ENH: speed up DataFrame.plot using LineCollection #61532
Description
Description:
When plotting line charts with many columns or rows, DataFrame.plot() currently adds one Line2D object per column. This incurs significant overhead in large datasets.
Replacing this with a single LineCollection (from matplotlib.collections) can yield substantial speedups. In my benchmarks, plotting via LineCollection was ~2.5× faster on large DataFrames with many columns.
Minimal example:
# Imports and data generation import itertools import matplotlib.pyplot as plt import numpy as np import pandas as pd from matplotlib.collections import LineCollection num_rows = 500 num_cols = 2000 test_df = pd.DataFrame(np.random.randn(num_rows, num_cols).cumsum(axis=0)) # Simply using DataFrame.plot, (5.6 secs) test_df.plot(legend=False, figsize=(12, 8)) plt.show() # Optimized version using LineCollection (2.2 secs) x = np.arange(len(test_df.index)) lines = [np.column_stack([x, test_df[col].values]) for col in test_df.columns] default_colors = plt.rcParams["axes.prop_cycle"].by_key()["color"] color_cycle = list(itertools.islice(itertools.cycle(default_colors), len(lines))) line_collection = LineCollection(lines, colors=color_cycle) fig, ax = plt.subplots(figsize=(12, 8)) ax.add_collection(line_collection) ax.margins(0.05) plt.show()Note: the ~2.5x speed improvement is specific to dataframes with integer index. For dataframes with DatetimeIndex the actual speed improvement is ~27x when combined with the workaround here: #61398
Thank you for considering this suggestion!