plotly.figure_factory.create_violin

plotly.figure_factory.create_violin(data, data_header=None, group_header=None, colors=None, use_colorscale=False, group_stats=None, rugplot=True, sort=False, height=450, width=600, title='Violin and Rug Plot')

deprecated, use instead the plotly.graph_objects trace plotly.graph_objects.Violin.

Parameters
  • data ((list|array)) – accepts either a list of numerical values, a list of dictionaries all with identical keys and at least one column of numeric values, or a pandas dataframe with at least one column of numbers.

  • data_header ((str)) – the header of the data column to be used from an inputted pandas dataframe. Not applicable if ‘data’ is a list of numeric values.

  • group_header ((str)) – applicable if grouping data by a variable. ‘group_header’ must be set to the name of the grouping variable.

  • colors ((str|tuple|list|dict)) – either a plotly scale name, an rgb or hex color, a color tuple, a list of colors or a dictionary. An rgb color is of the form ‘rgb(x, y, z)’ where x, y and z belong to the interval [0, 255] and a color tuple is a tuple of the form (a, b, c) where a, b and c belong to [0, 1]. If colors is a list, it must contain valid color types as its members.

  • use_colorscale ((bool)) – only applicable if grouping by another variable. Will implement a colorscale based on the first 2 colors of param colors. This means colors must be a list with at least 2 colors in it (Plotly colorscales are accepted since they map to a list of two rgb colors). Default = False

  • group_stats ((dict)) – a dictioanry where each key is a unique value from the group_header column in data. Each value must be a number and will be used to color the violin plots if a colorscale is being used.

  • rugplot ((bool)) – determines if a rugplot is draw on violin plot. Default = True

  • sort ((bool)) – determines if violins are sorted alphabetically (True) or by input order (False). Default = False

  • height ((float)) – the height of the violin plot.

  • width ((float)) – the width of the violin plot.

  • title ((str)) – the title of the violin plot.

Example 1: Single Violin Plot

>>> from plotly.figure_factory import create_violin
>>> import plotly.graph_objects as graph_objects
>>> import numpy as np
>>> from scipy import stats
>>> # create list of random values
>>> data_list = np.random.randn(100)
>>> # create violin fig
>>> fig = create_violin(data_list, colors='#604d9e')
>>> # plot
>>> fig.show()

Example 2: Multiple Violin Plots with Qualitative Coloring

>>> from plotly.figure_factory import create_violin
>>> import plotly.graph_objects as graph_objects
>>> import numpy as np
>>> import pandas as pd
>>> from scipy import stats
>>> # create dataframe
>>> np.random.seed(619517)
>>> Nr=250
>>> y = np.random.randn(Nr)
>>> gr = np.random.choice(list("ABCDE"), Nr)
>>> norm_params=[(0, 1.2), (0.7, 1), (-0.5, 1.4), (0.3, 1), (0.8, 0.9)]
>>> for i, letter in enumerate("ABCDE"):
...     y[gr == letter] *=norm_params[i][1]+ norm_params[i][0]
>>> df = pd.DataFrame(dict(Score=y, Group=gr))
>>> # create violin fig
>>> fig = create_violin(df, data_header='Score', group_header='Group',
...                    sort=True, height=600, width=1000)
>>> # plot
>>> fig.show()

Example 3: Violin Plots with Colorscale

>>> from plotly.figure_factory import create_violin
>>> import plotly.graph_objects as graph_objects
>>> import numpy as np
>>> import pandas as pd
>>> from scipy import stats
>>> # create dataframe
>>> np.random.seed(619517)
>>> Nr=250
>>> y = np.random.randn(Nr)
>>> gr = np.random.choice(list("ABCDE"), Nr)
>>> norm_params=[(0, 1.2), (0.7, 1), (-0.5, 1.4), (0.3, 1), (0.8, 0.9)]
>>> for i, letter in enumerate("ABCDE"):
...     y[gr == letter] *=norm_params[i][1]+ norm_params[i][0]
>>> df = pd.DataFrame(dict(Score=y, Group=gr))
>>> # define header params
>>> data_header = 'Score'
>>> group_header = 'Group'
>>> # make groupby object with pandas
>>> group_stats = {}
>>> groupby_data = df.groupby([group_header])
>>> for group in "ABCDE":
...     data_from_group = groupby_data.get_group(group)[data_header]
...     # take a stat of the grouped data
...     stat = np.median(data_from_group)
...     # add to dictionary
...     group_stats[group] = stat
>>> # create violin fig
>>> fig = create_violin(df, data_header='Score', group_header='Group',
...                     height=600, width=1000, use_colorscale=True,
...                     group_stats=group_stats)
>>> # plot
>>> fig.show()