The problem is that you’re using = instead of < in the XPath expression. The correct XPath expression should be: $NEWXML/*:ReceiptDesc/*:Receipt[./*:ReceiptDtl/*:unit_qty/text() = $NAME] should be changed to: $NEWXML/*:ReceiptDesc/*:Receipt[./*:ReceiptDtl/*:unit_qty/text() = '0.0000'] Here’s the corrected code: with XML_TABLE as ( select xmltype( q'[&lt;?xml version="1.0" encoding="UTF-8" standalone="yes"?&gt; &lt;ReceiptDesc xmlns="http //www.w3.org/2000/svg"&gt; &lt;appt_nbr&gt;0&lt;/appt_nbr&gt; &lt;Receipt&gt; &lt;dc_dest_id&gt;ST&lt;/dc_dest_id&gt; &lt;po_nbr&gt;1232&lt;/po_nbr&gt; &lt;document_type&gt;T&lt;/document_type&gt; &lt;asn_nbr&gt;0033&lt;/asn_nbr&gt; &lt;ReceiptDtl&gt; &lt;item_id&gt;100233127&lt;/item_id&gt; &lt;unit_qty&gt;0.0000&lt;/unit_qty&gt; &lt;user_id&gt;EXTERNAL&lt;/user_id&gt; &lt;shipped_qty&gt;6.0000&lt;/shipped_qty&gt; &lt;/ReceiptDtl&gt; &lt;from_loc&gt;WH&lt;/from_loc&gt; &lt;from_loc_type&gt;W&lt;/from_loc_type&gt; &lt;/Receipt&gt; &lt;Receipt&gt; &lt;dc_dest_id&gt;ST&lt;/dc_dest_id&gt; &lt;po_nbr&gt;1233&lt;/po_nbr&gt; &lt;document_type&gt;T&lt;/document_type&gt; &lt;asn_nbr&gt;0033&lt;/asn_nbr&gt; &lt;ReceiptDtl&gt; &lt;item_id&gt;355532244&lt;/item_id&gt; &lt;unit_qty&gt;2.0000&lt;/unit_qty&gt; &lt;user_id&gt;EXTERNAL&lt;/user_id&gt; &lt;shipped_qty&gt;2.

Adding (Mean +/- 2 Sigma) Lines in Box Plot Introduction In this post, we will explore how to add lines representing mean plus/minus 2 sigma (or mean plus/minus 3 sigma) to a box plot in R. The original question posed by the user involves creating a box plot with two sets of data and adding these lines on top of it. Understanding Box Plots A box plot is a graphical representation of the distribution of data, showing the median, quartiles, and outliers.

Selecting the Maximum Address Sequence Number and Vendor ID In this blog post, we will explore how to write an SQL query that returns the VENDOR_ID with the maximum ADDRESS_SEQ_NUM. We will discuss the various approaches to achieve this, including using aggregate functions, grouping by a specific column, and sorting data. Understanding the Problem The problem arises when you want to retrieve only the VENDOR_ID and the corresponding maximum ADDRESS_SEQ_NUM from a table.

Implementing Search in Objective-C Overview In this article, we will explore how to implement search functionality in an Objective-C application. We will use the UISearchBar control and UITableView to filter data based on user input. Understanding the Problem The problem presented in the question is a common issue when implementing search functionality in table views. The user types a keyword into the UISearchBar, which filters the data and displays only the records that match the keyword.

Based on the provided code and error message, I’ll provide a step-by-step solution. Step 1: Identify the issue The make_prediction_nlm function is trying to use the lme function with a formula as an argument. However, when called with new_data = fake_data_complicated_1, it throws an error saying that the object ‘formula_used_nlm’ is not found. Step 2: Understand the lme function’s behavior The lme function expects to receive literal formulas as arguments, rather than variables or expressions containing variables.

Filtering Out Values in a Pandas DataFrame Based on a Specific Pattern In this article, we will explore how to exclude values in a pandas DataFrame that occur in a specific pattern. We’ll use the example provided by the Stack Overflow user who wants to remove rows from 15 to 22 based on a rule where the value of ‘step’ at row [i] should be +/- 1 of the value at row [i+1].

Understanding Table View Cells and Image Display in iPhone Development When building iOS applications, one of the fundamental components is the table view cell. A table view cell is a reusable container that holds the data and visual elements for a single row in a table view. In this article, we will delve into the specifics of creating table view cells with images, exploring common issues and solutions. Table View Cells and Delegation In iOS development, table view cells are created using a class that conforms to the UITableViewDataSource and UITableViewDelegate protocols.

Merging Row Values in Two Consecutive Rows Using Pandas Introduction Pandas is a powerful data manipulation library in Python that provides efficient data structures and operations for manipulating numerical data. In this article, we will explore how to merge the values of two consecutive rows in a pandas DataFrame. Understanding the Problem The problem at hand involves merging the values from two consecutive rows in a pandas DataFrame. The resulting row should have the same index as the original second row, and its values should be combined using a specified separator (in this case, the pipe character).

In order to find the time of year, we can use lubridate::year and lubridate::quarter, which return the number of years and quarters between two dates, respectively. library(lubridate) toy_df$years <- as.numeric(as.period(interval(start = birthdate, end = givendate))$year) toy_df$quarters <- quarter(as.period(interval(start = birthdate, end = givendate))) # Print the resulting data frame toy_df birthdate givendate years quarters 1 1978-12-30 2015-12-31 37 4 2 1978-12-31 2015-12-31 36 4 3 1979-01-01 2015-12-31 36 1 4 1962-12-30 2015-12-31 53 4 5 1962-12-31 2015-12-31 52 4 6 1963-01-01 2015-12-31 52 1 7 2000-06-16 2050-06-17 50 2 8 2000-06-17 2050-06-17 49 2 9 2000-06-18 2050-06-17 49 2 10 2007-03-18 2008-03-19 1 1 11 2007-03-19 2008-03-19 1 1 12 2007-03-20 2008-03-19 0 1 13 1968-02-29 2015-02-28 47 4 14 1968-02-29 2015-03-01 47 1 15 1968-02-29 2015-03-02 47 2 We can also calculate the quarter of the year using lubridate::quarter which returns a value between 1 and 4, where:

Function in a DataFrame: Comparing Two Columns In this article, we will explore how to apply a function to compare two columns of data in a pandas DataFrame. We’ll provide an example using R programming language and discuss various techniques for computing date differences. Introduction When working with data, it’s common to want to perform calculations or comparisons on specific columns. One way to achieve this is by creating a new column that contains the results of these operations.