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Ultimate Visualization with Python
course content

Course Content

Ultimate Visualization with Python

Ultimate Visualization with Python

1. Matplotlib Introduction
Plotting ApplicationsWhy Use Matplotlib?Creating a CanvasBasic Plotting
2. Creating Commonly Used Plots
Line PlotMultiple Line PlotsScatter PlotBar ChartStacked Bar ChartsGrouped Bar Charts
3. Plots Customization
Adding Title to the PlotAdding LegendAxes CustomizationColors and Transparency Customizing GridSubplots
4. More Statistical Plots
HistogramBox PlotPie Chart
5. Plotting with Seaborn
Seaborn IntroductionCountplotPlots DecorationKDE PlotJoint PlotPair PlotHeatmap

bookPlots Decoration

Setting Style

First, let’s start with setting the style for the plot. seaborn has a set_style() function exactly for this purpose. We are interested in its only required parameter called style. It has several possible values, which are all different styles:

  • 'white';
  • 'dark';
  • 'whitegrid';
  • 'darkgrid';
  • 'ticks'.

Feel free to experiment with them:


              
1234567

            
import seaborn as sns
import matplotlib.pyplot as plt
# Setting the style
sns.set_style('darkgrid')
titanic_df = sns.load_dataset('titanic')
sns.countplot(data=titanic_df, x='class')
plt.show()
copy

Setting Palette

Another option you have is to change the colors of the plot elements in seaborn using the set_palette() function. We'll focus on its only required parameter palette. Here are a few possible palettes:

  • Circular palettes: 'hls', 'husl';
  • Perceptually uniform palettes: 'rocket', 'magma', 'mako', etc;
  • Diverging color palettes: 'RdBu', 'PRGn', etc;
  • Sequential color palettes: 'Greys', 'Blues', etc.

You can explore more of them here. Once again, feel free to experiment with different palettes:


              
12345678910

            
import seaborn as sns
import matplotlib.pyplot as plt
# Setting the style
sns.set_style('darkgrid')
# Setting the palette
sns.set_palette('magma')
# Loading a built-in dataset of the Titanic passengers
titanic_df = sns.load_dataset('titanic')
sns.countplot(data=titanic_df, x='class')
plt.show()
copy

Setting Context

There is another function in the seaborn library, set_context(). It affects such aspects as the size of the labels, lines, and other elements of the plot (the overall style is not affected).

Its most important parameter context which can either be a dictionary (dict) of parameters or a name of a preconfigured set (string type).

'notebook' is the default context, other contexts are 'paper', 'talk', and 'poster', which are essentially just versions of the notebook parameters scaled by a certain value.

Here is an example, you can try different contexts and see the difference:


              
123456789101112

            
import seaborn as sns
import matplotlib.pyplot as plt
# Setting the style
sns.set_style('darkgrid')
# Setting the palette
sns.set_palette('magma')
# Setting the context
sns.set_context('paper')
# Loading a built-in dataset of the Titanic passengers
titanic_df = sns.load_dataset('titanic')
sns.countplot(data=titanic_df, x='class')
plt.show()
copy

You can explore more about set_context() in its documentation.

Task

  1. Use the correct function to set the style to 'dark'.
  2. Use the correct function to set the palette to 'rocket'.
  3. Use the correct function to set the context to 'talk'.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 5. Chapter 3
toggle bottom row

bookPlots Decoration

Setting Style

First, let’s start with setting the style for the plot. seaborn has a set_style() function exactly for this purpose. We are interested in its only required parameter called style. It has several possible values, which are all different styles:

  • 'white';
  • 'dark';
  • 'whitegrid';
  • 'darkgrid';
  • 'ticks'.

Feel free to experiment with them:


              
1234567

            
import seaborn as sns
import matplotlib.pyplot as plt
# Setting the style
sns.set_style('darkgrid')
titanic_df = sns.load_dataset('titanic')
sns.countplot(data=titanic_df, x='class')
plt.show()
copy

Setting Palette

Another option you have is to change the colors of the plot elements in seaborn using the set_palette() function. We'll focus on its only required parameter palette. Here are a few possible palettes:

  • Circular palettes: 'hls', 'husl';
  • Perceptually uniform palettes: 'rocket', 'magma', 'mako', etc;
  • Diverging color palettes: 'RdBu', 'PRGn', etc;
  • Sequential color palettes: 'Greys', 'Blues', etc.

You can explore more of them here. Once again, feel free to experiment with different palettes:


              
12345678910

            
import seaborn as sns
import matplotlib.pyplot as plt
# Setting the style
sns.set_style('darkgrid')
# Setting the palette
sns.set_palette('magma')
# Loading a built-in dataset of the Titanic passengers
titanic_df = sns.load_dataset('titanic')
sns.countplot(data=titanic_df, x='class')
plt.show()
copy

Setting Context

There is another function in the seaborn library, set_context(). It affects such aspects as the size of the labels, lines, and other elements of the plot (the overall style is not affected).

Its most important parameter context which can either be a dictionary (dict) of parameters or a name of a preconfigured set (string type).

'notebook' is the default context, other contexts are 'paper', 'talk', and 'poster', which are essentially just versions of the notebook parameters scaled by a certain value.

Here is an example, you can try different contexts and see the difference:


              
123456789101112

            
import seaborn as sns
import matplotlib.pyplot as plt
# Setting the style
sns.set_style('darkgrid')
# Setting the palette
sns.set_palette('magma')
# Setting the context
sns.set_context('paper')
# Loading a built-in dataset of the Titanic passengers
titanic_df = sns.load_dataset('titanic')
sns.countplot(data=titanic_df, x='class')
plt.show()
copy

You can explore more about set_context() in its documentation.

Task

  1. Use the correct function to set the style to 'dark'.
  2. Use the correct function to set the palette to 'rocket'.
  3. Use the correct function to set the context to 'talk'.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Section 5. Chapter 3
toggle bottom row

bookPlots Decoration

Setting Style

First, let’s start with setting the style for the plot. seaborn has a set_style() function exactly for this purpose. We are interested in its only required parameter called style. It has several possible values, which are all different styles:

  • 'white';
  • 'dark';
  • 'whitegrid';
  • 'darkgrid';
  • 'ticks'.

Feel free to experiment with them:


              
1234567

            
import seaborn as sns
import matplotlib.pyplot as plt
# Setting the style
sns.set_style('darkgrid')
titanic_df = sns.load_dataset('titanic')
sns.countplot(data=titanic_df, x='class')
plt.show()
copy

Setting Palette

Another option you have is to change the colors of the plot elements in seaborn using the set_palette() function. We'll focus on its only required parameter palette. Here are a few possible palettes:

  • Circular palettes: 'hls', 'husl';
  • Perceptually uniform palettes: 'rocket', 'magma', 'mako', etc;
  • Diverging color palettes: 'RdBu', 'PRGn', etc;
  • Sequential color palettes: 'Greys', 'Blues', etc.

You can explore more of them here. Once again, feel free to experiment with different palettes:


              
12345678910

            
import seaborn as sns
import matplotlib.pyplot as plt
# Setting the style
sns.set_style('darkgrid')
# Setting the palette
sns.set_palette('magma')
# Loading a built-in dataset of the Titanic passengers
titanic_df = sns.load_dataset('titanic')
sns.countplot(data=titanic_df, x='class')
plt.show()
copy

Setting Context

There is another function in the seaborn library, set_context(). It affects such aspects as the size of the labels, lines, and other elements of the plot (the overall style is not affected).

Its most important parameter context which can either be a dictionary (dict) of parameters or a name of a preconfigured set (string type).

'notebook' is the default context, other contexts are 'paper', 'talk', and 'poster', which are essentially just versions of the notebook parameters scaled by a certain value.

Here is an example, you can try different contexts and see the difference:


              
123456789101112

            
import seaborn as sns
import matplotlib.pyplot as plt
# Setting the style
sns.set_style('darkgrid')
# Setting the palette
sns.set_palette('magma')
# Setting the context
sns.set_context('paper')
# Loading a built-in dataset of the Titanic passengers
titanic_df = sns.load_dataset('titanic')
sns.countplot(data=titanic_df, x='class')
plt.show()
copy

You can explore more about set_context() in its documentation.

Task

  1. Use the correct function to set the style to 'dark'.
  2. Use the correct function to set the palette to 'rocket'.
  3. Use the correct function to set the context to 'talk'.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Everything was clear?

How can we improve it?

Thanks for your feedback!

Setting Style

First, let’s start with setting the style for the plot. seaborn has a set_style() function exactly for this purpose. We are interested in its only required parameter called style. It has several possible values, which are all different styles:

  • 'white';
  • 'dark';
  • 'whitegrid';
  • 'darkgrid';
  • 'ticks'.

Feel free to experiment with them:


              
1234567

            
import seaborn as sns
import matplotlib.pyplot as plt
# Setting the style
sns.set_style('darkgrid')
titanic_df = sns.load_dataset('titanic')
sns.countplot(data=titanic_df, x='class')
plt.show()
copy

Setting Palette

Another option you have is to change the colors of the plot elements in seaborn using the set_palette() function. We'll focus on its only required parameter palette. Here are a few possible palettes:

  • Circular palettes: 'hls', 'husl';
  • Perceptually uniform palettes: 'rocket', 'magma', 'mako', etc;
  • Diverging color palettes: 'RdBu', 'PRGn', etc;
  • Sequential color palettes: 'Greys', 'Blues', etc.

You can explore more of them here. Once again, feel free to experiment with different palettes:


              
12345678910

            
import seaborn as sns
import matplotlib.pyplot as plt
# Setting the style
sns.set_style('darkgrid')
# Setting the palette
sns.set_palette('magma')
# Loading a built-in dataset of the Titanic passengers
titanic_df = sns.load_dataset('titanic')
sns.countplot(data=titanic_df, x='class')
plt.show()
copy

Setting Context

There is another function in the seaborn library, set_context(). It affects such aspects as the size of the labels, lines, and other elements of the plot (the overall style is not affected).

Its most important parameter context which can either be a dictionary (dict) of parameters or a name of a preconfigured set (string type).

'notebook' is the default context, other contexts are 'paper', 'talk', and 'poster', which are essentially just versions of the notebook parameters scaled by a certain value.

Here is an example, you can try different contexts and see the difference:


              
123456789101112

            
import seaborn as sns
import matplotlib.pyplot as plt
# Setting the style
sns.set_style('darkgrid')
# Setting the palette
sns.set_palette('magma')
# Setting the context
sns.set_context('paper')
# Loading a built-in dataset of the Titanic passengers
titanic_df = sns.load_dataset('titanic')
sns.countplot(data=titanic_df, x='class')
plt.show()
copy

You can explore more about set_context() in its documentation.

Task

  1. Use the correct function to set the style to 'dark'.
  2. Use the correct function to set the palette to 'rocket'.
  3. Use the correct function to set the context to 'talk'.

Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
Section 5. Chapter 3
Switch to desktopSwitch to desktop for real-world practiceContinue from where you are using one of the options below
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