plotly.figure_factory.create_facet_grid

plotly.figure_factory.create_facet_grid(df, x=None, y=None, facet_row=None, facet_col=None, color_name=None, colormap=None, color_is_cat=False, facet_row_labels=None, facet_col_labels=None, height=None, width=None, trace_type='scatter', scales='fixed', dtick_x=None, dtick_y=None, show_boxes=True, ggplot2=False, binsize=1, **kwargs)

Returns figure for facet grid; this function is deprecated, since plotly.express functions should be used instead, for example

>>> import plotly.express as px
>>> tips = px.data.tips()
>>> fig = px.scatter(tips,
...     x='total_bill',
...     y='tip',
...     facet_row='sex',
...     facet_col='smoker',
...     color='size')
Parameters

  • df ((pd.DataFrame)) the dataframe of columns for the facet grid.

  • x ((str)) the name of the dataframe column for the x axis data.

  • y ((str)) the name of the dataframe column for the y axis data.

  • facet_row ((str)) the name of the dataframe column that is used to facet the grid into row panels.

  • facet_col ((str)) the name of the dataframe column that is used to facet the grid into column panels.

  • color_name ((str)) the name of your dataframe column that will function as the colormap variable.

  • colormap ((str|list|dict)) the param that determines how the color_name column colors the data. If the dataframe contains numeric data, then a dictionary of colors will group the data categorically while a Plotly Colorscale name or a custom colorscale will treat it numerically. To learn more about colors and types of colormap, run help(plotly.colors).

  • color_is_cat ((bool))

    determines whether a numerical column for the colormap will be treated as categorical (True) or sequential (False).

    Default = False.

  • facet_row_labels ((str|dict)) set to either name or a dictionary of all the unique values in the faceting row mapped to some text to show up in the label annotations. If None, labeling works like usual.

  • facet_col_labels ((str|dict)) set to either name or a dictionary of all the values in the faceting row mapped to some text to show up in the label annotations. If None, labeling works like usual.

  • height ((int)) the height of the facet grid figure.

  • width ((int)) the width of the facet grid figure.

  • trace_type ((str)) decides the type of plot to appear in the facet grid. The options are scatter, scattergl, histogram, bar, and box. Default = scatter.

  • scales ((str)) determines if axes have fixed ranges or not. Valid settings are fixed (all axes fixed), free_x (x axis free only), free_y (y axis free only) or free (both axes free).

  • dtick_x ((float)) determines the distance between each tick on the x-axis. Default is None which means dtick_x is set automatically.

  • dtick_y ((float)) determines the distance between each tick on the y-axis. Default is None which means dtick_y is set automatically.

  • show_boxes ((bool)) draws grey boxes behind the facet titles.

  • ggplot2 ((bool)) draws the facet grid in the style of ggplot2. See http://ggplot2.tidyverse.org/reference/facet_grid.html for reference. Default = False

  • binsize ((int)) groups all data into bins of a given length.

  • kwargs ((dict)) a dictionary of scatterplot arguments.

Examples 1: One Way Faceting

>>> import plotly.figure_factory as ff
>>> import pandas as pd
>>> mpg = pd.read_table('https://raw.githubusercontent.com/plotly/datasets/master/mpg_2017.txt')

>>> fig = ff.create_facet_grid(
...     mpg,
...     x='displ',
...     y='cty',
...     facet_col='cyl',
... )
>>> fig.show()

Example 2: Two Way Faceting

>>> import plotly.figure_factory as ff

>>> import pandas as pd

>>> mpg = pd.read_table('https://raw.githubusercontent.com/plotly/datasets/master/mpg_2017.txt')

>>> fig = ff.create_facet_grid(
...     mpg,
...     x='displ',
...     y='cty',
...     facet_row='drv',
...     facet_col='cyl',
... )
>>> fig.show()

Example 3: Categorical Coloring

>>> import plotly.figure_factory as ff
>>> import pandas as pd
>>> mtcars = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/mtcars.csv')
>>> mtcars.cyl = mtcars.cyl.astype(str)
>>> fig = ff.create_facet_grid(
...     mtcars,
...     x='mpg',
...     y='wt',
...     facet_col='cyl',
...     color_name='cyl',
...     color_is_cat=True,
... )
>>> fig.show()