Read Part 2 and Part 3 In the evolving landscape of artificial intelligence, combining advanced techniques like Retrieval-Augmented Generation (RAG) and Named Entity Recognition (NER) has opened new avenues for extracting and structuring information from complex documents. This blog delves into the intricacies of building a 10-Q Analyzer—a tool I designed to process SEC 10-Q … Continue reading Building a 10-Q Analyzer: Part 1 | Extracting Financial Insights with AI
Category: Data Analysis
Explaining P-value to a non technical audience
Wikipedia defines p-value as "the probability of obtaining test results at least as extreme as the results actually observed, under the assumption that the null hypothesis is correct". Well if we give this definition, say in a presentation to a product or a business team, you're most probably gonna receive piercing puzzled looks. One of the major … Continue reading Explaining P-value to a non technical audience
Unnest (explode) a column of list in Pandas
In python, when you have a list of lists and convert it directly to a pandas dataframe, you get columns of lists. This may seem overwhelming, but fear not! Pandas comes to our rescue once again - use pandas.DataFrame.explode() import pandas as pd df = pd.DataFrame({'col1': [[0, 1, 2], 'foo', [], [3, 4]], 'col2': 1, … Continue reading Unnest (explode) a column of list in Pandas

