percentage to a floating number with one digit after the decimal point via the third argument.

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

Everything was clear?

Thanks for your feedback!

Pie chart is a graph which uses a circle divided into slices (segments) to represent the numerical proportion (percentage distribution) of nominal data. Here is an example of a pie chart:

This chart represents the percentage distribution of the population by region. Looks pretty neat, doesn’t it?

Note

Despite being neat, pie charts should mostly be avoided, since they distort the view of the data. A category with a lot of instances will seem even bigger, a category with few instances will seem even smaller.

Now we’ll discuss how to create such a chart in matplotlib.

Pie Chart with Labels

The function we will use is pie() from the pyplot module, and its first and the only required parameter is our data (called x).

Another important parameter is labels which specifies the labels of the segments (it should be a sequence of strings).

Let’s first have a look at the data for our example:


              1234
            
import pandas as pd
url = 'https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/population.csv'
population_df = pd.read_csv(url)
print(population_df)

This DataFrame contains the population of each region. Now to our example:


              123456
            
import matplotlib.pyplot as plt
import pandas as pd
population_df = pd.read_csv('https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/population.csv')
# Creating a pie chart and setting the labels for each region 
plt.pie(population_df['Population'], labels=population_df['Region'])
plt.show()

We called the pie() function passing the Series with population data as x parameter and the Series with region names as the labels for the segments.

Adding Percents

You can either pass a format string or a function, however, we’ll focus here on the format string. Let’s now modify our example:


              123456
            
import matplotlib.pyplot as plt
import pandas as pd
population_df = pd.read_csv('https://codefinity-content-media-v2.s3.eu-west-1.amazonaws.com/courses/47339f29-4722-4e72-a0d4-6112c70ff738/population.csv')
# Setting the label for each region and its percentage
plt.pie(population_df['Population'], labels=population_df['Region'], autopct='%1.1f%%')
plt.show()

Format String

The percent signs specify that it is a format string and that the number should be followed by the percent sign (%).

If you want to explore more parameters of the .pie() method, here is its documentation for you.

Task

Swipe to show code editor

Use the correct function to create a pie chart.
Use incomes as the data for the pie chart (the first argument).
Set the labels to names via the second argument.
Set the format of the percentage to a floating number with one digit after the decimal point via the third argument.

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

Section 4. Chapter 3

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