How can I prevent Plotly from offsetting month labels when using month-end dates in a bar chart?

The approach you showed is one common workaround, but it isn’t the only solution. In Plotly, the unexpected x‑axis behavior is due to how dates are interpreted and automatically rounded for continuous axes. Here are two more idiomatic ideas:

1. Convert the Dates to Categories

If your data naturally represents discrete months, you can treat the x‑values as categorical labels rather than dates. This bypasses automatic date–rounding entirely. For example:

PYTHONCopied
import pandas as pd
import streamlit as st
import plotly.express as px

data = {
    '2024-01-31':   1044,
    '2024-02-29':   2310,
    '2024-03-31':    518,
    '2024-04-30':  -1959,
    '2024-05-31':      0,
    '2024-06-30':  -1010,
    '2024-07-31':   1500,
    '2024-08-31': -15459,
    '2024-09-30': -14153,
    '2024-10-31': -12604,
    '2024-11-30':  -5918,
    '2024-12-31':  -3897
}

df = pd.DataFrame(data.items(), columns=['date', 'value'])
# Keep the dates as strings, so they are treated as categories:
fig = px.bar(data_frame=df, x='date', y='value')
st.plotly_chart(fig)

This method shows the labels as provided (you might need to adjust tick labels if you want a different format).

2. Adjust Axis Tick Format

If you prefer to keep the data as dates rather than categories, you can update the x‑axis configuration to control tick placement and formatting. For example, you can force ticks to appear on the month–ends by updating the axis:

PYTHONCopied
import pandas as pd
import streamlit as st
import plotly.express as px

data = {
    '2024-01-31':   1044,
    '2024-02-29':   2310,
    '2024-03-31':    518,
    '2024-04-30':  -1959,
    '2024-05-31':      0,
    '2024-06-30':  -1010,
    '2024-07-31':   1500,
    '2024-08-31': -15459,
    '2024-09-30': -14153,
    '2024-10-31': -12604,
    '2024-11-30':  -5918,
    '2024-12-31':  -3897
}

df = pd.DataFrame(data.items(), columns=['date', 'value'])
df['date'] = pd.to_datetime(df['date'])
fig = px.bar(data_frame=df, x='date', y='value')

# Update the x-axis: specify tick format and tick intervals
fig.update_xaxes(
    dtick="M1",           # tick every month
    tickformat="%b %Y",   # format ticks as "Jan 2024", etc.
    tick0=df['date'].min()  # set the starting tick
)

st.plotly_chart(fig)

This approach leaves the data as a continuous date axis while overriding the automatic tick behavior. Note that sometimes Plotly’s internal date handling (especially with bar charts) might still need careful tuning of the range or offset parameters.

Summary

• Converting dates (as in your workaround) to the first day of the month is a reliable method because it makes the date values “round” naturally.
• Using categorical x‐axis values avoids date–rounding altogether.
• Customizing tick formatting on a continuous axis is also possible, but may require additional tweaks.

Each method has its tradeoffs. Your existing workaround is perfectly acceptable, and many people choose it for its clarity and reliability. The “more idiomatic” solution really depends on whether you prefer treating dates as categorical labels or want to preserve the continuous date axis with precise control over tick formatting.