From the course: Advanced Python in Excel for Finance: A Hands-On Approach
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Data cleaning and preparation
From the course: Advanced Python in Excel for Finance: A Hands-On Approach
Data cleaning and preparation
- [Instructor] As financial analysts, the quality of your data underpins the accuracy of your analysis. We will now address the common pitfalls, like missing values and former discrepancies. We start by identifying and removing irrelevant data that clutters our analysis. Next, we focus on reduplication, ensuring our dataset is free of repeat entries. These initial steps set the stage for more detailed cleaning techniques. Missing values can skew our financial analysis if not properly addressed. We'll explore several strategies for dealing with them, including data imputation and complete case analysis. Tailoring our approach to the context of our data, we ensure we maintain integrity. Financial data often comes in various formats, sometimes leading to inconsistencies. We'll tackle converting data types, ensuring dates, currencies, and percentages are correctly formatted for analysis. Proper formatting is crucial for accurate calculations and comparisons. To conclude our cleaning…
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