Formatting Datetime Index with Pandas DataFrame Styler

Question

I'm using the style property of Pandas DataFrames to create HTML tables for emailing.

The issue I am having is that I have a datetime index that shows up as a datetime stamp when I'd like it to show as a date instead. I'm not interested in the time part. In the interpreter, the DataFrame does print out correctly (only shows the date part). But when I render after stylizing using the style property of the table, it generates HTML that puts out the time part as well. I've looked into using style.format() but I can't access the index column. I would reset the index in order to make the datetime column a normal column... but my header columns are MultIndex. If I flatten out and don't use indexes the table looks weird.

Unfortunately I found this in the .style documentation:

Limitations

DataFrame only (use Series.to_frame().style) The index and columns must be unique No large repr, and performance isn’t great; this is intended for summary DataFrames You can only style the values, not the index or columns You can only apply styles, you can’t insert new HTML entities Some of these will be addressed in the future.

https://pandas.pydata.org/pandas-docs/stable/style.html#Limitations

I'm posting to see if anyone has any ideas for how I can get around this. Thanks!

Example table that shows the issue: example_table_link

Code for generating table:

account_day_df = merged[['Day', 'Metric1', 'Metric2', 'Metric3', 'Metric4', 'Campaign type']]
account_day_df = account_day_df.groupby(['Day', 'Campaign type']).sum()
account_day_df.loc[:, 'Metric5'] = account_day_df['Metric1'] / account_day_df['Metric4']
account_day_df = account_day_df.unstack('Campaign type')

html += (
    account_day_df.style
        .background_gradient(cmap=cm, subset=['Metric5'])
        .set_table_attributes('border="1" class="dataframe table table-hover table-bordered"')
        .render(i)
)

Answer 1

You can convert your index to object instead of datetime using strftime() by doing df.index = df.index.strftime("%Y-%d-%m"). Here's an example:

data = np.random.randn(10, 5)
index = pd.date_range('20130101', periods=10, freq='D')
pd.DataFrame(data, index=index).style.format("{:.2}")

pd.DataFrame(data, index=index.strftime("%Y-%m-%d")).style.format("{:.2}")