Reading Pretty-Printed JSON in Python Introduction JSON (JavaScript Object Notation) is a popular data interchange format that has become widely adopted in various industries. One of the advantages of JSON is its human-readable format, which makes it easy to read and write. However, when dealing with large datasets or files containing pretty-printed JSON, it can be challenging to parse them using standard libraries like Python’s built-in json module. In this article, we’ll explore how to read pretty-printed JSON in Python, including some common pitfalls and workarounds.

Enumerating Weeks Over Years in SQL/SNOWFLAKE When working with data models that involve a calendar, it’s essential to be able to easily order and visualize the weeks. In this article, we’ll explore how to enumerate weeks over years in SQL/SNOWFLAKE, including strategies for handling year changes and creating a grouped output. Understanding the Problem The problem statement provides a scenario where you want to create a data model that houses a calendar in SQL.

Converting and Casting NVARCHAR Time to Decimal in SQL Server As a developer working with legacy databases, you may encounter situations where you need to convert data types or formats from one database system to another. In this article, we’ll focus on converting the NVARCHAR time format to decimal in SQL Server. Understanding the Problem The problem arises when trying to convert a time value stored as an NVARCHAR (e.g., ‘07:30’) to a decimal data type.

Understanding the Google Analytics Data in BigQuery When working with data from Google Analytics in BigQuery, it’s not uncommon to encounter unexpected behavior or errors due to the specific structure of the data. In this article, we’ll explore a common issue where filtering using WHERE clauses fails due to an array value type. Introduction to BigQuery and Google Analytics Data BigQuery is a fully-managed enterprise data warehouse service by Google Cloud Platform (GCP).

Handling Missing Values in Resampled Data When resampling data, it’s common to encounter missing values due to the aggregation process. In this example, we’ll demonstrate how to handle missing values in a resampled dataset. Problem Statement Given a time series dataset with daily observations, we want to resample it to 15-minute intervals while keeping track of any missing values that may arise during the aggregation process. Solution We’ll use the pandas library to perform the resampling and handle missing values.

Parsing SQL Scripts in Python: A Deep Dive into Field, Name, and Table Extraction In today’s data-driven world, understanding the structure of SQL scripts is crucial for data analysis, visualization, and manipulation. This article delves into the process of parsing SQL scripts using Python to extract essential information such as field names, business names, and table names. Introduction SQL (Structured Query Language) is a standard language for managing relational databases. It provides a way to store, retrieve, and manipulate data in a database.

Understanding the Problem and Requirements The problem presented involves pagination of groups of records in a MySQL table, rather than individual records. The goal is to retrieve a specified number of groups (not just individual records) from the database based on certain criteria. Key Requirements Retrieve all records from the specified group without referencing the ID column. Sort or filter data as needed for individual records if required Paginate records by retrieving multiple groups with a specific page and record count.

Computing Z-Scores for All Columns in a Pandas DataFrame When working with numerical data, it’s common to normalize or standardize the values to have zero mean and unit variance. This process is known as z-scoring or standardization. In this article, we’ll explore how to compute z-scores for all columns in a pandas DataFrame, ignoring NaN values. Introduction to Z-Score Calculation The z-score is defined as: z = (X - μ) / σ

Handling Duplicate Rows and Applying Changes to Original DataFrame In this article, we will explore how to handle duplicate rows in a pandas DataFrame and apply changes to the original DataFrame. We will also discuss various methods for finding the maximum or latest value for each duplicated column. Introduction When working with datasets, it is common to encounter duplicate rows. These duplicates can be due to various reasons such as typos, errors in data entry, or identical records.

Understanding the Problem Converting Character Sequence of Hourly French Times to POSIXct with Lubridate As a technical blogger, I’ve encountered several questions related to time zone conversions and handling duplicate dates. In this article, we’ll delve into the world of lubridate and explore how to set the dst (daylight saving time) attribute when converting character sequences of hourly French times to POSIXct. Introduction to Lubridate Lubridate is a popular R package for working with dates and times.