Use Python in Excel:

Microsoft has unveiled its integration of the widely used programming language Python into Excel, offering a public preview of this feature. As a result, proficient users can now seamlessly incorporate Python code into their spreadsheets and analyse data.

Incorporating Python within Excel is effortless—all you need to do is input "=PY (" in your Excel cell, followed by your Python code. Subsequently, the outcomes of your Python calculations or visualizations will seamlessly populate your Excel worksheet.

Within Excel, Python has the capacity to generate various forms of data in the form of Python objects. One particularly valuable data type available through Python in Excel is the DataFrame object.

Python integrated with Excel provides two distinct methods for generating a DataFrame: as a Python object or as Excel-compatible values.

In Python, the. describe () method is typically used with pandas DataFrames, a popular library for data manipulation and analysis. When called on a DataFrame, the. describe () method provides summary statistics for the numeric columns in the DataFrame.

Analyze your data by Python:

Chart in Excel by Python:

You can create charts in Excel using Python. Examples of how to create a chart in Excel using Python:

Retrieve the image object as an Excel value, follow these steps:

1. Highlight the cell containing the image object.
2. Navigate to the Python output menu located in the formula bar.
3. Choose the "Excel Value" option from the menu.
4. The visualization will now appear directly within the cell. For further guidance, refer to the accompanying screenshots below.

Joint Distribution Plot:

A joint distribution plot, also known as a bivariate distribution plot, is a type of data visualization that displays the joint distribution of two variables. It combines multiple types of plots to provide a comprehensive view of the relationship between these variables. The main components of a joint distribution plot typically include:

Scatter Plot: A scatter plot is at the centre of the joint distribution plot. It displays individual data points as dots on a two-dimensional plane, with one variable on the x-axis and the other variable on the y-axis.

Marginal Distribution Plots: On the top and right sides of the scatter plot, there are marginal distribution plots.

This code will generate a scatter plot with a marginal distribution plot for "Sales Amount" on the y-axis and "Region" on the x-axis, allowing you to visualize the joint distribution of sales amounts across different regions.

The chart illustrates the relationship between "Sales Amount" and "Region." Understanding this relationship is essential for several reasons:

1. Regional Performance Analysis: It helps analyze the performance of different regions in terms of sales.
2. Resource Allocation: Businesses can use this information to allocate resources strategically.
3. Marketing and Strategy: The data can inform marketing strategies.

Best regards,

Debjit Chakraborty