plotnine.mapping.aes

class plotnine.mapping.aes(*args, **kwargs)

Create aesthetic mappings

Parameters:

xexpression | array_like | scalar

x aesthetic mapping

yexpression | array_like | scalar

y aesthetic mapping

**kwargsdict

Other aesthetic mappings

See also

after_stat()

For how to map aesthetics to variable calculated by the stat

after_scale()

For how to alter aesthetics after the data has been mapped by the scale.

stage

For how to map to evaluate the mapping to aesthetics at more than one stage of the plot building pipeline.

Notes

Only the x and y aesthetic mappings can be specified as positional arguments. All the rest must be keyword arguments.

The value of each mapping must be one of:

• string:

  import pandas as pd
  import numpy as np
  
  arr = [11, 12, 13]
  df = pd.DataFrame({'alpha': [1, 2, 3],
                     'beta': [1, 2, 3],
                     'gam ma': [1, 2, 3]})
  
  # Refer to a column in a dataframe
  ggplot(df, aes(x='alpha', y='beta'))
  

• array_like:

  # A variable
  ggplot(df, aes(x='alpha', y=arr))
  
  # or an inplace list
  ggplot(df, aes(x='alpha', y=[4, 5, 6]))
  

• scalar:

  # A scalar value/variable
  ggplot(df, aes(x='alpha', y=4))
  
  # The above statement is equivalent to
  ggplot(df, aes(x='alpha', y=[4, 4, 4]))
  

• String expression:

  ggplot(df, aes(x='alpha', y='2*beta'))
  ggplot(df, aes(x='alpha', y='np.sin(beta)'))
  ggplot(df, aes(x='df.index', y='beta'))
  
  # If `count` is an aesthetic calculated by a stat
  ggplot(df, aes(x='alpha', y=after_stat('count')))
  ggplot(df, aes(x='alpha', y=after_stat('count/np.max(count)')))
  

  The strings in the expression can refer to;

  1. columns in the dataframe

  2. variables in the namespace

  3. aesthetic values (columns) calculated by the stat

  with the column names having precedence over the variables. For expressions, columns in the dataframe that are mapped to must have names that would be valid python variable names.

  This is okay:

  # 'gam ma' is a column in the dataframe
  ggplot(df, aes(x='df.index', y='gam ma'))
  

  While this is not:

  # 'gam ma' is a column in the dataframe, but not
  # valid python variable name
  ggplot(df, aes(x='df.index', y='np.sin(gam ma)'))
  

aes has 2 internal methods you can use to transform variables being mapped.

1. factor - This function turns the variable into a factor.

   It is just an alias to pd.Categorical:

   ggplot(mtcars, aes(x='factor(cyl)')) + geom_bar()
   

2. reorder - This function changes the order of first variable

   based on values of the second variable:

   df = pd.DataFrame({
       'x': ['b', 'd', 'c', 'a'],
       'y': [1, 2, 3, 4]
   })
   
   ggplot(df, aes('reorder(x, y)', 'y')) + geom_col()
   

The group aesthetic

group is a special aesthetic that the user can map to. It is used to group the plotted items. If not specified, it is automatically computed and in most cases the computed groups are sufficient. However, there may be cases were it is handy to map to it.

copy() → a shallow copy of D

inherit(other: dict[str, Any] | aes) → aes

Create a mapping that inherits aesthetics in other

Parameters:

other: aes | dict[str, Any]

Default aesthetics

Returns:

newaes

Aesthetic mapping

Examples

import pandas as pd
import numpy as np

from plotnine import (
    ggplot,
    aes,
    geom_point
)

aes

Mapping variables to the visual properties of a plot.

df = pd.DataFrame({
    'col1': np.arange(11),
    'col2': np.arange(11)
})

(ggplot(df, aes(x='col1', y='col2'))
 + geom_point()
)

<Figure Size: (640 x 480)>

(ggplot(df, aes(x='col1', y='col2 ** 2'))
 + geom_point()
)

<Figure Size: (640 x 480)>

(ggplot(df, aes(x='col1', y='np.square(col2)'))
 + geom_point()
)

<Figure Size: (640 x 480)>

The first two positional arguments are x and y aesthetics. Any other aesthetic must be mapped with a keyword argument.

(ggplot(df, aes('col1', 'np.square(col2)', color='col2'))
 + geom_point(size=3)
)

<Figure Size: (640 x 480)>