如何在堆叠柱状图中指定X轴标签显示间隔（每3个月）

Hey Helen, let's tackle this step by step—your goal is totally achievable with pandas and matplotlib. Here's a detailed breakdown of how to get that stacked bar chart with readable X-axis labels:

1. Data Preprocessing First #

First, we need to clean up the date data and aggregate the catch by month and size code. Let's start with that:

import pandas as pd
import matplotlib.pyplot as plt

# Convert Date column to datetime (critical for time-based grouping)
df['Date'] = pd.to_datetime(df['Date'])

# Create a 'Month' column that represents the first day of each month (keeps datetime format)
df['Month'] = df['Date'].dt.to_period('M').dt.to_timestamp()

# Aggregate total weight by Month and Size.Code, then pivot to wide format for stacking
aggregated_df = df.groupby(['Month', 'Size.Code'])['Weight'].sum().unstack(fill_value=0)

Quick Notes: #

• Using to_period('M').dt.to_timestamp() ensures our monthly data is ordered correctly (no messy string sorting).
• unstack() turns Size.Code values into columns—this is what lets matplotlib create stacked bars automatically.

2. Plot the Stacked Bar Chart #

Now let's build the chart with the X-axis label spacing you want:

fig, ax = plt.subplots(figsize=(14, 7))

# Plot stacked bars directly from the aggregated dataframe
aggregated_df.plot(kind='bar', stacked=True, ax=ax, colormap='viridis')

# Customize X-axis labels to show every 3 months
# Get all tick positions and their corresponding labels
all_ticks = ax.get_xticks()
all_labels = [date.strftime('%Y-%m') for date in aggregated_df.index]

# Only keep labels every 3 months, leave others blank
visible_labels = [label if i % 3 == 0 else '' for i, label in enumerate(all_labels)]
ax.set_xticklabels(visible_labels, rotation=45, ha='right')

# Add titles and labels for clarity
ax.set_title('10-Year Monthly Catch by Size Code', fontsize=16)
ax.set_xlabel('Month', fontsize=12)
ax.set_ylabel('Total Catch Weight', fontsize=12)

# Move legend outside the plot to avoid overlapping bars
ax.legend(title='Size Code', bbox_to_anchor=(1.02, 1), loc='upper left')

plt.tight_layout()
plt.show()

3. If You Want Separate Charts for Each Vessel #

If you need individual stacked charts for each Vessel.Pln, wrap the plotting code in a loop:

# Get all unique vessel identifiers
unique_vessels = df['Vessel.Pln'].unique()

for vessel in unique_vessels:
    # Filter data for the current vessel
    vessel_data = df[df['Vessel.Pln'] == vessel]
    # Aggregate catch for this vessel
    vessel_agg = vessel_data.groupby(['Month', 'Size.Code'])['Weight'].sum().unstack(fill_value=0)
    
    # Create plot
    fig, ax = plt.subplots(figsize=(12, 6))
    vessel_agg.plot(kind='bar', stacked=True, ax=ax, colormap='viridis')
    
    # Apply the same 3-month label spacing
    all_labels = [date.strftime('%Y-%m') for date in vessel_agg.index]
    visible_labels = [label if i % 3 == 0 else '' for i, label in enumerate(all_labels)]
    ax.set_xticklabels(visible_labels, rotation=45, ha='right')
    
    # Customize titles for each vessel
    ax.set_title(f'Monthly Catch by Size Code - Vessel {vessel}', fontsize=14)
    ax.set_xlabel('Month', fontsize=12)
    ax.set_ylabel('Total Catch Weight', fontsize=12)
    ax.legend(title='Size Code', bbox_to_anchor=(1.02, 1), loc='upper left')
    
    plt.tight_layout()
    plt.show()

Key Tips to Avoid Issues: #

• Handle Missing Data: If you have missing dates or weights, add df = df.dropna(subset=['Date', 'Weight']) and df['Weight'] = pd.to_numeric(df['Weight'], errors='coerce') to clean the data first.
• Adjust Figure Size: Tweak the figsize parameter if your labels still feel crowded—wider figures work better for long time periods.
• Color Customization: Replace colormap='viridis' with any matplotlib colormap you prefer (e.g., 'tab20' for distinct colors).

内容的提问来源于stack exchange，提问作者helen.h