The filter() method of Array instances creates a shallow copy of a portion of a given array, filtered down to just the elements from the given array that pass the test implemented by the provided function.

The filter() method is an iterative method. It calls a provided callbackFn function once for each element in an array, and constructs a new array of all the values for which callbackFn returns a truthy value. Array elements which do not pass the callbackFn test are not included in the new array. Read the iterative methods section for more information about how these methods work in general.

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The array argument is useful if you want to access another element in the array, especially when you don't have an existing variable that refers to the array. The following example first uses map() to extract the numerical ID from each name and then uses filter() to select the ones that are greater than its neighbors.

Note that filter(function, iterable) is equivalent to the generatorexpression (item for item in iterable if function(item)) if function isnot None and (item for item in iterable if item) if function isNone.

Filtered data displays only the rows that meet criteria that you specify and hides rows that you do not want displayed. After you filter data, you can copy, find, edit, format, chart, and print the subset of filtered data without rearranging or moving it.

Using AutoFilter, you can create two types of filters: by a list value or by criteria. Each of these filter types is mutually exclusive for each range of cells or column table. For example, you can filter by a list of numbers, or a criteria, but not by both; you can filter by icon or by a custom filter, but not by both.

For best results, do not mix data types, such as text and number, or number and date in the same column, because only one type of filter command is available for each column. If there is a mix of data types, the command that is displayed is the data type that occurs the most. For example, if the column contains three values stored as number and four as text, the Text Filters command is displayed .

You can quickly filter data based on visual criteria, such as font color, cell color, or icon sets. And you can filter whether you have formatted cells, applied cell styles, or used conditional formatting.

When you filter data, only the data that meets your criteria appears. The data that doesn't meet that criteria is hidden. After you filter data, you can copy, find, edit, format, chart, and print the subset of filtered data.

Filters are additive. This means that each additional filter is based on the current filter and further reduces the subset of data. You can make complex filters by filtering on more than one value, more than one format, or more than one criteria. For example, you can filter on all numbers greater than 5 that are also below average. But some filters (top and bottom ten, above and below average) are based on the original range of cells. For example, when you filter the top ten values, you'll see the top ten values of the whole list, not the top ten values of the subset of the last filter.

In Excel, you can create three kinds of filters: by values, by a format, or by criteria. But each of these filter types is mutually exclusive. For example, you can filter by cell color or by a list of numbers, but not by both. You can filter by icon or by a custom filter, but not by both.

The filter() function is used to subset a data frame,retaining all rows that satisfy your conditions.To be retained, the row must produce a value of TRUE for all conditions.Note that when a condition evaluates to NAthe row will be dropped, unlike base subsetting with [.

The filter() function is used to subset the rows of.data, applying the expressions in ... to the column values to determine whichrows should be retained. It can be applied to both grouped and ungrouped data (see group_by() andungroup()). However, dplyr is not yet smart enough to optimise the filteringoperation on grouped datasets that do not need grouped calculations. For thisreason, filtering is often considerably faster on ungrouped data.

Because filtering expressions are computed within groups, they mayyield different results on grouped tibbles. This will be the caseas soon as an aggregating, lagging, or ranking function isinvolved. Compare this ungrouped filtering:

In the ungrouped version, filter() compares the value of mass in each row tothe global average (taken over the whole data set), keeping only the rows withmass greater than this global average. In contrast, the grouped version calculatesthe average mass separately for each gender group, and keeps rows with mass greaterthan the relevant within-gender average.

Given a jQuery object that represents a set of DOM elements, the .filter() method constructs a new jQuery object from a subset of the matching elements. The supplied selector is tested against each element; all elements matching the selector will be included in the result.

The second form of this method allows us to filter elements against a function rather than a selector. For each element, if the function returns true (or a "truthy" value), the element will be included in the filtered set; otherwise, it will be excluded. Suppose we have a somewhat more involved HTML snippet:

This code will alter the first list item only, as it contains exactly one  tag. Within the filter function, this refers to each DOM element in turn. The parameter passed to the function tells us the index of that DOM element within the set matched by the jQuery object.

Note: When a CSS selector string is passed to .filter(), text and comment nodes will always be removed from the resulting jQuery object during the filtering process. When a specific node or array of nodes are provided, a text or comment node will be included in the resulting jQuery object only if it matches one of the nodes in the filtering array.

When possible, Power Apps will delegate filter and sort operations to the data source and page through the results on demand. For example, when you start an app that shows a Gallery control filled with data, only the first set of records will be initially brought to the device. As the user scrolls, additional data is brought down from the data source. The result is a faster start time for the app and access to very large data sets.

As the user types characters in SearchInput, the results in the gallery are automatically filtered. In this case, the gallery is configured to show records for which the name of the customer (not the name of the company) starts with the sequence of characters in SearchInput. If the user types co in the search box, the gallery shows these results:

The following example creates a report of Internet sales outside the United States by using a measure that filters out sales in the United States, and then slicing by calendar year and product categories. To create this measure, you filter the table, Internet Sales USD, by using Sales Territory, and then use the filtered table in a SUMX function.

The following table demonstrates the proof of concept for the measure, NON USA Internet Sales, the formula for which is provided in the code section below. The table compares all Internet sales with non- USA Internet sales, to show that the filter expression works, by excluding United States sales from the computation.

Note: If you want to add to or clarify this documentation, please follow the style of the existing entries. Describe what data the filter is applied to, and if the filter function takes additional arguments, describe the argument list.

comment_flood_filter : applied when someone appears to be flooding your blog with comments. Filter function arguments: already blocked (true/false, whether a previous filtering plugin has already blocked it; set to true and return true to block this comment in a plugin), time of previous comment, time of current comment.

get_comments_number : applied to the comment count read from the $post global variable by the get_comments_number function (which is also called by the comments_number function; see also comments_number filter).

bloginfo : applied to the blog option information retrieved from the database by the bloginfo function, after first retrieving the information with the get_bloginfo function. A second argument $show gives the name of the bloginfo option that was requested. Note that bloginfo("url"), bloginfo("directory") and bloginfo("home") do not use this filtering function (see bloginfo_url).

pre_get_space_used: applied to the get_space_used() function to provide an alternative way of displaying storage space used. Returning false from this filter will revert to default display behavior (used wp_upload_dir() directory space in megabytes).

upload_mimes : allows a filter function to return a list of MIME types for uploads, if there is no MIME list input to the wp_check_filetype function. Filter function argument is an associated list of MIME types whose component names are file extensions (separated by vertical bars) and values are the corresponding MIME types. 0852c4b9a8

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