Like the [2D scatter plot](https://plot.ly/python/line-and-scatter/) `px.scatter`, the 3D function `px.scatter_3d` plots individual data in three-dimensional space.

In a box plot created by `px.box`, the distribution of the column given as `y` argument is represented.

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With ``px.scatter``, each data point is represented as a marker point, which location is given by the `x` and `y` columns.

fig = px.scatter(x=[0, 1, 2, 3, 4], y=[0, 1, 4, 9, 16])

fig.show()

import plotly.express as px

iris = px.data.iris() # iris is a pandas DataFrame

import plotly.express as px

import numpy as np

t = np.linspace(0, 2*np.pi, 100)

fig = px.line(x=t, y=np.cos(t), labels={'x':'t', 'y':'cos(t)'})

fig.show()

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This notebook demonstrates various `plotly.express` features. [Reference documentation](https://plotly.github.io/plotly_express/plotly_express/) is also available, as well as a [tutorial on input argument types](./px-arguments).

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description: Arguments accepted by plotly express functions

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name: Plotly Express Arguments

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title: Plotly Express Arguments

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See also the [introduction to plotly express](./plotly-express).

`px` functions supports natively pandas DataFrame. Arguments can either be passed as dataframe columns, or as column names if the `data_frame` argument is provided.

import plotly.express as px

iris = px.data.iris()

fig = px.scatter(iris, x=iris.sepal_length, y=iris.sepal_width, color=iris.species, size=iris.petal_length)

fig.show()

import plotly.express as px

iris = px.data.iris()

fig = px.scatter(iris, x='sepal_length', y='sepal_width', color='species', size='petal_length')

fig.show()

In addition to columns, it is also possible to pass the index of a DataFrame as argument. In the example below the index is displayed in the hover data.

import plotly.express as px

iris = px.data.iris()

fig = px.scatter(iris, x=iris.sepal_length, y=iris.sepal_width, size=iris.petal_length,

hover_data=[iris.index])

fig.show()

In the addition to columns from the `data_frame` argument, one may also pass columns from a different DataFrame, *as long as all columns have the same length*. It is also possible to pass columns without passing the `data_frame` argument.

However, column names are used only if they correspond to columns in the `data_frame` argument, in other cases, the name of the keyword argument is used. As explained below, the `labels` argument can be used to set names.

import plotly.express as px

import pandas as pd

df1 = pd.DataFrame(dict(time=[10, 20, 30], sales=[10, 8, 30]))

df2 = pd.DataFrame(dict(market=[4, 2, 5]))

fig = px.bar(df1, x=df1.time, y=df2.market, color=df1.sales)

fig.show()

The `labels` argument can be used to override the names used for axis titles, legend entries and hovers.

import plotly.express as px

import pandas as pd

gapminder = px.data.gapminder()

gdp = gapminder['pop'] * gapminder['gdpPercap']

fig = px.bar(gapminder, x='year', y=gdp, color='continent', labels={'y':'gdp'},

hover_data=['country'],

title='Evolution of world GDP')

fig.show()

`px` arguments can also be array-like objects such as lists, NumPy arrays.

import plotly.express as px

fig = px.line(x=[1, 2, 3, 4], y=[3, 5, 4, 8])

fig.show()

import numpy as np

import plotly.express as px

t = np.linspace(0, 10, 100)

fig = px.scatter(x=t, y=np.sin(t), labels={'x':'t', 'y':'sin(t)'}) # override keyword names with labels

fig.show()

The column-based argument `data_frame` can also be passed with a `dict` or `array`. Using a dictionary can be a convenient way to pass column names used in axis titles, legend entries and hovers without creating a pandas DataFrame.

import plotly.express as px

import numpy as np

N = 10000

np.random.seed(0)

fig = px.density_contour(dict(effect_size=5 + np.random.randn(N),

waiting_time=np.random.poisson(size=N)),

x="effect_size", y="waiting_time")

fig.show()

When the `data_frame` argument is a NumPy array, column names are integer corresponding to the columns of the array. In this case, keyword names are used in axis, legend and hovers. This is also the case for a pandas DataFrame with integer column names. Use the `labels` argument to override these names.

import numpy as np

import plotly.express as px

ar = np.arange(100).reshape((10, 10))

fig = px.scatter(ar, x=2, y=6, size=1, color=5)

fig.show()

It is possible to mix DataFrame columns, NumPy arrays and lists as arguments. Remember that the only column names to be used correspond to columns in the `data_frame` argument, use `labels` to override names displayed in axis titles, legend entries or hovers.

import plotly.express as px

import numpy as np

import pandas as pd

gapminder = px.data.gapminder()

gdp = np.log(gapminder['pop'] * gapminder['gdpPercap']) # NumPy array

fig = px.bar(gapminder, x='year', y=gdp, color='continent', labels={'y':'log gdp'},

hover_data=['country'],

title='Evolution of world GDP')

fig.show()

Sunburst plot visualizes hierarchical data spanning outwards radially from root to leaves. The sunburst sectors are determined by the entries in "labels" and in "parents". The root starts from the center and children are added to the outer rings.

Main arguments:

1. **labels**: sets the labels of sunburst sectors.

2. **parents**: sets the parent sectors of sunburst sectors. An empty string '' is used for the root node in the hierarchy. In this example, the root is "Eve".

3. **values**: sets the values associated with sunburst sectors, determining their width (See the "Branchvalues" section below for different modes for setting the width).