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Table and Chart Subplots in Python

How to create a subplot with tables and charts in Python with Plotly.


If you're using Dash Enterprise's Data Science Workspaces, you can copy/paste any of these cells into a Workspace Jupyter notebook.
Alternatively, download this entire tutorial as a Jupyter notebook and import it into your Workspace.
Find out if your company is using Dash Enterprise.


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.

Import CSV Data

In [1]:
import plotly.graph_objects as go
from plotly.subplots import make_subplots

import pandas as pd
import re

df = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/Mining-BTC-180.csv")

for i, row in enumerate(df["Date"]):
    p = re.compile(" 00:00:00")
    datetime = p.split(df["Date"][i])[0]
    df.iloc[i, 1] = datetime

fig = make_subplots(
    rows=3, cols=1,
    shared_xaxes=True,
    vertical_spacing=0.03,
    specs=[[{"type": "table"}],
           [{"type": "scatter"}],
           [{"type": "scatter"}]]
)

fig.add_trace(
    go.Scatter(
        x=df["Date"],
        y=df["Mining-revenue-USD"],
        mode="lines",
        name="mining revenue"
    ),
    row=3, col=1
)

fig.add_trace(
    go.Scatter(
        x=df["Date"],
        y=df["Hash-rate"],
        mode="lines",
        name="hash-rate-TH/s"
    ),
    row=2, col=1
)

fig.add_trace(
    go.Table(
        header=dict(
            values=["Date", "Number<br>Transactions", "Output<br>Volume (BTC)",
                    "Market<br>Price", "Hash<br>Rate", "Cost per<br>trans-USD",
                    "Mining<br>Revenue-USD", "Trasaction<br>fees-BTC"],
            font=dict(size=10),
            align="left"
        ),
        cells=dict(
            values=[df[k].tolist() for k in df.columns[1:]],
            align = "left")
    ),
    row=1, col=1
)
fig.update_layout(
    height=800,
    showlegend=False,
    title_text="Bitcoin mining stats for 180 days",
)

fig.show()

Reference

See https://plotly.com/python/reference/table/ for more information regarding chart attributes!
For examples of Plotly Tables, see: https://plotly.com/python/table/

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