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In this tutorial, we’ll show how to use @AttributeOverride to change a column mapping. We’ll explain how to use it when extending or embedding an entity, and we’ll cover single and collection embedding.
#developer
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In this tutorial, we are going to discuss different ways to add a new column to pandas data frame.
Table of Contents
Pandas data frameis a two-dimensional heterogeneous data structure that stores the data in a tabular form with labeled indexes i.e. rows and columns.
Usually, data frames are used when we have to deal with a large dataset, then we can simply see the summary of that large dataset by loading it into a pandas data frame and see the summary of the data frame.
In the real-world scenario, a pandas data frame is created by loading the datasets from an existing CSV file, Excel file, etc.
But pandas data frame can be also created from the list, dictionary, list of lists, list of dictionaries, dictionary of ndarray/lists, etc. Before we start discussing how to add a new column to an existing data frame we require a pandas data frame.
#pandas #dataframe #pandas dataframe #column #add a new column #how to add a new column to pandas dataframe
1621501920
In this tutorial, we’ll show how to use @AttributeOverride to change a column mapping. We’ll explain how to use it when extending or embedding an entity, and we’ll cover single and collection embedding.
#developer
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At a very surface level, column-store databases do exactly what is advertised on the tin: namely, that instead of organizing information into rows, it does so in columns. This essentially makes them function the same way that tables work in relational databases. Of course, since this is a NoSQL database, this data model makes them much more flexible.
More specifically, column databases use the concept of keyspace , which is sort of like a schema in relational models. This keyspace contains all the column families, which then contain rows, which then contain columns. It’s a bit tricky to wrap your head around at first but it’s relatively straightforward.
By taking a quick look, we can see that a column family has several rows. Within each row, there can be several different columns, with different names, links, and even sizes (meaning they don’t need to adhere to a standard). Furthermore, these columns only exist within their own row and can contain a value pair, name, and a timestamp.
#database #column-oriented database #columns
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In this post we’ll go over how to bind and update column definitions in ag-Grid in each major framework. You’ll see how specific aspects of column state are preserved during these updates, allowing you to easily update columns without having to implement you own logic to reapply column state.
We demonstrate this in live examples in Angular, React, Vue.JS and JavaScript.
Note: As of ag-Grid version 24, there is no longer a need to set immutableColumns
in your gridOptions
(previously known as deltaColumnMode
) as columns are reactive by default!
Whenever new column definitions are set in ag-Grid, specific aspects of state of the existing columns are automatically preserved. This allows you to easily update column definitions without having to write your own code to save and reapply column state.
Column state is kept for sorting, filtering, column width, pinned columns, column order etc. See the full list of stateful attributes of column definitions and how to save and apply column state in our documentation.
The GIF below shows how adding, removing and even updating columns does not reset column state - we sort the AGE column, resize the COUNTRY column, filter the SPORT column, and all this state is preserved when we add a new ATHLETE column or set header names by clicking the buttons above the grid.
#ag-grid #angular #columns #javascript #react #state #vuejs
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This post is the first in a series about Big Data. In it, we’d like to tell you how we at JetBrains see Big Data, and consequently, how we’re creating products for it.
Next parts:
Table of contents:
The world of big data can seem mysterious, hidden behind a curtain of unknown and weird words. It’s time to clear up this mystery and define Big Data.
#big-data #big data world #definitions #big data world, part 1: definitions