I have often found transforming an arbitrary pandas df into a dash datatable a bit tedious. It also requires a bit more work if you have numeric column names or multi-index. For this reason, I wanted to share this useful code snippet I use in a private python package that allows me to prepare my dfs for dash datatable with minimum effort. If you find such functionalities useful, let me know and i will package it into a public pypi package 
.
In this example, the function in question is dataframe_to_dash_datable. It works with flat dfs and multi-indexed dfs. You can also specify properties to be applied to all columns or to a single column. Let me know if you have any comments or questions:
from typing import Dict, Tuple
from dash import Dash, html, dash_table
import pandas as pd
import plotly.express as px
def dataframe_to_dash_datable(
df: pd.DataFrame,
separator: str = "_",
properties: Dict = {},
column_properties: Dict = {},
) -> Tuple:
column_dicts, column_renaming = [], []
for column in df.columns:
if isinstance(column, (list, tuple)):
column_str = [str(x) for x in column if pd.notnull(x)]
if "" in column_str:
n_empty = column_str.count("")
column_str.remove("")
column_str = [""]*n_empty + column_str
column_id = f"{separator}".join([x for x in column_str if x != ""])
else:
column_id = str(column)
column_renaming.append(column_id)
column_dict = {"id": column_id, "name": column_str, **properties}
if column_id in column_properties:
column_dict.update(column_properties[column_id])
column_dicts.append(column_dict)
df.columns = column_renaming
df = df.to_dict("records")
return df, column_dicts
# App
app = Dash(__name__, title="Multi-index data")
server = app.server
data = px.data.gapminder()
data["country"] = (
"[" + data["country"] + "](https://en.wikipedia.org/wiki/" + data["country"].str.replace(" ", "_") + ")"
)
data = data.sort_values(["continent", "country", "year"])
data = data.pivot(
index=["continent", "country"],
columns="year",
values=["gdpPercap", "pop"],
).round(2).reset_index()
data, columns = dataframe_to_dash_datable(
data, column_properties={"country": {"presentation": "markdown"}}
)
app.layout = html.Div(
children=[
dash_table.DataTable(
data=data,
columns=columns,
sort_action="native",
sort_mode="single",
filter_action="native",
merge_duplicate_headers=True,
),
]
)
if __name__ == "__main__":
app.run_server(debug=True)
Result:
