Category: Python

Exploratory Data Analysis (EDA) is crucial for gaining a solid understanding of your data and uncovering potential insights. However, this process is typically manual and involves a number of routine functions. Despite numerous technological advancements, EDA still requires significant manual effort, technical skills, and substantial computational power. In this post, we will explore why EDA…

Effective data analysis hinges on having complete data sets. Commonly, grouping data by days or months can result in significant gaps due to missing data points. In this post, I’ll guide you through a more efficient strategy: dynamically creating date ranges in BigQuery. This approach allows for on-the-fly date range generation without the overhead of…

Data gaps can occur when data is organized into time intervals but observations are missing for certain intervals. For example, let’s say you are tracking sales of snow shovels by month. Snow shovels are typically only in demand during winter months, so it is likely that there will be months with no sales at all.…

Managing MySQL databases can often be costly and time-consuming. If you’re working with databases containing static data, an effective alternative is to convert your database tables into individual Parquet files. By storing these files and leveraging Python for direct querying, you’ll maintain your existing querying capabilities and benefit from improved query performance, cost reduction, and…

The Pandas package in Python allows you to generate a list of dates dynamically and then extract their attributes with various datetime functions.