From the course: Advanced Python in Excel for Finance: A Hands-On Approach

Importing and exporting financial data

From the course: Advanced Python in Excel for Finance: A Hands-On Approach

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Importing and exporting financial data

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- [Instructor] Let's harness the power of Python directly within Excel for your financial data needs by bridging the gap between Python's analytical capabilities and Excel's user-friendly interface. Let's jump right in. Efficiency is key in financial analysis by automating repetitive tasks, using Python scripts, like updating financial reports or fetching real time market data. By automating these processes, Python within Excel not only saves you time, but also minimizes error, allowing for real time data analysis and decision making. Suppose you've got data in Excel that you want to analyze using Python's libraries, a script or Python command to read Excel data and perform a basic analysis. So once you have your new Excel worksheet, and we can still access any data frames we've created in the notebook. So once I can activate and check that I still have access to stock data data frame. And the fact that I don't get an error after the busy for a second, means I can still access it. So we've already imported the libraries. We have Pandas, we have MPI, Matplotlib, Seaborn, Datetime. So we want to do a simple calculation or analysis really for the same dataset. So I can do a simple visual and that would be okay to just plot the dataset. And then we can do a returns also and plot the same. So now we have the data frame, which is the data, and it has the adjust close, and we want to call the plot method. And this one just takes in the size, which I like using 10, 5 or 10, 6. And if I control enter, I should be able to get an image, a time series image of the adjusted close price of the stock data. And I can just view this image and still click here and show it over the cells. Okay, it's too big, but I can just reduce it. As you can see, this is the image of the stock data adjusted close column over time. Now we can compute the return of the same, based on their adjusted close. So you can see, create another column in the stock data called "return," and it'll be given by the stock data column of adjusted close percentage change, which is a method available with Pandas. And so when I do control enter, I should be able to get a return rate. Awesome. Should be a series. We plot that, so let me just activate that and then do stock data. This time I'm not plotting an adjusted close. I want the return rate. And then just call the plot method in Matplotlib and give the same size of input of visual. Yeah, that should work. Now it gives me busy for a sec before gives me the image or an error in case there's something that didn't work. So now I can just display over the cells. It's too big. Let me also reduce this. As you can see, now this is a plot of the return, which we computed by calling the percentage change method available with data frames in Pandas. There, we've just used Python to calculate returns from our Excel data. Ensuring data integrity during these processes is critical. Here are a few best practices. Always validate your data before and after transfer. Watch out for data type mismatches, and handle missing values gracefully. These steps are crucial for maintaining the accuracy of your financial analysis. Now know how to seamlessly import and export financial data between Excel and Python, and apply best practices to ensure data integrity. Dive into these practices and you'll unlock a new level of efficiency and depth in your financial analysis.

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