如何用Matplotlib绘制目标样式散点图？Python新手代码调试求助

DevOps 小满

2026-7-2

问题：无法绘制符合要求的散点图

我想要绘制图中所示的散点图，但我的代码似乎无法达到要求的效果。我是Python新手，没有太多相关经验。

目标散点图

原代码：

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

Production_steps = [13,12,21,1,29,20,15,12,21,18,20,15,20,22,26,7,8,12,22,25,15,23,24,13,4,22,12]
Step_name = ['Start','NA2S','waiting','NA+','vacume','NA-','90min_Wait','waiting','NA+','waiting','vacume','vacume','1h_wait','Pressue','vacume','vacume','NA+/-','NA+','Pressure','90min_Wait','Pressure','cool-','vacume','vacume','NA-','Pressure','Batch Start']
Parameter1 = [80,932,21,525,20,20,21,1090,19,60,18,33,19,936,1083,61,102,1088,935,38,20,83,21,79,1006,930,1088]

# Create a DataFrame using the given data
df = pd.DataFrame({'Production_steps': Production_steps, 
'Step_name': Step_name, 'Parameter1': Parameter1})

# Get unique values of 'Step_name'
unique_steps = df['Step_name'].unique()

# Set up colors for each unique 'Step_name'
colors = plt.cm.rainbow(np.linspace(0, 1, len(unique_steps)))

# Create a figure and axis
fig, ax = plt.subplots(figsize=(13, 6))

# Loop through unique 'Step_name' values, filter data, and plot scatter points
for i, step in enumerate(unique_steps):
  step_data = df[df['Step_name'] == step]
  ax.scatter(step_data['Production_steps'],step_data['Parameter1'], label=step, color=colors[i])

# Set labels and show legend
 ax.set_xlabel('Production_steps')
ax.set_ylabel('Parameter1')

# Create legend outside of plot to position it at the bottom
fig.legend(loc='lower center', ncol=4, bbox_to_anchor=(0.5,-0.2))

# Show the scatter plot
plt.show()

问题分析与修正

你的代码存在几个关键问题，导致无法匹配目标图效果：

缩进错误：ax.set_xlabel行的缩进不正确，会直接导致代码运行报错。
X轴逻辑错误：目标图的X轴是按数据顺序排列的序列点，但你用Production_steps的数值作为X轴，导致相同数值的点重叠、顺序混乱。
图例布局问题：原图例位置超出画布范围，会导致部分内容被截断。

修正后的代码：

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

Production_steps = [13,12,21,1,29,20,15,12,21,18,20,15,20,22,26,7,8,12,22,25,15,23,24,13,4,22,12]
Step_name = ['Start','NA2S','waiting','NA+','vacume','NA-','90min_Wait','waiting','NA+','waiting','vacume','vacume','1h_wait','Pressue','vacume','vacume','NA+/-','NA+','Pressure','90min_Wait','Pressure','cool-','vacume','vacume','NA-','Pressure','Batch Start']
Parameter1 = [80,932,21,525,20,20,21,1090,19,60,18,33,19,936,1083,61,102,1088,935,38,20,83,21,79,1006,930,1088]

# 创建DataFrame，添加顺序索引作为X轴基准
df = pd.DataFrame({
    'sequence': range(len(Production_steps)),  # 新增顺序索引，保证点按排列顺序展示
    'Production_steps': Production_steps, 
    'Step_name': Step_name, 
    'Parameter1': Parameter1
})

# 获取唯一步骤名称
unique_steps = df['Step_name'].unique()

# 为每个步骤分配对应颜色
colors = plt.cm.rainbow(np.linspace(0, 1, len(unique_steps)))

# 创建画布与坐标轴
fig, ax = plt.subplots(figsize=(15, 7))

# 循环绘制每个步骤的散点
for i, step in enumerate(unique_steps):
    step_data = df[df['Step_name'] == step]
    ax.scatter(step_data['sequence'], step_data['Parameter1'], label=step, color=colors[i], s=100)

# 设置坐标轴标签
ax.set_xlabel('Production_steps')
ax.set_ylabel('Parameter1')
# 将X轴刻度替换为Production_steps的数值，同时保持点的顺序
ax.set_xticks(df['sequence'])
ax.set_xticklabels(df['Production_steps'])

# 调整图例位置与布局，避免内容截断
fig.legend(loc='lower center', ncol=4, bbox_to_anchor=(0.5, -0.15))
plt.tight_layout()

# 显示图表
plt.show()

关键修改说明

新增顺序索引：添加sequence列作为X轴基准，确保每个点按数据原始顺序排列，和目标图一致。
修正缩进错误：调整ax.set_xlabel的缩进，保证代码正常运行。
优化X轴刻度：用set_xticks和set_xticklabels实现“按顺序排列、显示Production_steps数值”的效果。
调整布局：通过tight_layout()自动适配画布空间，同时微调图例位置避免截断。
增大散点尺寸：用s=100参数让散点更清晰，匹配目标图的视觉效果。

内容的提问来源于stack exchange，提问作者wasif ahmed