To read data from a table into a dataframe outside of Databricks environment, you can use one of the many available Python libraries, such as Pandas or PyODBC, depending on the type of table and database you are using. Here are the general steps you can follow:

Install the necessary library: If you are using a library like Pandas, you can install it using pip. For example, you can open a terminal or command prompt and type: pip install pandas.

Import the library: In your Python script or notebook, import the library using the import statement. For example: import pandas as pd.

Connect to the database: Depending on the type of database you are using, you will need to provide connection details, such as the server address, database name, username, and password. If you are using PyODBC, you can use the pyodbc.connect function to create a connection object. For example:

import pyodbc

conn = pyodbc.connect('Driver={SQL Server};'

           'Server=myServerName;'

           'Database=myDatabaseName;'

           'Trusted_Connection=yes;')

Read the data into a dataframe: Once you have established a connection, you can use the pd.read_sql function in Pandas to read the data into a dataframe. For example:

df = pd.read_sql('SELECT * FROM myTable', conn)

This will read all the data from the "myTable" table into a dataframe called "df". You can then manipulate the data as needed using Pandas functions.