description: How to adjust axes properties in Python: axes titles, styling and coloring axes and grid lines, ticks, tick labels

This tutorial explain how to set the properties of [2-dimensional Cartesian axes](/python/figure-structure/#2d-cartesian-trace-types-and-subplots), namely [`go.layout.XAxis`](/python/reference/layout/xaxis/) and [`go.layout.YAxis`](python/reference/layout/xaxis/).

Other kinds of subplots and axes are described in other tutorials:

**See also** the tutorials on [facet plots](/python/facet-plots/), [subplots](/python/subplots) and [multiple axes](/python/multiple-axes/).

The different types of Cartesian axes are configured via the `xaxis.type` or `yaxis.type` attribute, which can take on the following values:

- `'linear'` as described in this page

- `'log'` (see the [log plot tutorial](/python/log-plots/))

- `'date'` (see the [tutorial on timeseries](/python/time-series/))

- `'category'` (see the [categorical axes tutorial](/python/categorical-axes/))

- `'multicategory'` (see the [categorical axes tutorial](/python/categorical-axes/))

The axis type is auto-detected by looking at data from the first [trace](/python/figure-structure/) linked to this axis:

* First check for `multicategory`, then `date`, then `category`, else default to `linear` (`log` is never automatically selected)

* `multicategory` is just a shape test: is the array nested?

* `date` and `category`: require **more than twice as many distinct date or category strings** as distinct numbers or numeric strings in order to choose that axis type.

* Both of these test an evenly-spaced sample of at most 1000 values

* Small detail: the `category` test sorts every value into either number or category, whereas for dates, 2- and 4-digit integers count as both dates and numbers.

If you pass string values for the `x` or `y` parameter, plotly will automatically set the corresponding axis type to `category`, *except if enough of these strings contain numbers* as detailed above, in which case the axis is automatically set to `linear`. It is however possible to force the axis type by setting explicitely `xaxis_type` to be `category`.

fig.update_xaxes(type='category')

- tick values (locations of tick marks) and tick labels. Tick labels and grid lines are placed at tick values.

The examples on this page apply to axes of any type, but extra attributes are available for [axes of type `category`](/pythone/categorical-axes/) and [axes of type `date`](/python/time-series/).

See https://plotly.com/python/reference/layout/xaxis/ and https://plotly.com/python/reference/layout/yaxis/ for more information and chart attribute options!

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df = px.data.tips()

df = px.data.tips()

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description: How to use categorical axes in Python with Plotly.

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This page shows examples of how to configure [2-dimensional Cartesian axes](/python/figure-structure/#2d-cartesian-trace-types-and-subplots) to visualize categorical (i.e. qualitative, nominal or ordinal data as opposed to continuous numerical data). Such axes are a natural fit for bar charts, waterfall charts, funnel charts, heatmaps, violin charts and box plots, but can also be used with scatter plots and line charts. [Configuring gridlines, ticks, tick labels and axis titles](/python/axes/) on logarithmic axes is done the same was as with [linear axes](/python/axes/).

The different types of Cartesian axes are configured via the `xaxis.type` or `yaxis.type` attribute, which can take on the following values:

- `'linear'` (see the [linear axes tutoria](/python/axes/))

- `'log'` (see the [log plot tutorial](/python/log-plots/))

- `'date'` (see the [tutorial on timeseries](/python/time-series/))

- `'category'` see below

- `'multicategory'` see below

The axis type is auto-detected by looking at data from the first [trace](/python/figure-structure/) linked to this axis:

* First check for `multicategory`, then `date`, then `category`, else default to `linear` (`log` is never automatically selected)

* `multicategory` is just a shape test: is the array nested?

* `date` and `category`: require **more than twice as many distinct date or category strings** as distinct numbers or numeric strings in order to choose that axis type.

* Both of these test an evenly-spaced sample of at most 1000 values

* Small detail: the `category` test sorts every value into either number or category, whereas for dates, 2- and 4-digit integers count as both dates and numbers.

If you pass string values for the `x` or `y` parameter, plotly will automatically set the corresponding axis type to `category`, *except if enough of these strings contain numbers* as detailed above, in which case the axis is automatically set to `linear`. It is however possible to force the axis type by setting explicitely `xaxis_type` to be `category`.

import plotly.express as px

fig = px.bar(x=[1, 2, 4, 10], y =[8, 6, 11, 5])

fig.update_xaxes(type='category')

fig.show()

[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on a variety of types of data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/).

By default, Plotly Express lays out categorical data in the order in which it appears in the underlying data. Every 2-d cartesian Plotly Express function also includes a `category_orders` keyword argument which can be used to control the order in which categorical axes are drawn, but beyond that can also control [the order in which discrete colors appear in the legend](/python/discrete-color/), and [the order in which facets are laid out](/python/facet-plots/).

import plotly.express as px

df = px.data.tips()

fig = px.bar(df, x="day", y="total_bill", color="smoker", barmode="group", facet_col="sex",

category_orders={"day": ["Thur", "Fri", "Sat", "Sun"],

"smoker": ["Yes", "No"],

"sex": ["Male", "Female"]})

fig.show()

Whether using Plotly Express or not, categories can be sorted alphabetically or by value using the `categoryorder` attribute:

Set `categoryorder` to `"category ascending"` or `"category descending"` for the alphanumerical order of the category names or `"total ascending"` or `"total descending"` for numerical order of values. [categoryorder](https://plotly.com/python/reference/layout/xaxis/#layout-xaxis-categoryorder) for more information. Note that sorting the bars by a particular trace isn't possible right now - it's only possible to sort by the total values. Of course, you can always sort your data _before_ plotting it if you need more customization.

This example orders the categories **alphabetically** with `categoryorder: 'category ascending'`

import plotly.graph_objects as go

x=['b', 'a', 'c', 'd']

fig = go.Figure(go.Bar(x=x, y=[2,5,1,9], name='Montreal'))

fig.add_trace(go.Bar(x=x, y=[1, 4, 9, 16], name='Ottawa'))

fig.add_trace(go.Bar(x=x, y=[6, 8, 4.5, 8], name='Toronto'))

fig.update_layout(barmode='stack')

fig.update_xaxes(categoryorder='category ascending')

fig.show()

This example orders the categories **by total value** with `categoryorder: 'total descending'`

import plotly.graph_objects as go

x=['b', 'a', 'c', 'd']

fig = go.Figure(go.Bar(x=x, y=[2,5,1,9], name='Montreal'))

fig.add_trace(go.Bar(x=x, y=[1, 4, 9, 16], name='Ottawa'))

fig.add_trace(go.Bar(x=x, y=[6, 8, 4.5, 8], name='Toronto'))

fig.update_layout(barmode='stack')

fig.update_xaxes(categoryorder='total ascending')

fig.show()

This example shows how to control category order when using `plotly.graph_objects` by defining `categoryorder` to "array" to derive the ordering from the attribute `categoryarray`.

import plotly.graph_objects as go

x=['b', 'a', 'c', 'd']

fig = go.Figure(go.Bar(x=x, y=[2,5,1,9], name='Montreal'))

fig.add_trace(go.Bar(x=x, y=[1, 4, 9, 16], name='Ottawa'))

fig.add_trace(go.Bar(x=x, y=[6, 8, 4.5, 8], name='Toronto'))

fig.update_layout(barmode='stack')

fig.update_xaxes(categoryorder='array', categoryarray:['d','a','c','b'])

fig.show()

By default, gridlines and ticks are not shown on categorical axes but they can be activated:

import plotly.express as px

fig = px.bar(x=["A","B","C"], y=[1,3,2])

fig.update_xaxes(showgrid=True, ticks="outside")

fig.show()

By default, ticks and gridlines appear on the categories but the `tickson` attribute can be used to move them to the category boundaries:

import plotly.express as px

fig = px.bar(x=["A","B","C"], y=[1,3,2])

fig.update_xaxes(showgrid=True, ticks="outside", tickson="boundaries")

fig.show()

A two-level categorical axis (also known as grouped or hierarchical categories, or sub-categories) can be created by specifying a trace's `x` or `y` property as a 2-dimensional lists. The first sublist represents the outer categorical value while the second sublist represents the inner categorical value. This is only possible with `plotly.graph_objects` at the moment, and not Plotly Express.

Passing in a two-dimensional list as the `x` or `y` value of a trace causes [the `type` of the corresponding axis](/python/axes/) to be set to `multicategory`.

Here is an example that creates a figure with 2 `bar` traces with a 2-level categorical x-axis.

import plotly.graph_objects as go

fig = go.Figure()

fig.add_trace(go.Bar(

x = [['First', 'First', 'Second', 'Second'],

["A", "B", "A", "B"]],

y = [2, 3, 1, 5],

name = "Adults",

))

fig.add_trace(go.Bar(

x = [['First', 'First', 'Second', 'Second'],

["A", "B", "A", "B"]],

y = [8, 3, 6, 5],

name = "Children",

))

fig.update_layout(title_text="Multi-category axis")

fig.show()

See https://plotly.com/python/reference/layout/xaxis/ for more information and chart attribute options!