尝试使用Pandas合并您的数据如下：

import pandas as pd

# read each file to Pandas DataFrame
df1 = pd.read_csv('file1.csv')
df2 = pd.read_csv('file2.csv')
df3 = pd.read_csv('file3.csv')

# concatenate frames with equal structure
df = pd.concat([df1, df3])

# merge frames by common columns
df_merged = df.merge(df2, how='outer', on=['firstName', 'lastName', 'age'])
df_merged.to_csv('merged.csv', sep=',')

您不仅可以将合并后的帧保存到csv文件中，还可以使用df.to_sql（）方法直接保存到数据库中，并且可以根据您的需要对数据进行操作。

尝试使用标准Python工具解决问题

import csv

files= ['file1.csv', 'file2.csv', 'file3.csv']

rows = list()
column_names = list()

"""
Add each row of file to list as list of dictionaries,
e.g. [{'field1': 'value'}, {'field2': 'value'}, ...]
and add column names from header of each file to another sequence
"""
for f in files:
    with open(f, newline='', encoding='utf8') as csvfile:
        reader = csv.DictReader(csvfile)
        for row in reader:
            rows.append(row)
        for item in reader.fieldnames:
            if item not in column_names:
                column_names.append(item)

"""
Write rows to new csv file from previously formed dictionaries
"""
with open('merged.csv', 'w', newline='', encoding='utf8') as csvfile:
    writer = csv.DictWriter(csvfile, fieldnames=column_names)
    writer.writeheader()
    writer.writerows(rows)

不是很优雅，但应该可以。