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Excel is great for working with data you already have. But what if you want to fill a new column with values that cannot be calculated with a simple formula? Imagine that you work with insurance data. You have information about previous customers, includi

You have a customer list with 40 rows and want to check it for duplicates. You start with the obvious tests. Every customer ID is unique. Every company name is unique. Every email address is unique. Every phone number is unique. There are no exact duplica

You have a sales spreadsheet, and every week someone asks you a slightly different question about it. How did the North region do last month? Can you show only Keyboard sales? Can you remove Standing Desks? Can you send me the numbers for the first two we

If you already work with Excel, learning Python does not mean forgetting everything you know and starting again. You already know what you want to do with data: look at the last rows, keep only a few columns, apply filters, sort from highest to lowest, ad

You want to know how many customers you have in each country. It sounds like one of the simplest questions you can ask about a spreadsheet. You count the values in country, and Poland comes out on top with 156 customers. The result looks reasonable. Polan

You have a customer export with 530 rows, and you suspect that some records appear more than once. Maybe an export was run twice and appended to the same file. Maybe two people imported the same batch. The spreadsheet still opens normally, so nothing imme

Counting missing values sounds like one of the easiest parts of data cleaning. You load a spreadsheet, ask Python how many empty cells there are in each column, and move on. The problem is that a spreadsheet can represent "we don't know" in several differ

You open an Excel file and look at the date column. One row contains 14/09/2024, another says April 29, 2023, and the next one uses 2024-09-13. As a person, you can read all three without much trouble. They are different ways of writing a date. For Python

If you already know Excel, you already know how to answer many common questions about data. You know how to use SUM, AVERAGE, COUNTIF, and probably SUMIFS when you need more than one condition. Learning Python does not mean starting again from zero. In ma

A customer export lands in your inbox. At first glance, it looks fine. There are 530 rows, seven columns, and nothing obviously broken. Then you try to calculate total monthly spend and the result looks strange. You sort customers by signup date and the o