如何发送带DataFrame附件的邮件？CSV附件版本异常问题求助

Hey there, let's break down your two main issues and fix them one by one:

1. Why Are You Sending the Old CSV File? #

The most likely culprits here are unflushed file caches or mismatched working directories—super common pitfalls when dealing with file I/O and automation. Here's how to fix it:

a. Make Sure Your CSV Write Operation Is Fully Completed #

If you're using pandas to modify and save the CSV, double-check that the save operation finishes before you send the email. Pandas' to_csv() method handles file closing automatically, but if you're manually opening files, always use a with statement to avoid leaving files in an unflushed state:

import pandas as pd
# Load and modify your data
df = pd.read_csv('CC.csv')
# ... your data edits here ...
# Save properly (pandas handles closing the file)
df.to_csv('CC.csv', index=False)

b. Use Absolute File Paths Instead of Relative Ones #

When you use filename='CC.csv', your script looks for the file in its current working directory—which might not be the folder you think, especially when launching via a Unix executable on Mac. To eliminate this ambiguity, use an absolute path:

import os
# Get the folder where your script lives
script_folder = os.path.dirname(os.path.abspath(__file__))
# Build the full path to your CSV
filename = os.path.join(script_folder, 'CC.csv')

c. Force Fresh File Reads #

If you're still seeing old data, disable buffering when opening the file to ensure you read the latest version from disk:

attachment = open(filename, 'rb', buffering=0)

2. How to Send a DataFrame Directly as an Attachment #

You don't need to save the DataFrame to a CSV file first! You can convert it to an in-memory CSV stream and attach that directly—this skips all the file I/O headaches entirely. Here's a complete working example:

import smtplib
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from email.mime.base import MIMEBase
from email import encoders
import pandas as pd
from io import BytesIO

# Email configs
email_user = 'Bot@gmail.com'
email_password = 'Business101'
email_send = ('myemail@gmail.com', 'myfriendsemail@gmail.com')
subject = 'TOP 5 CONTRACTS'

# Set up the email
msg = MIMEMultipart()
msg['From'] = email_user
msg['To'] = ",".join(email_send)
msg['Subject'] = subject

# Add email body
body = 'These are the latest contracts for this week!'
msg.attach(MIMEText(body, 'plain'))

# Replace this with your actual DataFrame
top_5_contracts = pd.DataFrame({
    'Contract ID': ['C001', 'C002', 'C003', 'C004', 'C005'],
    'Client': ['Alpha Corp', 'Beta Inc', 'Gamma LLC', 'Delta Group', 'Eta Co'],
    'Value': [12000, 25000, 18000, 32000, 21000]
})

# Convert DataFrame to an in-memory CSV stream
csv_stream = BytesIO()
top_5_contracts.to_csv(csv_stream, index=False)
csv_stream.seek(0)  # Reset the stream to the start so we can read it

# Create the attachment
part = MIMEBase('application', 'octet-stream')
part.set_payload(csv_stream.read())
encoders.encode_base64(part)
part.add_header('Content-Disposition', f"attachment; filename=CC.csv")

msg.attach(part)

# Send the email
text = msg.as_string()
server = smtplib.SMTP('smtp.gmail.com', 587)
server.starttls()
server.login(email_user, email_password)
server.sendmail(email_user, email_send, text)
server.quit()

print("Emailed Recipients")

Why This Method Is Better: #

• No more worrying about file caches or wrong paths—everything stays in memory
• Faster and cleaner for automation workflows (no temporary files cluttering up your system)
• Eliminates the risk of sending old files entirely

Quick Check for Your Mac Unix Executable #

When launching via a Unix executable, confirm your script's working directory matches where your CSV lives. Add this line at the start of your script to debug:

import os
print("Current working directory:", os.getcwd())

If it's not the right folder, use the absolute path trick from earlier to fix it.

内容的提问来源于stack exchange，提问作者user14425857