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Tables

Tables#

plotly.express does not include a table function, but we can create a graph object table using go.Figure() in combination with go.Table(). We can create two columns of data with sample scores for A and B letter grades.

import plotly.graph_objects as go # import statement
# create figure with data
fig = go.Figure(data=[go.Table(header=dict(values=['A Scores', 'B Scores']),
                 cells=dict(values=[[100, 90, 80, 90], [95, 85, 75, 95]]))
                     ])
# show figure
fig.show()

There’s a lot to get into in terms of the differences between plotly.express function syntax and plotly.graph_object syntax.

For our purposes, we can focus on how the table header takes a dictionary with column labels, and the cells also take a dictionary with two lists of values. These dictionaries are passed to go.Figure() and go.Table() to create the plot.

DataFrames & Tables#

We could also generate a table from data stored in a pandas DataFrame.

This example also includes style parameters for the table.

import pandas as pd # import statement
df = pd.read_csv('https://raw.githubusercontent.com/kwaldenphd/elements-of-computing/main/book/data/ch11/2014_usa_states.csv') # load data
df # inspect data

# create figure
fig = go.Figure(data=[go.Table(
    header=dict(values=list(df.columns),
                fill_color='paleturquoise',
                align='left'),
    cells=dict(values=[df.Rank, df.State, df.Postal, df.Population],
               fill_color='lavender',
               align='left'))
])

# show figure
fig.show()

This example includes style attributes like fill_color and align for both header and cells.

For header, this example takes a dictionary with the DataFrame column labels as a list, and the DataFrame column values as values for the cells.

Additional Resources#

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