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import openpyxl

# Load the Excel file
wb = openpyxl.load_workbook('input_file.xlsx')
sheet = wb.active

# Print data from each row
for row in sheet.iter_rows(min_row=2, values_only=True):  # Exclude the first row as it is the header
    month, product, sales, price, revenue = row
    print(f"Month: {month}, Product: {product}, Sales: {sales}, Price: {price}, Revenue: {revenue}")

# Importing Sales Performance Excel Data into Python

In this task, we will convert sales performance data downloaded as an Excel file into a `table`, extract **quantitative insights**, and create a Word document.

First, we will use the *openpyxl* library to read the Excel file and extract the necessary data to compile the data needed for the report.

<br />

## Example of Sales Performance Data

The sales performance data used for this task represents the sales performance of cameras, computers, and smartphones for January, February, and March.

This data includes the unit price, sales volume, and monthly revenue information for each product.

| Month | Product   | Sales | Unit Price | Revenue        |
|-------|-----------|-------|------------|----------------|
| Jan   | Camera    | 50    | 500,000    | 25,000,000     |
| Jan   | Computer  | 30    | 1,200,000  | 36,000,000     |
| Jan   | Smartphone| 100   | 900,000    | 90,000,000     |
| Feb   | Camera    | 45    | 480,000    | 21,600,000     |
| Feb   | Computer  | 35    | 1,150,000  | 40,250,000     |
| Feb   | Smartphone| 90    | 850,000    | 76,500,000     |
| Mar   | Camera    | 40    | 470,000    | 18,800,000     |
| Mar   | Computer  | 40    | 1,100,000  | 44,000,000     |
| Mar   | Smartphone| 95    | 800,000    | 76,000,000     |

<br />

## Importing Excel Data into Python

Let's use the *openpyxl* library to read the Excel file and print each row of data.

```python title="Loading Excel File"
import openpyxl

# Load the Excel file
wb = openpyxl.load_workbook('input_file.xlsx')
sheet = wb.active

# Print each row of data
# Exclude the first row (header)
for row in sheet.iter_rows(min_row=2, values_only=True):
    month, product, sales, price, revenue = row
    print(f"Month: {month}, Product: {product}, Sales: {sales}, Unit Price: {price}, Revenue: {revenue}")
```

<br />

### Code Explanation

1. `openpyxl.load_workbook('input_file.xlsx')`: Load the Excel file into the wb object. Here, you need to enter the actual path and name of the Excel file.

2. `sheet = wb.active`: Select the currently active Excel sheet. By default, the first sheet is selected.

3. `sheet.iter_rows(min_row=2, values_only=True)`: Read the data starting from the second row. *min_row=2* means to exclude the first row which contains headers. *values_only=True* returns only the cell values, excluding the cell object information.

4. `for row in sheet.iter_rows`: Iterate over each row of data, storing Month, Product, Sales, Unit Price, and Revenue information in variables and printing them.

<br />

```text title="Excel Data Output"
Month: Jan, Product: Camera, Sales: 50, Unit Price: 500000, Revenue: 25000000
Month: Jan, Product: Computer, Sales: 30, Unit Price: 1200000, Revenue: 36000000
Month: Jan, Product: Smartphone, Sales: 100, Unit Price: 900000, Revenue: 90000000
Month: Feb, Product: Camera, Sales: 45, Unit Price: 480000, Revenue: 21600000
Month: Feb, Product: Computer, Sales: 35, Unit Price: 1150000, Revenue: 40250000
Month: Feb, Product: Smartphone, Sales: 90, Unit Price: 850000, Revenue: 76500000
Month: Mar, Product: Camera, Sales: 40, Unit Price: 470000, Revenue: 18800000
Month: Mar, Product: Computer, Sales: 40, Unit Price: 1100000, Revenue: 44000000
Month: Mar, Product: Smartphone, Sales: 95, Unit Price: 800000, Revenue: 76000000
```

Month	Product	Sales	Unit Price	Revenue
Jan	Camera	50	500,000	25,000,000
Jan	Computer	30	1,200,000	36,000,000
Jan	Smartphone	100	900,000	90,000,000
Feb	Camera	45	480,000	21,600,000
Feb	Computer	35	1,150,000	40,250,000
Feb	Smartphone	90	850,000	76,500,000
Mar	Camera	40	470,000	18,800,000
Mar	Computer	40	1,100,000	44,000,000
Mar	Smartphone	95	800,000	76,000,000