如何用Python在单张图中绘制车辆速度数据并标注城市信息？

Hey there! Let's walk through this together—since you're new to Python data visualization, I'll keep things clear and actionable. Here's how to get your desired chart with a proper time axis and city labels for speed=0 points:

1. First, Fix the Time Data Type #

Chances are your time column is stored as a string right now, which will mess up the X-axis ordering. Let's convert it to a proper datetime format first:

import pandas as pd
import matplotlib.pyplot as plt

# If you haven't loaded your Excel data yet, add this line:
# Dataset = pd.read_excel("your_data_file.xlsx")

# Convert time column to datetime (critical for correct time axis behavior)
Dataset['time'] = pd.to_datetime(Dataset['time'])

2. Plot the Basic Speed-Time Curve #

Now let's draw the core speed curve with a properly formatted time X-axis:

plt.figure(figsize=(12, 6))  # Set a comfortable chart size

# Plot speed over time
plt.plot(Dataset['time'], Dataset['speed'], label='Speed', color='steelblue', linewidth=2)

# Add clear labels and title
plt.xlabel('Time', fontsize=12)
plt.ylabel('Speed', fontsize=12)
plt.title('Speed Over Time (City Labels for Speed=0)', fontsize=14)
plt.legend()

# Rotate X-axis ticks to avoid overlap (a must for time-based data)
plt.xticks(rotation=45)
plt.tight_layout()  # Adjusts layout so labels don't get cut off

3. Add City Annotations for Speed=0 Points #

Next, we'll target all rows where speed is 0 and label the corresponding city at those points:

# Filter rows where speed equals 0
zero_speed_rows = Dataset[Dataset['speed'] == 0]

# Loop through each zero-speed entry to add annotations
for idx, row in zero_speed_rows.iterrows():
    # Place the city label slightly above the speed=0 point to avoid curve overlap
    plt.annotate(
        text=row['city'],
        xy=(row['time'], row['speed']),
        xytext=(0, 10),  # Offset text 10 points upward from the data point
        textcoords='offset points',
        fontsize=10,
        color='crimson',
        weight='bold'
    )

# Display the final chart
plt.show()

Quick Tips for Your Workflow: #

• If your speed column has non-numeric values, clean it first with Dataset['speed'] = pd.to_numeric(Dataset['speed'], errors='coerce') to handle any bad data entries.
• Tweak the xytext value (the 10) if you want labels closer or farther from the speed=0 points.
• If you prefer a fancier style, swap plt.plot with sns.lineplot(data=Dataset, x='time', y='speed')—the annotation code works the same way!

内容的提问来源于stack exchange，提问作者Pierpaolo Regnante