{"library":"plotly-resampler","title":"Plotly Resampler","description":"Plotly-resampler is a Python library designed for visualizing large time series datasets efficiently with Plotly. It extends Plotly's capabilities by dynamically resampling data based on the zoom level, ensuring smooth performance even with millions of data points. The current version is 0.11.0, and the project maintains an active development pace with frequent releases.","language":"python","status":"active","last_verified":"Sat May 16","install":{"commands":["pip install plotly-resampler","pip install plotly-resampler[full]"],"cli":null},"imports":["from plotly_resampler import FigureResampler","from plotly_resampler import FigureResampler\n# ... later ...\nFigureResampler.register_plotly_resampler()"],"auth":{"required":false,"env_vars":[]},"quickstart":{"code":"import pandas as pd\nimport numpy as np\nfrom plotly_resampler import FigureResampler\nimport plotly.graph_objects as go\n\n# Create some large time series data\nn_points = 1_000_000\ntime_index = pd.date_range(\"2020-01-01\", periods=n_points, freq=\"S\")\ndata_y = np.cumsum(np.random.randn(n_points))\n\n# Initialize FigureResampler, wrapping a Plotly figure\nfig = FigureResampler(go.Figure())\n\n# Add a trace with resampling enabled by providing hf_x and hf_y\nfig.add_trace(go.Scattergl(name='High-Frequency Data'), hf_x=time_index, hf_y=data_y)\n\n# Show the figure (opens in browser or displays in compatible environments)\nfig.show()","lang":"python","description":"This quickstart demonstrates how to create a `FigureResampler` instance to visualize a large time series dataset. 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