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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!