How to use pyplot to show data with no scale on xaxis and allow to pan (shift) via mouse

So in the current matplotlib there is a pan button and zoom button and the plot show all data initially and scales it to fit in the visible window. To see the details, I have to zoom in and then pan.

What I want to do is to show the data in a detailed view as default and then I can pan it as needed.

For example, instead of this:

I want this (by default):

Then I can pan it as needed.

I don't want to truncate the data because I need all data but just view them in parts. Any easier way out of this without manipulating the data as I pan it?

With Matplotlib you could use a Span Selector like in this example, to show your entire data on the top subplot, while showing the "zoomed" part of the data on the bottom subplot:

import matplotlib.pyplot as plt
import numpy as np
import mplfinance as mpf
import yfinance as yf
import pandas as pd

from matplotlib.widgets import SpanSelector

# get market data
df = yf.download("SPY", start="2024-02-04", end="2024-03-04", interval="1h")

# x data range
timestamps = list(df.index)
short_timestamps = [str(t)[0:16] for t in timestamps]
x = range(len(timestamps))

fig, (ax1, ax2) = plt.subplots(2)

# diagram to select a region from
mpf.plot(df,
         ax=ax1,
         type="candle"
    )

ax1.set_title('Press left mouse button and drag '
              'to select a region in the top graph')

# zoomed region
mpf.plot(df,
         ax=ax2,
         type="candle"
    )

# set initial range
ax2.set_xlim(x[short_timestamps.index('2024-02-05 09:30')], x[short_timestamps.index('2024-02-06 09:30')])

def onselect(xmin, xmax):
    indmin, indmax = np.searchsorted(x, (xmin, xmax))
    indmax = min(len(x) - 1, indmax)
    region_x = x[indmin:indmax]

    if len(region_x) >= 2:
        # plot the original diagram but with a different x range
        ax2.clear()
        mpf.plot(df,
                 ax=ax2,
                 type="candle"
            )
        ax2.set_xlim(region_x[0], region_x[-1])
        ax2.set_title('Zoomed')
        fig.canvas.draw_idle()

span = SpanSelector(
    ax1,
    onselect,
    "horizontal",
    useblit=True,
    interactive=True,
    drag_from_anywhere=True
)

fig.tight_layout()
plt.show()

In this way you can select the desired range on the top subplot:

But Plotly allows you to do something similar much easier, like in these examples:

import plotly.graph_objects as go
import pandas as pd

df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv')

fig = go.Figure(data=[go.Candlestick(x=df['Date'],
                open=df['AAPL.Open'],
                high=df['AAPL.High'],
                low=df['AAPL.Low'],
                close=df['AAPL.Close'])])

# limit the x range to a required interval
fig.update_xaxes(range=['2016-01-01', '2016-02-01'])
fig.show()

In this case, you can see your entire data and select some part of it on the bottom subplot (called rangeslider), while seeing the selected part on the top subplot: