UIAlertView Dismiss Not Really Dismissed ===================================================== As a developer, it’s frustrating when unexpected issues arise with our code. In this post, we’ll delve into the world of UIAlertView and explore why its dismiss method doesn’t quite do what we expect. Background In iOS development, UIAlertView is used to display alert messages to the user. When an app attempts to log in using Facebook Connect (FBConnect), it creates a subview that overlays the entire window, including the UIAlertView.

Dynamic Date Parameter in Airflow DAG for Snowflake SQL Query In this article, we’ll explore how to implement a dynamic date parameter in an Airflow DAG that runs a Snowflake SQL query. We’ll cover the steps required to set up a conditional statement to determine the desired date and reuse it throughout the query. Introduction to Airflow and Snowflake Integration Airflow is an open-source platform for programmable workflows, allowing users to create, schedule, and manage data pipelines.

Understanding the Issue with Dropping Rows from a Pandas DataFrame and KeyErrors in PyTorch Dataloader In this article, we’ll delve into the issue of KeyError exceptions that occur when dropping rows from a pandas DataFrame using the dropna() method. We’ll explore why this happens and provide solutions to avoid these errors when working with PyTorch datasets. Introduction to Pandas DataFrames and Dataloaders Pandas is a powerful library for data manipulation and analysis in Python.

Maintaining the Order of a Vector When Applying it to setNames of a List In this article, we will delve into the world of R programming language and explore how to maintain the order of a vector when applying it to setNames of a list. This is a common problem faced by many data analysts and scientists who work with lists of dataframes. Introduction The R programming language is widely used for statistical computing, data analysis, and visualization.

Retrieving Rows Between Two Dates in PostgreSQL PostgreSQL provides several ways to retrieve rows that fall within a specific date range. In this article, we will explore one such approach using the date data type and its various operators. Introduction to Date Data Type The date data type is used to represent dates without time components. This data type is useful when you need to store or compare dates without considering their time parts.

Based on the provided specification, I will write the code in SQL Server programming language. CREATE FUNCTION fn_hash_string (@str nvarchar(4000)) RETURNS BIGINT AS BEGIN DECLARE @result_num BIGINT = 0; -- Check if string is empty IF LEN(@str) = 0 RETURN 0; -- Initialize variables for loop DECLARE @hash_lo BIGINT; DECLARE @hash_md BIGINT; DECLARE @hash_hi BIGINT; DECLARE @mult_lo BIGINT; DECLARE @mult-md BIGINT; DECLARE @mult_hi BIGINT; -- Convert string to UNICODE SET @str = N'%' + REPT(N''', 1) + @str + REPT(N''', 1); -- Get the true length of string, including possible trailing spaces DECLARE @len INT = LEN(@str); DECLARE @pos INT = 0; WHILE @pos < @len BEGIN SET @pos += 1; DECLARE @value BIGINT = UNICODE(SUBSTRING(@str, @pos, 1)); -- Add with carry DECLARE @sum_lo BIGINT = @hash_lo + @value; DECLARE @sum_md BIGINT = @hash_md + (@sum_lo >> 24); DECLARE @sum_hi BIGINT = @hash_hi + (@sum_md >> 24); SET @hash_lo = @sum_lo & 0xFF; SET @hash_md = @sum_md & 0xFFFF; SET @hash_hi = @sum_hi & 0xFFFF; -- Cross-multiply with carry DECLARE @prod_lo BIGINT = (@hash_lo * @mult_lo); DECLARE @prod_md BIGINT = (@hash_md * @mult_lo) + (@hash_lo * @mult-md) + (@prod_lo >> 24); DECLARE @prod_hi BIGINT = (@hash_hi * @mult_lo) + (@hash_md * @mult-md) + (@hash_lo * @mult_hi) + (@prod_md >> 24); -- Update hash values SET @hash_lo = @prod_lo & 0xFF; SET @hash_md = @prod_md & 0xFFFF; SET @hash_hi = @prod_hi & 0xFFFF; SET @mult_lo = (@mult_lo << 8) + @value; SET @mult-md = (@mult_lo >> 24) * 65536 + ( (@mult_lo & 0xFFFF0000) >> 16) + (@multip-hi << 16) ; SET @mult_hi = (@mult_hi << 8) + @value; END -- Combine slices SET @result_hi = @hash_hi << 48; SET @result_md = @hash_md << 24; SET @result_lo = @hash_lo; -- Convert to numeric and adjust for negative IF @result_hi < 0 SET @result_num += 18446744073709551616; IF @result_md < 0 SET @result_num += 18446744073709551616; IF @result_lo < 0 SET @result_num += 18446744073709551616; -- Format and return as string RETURN (@result_num); END GO This SQL function takes a string input and returns its hash value in BIGINT format.

Creating a Subset by Removing Factors in R Introduction In this blog post, we will explore how to create a subset of data by removing factors, which are categorical variables. We’ll use the dplyr library and provide examples with code snippets. Understanding Factors In R, factors are a type of vector that can contain a limited number of unique levels or categories. They are often used in data analysis to represent categorical variables.

NetworkX - Setting Node Attributes from DataFrame Introduction to NetworkX and DataFrames in Python NetworkX is a Python library for the creation, manipulation, and study of the structure, dynamics, and functions of complex networks. It provides an object-oriented interface for creating network objects and allows users to manipulate network structures using various methods. DataFrames are a data structure in pandas, a popular Python library for data analysis and manipulation. They provide a convenient way to store and manipulate tabular data, such as tables or spreadsheets.

To solve this problem in a more efficient manner, we can use the rowwise() function from the dplyr package to repeat elements a certain number of times and then use unnest() to convert the resulting list of vectors into separate rows. Here’s how you can do it: library(tidyverse) sched <- weekly_data %>% mutate(max_weeks = max(cd_dur_weeks + ca_dur_weeks)) %>% rowwise() %>% mutate( week = list( c(rep(hrs_per_week_cd, cd_dur_weeks), rep(0, (max_weeks - cd_dur_weeks)), rep(hrs_per_week_ca, ca_dur_weeks)), c(rep(0, (max_weeks - cd_dur_weeks)), rep(hrs_per_week_cd, cd_dur_weeks), rep(0, ca_dur_weeks)) ) ) %>% ungroup() %>% select(dsk_proj_number = dsk_proj_number) %>% # rename the columns pivot_wider(names_from = "dsk_proj_number", values_from = week) This code achieves the same result as your original code but with less manual repetition and error-prone logic.

Formatting Percentages in Pandas DataFrames As a data analyst or scientist working with Python, you’ve likely encountered the need to format percentages within your data frames using pandas and other relevant modules. While performing calculations on percentage changes is crucial for understanding growth rates or declines in various metrics, displaying these values as percentages can significantly enhance readability and comprehension. In this article, we’ll delve into how to format percent values while calculating percentages in Python, specifically focusing on the pandas library for data manipulation and analysis.