Formatting Ticks in Python

How to format axes ticks in Python with Plotly.

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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.

Tickmode - Linear¶

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

In [1]:

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.

In [2]:

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.

Out[3]:

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Using Tickformat Attribute¶

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

In [4]:

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

In [5]:

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¶

In [6]:

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¶

In [7]:

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¶

In [8]:

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!

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( ... )

from dash import Dash, dcc, html

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

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