Data Scientyst

If you spend your days moving between Python and R, wrangling dataframes, or switching between RStudio and VS Code depending on the task, there's a new tool worth putting on your radar: Positron, a free IDE built specifically for data science. It's new to me, and relatively new itself — dating from around mid-2024. I learned about it by checking out the GitHub profile of Wes McKinney, author of pandas. I'd been searching for a good data science IDE for a long time, so I decided to give it a chance.

To filter a DataFrame for numeric values in Pandas we can: (1) Use str.isnumeric() with boolean indexing df[df['col'].str.isnumeric()] (2) Use pd.to_numeric() with errors='coerce' df[pd.to_numeric(df['col'], errors='coerce').notna()] (3) Use regular expressions with str.match() df[df['col'].str.match(r'^\d+$')] Assume we have a DataFrame with a string column that contains both numeric and non-numeric values: import pandas as pd data = { 'col': ['123', 'abc', '45', 'xyz', '678', 'hello'] } df...

FRED (Federal Reserve Economic Data) is a free database run by the Federal Reserve Bank of St. Louis. It holds hundreds of thousands of economic time series from national, international, public, and private sources, plus tools to chart, customize, and export the data. No account needed to browse. šŸ”— Main site: fred.stlouisfed.org Quick Reference: Where to Go for What I want to... Go here Learn what FRED is / its history What is FRED?

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.