Bijay Kumar 
When analyzing data in Python, I often need to see relationships between categorical variables. Creating cross-tabulations with percentage values has been one of my go-to techniques for years. While I was working on a project analyzing voter demographics, I needed to see the percentage breakdown across different categories. The solution? Pandas crosstab with normalization. In … Read more >>
While working with data in Python, the pandas library is an indispensable tool that I’ve relied on for years. One of the most common operations in data cleaning and preparation is removing unwanted rows or columns from your dataset. This is where the DataFrame drop() function comes into play. Whether you’re dealing with missing values, … Read more >>
When I was working on a data analysis project for a U.S. retail chain where I needed to examine the relationship between customer demographics and purchasing patterns. I needed to create a cross-tabulation of these variables to identify trends. That’s when the pandas crosstab function became my go-to solution. In this article, I’ll explain what … Read more >>
When working with real-world data, I often encounter messy text containing a mix of numbers and other characters. Sometimes, I need to extract the numeric values from these strings for calculations or analysis. Pandas makes this data cleaning process much easier, but there’s no single built-in function called “remove_non_numeric()”. Instead, we need to use a … Read more >>
While building an e-commerce application in TypeScript, you might define a ProductCategory enum for valid categories like ‘electronics’, ‘clothing’, and ‘food’. When receiving data from a user or an API, it’s important to check if the provided category actually exists in the enum. This prevents invalid values from being processed or stored. This ensures your … Read more >>
While working on a recent feature that displayed important dates in a user-friendly format, I encountered an issue. The ISO date string, like “2025-07-04T00:00:00.000Z,” was showing up as July 3 instead of July 4 in the UI. At first, it seemed like a simple formatting issue, but it turned out to be a classic time … Read more >>
As a developer working on a data analysis project in Python for one of my clients, I often encounter datasets that contain a mix of numeric and non-numeric columns. Sometimes, I need to perform calculations that only work with numeric data, and in these situations, I need to filter out all non-numeric columns from my … Read more >>
When working with multiple datasets in Python, I often need to combine information from different dataframes. One common task is adding a column from one dataframe to another. This might seem simple, but there are several methods to accomplish this, each with its advantages. In this article, I’ll walk you through six practical methods to … Read more >>
When I was working with data in Python, I often needed to locate specific rows in my Pandas DataFrames. Finding the row index is a common task that can be approached in several ways depending on your specific needs. In this article, I’ll share multiple methods to get the index of a row in a … Read more >>
When working with data in Python, I often encounter datasets with missing values. These values, represented as NaN (Not a Number) in pandas, can sometimes interfere with my analysis, especially when I need to identify unique values in a column. Recently, I was analyzing a customer dataset for a US retail chain where I needed … Read more >>
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