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To extract numerical values from designated positions on a data map using Python, you can follow the following steps:

1. Import necessary libraries: The first step is to import the required libraries, including pandas or numpy, which are powerful tools for data analysis and manipulation.

2. Read the data file: Read the data file using pandas or numpy. You can use pandas readcsv or readexcel functions to read the files in csv or excel formats respectively.

3. Locate the positions: Identify the positions of the numerical values you want to extract from the data map. You can use the row and column indices or labels to locate the positions.

4. Extract values: After locating the positions, extract the numerical data using the iloc or loc functions. The iloc function helps you to access the data using the position indices, while the loc function allows you to access data using the row and column labels.

5. Store the extracted values: Programmatically store the extracted numerical values into a new file, or use them for further analysis.

Here is an example Python code to extract numerical values from designated positions on a data map:

import pandas as pd

# Read the data file
data = pd.read_csv('data_file.csv')

# Locate the position of the numerical data to extract
row_index = 2
column_label = 'column2'

# Extract the numerical value
num_value = data.loc[row_index, column_label]

# Print the numerical value
print(num_value)

# Store the extracted value in a new file
with open('extracted_values.txt', 'w') as output_file:
    output_file.write(str(num_value))

This code reads a data file in csv format, locates the position of a numerical value using row and column labels, extracts the numerical value, and stores it in a text file.