# Explore
<!DOCTYPE html>
<html lang="en">

<head>
<meta charset="UTF-8">

<title>
Read Excel EMB 2025 - Python Code Explanation
</title>

<link rel="stylesheet" href="../custom.css">

</head>

<body>

<h1>
Read Excel EMB 2025 - Python Code Explanation
</h1>


<h2>
Goal of the Script
</h2>

<p>
This Python script reads multiple worksheets from the 2025 Excel workbook
containing minority data, combines the worksheets into a single pandas
DataFrame, creates a district variable from location information, and exports
the final dataset to SAS.
</p>


<h2>
Code Description
</h2>

<p>
The script uses <strong>pandas</strong> to read and manipulate Excel data and
<strong>saspy</strong> to transfer the resulting DataFrame into a SAS dataset.
A SAS session is started using the configured SASPy connection.
</p>


<h2>
Reading Excel Worksheets
</h2>

<p>
The program imports Tables 2 through 72 from the Excel workbook. Each worksheet
is read using pandas, with all columns treated as text to preserve the original
values and prevent data truncation.
</p>

<p>
Missing values are replaced with empty strings. The script also standardizes
multi-line text in the <strong>i_date</strong> variable by replacing line breaks
with spaces.
</p>


<h2>
Combining and Preparing Data
</h2>

<p>
After all worksheets are read, the individual DataFrames are concatenated into
one combined dataset. A district lookup dictionary is used to identify
Bangladesh districts from variations in location names.
</p>

<p>
The script applies a function to the <strong>i_loc</strong> variable to create
a new <strong>district</strong> variable. Common spelling variations, such as
alternative English spellings of district names, are handled through the
lookup dictionary.
</p>


<h2>
Creating the SAS Dataset
</h2>

<p>
The final DataFrame is reordered to match the expected SAS structure and is
sent to SAS using SASPy. The resulting SAS dataset is stored as
<strong>MYDATA.EMB_2025</strong>.
</p>


<h2>
Summary
</h2>

<p>
This script demonstrates an integrated Python and SAS workflow. It automates
Excel data ingestion, data cleaning, geographic classification, and conversion
of a pandas DataFrame into a SAS dataset for further analysis.
</p>


</body>

</html>