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Plots 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()

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()

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()

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

Tarea

Swipe to start coding

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

Solución

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

¡Gracias por tus comentarios!

Sección 5. Capítulo 3

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Plots Decoration

Setting Style

'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()

Setting Palette

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()

Setting Context

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()

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

Tarea

Swipe to start coding

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

Solución

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones

¿Todo estuvo claro?

¡Gracias por tus comentarios!

Sección 5. Capítulo 3

Cambia al escritorio para practicar en el mundo realContinúe desde donde se encuentra utilizando una de las siguientes opciones