读取CSV文件时出现SyntaxError语法错误，请求Python代码调试帮助

问题背景 #

我需要编写Python代码处理两个CSV文件，目标是找出源文件中满足**阈值（≥'T'列均值的50%）**且在目标文件中缺失的行，方便手动复制到目标文件。运行代码时触发语法错误，相关代码与报错信息如下：

原代码 #

import pandas as pd

# Define file paths (replace with your actual paths)
source_file = "C:/Users/sharsa07/Desktop/pipeline/gso_orb_ticket_list.csv"
dest_file = "C:/Users/sharsa07/Desktop/pipeline/gso_pipeline.csv"

# Read excel files into DataFrames
df_source = pd.read_csv(C:/Users/sharsa07/Desktop/pipeline/gso_orb_ticket_list.csv)
df_dest = pd.read_csv(C:/Users/sharsa07/Desktop/pipeline/gso_pipeline.csv)

# Find the common column name (assuming the same column name in both files, case-insensitive)
common_col = "a"  # Assuming column names are the same (case-insensitive)

# Merge DataFrames based on the common column (outer join to keep unmatched rows)
merged_df = df_source.merge(df_dest[[common_col]], how="outer", on=common_col.lower())

# Calculate threshold value based on 'T' column mean in the source DataFrame
threshold_value = df_source['T'].mean() * 0.5

# Filter merged DataFrame to rows where 'T' is greater than or equal to the threshold and source column is missing in destination
filtered_df = merged_df[(merged_df['T'] >= threshold_value) & (merged_df[common_col.lower()].isna())]

# Get source column names from the filtered DataFrame (excluding the common column)
source_cols = set(filtered_df.columns) - {common_col.lower()}

# Print the column names that meet the criteria
print("Columns to be checked:", source_cols)

报错信息 #

Cell In[2], line 8
df_source = pd.read_csv(C:/Users/sharsa07/Desktop/pipeline/gso_orb_ticket_list.csv)
^
SyntaxError: invalid syntax

错误原因与修复方案 #

1. 语法错误修复 #

报错行的核心问题是文件路径没有用引号包裹，Python无法识别未加引号的路径字符串。另外你已经定义了source_file和dest_file变量，直接复用变量更简洁，无需重复写路径：
将原代码中读取文件的两行替换为：

df_source = pd.read_csv(source_file)
df_dest = pd.read_csv(dest_file)

2. 逻辑错误修正 #

原代码的合并与过滤逻辑存在问题：

• 用outer join会保留两边所有行，而我们只需要源文件中存在、目标文件中缺失的行，用left join更合适
• 判断缺失的条件应该是目标文件的对应列为空，而非公共列本身为空

同时，公共列的大小写处理需要统一，避免因列名大小写不一致导致匹配失败。

修正后的完整代码 #

import pandas as pd

# 定义文件路径
source_file = "C:/Users/sharsa07/Desktop/pipeline/gso_orb_ticket_list.csv"
dest_file = "C:/Users/sharsa07/Desktop/pipeline/gso_pipeline.csv"

# 读取CSV文件为DataFrame
df_source = pd.read_csv(source_file)
df_dest = pd.read_csv(dest_file)

# 统一列名为小写（解决大小写不匹配问题）
df_source.columns = df_source.columns.str.lower()
df_dest.columns = df_dest.columns.str.lower()

# 指定公共匹配列（请替换为实际的唯一标识列，比如工单ID）
common_col = "a"  # 示例列名，需根据实际数据修改

# 左连接：保留源文件所有行，匹配目标文件的公共列
merged_df = df_source.merge(df_dest[[common_col]], how="left", on=common_col, indicator=True)

# 计算阈值：T列均值的50%
threshold_value = df_source['t'].mean() * 0.5

# 过滤条件：T列值≥阈值，且目标文件中无匹配行
filtered_df = merged_df[(merged_df['t'] >= threshold_value) & (merged_df['_merge'] == 'left_only')]

# 输出符合条件的行（包含源文件所有列）
print("需要复制到目标文件的行：")
print(filtered_df.drop(columns=['_merge']))

# 如果需要保存结果到CSV
# filtered_df.drop(columns=['_merge']).to_csv("missing_rows.csv", index=False)

内容的提问来源于stack exchange，提问作者Satyajit Sharma