Analysis and solution of common problems encountered in Python processing Excel files

1. Introduction

Excel (.xlsx) is one of the most commonly used data storage formats in data processing and automation tasks. Python's pandas library provides convenient read_excel() methods, but in actual use, we may encounter various problems, such as:

Excel xlsx file; not supported (not supported .xlsx format)
File path error
Missing necessary dependency library
Missing data columns or irregular format

This article analyzes these common errors and provides Python and Java solutions to help developers process Excel files efficiently.

2. Analysis of common errors in Excel file processing

2.1 Excel xlsx file; not supported Error

Cause of error:

Pandas may not contain a parsing engine for .xlsx files by default, and requires additional installation of openpyxl or xlrd (legacy version support).

Solution:

pip install openpyxl

Then specify the engine in the code:

df = pd.read_excel(file_path, engine='openpyxl')

2.2 File path issues

Cause of error:

File path error (such as the relative path not parsed correctly)
File does not exist or permissions are insufficient

Solution:

import os

if not (file_path):
    raise FileNotFoundError(f"The file does not exist: {file_path}")

2.3 Dependency library missing

Cause of error:

If openpyxl or xlrd is not installed, pandas cannot parse .xlsx files.

Solution:

pip install pandas openpyxl

2.4 File corrupt or incompatible format

Cause of error:

Files may be partially uploaded or corrupted
Incompatible Excel versions (such as .xls and .xlsx mixed)

Solution:

Manually use Excel to open the file to confirm whether it is readable
Try to regenerate the file or convert the format

3. Python solutions and optimization code

3.1 Read .xlsx files using openpyxl

import pandas as pd

def read_excel_safely(file_path):
    try:
        return pd.read_excel(file_path, engine='openpyxl')
    except ImportError:
        return pd.read_excel(file_path)  # Fallback to default engine

3.2 Check whether the file path exists

import os

def validate_file_path(file_path):
    if not (file_path):
        raise FileNotFoundError(f"The file does not exist: {file_path}")
    if not file_path.endswith(('.xlsx', '.xls')):
        raise ValueError("Support only .xlsx or .xls files")

3.3 Handling column missing issues

def check_required_columns(df, required_columns):
    missing_columns = [col for col in required_columns if col not in ]
    if missing_columns:
        raise ValueError(f"Necessary columns are missing: {missing_columns}")

3.4 Data cleaning and standardization

import re

def clean_text(text):
    return () if text else ""

def extract_province_city(address):
    province_pattern = r'(Beijing|Tianjin|...|Macao Special Administrative Region)'
    match = (province_pattern, address)
    province = (1) if match else ""
    
    if province:
        remaining = address[():]
        city_match = (r'([^city]+city)', remaining)
        city = city_match.group(1) if city_match else ""
    return province, city

Complete optimization code

import pandas as pd
import os
import re

def process_recipient_info(file_path):
    try:
        validate_file_path(file_path)
        df = read_excel_safely(file_path)
        check_required_columns(df, ['Recipient', 'Way bill number', 'System Order Number', 'Recipient', 'Recipient'])
        
        processed_data = []
        for _, row in ():
            name = clean_text(str(row['Recipient']))
            phone = (r'\D', '', str(row['Recipient']))
            province, city = extract_province_city(str(row['Recipient']))
            
            processed_data.append({
                'name': name,
                'phone': phone,
                'province': province,
                'city': city
            })
        return processed_data
    except Exception as e:
        print(f"Processing failed: {e}")
        return []

4. Java Comparative Implementation (POI Library)

In Java, you can use Apache POI to process Excel files:

Maven dependencies

<dependency>
    <groupId></groupId>
    <artifactId>poi</artifactId>
    <version>5.2.3</version>
</dependency>
<dependency>
    <groupId></groupId>
    <artifactId>poi-ooxml</artifactId>
    <version>5.2.3</version>
</dependency>

Java Read Excel Example

import .*;
import ;

import ;
import ;
import ;
import ;

public class ExcelReader {
    public static List&lt;Recipient&gt; readRecipients(String filePath) {
        List&lt;Recipient&gt; recipients = new ArrayList&lt;&gt;();
        try (FileInputStream fis = new FileInputStream(new File(filePath));
             Workbook workbook = new XSSFWorkbook(fis)) {
            
            Sheet sheet = (0);
            for (Row row : sheet) {
                String name = (0).getStringCellValue();
                String phone = (1).getStringCellValue();
                String address = (2).getStringCellValue();
                
                (new Recipient(name, phone, address));
            }
        } catch (Exception e) {
            ("Read Excel failed: " + ());
        }
        return recipients;
    }
}

class Recipient {
    private String name;
    private String phone;
    private String address;
    
    // Constructor, Getters, Setters...
}

5. Summary and best practices

Python best practices

Use openpyxl to handle .xlsx
Check file path and format
Handle column missing and null values
Data cleaning (such as mobile phone number and address resolution)

Java best practices

Process Excel with Apache POI
Close resources (try-with-resources)
Handle exceptions and empty cells

General recommendations

Use logging errors (such as Python logging/Java SLF4J)
Unit testing ensures that data parses correctly
Use streaming reading when considering large data volumes (such as pandas chunksize / POI SXSSF)

With this article's solution, Excel files can be processed efficiently and stably, avoiding common errors.

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