How do I export a pandas DataFrame to Microsoft Access?

First convert the dataframe into .csv file using the below command

name_of_your_dataframe.to_csv("filename.csv", sep='\t', encoding='utf-8')

Then load .csv to .mdb using pyodbc

MS Access can directly query CSV files and run a Make-Table Query(https://support.office.com/en-us/article/Create-a-make-table-query-96424f9e-82fd-411e-aca4-e21ad0a94f1b) to produce a resulting table. However, some cleaning is needed to remove the rubbish rows. Below opens two files one for reading and other for writing. Assuming rubbish is in first column of csv, the if logic writes any line that has some data in second column (adjust as needed):

import os
import csv
import pyodbc

# TEXT FILE CLEAN
with open('C:\Path\To\Raw.csv', 'r') as reader, open('C:\Path\To\Clean.csv', 'w') as writer:
    read_csv = csv.reader(reader); write_csv = csv.writer(writer,lineterminator='\n')

    for line in read_csv:
        if len(line[1]) > 0:            
            write_csv.writerow(line)

# DATABASE CONNECTION
access_path = "C:\Path\To\Access\\DB.mdb"
con = pyodbc.connect("DRIVER={{Microsoft Access Driver (*.mdb, *.accdb)}};DBQ={};" \
                 .format(access_path))

# RUN QUERY
strSQL = "SELECT * INTO [TableName] FROM [text;HDR=Yes;FMT=Delimited(,);" + \
     "Database=C:\Path\To\Folder].Clean.csv;"    
cur = con.cursor()
cur.execute(strSQL)
con.commit()

con.close()                            # CLOSE CONNECTION
os.remove('C\Path\To\Clean.csv')       # DELETE CLEAN TEMP 

2020 Update

There is now a supported external SQLAlchemy dialect for Microsoft Access ...

https://github.com/gordthompson/sqlalchemy-access

... which enables you to use pandas' to_sql method directly via pyodbc and the Microsoft Access ODBC driver (on Windows).

I would recommend to export the pandas dataframe to csv as usual like this:

dataframe_name.to_csv("df_filename.csv", sep=',', encoding='utf-8')

Then you can convert it to .mdb file as this stackoverflow answer shows