Data Scientyst

1. Introduction Did you know that the Ancient Olympics date back to 776 BC? Even the philosopher Plato is believed to have competed in wrestling. Plato, Socrates, and Aristotle frequented gymnasia, where the rigorous training for games like the Olympics was seen as a metaphor for intellectual competition and the pursuit of truth. Today, the Games look very different — but age is still a fascinating factor in athletic success. The Olympic Games bring together athletes from hundreds of countries.

In this short post we will see how to split a column into multiple parts and then extract only the last component or the last not null value. This is common with file paths, URLs, codes, delimiter-separated strings and dirty data. For example, if your column contains: "user/home/file.txt", you might want "file.txt". New York,US - US etc Let's learn how to split a Pandas column and get the last part using simple, efficient methods.

When working with pandas, comparing DataFrames that contain NaN values can be confusing and error prone. By defaultin Python, NaN is not equal to NaN in standard element-wise comparisons, which often leads to unexpected results. Sample data: import numpy as np import pandas as pd df1 = pd.DataFrame([[np.nan,1, np.nan, 3],[2, 1, np.nan,3]]) df2 = df1.copy() 0 1 2 3 0 NaN 1 NaN 3 1 2.0 1 NaN 3 Why NaN breaks equality checks? In NumPy and pandas, NaN represents missing data.

If you're working with textual data in a Pandas DataFrame and want to find all words written in uppercase, there are several simple ways to do it using Python. A "capital word" here means a word where every letter is uppercase (like JAVA or PYTHON). This is useful when cleaning data, detecting acronyms, or filtering for entries that stand out in text data.

Working with dates in Pandas often requires precise manipulations, such as flooring a date to the beginning of its month. This is particularly useful when aggregating data monthly or standardizing timestamps for reporting. In this short guide, we'll explore several efficient methods to achieve this, drawing from proven Pandas techniques.

Learn how to compare every value in a pandas DataFrame column with all following values efficiently. Sample Data import pandas as pd val = [16, 19, 15, 19, 15] df = pd.DataFrame({'val': val}) val 0 16 1 19 2 15 3 19 4 15 1.

When working with Pandas Series, you may need to add an item at the beginning rather than at the end. While Pandas doesn't have a built-in prepend method, there are several effective ways to accomplish this task. In this short guide, you'll see how to insert an item at the beginning of a Pandas Series.

When working with user input, web scraping, or data validation, you often need to verify whether a string represents a valid domain name. Python offers several approaches to accomplish this task, from simple regex patterns to specialized libraries. In this guide, you'll learn different methods to validate domain names in Python with practical examples.

If you're seeing the warning: FutureWarning: Passing literal html to 'read_html' is deprecated and will be removed in a future version this means you're using pandas' read_html() function in a way that will soon be unsupported. Why the Change? The pandas development team is deprecating the direct passing of HTML strings to read_html(). This change promotes better practices and prepares for future improvements to the function.

Pandas can read CSV files directly from a URL by passing the URL to the read_csv() method. This is useful when working with datasets hosted online and for ad hoc tests.