matplotlib / matplotlib/mplfinance
Update plot, remove old data show new data
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- Dominant language
- Python
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Description
Thanks for an amazing library!
I'm trying to create an application where the user can select a ticker and then click a button that prints candlesticks for the ticker. I'm plotting the graph inside a frame in Tkinter.

The user can select a ticker in the drop down and Graph the data for the ticker.

I first create the empty graph
def _make_empty_graph(self):
self.fig = mpf.figure()
self.canvas = FigureCanvasTkAgg(self.fig, self.graph_frame)
self.canvas.get_tk_widget().pack()
I then create the graph with data from a dataframe, after the user has selected a ticker and clicked "Graph". I destroy the children in the frame to not add another graph under the previous graph. The user can then select yet another ticker and graph it.
def _make_graph(self, ticker_data=None):
self._get_ticker_data()
for widgets in self.graph_frame.winfo_children():
widgets.destroy()
self.fig, _ = mpf.plot(self.ticker_df, type='candle',
title = f'{self.ticker_var.get()}',
volume=True, style='binance', returnfig=True)
self.canvas = FigureCanvasTkAgg(self.fig, self.graph_frame)
self.canvas.get_tk_widget().pack()
Can I solve this a bit more elegant by using subplots or similar and then just clear the graph before I create the new graph? I still want to show an empty graph in the beginning. Do I need to create the empty graph in some other way to be able to update it later? Later on I like to add buttons to be able to RSI, Bollinger bands etc. on top the candlesticks.
Suggestions are greatly appreciated.
Contributor guide
First steps
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Research direction
Start with the _make_empty_graph and _make_graph entry points shown in the issue, including the FigureCanvasTkAgg setup and graph_frame cleanup. Check how the plotting API handles replacing an existing figure and whether the requested empty initial graph and later indicators are supported. Done would require a documented, agreed approach, but no specific repository file or test is identified.
Written by the indexing model from the issue text.
Assessment
- Tech stack
- python
- Domain
- data-visualization, desktop
- Issue type
- Feature
- Difficulty
- 4/5
- Estimated time
- 3-5 days
- Activity status
- Stale
- Clarity
- Needs clarification
- Newbie friendliness
- 20/100