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:

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()


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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:

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()


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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:

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()


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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.

Opgave

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'.

Løsning

import seaborn as sns

import matplotlib.pyplot as plt

# Set the style

sns.set_style('dark')

# Set the palette

sns.set_palette('rocket')

# Set the context

sns.set_context('talk')

# Loading a built-in dataset of the Titanic passengers

titanic_df = sns.load_dataset('titanic')

sns.countplot(data=titanic_df, x='class')

plt.show()

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Sektion 5. Kapitel 3

import seaborn as sns

import matplotlib.pyplot as plt

# Set the style

___.___(___)

# Set the palette

___.___(___)

# Set the context

___.___(___)

# Loading a built-in dataset of the Titanic passengers

titanic_df = sns.load_dataset('titanic')

sns.countplot(data=titanic_df, x='class')

plt.show()

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