To get the file size in Python, call os.path.getsize(“file.csv”) or Path(“file.csv”).stat().st_size. Both return the size in bytes; divide by 1024 ** 2 to get megabytes. This guide shows each way to get a file size in Python, converts bytes to KB, MB and GB (and explains why Windows and disk makers disagree), adds up the … Read more >>
As a Python developer with over a decade of experience, I’ve found that data manipulation tasks often come down to filtering and finding specific values. One of the most efficient tools in the Python data science ecosystem is np.where(), especially when used with Pandas. In this comprehensive guide, I’ll walk you through everything you need … Read more >>
Are you working with data in Python and need to export your analysis results to Excel? I do this almost daily in my data analysis workflow. Pandas makes it incredibly easy to write DataFrames to Excel files with just a few lines of code. In this tutorial, I’ll show you multiple ways to write Pandas … Read more >>
When working with data in Python, I often encounter situations where I need to replace values in a DataFrame based on specific conditions. Pandas makes this process easy, offering several methods to accomplish this task efficiently. In this guide, I will show you multiple ways to replace values in a pandas DataFrame column based on … Read more >>
While I was working on a data analysis project, I needed to replace multiple values in a Pandas DataFrame. This is a common task when cleaning and preparing data for analysis. In this article, I’ll share five useful methods to replace multiple values in Pandas DataFrames. These techniques will help you clean your data faster … Read more >>
Working with data often requires filtering and counting specific information. Recently, I was analyzing US sales data and needed to count the number of transactions that exceeded $1,000. This is a common requirement when processing large datasets. Pandas makes this task simple with several different approaches. In this article, I’ll share nine practical ways to … Read more >>
When I was working on a large dataset containing US sales figures, I needed to quickly preview the data without loading the entire DataFrame. This is a common task when dealing with large datasets, and printing the first few rows can give you a quick overview of your data structure. In this article, I will … Read more >>
When I was working with DataFrames in Pandas, I often found myself needing to set the first column as an index. This is a common task when dealing with datasets where the first column contains unique identifiers, names, or dates that would serve better as row labels rather than regular data. In this article, I … 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 >>
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 >>