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# Axes in Python

How to adjust axes properties in Python - axes titles, styling and coloring axes and grid lines, ticks, tick labels and more.

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New to Plotly?

Plotly is a free and open-source graphing library for Python. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

This tutorial explain how to set the properties of 2-dimensional Cartesian axes, namely go.layout.XAxis and go.layout.YAxis.

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

See also the tutorials on facet plots, subplots and multiple axes.

### 2-D Cartesian Axis Types and Auto-Detection¶

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

The axis type is auto-detected by looking at data from the first trace 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.

### Forcing an axis to be categorical¶

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.

In [1]:
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()


#### General Axis properties¶

The different groups of Cartesian axes properties are

• title of the axis
• tick values (locations of tick marks) and tick labels. Tick labels and grid lines are placed at tick values.
• lines: grid lines (passing through tick values), axis lines, zero lines
• range of the axis
• domain of the axis

The examples on this page apply to axes of any type, but extra attributes are available for axes of type category and axes of type date.

#### Set and Style Axes Title Labels¶

##### Set axis title text with Plotly Express¶

Axis titles are automatically set to the column names when using Plotly Express with a data frame as input.

In [2]:
import plotly.express as px
df = px.data.tips()
fig = px.scatter(df, x="total_bill", y="tip", color="sex")
fig.show()


Axis titles (and legend titles) can also be overridden using the labels argument of Plotly Express functions:

In [3]:
import plotly.express as px
df = px.data.tips()
fig = px.scatter(df, x="total_bill", y="tip", color="sex",
labels=dict(total_bill="Total Bill ($)", tip="Tip ($)", sex="Payer Gender")
)
fig.show()


The PX labels argument can also be used without a data frame argument:

In [4]:
import plotly.express as px
fig = px.bar(df, x=["Apples", "Oranges"], y=[10,20], color=["Here", "There"],
labels=dict(x="Fruit", y="Amount", color="Place")
)
fig.show()

##### Set axis title text with Graph Objects¶

Axis titles are set using the nested title.text property of the x or y axis. Here is an example of creating a new figure and using update_xaxes and update_yaxes, with magic underscore notation, to set the axis titles.

In [5]:
import plotly.express as px

fig = px.line(y=[1, 0])

fig.update_xaxes(title_text='Time')
fig.update_yaxes(title_text='Value A')

fig.show()


### Set axis title position¶

This example sets standoff attribute to cartesian axes to determine the distance between the tick labels and the axis title. Note that the axis title position is always constrained within the margins, so the actual standoff distance is always less than the set or default value. By default automargin is True in Plotly template for the cartesian axis, so the margins will be pushed to fit the axis title at given standoff distance.

In [6]:
import plotly.graph_objects as go

fig = go.Figure(go.Scatter(
mode = "lines+markers",
y = [4, 1, 3],
x = ["December", "January", "February"]))

fig.update_xaxes(
tickangle = 90,
title_text = "Month",
title_font = {"size": 20},
title_standoff = 25)

fig.update_yaxes(
title_text = "Temperature",
title_standoff = 25)

fig.show()

##### Set axis title font¶

Here is an example that configures the font family, size, and color for the axis titles in a figure created using Plotly Express.

In [7]:
import plotly.express as px
df = px.data.iris()

fig = px.scatter(df, x="sepal_width", y="sepal_length", facet_col="species")
fig.update_xaxes(title_font=dict(size=18, family='Courier', color='crimson'))
fig.update_yaxes(title_font=dict(size=18, family='Courier', color='crimson'))

fig.show()


#### Tick Placement, Color, and Style¶

##### Toggling axis tick marks¶

Axis tick marks are disabled by default for the default plotly theme, but they can easily be turned on by setting the ticks axis property to "inside" (to place ticks inside plotting area) or "outside" (to place ticks outside the plotting area).

Here is an example of turning on inside x-axis and y-axis ticks in a faceted figure created using Plotly Express. Note how the col argument to update_yaxes is used to only turn on the y-axis ticks for the left-most subplot.

In [8]:
import plotly.express as px
df = px.data.iris()

fig = px.scatter(df, x="sepal_width", y="sepal_length", facet_col="species")
fig.update_xaxes(ticks="inside")
fig.update_yaxes(ticks="inside", col=1)

fig.show()

##### Set number of tick marks (and grid lines)¶

The approximate number of ticks displayed for an axis can be specified using the nticks axis property.

Here is an example of updating the y-axes of a figure created using Plotly Express to display approximately 20 ticks.

In [9]:
import plotly.express as px
df = px.data.iris()

fig = px.scatter(df, x="sepal_width", y="sepal_length", facet_col="species")
fig.update_yaxes(nticks=20)

fig.show()

##### Set start position and distance between ticks¶

The tick0 and dtick axis properties can be used to control to placement of axis ticks as follows: If specified, a tick will fall exactly on the location of tick0 and additional ticks will be added in both directions at intervals of dtick.

Here is an example of updating the y axis of a figure created using Plotly Express to position the ticks at intervals of 0.5, starting at 0.25.

In [10]:
import plotly.express as px
df = px.data.iris()

fig = px.scatter(df, x="sepal_width", y="sepal_length", facet_col="species")
fig.update_yaxes(tick0=0.25, dtick=0.5)

fig.show()

##### Set exact location of axis ticks¶

It is possible to configure an axis to display ticks at a set of predefined locations by setting the tickvals property to an array of positions.

Here is an example of setting the exact location of ticks on the y axes of a figure created using Plotly Express.

In [11]:
import plotly.express as px
df = px.data.iris()

fig = px.scatter(df, x="sepal_width", y="sepal_length", facet_col="species")
fig.update_yaxes(tickvals=[5.1, 5.9, 6.3, 7.5])

fig.show()

##### Style tick marks¶

As discussed above, tick marks are disabled by default in the default plotly theme, but they can be enabled by setting the ticks axis property to "inside" (to place ticks inside plotting area) or "outside" (to place ticks outside the plotting area).

The appearance of these tick marks can be customized by setting their length (ticklen), width (tickwidth), and color (tickcolor).

Here is an example of enabling and styling the tick marks of a faceted figure created using Plotly Express. Note how the col argument to update_yaxes is used to only turn on and style the y-axis ticks for the left-most subplot.

In [12]:
import plotly.express as px
df = px.data.iris()

fig = px.scatter(df, x="sepal_width", y="sepal_length", facet_col="species")
fig.update_xaxes(ticks="outside", tickwidth=2, tickcolor='crimson', ticklen=10)
fig.update_yaxes(ticks="outside", tickwidth=2, tickcolor='crimson', ticklen=10, col=1)

fig.show()

##### Toggling axis labels¶

The axis tick mark labels can be disabled by setting the showticklabels axis property to False.

Here is an example of disabling tick labels in all subplots for a faceted figure created using Plotly Express.

In [13]:
import plotly.express as px
df = px.data.iris()

fig = px.scatter(df, x="sepal_width", y="sepal_length", facet_col="species")
fig.update_xaxes(showticklabels=False)
fig.update_yaxes(showticklabels=False)

fig.show()

##### Set axis label rotation and font¶

The orientation of the axis tick mark labels is configured using the tickangle axis property. The value of tickangle is the angle of rotation, in the clockwise direction, of the labels from vertical in units of degrees. The font family, size, and color for the tick labels are stored under the tickfont axis property.

Here is an example of rotating the x-axis tick labels by 45 degrees, and customizing their font properties, in a faceted histogram figure created using Plotly Express.

In [14]:
import plotly.express as px
df = px.data.tips()

fig = px.histogram(df, x="sex", y="tip", histfunc='sum', facet_col='smoker')
fig.update_xaxes(tickangle=45, tickfont=dict(family='Rockwell', color='crimson', size=14))

fig.show()


#### Enumerated Ticks with Tickvals and Ticktext¶

The tickvals and ticktext axis properties can be used together to display custom tick label text at custom locations along an axis. They should be set to lists of the same length where the tickvals list contains positions along the axis, and ticktext contains the strings that should be displayed at the corresponding positions.

Here is an example.

In [15]:
import plotly.graph_objects as go
import pandas as pd

# Load and filter Apple stock data for 2016
"https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv",
parse_dates=["Date"],
index_col="Date"
)

apple_df_2016 = apple_df["2016"]

# Create figure and add line
fig = go.Figure()
x=apple_df_2016.index,
y=apple_df_2016["AAPL.High"],
mode="lines"
))

# Set custom x-axis labels
fig.update_xaxes(
ticktext=["End of Q1", "End of Q2", "End of Q3", "End of Q4"],
tickvals=["2016-04-01", "2016-07-01", "2016-10-01", apple_df_2016.index.max()],
)

# Prefix y-axis tick labels with dollar sign
fig.update_yaxes(tickprefix="\$")

# Set figure title
fig.update_layout(title_text="Apple Stock Price")

fig.show()


### Axis lines: grid and zerolines¶

##### Toggling Axis grid lines¶

Axis grid lines can be disabled by setting the showgrid property to False for the x and/or y axis.

Here is an example of setting showgrid to False in the graph object figure constructor.

In [16]:
import plotly.express as px

fig = px.line(y=[1, 0])
fig.update_xaxes(showgrid=False)
fig.update_yaxes(showgrid=False)
fig.show()

##### Toggling Axis zero lines¶

The lines passing through zero can be disabled as well by setting the zeroline axis property to False

In [17]:
import plotly.express as px

fig = px.line(y=[1, 0])

fig.update_xaxes(showgrid=False, zeroline=False)
fig.update_yaxes(showgrid=False, zeroline=False)
fig.show()


#### Styling and Coloring Axes and the Zero-Line¶

##### Styling axis lines¶

The showline axis property controls the visibility of the axis line, and the linecolor and linewidth axis properties control the color and width of the axis line.

Here is an example of enabling the x and y axis lines, and customizing their width and color, for a faceted histogram created with Plotly Express.

In [18]:
import plotly.express as px
df = px.data.tips()

fig = px.histogram(df, x="sex", y="tip", histfunc='sum', facet_col='smoker')
fig.update_xaxes(showline=True, linewidth=2, linecolor='black')
fig.update_yaxes(showline=True, linewidth=2, linecolor='black')

fig.show()

##### Mirroring axis lines¶

Axis lines can be mirrored to the opposite side of the plotting area by setting the mirror axis property to True.

Here is an example of mirroring the x and y axis lines in a faceted histogram created using Plotly Express.

In [19]:
import plotly.express as px
df = px.data.tips()

fig = px.histogram(df, x="sex", y="tip", histfunc='sum', facet_col='smoker')
fig.update_xaxes(showline=True, linewidth=2, linecolor='black', mirror=True)
fig.update_yaxes(showline=True, linewidth=2, linecolor='black', mirror=True)

fig.show()

##### Styling grid lines¶

The width and color of axis grid lines are controlled by the gridwidth and gridcolor axis properties.

Here is an example of customizing the grid line width and color for a faceted scatter plot created with Plotly Express

In [20]:
import plotly.express as px
df = px.data.iris()

fig = px.scatter(df, x="sepal_width", y="sepal_length", facet_col="species")
fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='LightPink')
fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='LightPink')

fig.show()

##### Styling zero lines¶

The width and color of axis zero lines are controlled by the zerolinewidth and zerolinecolor axis properties.

Here is an example of configuring the zero line width and color for a simple figure using the update_xaxes and update_yaxes graph object figure methods.

In [21]:
import plotly.express as px

fig = px.line(y=[1, 0])

fig.update_xaxes(zeroline=True, zerolinewidth=2, zerolinecolor='LightPink')
fig.update_yaxes(zeroline=True, zerolinewidth=2, zerolinecolor='LightPink')

fig.show()


#### Setting the Range of Axes Manually¶

The visible x and y axis range can be configured manually by setting the range axis property to a list of two values, the lower and upper boundary.

Here's an example of manually specifying the x and y axis range for a faceted scatter plot created with Plotly Express.

In [22]:
import plotly.express as px
df = px.data.iris()

fig = px.scatter(df, x="sepal_width", y="sepal_length", facet_col="species")
fig.update_xaxes(range=[1.5, 4.5])
fig.update_yaxes(range=[3, 9])

fig.show()


#### Disabling Pan/Zoom on Axes (Fixed Range)¶

Pan/Zoom can be disabled for a given axis by setting fixedrange to True.

In [23]:
import plotly.express as px
df = px.data.iris()

fig = px.scatter(df, x="sepal_width", y="sepal_length", facet_col="species")
fig.update_xaxes(fixedrange=True)

fig.show()


### Fixed Ratio Axes¶

The scaleanchor and scaleratio axis properties can be used to force a fixed ratio of pixels per unit between two axes.

Here is an example of anchoring the scale of the x and y axis with a scale ratio of 1. Notice how the zoom box is constrained to prevent the distortion of the shape of the line plot.

In [24]:
import plotly.graph_objects as go

fig = go.Figure()

x = [0,1,1,0,0,1,1,2,2,3,3,2,2,3],
y = [0,0,1,1,3,3,2,2,3,3,1,1,0,0]
))

fig.update_layout(
width = 800,
height = 500,
title = "fixed-ratio axes"
)
fig.update_yaxes(
scaleanchor = "x",
scaleratio = 1,
)

fig.show()


### Fixed Ratio Axes with Compressed domain¶

If an axis needs to be compressed (either due to its own scaleanchor and scaleratio or those of the other axis), constrain determines how that happens: by increasing the "range" (default), or by decreasing the "domain".

In [25]:
import plotly.graph_objects as go
fig = go.Figure()
x = [0,1,1,0,0,1,1,2,2,3,3,2,2,3],
y = [0,0,1,1,3,3,2,2,3,3,1,1,0,0]
))
fig.update_layout(
width = 800,
height = 500,
title = "fixed-ratio axes with compressed axes"
)
fig.update_xaxes(
range=[-1,4],  # sets the range of xaxis
constrain="domain",  # meanwhile compresses the xaxis by decreasing its "domain"
)
fig.update_yaxes(
scaleanchor = "x",
scaleratio = 1
)
fig.show()

##### Decreasing the domain spanned by an axis¶

In the example below, the x and y axis are anchored together, and the range of the xaxis is set manually. By default, plotly extends the range of the axis (overriding the range parameter) to fit in the figure domain. You can restrict the domain to force the axis to span only the set range, by setting constrain='domain' as below.

In [26]:
import plotly.graph_objects as go

fig = go.Figure()

x = [0,1,1,0,0,1,1,2,2,3,3,2,2,3],
y = [0,0,1,1,3,3,2,2,3,3,1,1,0,0]
))

fig.update_layout(
width = 800,
height = 500,
title = "fixed-ratio axes"
)
fig.update_xaxes(
scaleanchor = "x",
scaleratio = 1,
)
fig.update_yaxes(
range=(-0.5, 3.5),
constrain='domain'
)

fig.show()


### Fixed Ratio Axes with Compressed domain¶

If an axis needs to be compressed (either due to its own scaleanchor and scaleratio or those of the other axis), constrain determines how that happens: by increasing the "range" (default), or by decreasing the "domain".

In [27]:
import plotly.graph_objects as go

fig = go.Figure()

x = [0,1,1,0,0,1,1,2,2,3,3,2,2,3],
y = [0,0,1,1,3,3,2,2,3,3,1,1,0,0]
))

fig.update_layout(
width = 800,
height = 500,
title = "fixed-ratio axes with compressed axes"
)
fig.update_xaxes(
range=[-1,4],  # sets the range of xaxis
constrain="domain",  # meanwhile compresses the xaxis by decreasing its "domain"
)
fig.update_yaxes(
scaleanchor = "x",
scaleratio = 1,
)

fig.show()


#### Reversed Axes¶

You can tell plotly's automatic axis range calculation logic to reverse the direction of an axis by setting the autorange axis property to "reversed".

Here is an example of reversing the direction of the y axes for a faceted scatter plot created using Plotly Express.

In [28]:
import plotly.express as px
df = px.data.iris()

fig = px.scatter(df, x="sepal_width", y="sepal_length", facet_col="species")
fig.update_yaxes(autorange="reversed")

fig.show()


#### Reversed Axes with Range ( Min/Max ) Specified¶

The direction of an axis can be reversed when manually setting the range extents by specifying a list containing the upper bound followed by the lower bound (rather that the lower followed by the upper) as the range axis property.

Here is an example of manually setting the reversed range of the y axes in a faceted scatter plot figure created using Plotly Express.

In [29]:
import plotly.express as px
df = px.data.iris()

fig = px.scatter(df, x="sepal_width", y="sepal_length", facet_col="species")
fig.update_yaxes(range=[9, 3])

fig.show()


### Axis range for log axis type¶

If you are using a log type of axis and you want to set the range of the axis, you have to give the log10 value of the bounds when using fig.update_xaxes or fig.update_layout. However, with plotly.express functions you pass directly the values of the range bounds (plotly.express then computes the appropriate values to pass to the figure layout).

In [30]:
import plotly.express as px
import numpy as np

x = np.linspace(1, 200, 30)
fig = px.scatter(x=x, y=x**3, log_x=True, log_y=True, range_x=[0.8, 250])
fig.show()

In [31]:
import plotly.graph_objects as go
import numpy as np

x = np.linspace(1, 200, 30)
fig = go.Figure(go.Scatter(x=x, y=x**3))
fig.update_xaxes(type="log", range=[np.log10(0.8), np.log10(250)])
fig.update_yaxes(type="log")
fig.show()


#### nonnegative, tozero, and normal Rangemode¶

The axis auto-range calculation logic can be configured using the rangemode axis parameter.

If rangemode is "normal" (the default), the range is computed based on the min and max values of the input data. If "tozero", the range will always include zero. If "nonnegative", the range will not extend below zero, regardless of the input data.

Here is an example of configuring a faceted scatter plot created using Plotly Express to always include zero for both the x and y axes.

In [32]:
import plotly.express as px
df = px.data.iris()

fig = px.scatter(df, x="sepal_width", y="sepal_length", facet_col="species")
fig.update_xaxes(rangemode="tozero")
fig.update_yaxes(rangemode="tozero")

fig.show()


#### Setting the domain of the axis¶

In [33]:
import plotly.graph_objects as go

fig = go.Figure()

x = [0,1,1,0,0,1,1,2,2,3,3,2,2,3],
y = [0,0,1,1,3,3,2,2,3,3,1,1,0,0]
))
fig.update_xaxes(domain=(0.25, 0.75))
fig.update_yaxes(domain=(0.25, 0.75))
fig.show()


#### Synchronizing axes in subplots with matches¶

Using facet_col from plotly.express let zoom and pan each facet to the same range implicitly. However, if the subplots are created with make_subplots, the axis needs to be updated with matches parameter to update all the subplots accordingly.

Zoom in one trace below, to see the other subplots zoomed to the same x-axis range. To pan all the subplots, click and drag from the center of x-axis to the side:

In [34]:
import plotly.graph_objects as go
from plotly.subplots import make_subplots
import numpy as np

N = 20
x = np.linspace(0, 1, N)

fig = make_subplots(1, 3)
for i in range(1, 4):
fig.add_trace(go.Scatter(x=x, y=np.random.random(N)), 1, i)
fig.update_xaxes(matches='x')
fig.show()


#### Reference¶

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

### What About Dash?¶

Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash at https://dash.plot.ly/installation.

Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:

import plotly.graph_objects as go # or plotly.express as px
fig = go.Figure() # or any Plotly Express function e.g. px.bar(...)
# fig.add_trace( ... )
# fig.update_layout( ... )

import dash
import dash_core_components as dcc
import dash_html_components as html

app = dash.Dash()
app.layout = html.Div([
dcc.Graph(figure=fig)
])

app.run_server(debug=True, use_reloader=False)  # Turn off reloader if inside Jupyter