SCI，CCF，EI及核心期刊绘图宝典，爆款持续更新，助力科研！
本期分享：
【SCI绘图】【曲线图系列2 python】多类别标签对比的曲线图，文末附完整代码。
1.环境准备
python 3
import proplot as pplt
import pandas as pd
from proplot import rc2.数据示例与读取
数据示例：
68.52769 1.01941334E-118 1.04975387E-287 3.07499527E-220 4.62365918E-169 2.32835613E-134
68.58945 3.94732386E-118 8.59329295E-287 1.94218811E-219 2.33402749E-168 9.88293492E-134
68.65121 1.51971781E-117 6.98480839E-286 1.21860660E-218 1.17091842E-167 4.17021496E-133
68.71298 5.81756808E-117 5.63747055E-285 7.59578697E-218 5.83793125E-167 1.74935402E-132
68.77474 2.21436321E-116 4.51815464E-284 4.70360236E-217 2.89277108E-166 7.29548983E-132
68.83650 8.38099508E-116 3.59581184E-283 2.89366667E-216 1.42463282E-165 3.02481879E-131
68.89826 3.15422077E-115 2.84186785E-282 1.76863385E-215 6.97327187E-165 1.24687538E-130
68.96003 1.18045311E-114 2.23045665E-281 1.07401485E-214 3.39254761E-164 5.11017864E-130
69.02179 4.39314840E-114 1.73851547E-280 6.48002835E-214 1.64052134E-163 2.08232725E-129
69.08355 1.62586232E-113 1.34576744E-279 3.88462479E-213 7.88524556E-163 8.43669246E-129
69.14532 5.98387683E-113 1.03461776E-278 2.31386509E-212 3.76735665E-162 3.39872639E-128
69.20708 2.19019452E-112 7.89987107E-278 1.36947767E-211 1.78919283E-161 1.36141743E-127
读取数据：
# 读取 txt 文件中的数据
data = pd.read_csv('uv_curve.txt', header=None, delim_whitespace=True)
# 颜色、曲线格式以及标签
color_list = ['black', 'red', 'orange', 'blue', 'green']
label_list = ['label1', 'label2', 'label3', 'label4', 'label5']
style_list = ['-', '--', '--', '--', '--']3.绘图展示
画布设置：
# 设置绘图的默认参数，如字体、字号等
rc['font.name'] = 'Arial'
rc['title.size'] = 14
rc['label.size'] = 12
rc['font.size'] = 10.5
rc['tick.width'] = 1.3
rc['meta.width'] = 1.3
rc['label.weight'] = 'bold'
rc['tick.labelweight'] = 'bold'
rc['ytick.major.size'] = 4.6
rc['ytick.minor.size'] = 2.5
rc['xtick.major.size'] = 4.6
rc['xtick.minor.size'] = 2.5绘制逻辑：
# 创建画布
fig, ax = pplt.subplots(figsize=(6 * 0.9, 4 * 0.9))
# 绘制多曲线图
x = data.iloc[:, 0]
for i in range(len(data.columns) - 1):
y = data.iloc[:, i + 1]
ax.plot(x, y, linewidth=1.3, color=color_list[i],
label=label_list[i], linestyle=style_list[i])
# 设置图例
ax.legend(loc='ur', ncols=1, fontweight='bold', frame=False)
# 格式化图像
fig.format(
grid=False, ylabel='Absorbance', xlabel='Wavelength (nm)',
xlim=(80, 200), xminorlocator=10, xlocator=20,
ylim=(0, 10000), yminorlocator=1000, ylocator=2000
)
fig.show()
fig.savefig('uv.png', bbox_inches='tight', dpi=300)完整代码：
# -*- coding: utf-8 -*-
import proplot as pplt
import pandas as pd
from proplot import rc
# 读取 txt 文件中的数据
data = pd.read_csv('uv_curve.txt', header=None, delim_whitespace=True)
# 颜色、曲线格式以及标签
color_list = ['black', 'red', 'orange', 'blue', 'green']
label_list = ['label1', 'label2', 'label3', 'label4', 'label5']
style_list = ['-', '--', '--', '--', '--']
# 设置绘图的默认参数，如字体、字号等
rc['font.name'] = 'Arial'
rc['title.size'] = 14
rc['label.size'] = 12
rc['font.size'] = 10.5
rc['tick.width'] = 1.3
rc['meta.width'] = 1.3
rc['label.weight'] = 'bold'
rc['tick.labelweight'] = 'bold'
rc['ytick.major.size'] = 4.6
rc['ytick.minor.size'] = 2.5
rc['xtick.major.size'] = 4.6
rc['xtick.minor.size'] = 2.5
# 创建画布
fig, ax = pplt.subplots(figsize=(6 * 0.9, 4 * 0.9))
# 绘制多曲线图
x = data.iloc[:, 0]
for i in range(len(data.columns) - 1):
y = data.iloc[:, i + 1]
ax.plot(x, y, linewidth=1.3, color=color_list[i],
label=label_list[i], linestyle=style_list[i])
# 设置图例
ax.legend(loc='ur', ncols=1, fontweight='bold', frame=False)
# 格式化图像
fig.format(
grid=False, ylabel='Absorbance', xlabel='Wavelength (nm)',
xlim=(80, 200), xminorlocator=10, xlocator=20,
ylim=(0, 10000), yminorlocator=1000, ylocator=2000
)
fig.show()
fig.savefig('uv.png', bbox_inches='tight', dpi=300)
