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

Joint Plot

Joint plot is a rather unique plot, since it combines multiple plots. Basically, it has three elements by default:

histogram on the top which represents the distribution of a certain variable;
histogram on the right which represents the distribution of another variable;
scatter plot in the middle which shows the relationship between these two variables.

Here is an example of a joint plot:

Data for the Joint Plot

seaborn has a joinplot() function which, similarly to countplot() and kdeplot(), has three most important parameters:

data;
x;
y.

x and y parameters are the variables we are interested in (the right and top histogram respectively), they can either be array-like objects or the names of the columns of a DataFrame (if we also set the data parameter as a DataFrame).

Let’s have a look at an example:


              123456
            
import seaborn as sns
import matplotlib.pyplot as plt
# Loading the dataset with data about three different iris flowers species
iris_df = sns.load_dataset("iris")
sns.jointplot(data=iris_df, x="sepal_length", y="sepal_width")
plt.show()

We have just recreated the example we had at the beginning by setting a DataFrame object for the data parameter and the names of the columns for x and y.

Plot in the Middle

Another quite useful parameter is kind which specifies the plot you have in the middle. 'scatter' is its default value. Here are other possible plots: 'kde', 'hist', 'hex', 'reg', 'resid'. Feel free to experiment with different plots:


              123456
            
import seaborn as sns
import matplotlib.pyplot as plt
# Loading the dataset with data about three different iris flowers species
iris_df = sns.load_dataset("iris")
sns.jointplot(data=iris_df, x="sepal_length", y="sepal_width", kind='reg')
plt.show()

Plot Kinds

Although scatter plot is mostly used for the plot in the middle, here are some other plots just for information:

'reg' creates a linear regression model fit along with the scatter plot, which is useful to check whether two variables are correlated;
'resid' plots the residuals of a linear regression (documentation);
'hist' creates a bivariate histogram (for two variables);
'kde' creates a KDE plot;
'hex' creates a hexbin plot. It's a scatter plot where hexagonal bins are used instead of individual data points, and the color of each bin indicates how many data points fall within it.

As usual, feel free to explore more parameters in the documentaion.

Task

Use the correct function to create a joint plot.
Use weather_df as the data for the plot (the first argument).
Set the 'Boston' column for the x-axis variable (the second argument).
Set the 'Seattle' column for the y-axis variable (the third argument).
Set the plot in the middle to have a regression line (the rightmost 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 5. Chapter 5

Joint Plot

Joint plot is a rather unique plot, since it combines multiple plots. Basically, it has three elements by default:

histogram on the top which represents the distribution of a certain variable;
histogram on the right which represents the distribution of another variable;
scatter plot in the middle which shows the relationship between these two variables.

Here is an example of a joint plot:

Data for the Joint Plot

seaborn has a joinplot() function which, similarly to countplot() and kdeplot(), has three most important parameters:

data;
x;
y.

Let’s have a look at an example:


              123456
            
import seaborn as sns
import matplotlib.pyplot as plt
# Loading the dataset with data about three different iris flowers species
iris_df = sns.load_dataset("iris")
sns.jointplot(data=iris_df, x="sepal_length", y="sepal_width")
plt.show()

We have just recreated the example we had at the beginning by setting a DataFrame object for the data parameter and the names of the columns for x and y.

Plot in the Middle


              123456
            
import seaborn as sns
import matplotlib.pyplot as plt
# Loading the dataset with data about three different iris flowers species
iris_df = sns.load_dataset("iris")
sns.jointplot(data=iris_df, x="sepal_length", y="sepal_width", kind='reg')
plt.show()

Plot Kinds

Although scatter plot is mostly used for the plot in the middle, here are some other plots just for information:

'reg' creates a linear regression model fit along with the scatter plot, which is useful to check whether two variables are correlated;
'resid' plots the residuals of a linear regression (documentation);
'hist' creates a bivariate histogram (for two variables);
'kde' creates a KDE plot;
'hex' creates a hexbin plot. It's a scatter plot where hexagonal bins are used instead of individual data points, and the color of each bin indicates how many data points fall within it.

As usual, feel free to explore more parameters in the documentaion.

Task

Use the correct function to create a joint plot.
Use weather_df as the data for the plot (the first argument).
Set the 'Boston' column for the x-axis variable (the second argument).
Set the 'Seattle' column for the y-axis variable (the third argument).
Set the plot in the middle to have a regression line (the rightmost 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 5. Chapter 5

Joint Plot

Joint plot is a rather unique plot, since it combines multiple plots. Basically, it has three elements by default:

histogram on the top which represents the distribution of a certain variable;
histogram on the right which represents the distribution of another variable;
scatter plot in the middle which shows the relationship between these two variables.

Here is an example of a joint plot:

Data for the Joint Plot

seaborn has a joinplot() function which, similarly to countplot() and kdeplot(), has three most important parameters:

data;
x;
y.

Let’s have a look at an example:


              123456
            
import seaborn as sns
import matplotlib.pyplot as plt
# Loading the dataset with data about three different iris flowers species
iris_df = sns.load_dataset("iris")
sns.jointplot(data=iris_df, x="sepal_length", y="sepal_width")
plt.show()

We have just recreated the example we had at the beginning by setting a DataFrame object for the data parameter and the names of the columns for x and y.

Plot in the Middle


              123456
            
import seaborn as sns
import matplotlib.pyplot as plt
# Loading the dataset with data about three different iris flowers species
iris_df = sns.load_dataset("iris")
sns.jointplot(data=iris_df, x="sepal_length", y="sepal_width", kind='reg')
plt.show()

Plot Kinds

Although scatter plot is mostly used for the plot in the middle, here are some other plots just for information:

'reg' creates a linear regression model fit along with the scatter plot, which is useful to check whether two variables are correlated;
'resid' plots the residuals of a linear regression (documentation);
'hist' creates a bivariate histogram (for two variables);
'kde' creates a KDE plot;
'hex' creates a hexbin plot. It's a scatter plot where hexagonal bins are used instead of individual data points, and the color of each bin indicates how many data points fall within it.

As usual, feel free to explore more parameters in the documentaion.

Task

Use the correct function to create a joint plot.
Use weather_df as the data for the plot (the first argument).
Set the 'Boston' column for the x-axis variable (the second argument).
Set the 'Seattle' column for the y-axis variable (the third argument).
Set the plot in the middle to have a regression line (the rightmost 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!

Joint plot is a rather unique plot, since it combines multiple plots. Basically, it has three elements by default:

histogram on the top which represents the distribution of a certain variable;
histogram on the right which represents the distribution of another variable;
scatter plot in the middle which shows the relationship between these two variables.

Here is an example of a joint plot:

Data for the Joint Plot

seaborn has a joinplot() function which, similarly to countplot() and kdeplot(), has three most important parameters:

data;
x;
y.

Let’s have a look at an example:


              123456
            
import seaborn as sns
import matplotlib.pyplot as plt
# Loading the dataset with data about three different iris flowers species
iris_df = sns.load_dataset("iris")
sns.jointplot(data=iris_df, x="sepal_length", y="sepal_width")
plt.show()

We have just recreated the example we had at the beginning by setting a DataFrame object for the data parameter and the names of the columns for x and y.

Plot in the Middle


              123456
            
import seaborn as sns
import matplotlib.pyplot as plt
# Loading the dataset with data about three different iris flowers species
iris_df = sns.load_dataset("iris")
sns.jointplot(data=iris_df, x="sepal_length", y="sepal_width", kind='reg')
plt.show()

Plot Kinds

Although scatter plot is mostly used for the plot in the middle, here are some other plots just for information:

'reg' creates a linear regression model fit along with the scatter plot, which is useful to check whether two variables are correlated;
'resid' plots the residuals of a linear regression (documentation);
'hist' creates a bivariate histogram (for two variables);
'kde' creates a KDE plot;
'hex' creates a hexbin plot. It's a scatter plot where hexagonal bins are used instead of individual data points, and the color of each bin indicates how many data points fall within it.

As usual, feel free to explore more parameters in the documentaion.

Task

Use the correct function to create a joint plot.
Use weather_df as the data for the plot (the first argument).
Set the 'Boston' column for the x-axis variable (the second argument).
Set the 'Seattle' column for the y-axis variable (the third argument).
Set the plot in the middle to have a regression line (the rightmost argument).

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

Section 5. Chapter 5

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