Step 1 Define a percentage conversion function and extract the raw data. Convert the percentage format into a computable floating-point number through regular expressions or string processing.
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
# 配置中文字体，确保图表标签正常显示
plt.rcParams['font.sans-serif'] = ['SimHei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False
def convert_percentage(val): """ Convert a percentage string to a floating-point number. Processing logic: remove the percent sign and convert to float; if it is already a numeric value, return it directly. """ if isinstance(val, str): return float(val.strip('%')) return val
Example data extraction logic (replace with extraction from a DataFrame in actual applications)time_labels = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun'] # generalized time axis cat1_raw = ['23.21%', '22.98%', '24.31%', '24.53%', '23.84%', '24.80%'] cat2_raw = ['25.17%', '25.67%', '25.77%', '25.98%', '25.17%', '25.61%'] cat3_raw = ['28.12%', '28.37%', '26.58%', '25.83%', '26.49%', '25.17%']
cat1_ratios = [convert_percentage(x) for x in cat1_raw] cat2_ratios = [convert_percentage(x) for x in cat2_raw] cat3_ratios = [convert_percentage(x) for x in cat3_raw]
Step2 构建结构化数据表，将清洗后的数值整合进 DataFrame 以便进行向量化计算。
```python
# 构建包含时间维度和各分类占比的结构化数据表
df = pd.DataFrame({
'group_col': time_labels,
'cat_1': cat1_ratios,
'cat_2': cat2_ratios,
'cat_3': cat3_ratios
})
Step 3 Calculate the proportion of the missing dimension. When the proportions of some dimensions are known, infer the value of the remaining dimension through the constraint that the total is 100%, and perform data validation.
# 计算已知维度的总占比
target_cols = ['cat_1', 'cat_2', 'cat_3']
df['current_total'] = df[target_cols].sum(axis=1)
# 推算剩余维度（如“其他”或特定分类）的占比
df['cat_remainder'] = 100 - df['current_total']
# 验证数据完整性：确保所有维度相加接近 100
df['final_check'] = df[target_cols + ['cat_remainder']].sum(axis=1)
Step 4 Visualize using a stacked bar chart. The core is to use the bottom parameter to accumulate the height layer by layer and optimize the chart aesthetic configuration.
# 设置绘图风格与画布
plt.figure(figsize=(12, 6), dpi=100)
sns.set_style('whitegrid')
# 核心堆叠逻辑：每一层的 bottom 是前几层高度的总和
plt.bar(df['group_col'], df['cat_1'], label='分类1', color='#5DADE2')
plt.bar(df['group_col'], df['cat_2'], bottom=df['cat_1'], label='分类2', color='#58D68D')
plt.bar(df['group_col'], df['cat_3'], bottom=df['cat_1'] + df['cat_2'], label='分类3', color='#EC7063')
plt.bar(df['group_col'], df['cat_remainder'], bottom=df['cat_1'] + df['cat_2'] + df['cat_3'], label='其他', color='#F4D03F')
# 图表辅助元素优化
plt.xlabel('统计周期')
plt.ylabel('占比 (%)')
plt.title('多维度占比变化趋势分析')
plt.legend(loc='upper right', bbox_to_anchor=(1.1, 1))
plt.xticks(rotation=45) # 避免标签重叠
plt.tight_layout()
Step 5 Export the analysis results. Save the generated chart as a high-resolution image and clean up memory.
# 保存图表，设置 dpi 确保清晰度，bbox_inches 确保标签不被截断
output_path = 'stacked_ratio_analysis.png'
plt.savefig(output_path, dpi=300, bbox_inches='tight')
plt.show()
plt.close()