我有一个大的数据集,想把整个数据集作为背景来绘制,然后通过在背景上进行子集和绘制来突出显示其中的过滤特征。我有这个工作的背景,每一次,但这是非常耗时的,因为我渲染了大约40个绘图的基础上。你知道吗
我的问题是,我似乎无法得到背景数据(第一散点图)留在原地。通过复制图形或尝试复制轴。你知道吗
完整功能代码示例:
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
df = pd.DataFrame(
{
"x": np.random.normal(size=100),
"y": np.random.rand(100),
"thing_1": np.concatenate((np.ones(50), np.zeros(50))),
"thing_2": np.concatenate((np.zeros(50), np.ones(50)))}
)
fig, ax = plt.subplots(figsize=(12, 8))
# This works but replots the background data each time (costly with the large datasets)
for thing in ['thing_1', 'thing_2']:
ax.clear()
# background data cloud Reuse instead of plotting
ax.scatter(df.x, df.y, c='grey', alpha=0.5, s=30)
# subset to highlight
ind = df[thing] == 1
ax.scatter(df.loc[ind, 'x'], df.loc[ind, 'y'], c='red', alpha=1, s=15)
plt.savefig('{}_filter.png'.format(thing))
我目前优化代码的最佳尝试:
# Want to do something like this (only plot background data once and copy the axis or figure)
fig_background, ax_background = plt.subplots(figsize=(12, 8))
ax_background.scatter(df.x, df.y, c='grey', alpha=0.5, s=30)
for thing in ['thing_1', 'thing_2']:
fig_filter = fig_background
axs = fig_filter.get_axes()
# subset to highlight
ind = df[thing] == 1
axs[0].scatter(df.loc[ind, 'x'], df.loc[ind, 'y'], c='red', alpha=1, s=15)
plt.savefig('{}_filter.png'.format(thing))
plt.cla()
在绘制新的循环步骤之前,可以删除每个循环步骤中的散点。你知道吗
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