## What will we cover in this tutorial?

We will have some data in a Pandas DataFrame, which we want to export to an Excel sheet. Then we want to create a Scatter plot graph and fit that to a Excel trendline.

## Step 1: Get the data

You might have some data already that you want to use. It can be from a HTML page (example) or CSV file.

For this purpose here we just generate some random data to use. We will use NumPy’s uniform function to generate it.

```
import pandas as pd
import numpy as np
# Generate some random increasing data
data = pd.DataFrame(
{'A': [np.random.uniform(0.1*i, 0.1*i + 1) for i in range(100)],
'B': [np.random.uniform(0.1*i, 0.1*i + 1) for i in range(100)]}
)
print(data)
```

Which will generate some slightly increasing data, which is nice to fit a graph to.

The output could look something like this.

```
A B
0 0.039515 0.778077
1 0.451888 0.210705
2 0.992493 0.961428
3 0.317536 1.046444
4 1.220419 1.388086
```

## Step 2: Create an Excel XlsxWriter engine

This step might require that you install the **XlsxWriter** library, which is needed from the Pandas library.

This can be done by the following command.

```
pip install xlsxwriter
```

Now we can create the engine in our code.

```
import pandas as pd
import numpy as np
# Generate some random increasing data
data = pd.DataFrame(
{'A': [np.random.uniform(0.1*i, 0.1*i + 1) for i in range(100)],
'B': [np.random.uniform(0.1*i, 0.1*i + 1) for i in range(100)]}
)
# Create a Pandas Excel writer using XlsxWriter
excel_file = 'output.xlsx'
sheet_name = 'Data set'
writer = pd.ExcelWriter(excel_file, engine='xlsxwriter')
```

This will setup a Excel writer engine and be ready to write to file **output.xlsx**.

## Step 3: Write the data to Excel and create a scatter graph with a fitted Trendline

This can be done by the following code, which uses the **add_series** function to insert a graph.

```
import pandas as pd
import numpy as np
# Generate some random increasing data
data = pd.DataFrame(
{'A': [np.random.uniform(0.1*i, 0.1*i + 1) for i in range(100)],
'B': [np.random.uniform(0.1*i, 0.1*i + 1) for i in range(100)]}
)
# Create a Pandas Excel writer using XlsxWriter
excel_file = 'output.xlsx'
sheet_name = 'Data set'
writer = pd.ExcelWriter(excel_file, engine='xlsxwriter')
data.to_excel(writer, sheet_name=sheet_name)
# Access the XlsxWriter workbook and worksheet objects from the dataframe.
workbook = writer.book
worksheet = writer.sheets[sheet_name]
# Create a scatter chart object.
chart = workbook.add_chart({'type': 'scatter'})
# Get the number of rows and column index
max_row = len(data)
col_x = data.columns.get_loc('A') + 1
col_y = data.columns.get_loc('B') + 1
# Create the scatter plot, use a trendline to fit it
chart.add_series({
'name': "Samples",
'categories': [sheet_name, 1, col_x, max_row, col_x],
'values': [sheet_name, 1, col_y, max_row, col_y],
'marker': {'type': 'circle', 'size': 4},
'trendline': {'type': 'linear'},
})
# Set name on axis
chart.set_x_axis({'name': 'Concentration'})
chart.set_y_axis({'name': 'Measured',
'major_gridlines': {'visible': False}})
# Insert the chart into the worksheet in field D2
worksheet.insert_chart('D2', chart)
# Close and save the Excel file
writer.save()
```

## Result

The result should be similar to this.

That is how it can be done.

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