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Tick formatting

Tickmode - Linear

If "linear", the placement of the ticks is determined by a starting position tick0 and a tick step dtick

import plotly.graph_objects as go

fig = go.Figure(go.Scatter(
    x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
    y = [28.8, 28.5, 37, 56.8, 69.7, 79.7, 78.5, 77.8, 74.1, 62.6, 45.3, 39.9]
))

fig.update_layout(
    xaxis = dict(
        tickmode = 'linear',
        tick0 = 0.5,
        dtick = 0.75
    )
)

fig.show()

Tickmode - Array

If "array", the placement of the ticks is set via tickvals and the tick text is ticktext.

import plotly.graph_objects as go

fig = go.Figure(go.Scatter(
    x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
    y = [28.8, 28.5, 37, 56.8, 69.7, 79.7, 78.5, 77.8, 74.1, 62.6, 45.3, 39.9]
))

fig.update_layout(
    xaxis = dict(
        tickmode = 'array',
        tickvals = [1, 3, 5, 7, 9, 11],
        ticktext = ['One', 'Three', 'Five', 'Seven', 'Nine', 'Eleven']
    )
)

fig.show()

Dynamic tickmode in Dash

Dash is the best way to build analytical apps in Python using Plotly figures. To run the app below, run pip install dash, click "Download" to get the code and run python app.py.

Get started with the official Dash docs and learn how to effortlessly style & deploy apps like this with Dash Enterprise.

```python hide_code=true from IPython.display import IFrame snippet_url = 'https://dash-gallery.plotly.host/python-docs-dash-snippets/' IFrame(snippet_url + 'tick-formatting', width='100%', height=630) 

#### Using Tickformat Attribute


For more formatting types, see: https://github.com/d3/d3-format/blob/master/README.md#locale_format

```python
import plotly.graph_objects as go

fig = go.Figure(go.Scatter(
    x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
    y = [28.8, 28.5, 37, 56.8, 69.7, 79.7, 78.5, 77.8, 74.1, 62.6, 45.3, 39.9]
))

fig.update_layout(yaxis_tickformat = '%')

fig.show()

Using Tickformat Attribute - Date/Time

For more date/time formatting types, see: https://github.com/d3/d3-time-format/blob/master/README.md

import plotly.graph_objects as go

import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv')

fig = go.Figure(go.Scatter(
    x = df['Date'],
    y = df['AAPL.High'],
))

fig.update_layout(
    title = 'Time Series with Custom Date-Time Format',
    xaxis_tickformat = '%d %B (%a)<br>%Y'
)

fig.show()

Using Exponentformat Attribute

import plotly.graph_objects as go

fig = go.Figure(go.Scatter(
    x = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12],
    y = [68000, 52000, 60000, 20000, 95000, 40000, 60000, 79000, 74000, 42000, 20000, 90000]
))

fig.update_layout(
    yaxis = dict(
        showexponent = 'all',
        exponentformat = 'e'
    )
)

fig.show()

Tickformatstops to customize for different zoom levels

import plotly.graph_objects as go

import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv')

fig = go.Figure(go.Scatter(
    x = df['Date'],
    y = df['mavg']
))

fig.update_layout(
    xaxis_tickformatstops = [
        dict(dtickrange=[None, 1000], value="%H:%M:%S.%L ms"),
        dict(dtickrange=[1000, 60000], value="%H:%M:%S s"),
        dict(dtickrange=[60000, 3600000], value="%H:%M m"),
        dict(dtickrange=[3600000, 86400000], value="%H:%M h"),
        dict(dtickrange=[86400000, 604800000], value="%e. %b d"),
        dict(dtickrange=[604800000, "M1"], value="%e. %b w"),
        dict(dtickrange=["M1", "M12"], value="%b '%y M"),
        dict(dtickrange=["M12", None], value="%Y Y")
    ]
)

fig.show()

Placing ticks and gridlines between categories

import plotly.graph_objects as go

fig = go.Figure(go.Bar(
    x = ["apples", "oranges", "pears"],
    y = [1, 2, 3]
))

fig.update_xaxes(
    showgrid=True,
    ticks="outside",
    tickson="boundaries",
    ticklen=20
)

fig.show()

Reference

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

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