line-chart-visualization

name: line-chart-visualization description: "提取结构化数据并进行特征清洗与聚类分析，生成包含趋势对比、分布特征与参数敏感性的多维度综合可视化图表，适用于各类趋势预测与多维对比场景。"

Step1 数据加载与预处理（支持大文件Parquet转换与动态表头识别）。

import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
from sklearn.cluster import KMeans
from sklearn.preprocessing import StandardScaler
import os
import re

# 设置中英文字体与图表美化
plt.rcParams['font.sans-serif'] = ['SimHei', 'WenQuanYi Zen Hei', 'DejaVu Sans']
plt.rcParams['axes.unicode_minus'] = False

file_path = 'input_data.xlsx'

# 处理大型Excel文件：统计总行数，若≥1万则转换为Parquet格式提升效率
xls = pd.ExcelFile(file_path)
total_rows = sum(pd.read_excel(xls, sheet_name=s, header=None).shape[0] for s in xls.sheet_names)

if total_rows >= 10000:
    parquet_path = "temp_converted_file.parquet"
    with pd.ExcelWriter(parquet_path, engine='pyarrow') as writer:
        for sheet in xls.sheet_names:
            df_sheet = pd.read_excel(xls, sheet_name=sheet, header=None)
            df_sheet.to_excel(writer, sheet_name=sheet, index=False, header=False)
    df = pd.read_excel(parquet_path, sheet_name='Sheet1', header=None)
else:
    df = pd.read_excel(file_path, sheet_name='Sheet1', header=None)

# 动态识别表头并提取数据
header_row_idx = None
target_cols = ['group_col', 'value_col1', 'value_col2'] # 占位示例列名
for idx, row in df.iterrows():
    row_vals = row.astype(str).tolist()
    if all(col in row_vals for col in target_cols):
        header_row_idx = idx
        break

if header_row_idx is not None:
    df.columns = df.iloc[header_row_idx].tolist()
    df_clean = df.iloc[header_row_idx + 1:].reset_index(drop=True)
else:
    df_clean = df.copy()