Using A Progressive-Search Optimization When Filtering Arrays In Angular 10.1.6

The other day, I looked at a search optimization in Angular 10 in which I use a single, pre-compiled keyword value as my search target. That optimization allows me to search across an aggregation of values with a single operation. Today, I wanted to follow-up with another search optimization that I enjoy: using a progressive-search to filter on an increasingly small number of records in Angular 10.1.6.

Using A Progressive-Search Optimization When Filtering Arrays In Angular 10.1.6 from Ben Nadel on Vimeo.

Run this demo in my JavaScript Demos project on GitHub.

View this code in my JavaScript Demos project on GitHub.

The idea with a progressive-search in Angular is that with every key-stroke, we can make some assumptions about the area over which we need to operate. Imagine that we’re searching a list of Friends and the user types-in the following set of characters in a search input:

  • M
  • Ma
  • Mar
  • Mari
  • Maria

This search is “progressive”, in that each character builds on top of the set of characters already entered: **Mari** is really just the **Mar** search followed by an additional **i** character constraint. Because of this, we know that the Mari search operation has already been limited by the Mar prefix. And, as such, we can apply the current search operation to the set of intermediary results that the Mar operation already produced.

What this means is that each subsequent search operation in a progressive-search is operating on an increasingly smaller set of records. Which, depending on the complexity of the page, could lead to a performance improvement.

To see this in action, I’ve put together a simple demo in which we can search over a set of Friends using a simple keyword search. And, with every key-input, we’re going to filter the list using the applySearchFilter() method; and, output the number of records that are being searched in each given operation:

// Import the core angular services.
import { Component } from "@angular/core";

// Import the application components and services.
import { Friend } from "./friends";
import { friends } from "./friends";

// ----------------------------------------------------------------------------------- //
// ----------------------------------------------------------------------------------- //

interface SearchResult {
	friend: Friend;
	sort: string;
	keywords: string;
}

var USE_FILTER_OPTIMIZATION = true;

@Component({
	selector: "app-root",
	styleUrls: [ "./app.component.less" ],
	templateUrl: "./app.component.html"
})
export class AppComponent {

	public allSearchResults!: SearchResult[];
	public filteredSearchResults!: SearchResult[];
	public searchFilter: string;

	private friends: Friend[];
	private previousSearchFilter: string;

	// I initialize the app component.
	constructor() {

		this.friends = friends;

		this.searchFilter = "";
		this.previousSearchFilter = "";
		this.setAllSearchResults();
		this.setFilteredSearchResults();

	}

	// ---
	// PUBLIC METHODS.
	// ---

	// I update the filtered search results to use the given filter.
	public applySearchFilter( searchFilter: string ) : void {

		this.searchFilter = searchFilter.trim();
		this.setFilteredSearchResults( USE_FILTER_OPTIMIZATION );

		// Now that we've applied the filtering for the given search filter, let's store
		// the given filter as the previous filter so that we can attempt to optimize
		// subsequent filter operations that build on top of the current one.
		this.previousSearchFilter = this.searchFilter;

	}

	// ---
	// PRIVATE METHODS.
	// ---

	// I setup the all-results collection based on the current friends.
	private setAllSearchResults() : void {

		this.allSearchResults = this.friends.map(
			( friend ) => {

				return({
					friend: friend,
					sort: friend.name.toLowerCase(),
					keywords: friend.name.toLowerCase()
				});

			}
		);

		this.allSearchResults.sort(
			( a, b ) => {

				return( a.sort.localeCompare( b.sort ) );

			}
		);

	}

	// I setup the filtered-results collection based on the current all-results.
	private setFilteredSearchResults( useFilterOptimization: boolean = false ) : void {

		var normalizedFilter = this.searchFilter.toLowerCase();

		if ( normalizedFilter ) {

			// PERFORMANCE OPTIMIZATION: If the current filter is just an ADDITION to the
			// previous filter, then we can improve performance of the search by using
			// the FILTERED SEARCH RESULTS as our target set. This means that as the user
			// types "forward", each operation will operate over an increasingly small
			// number of records.
			var canUseFilterOptization = (
				useFilterOptimization &&
				this.previousSearchFilter &&
				this.searchFilter.startsWith( this.previousSearchFilter )
			);

			var intermediaryResults = ( canUseFilterOptization )
				? this.filteredSearchResults
				: this.allSearchResults
			;

			// Let's output some debugging information about which list we are searching
			// so that we can see how the progressive-search filtering affects the
			// surface area of the search operation.
			console.group( "Searching List" );
			console.log( "Keywords:", normalizedFilter );
			console.log( "Record Count:", intermediaryResults.length );
			console.groupEnd();

			this.filteredSearchResults = intermediaryResults.filter(
				( result ) => {

					return( result.keywords.includes( normalizedFilter ) );

				}
			);

		} else {

			// If there is no search-filter, then we can just reset the filtered-results
			// to be the all-results collection.
			this.filteredSearchResults = this.allSearchResults;

		}

	}

}

#angular #javascript #programming #developer #web-development

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Using A Progressive-Search Optimization When Filtering Arrays In Angular 10.1.6
Veronica  Roob

Veronica Roob

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A Pure PHP Implementation Of The MessagePack Serialization Format

msgpack.php

A pure PHP implementation of the MessagePack serialization format.

Features

Installation

The recommended way to install the library is through Composer:

composer require rybakit/msgpack

Usage

Packing

To pack values you can either use an instance of a Packer:

$packer = new Packer();
$packed = $packer->pack($value);

or call a static method on the MessagePack class:

$packed = MessagePack::pack($value);

In the examples above, the method pack automatically packs a value depending on its type. However, not all PHP types can be uniquely translated to MessagePack types. For example, the MessagePack format defines map and array types, which are represented by a single array type in PHP. By default, the packer will pack a PHP array as a MessagePack array if it has sequential numeric keys, starting from 0 and as a MessagePack map otherwise:

$mpArr1 = $packer->pack([1, 2]);               // MP array [1, 2]
$mpArr2 = $packer->pack([0 => 1, 1 => 2]);     // MP array [1, 2]
$mpMap1 = $packer->pack([0 => 1, 2 => 3]);     // MP map {0: 1, 2: 3}
$mpMap2 = $packer->pack([1 => 2, 2 => 3]);     // MP map {1: 2, 2: 3}
$mpMap3 = $packer->pack(['a' => 1, 'b' => 2]); // MP map {a: 1, b: 2}

However, sometimes you need to pack a sequential array as a MessagePack map. To do this, use the packMap method:

$mpMap = $packer->packMap([1, 2]); // {0: 1, 1: 2}

Here is a list of type-specific packing methods:

$packer->packNil();           // MP nil
$packer->packBool(true);      // MP bool
$packer->packInt(42);         // MP int
$packer->packFloat(M_PI);     // MP float (32 or 64)
$packer->packFloat32(M_PI);   // MP float 32
$packer->packFloat64(M_PI);   // MP float 64
$packer->packStr('foo');      // MP str
$packer->packBin("\x80");     // MP bin
$packer->packArray([1, 2]);   // MP array
$packer->packMap(['a' => 1]); // MP map
$packer->packExt(1, "\xaa");  // MP ext

Check the "Custom types" section below on how to pack custom types.

Packing options

The Packer object supports a number of bitmask-based options for fine-tuning the packing process (defaults are in bold):

NameDescription
FORCE_STRForces PHP strings to be packed as MessagePack UTF-8 strings
FORCE_BINForces PHP strings to be packed as MessagePack binary data
DETECT_STR_BINDetects MessagePack str/bin type automatically
  
FORCE_ARRForces PHP arrays to be packed as MessagePack arrays
FORCE_MAPForces PHP arrays to be packed as MessagePack maps
DETECT_ARR_MAPDetects MessagePack array/map type automatically
  
FORCE_FLOAT32Forces PHP floats to be packed as 32-bits MessagePack floats
FORCE_FLOAT64Forces PHP floats to be packed as 64-bits MessagePack floats

The type detection mode (DETECT_STR_BIN/DETECT_ARR_MAP) adds some overhead which can be noticed when you pack large (16- and 32-bit) arrays or strings. However, if you know the value type in advance (for example, you only work with UTF-8 strings or/and associative arrays), you can eliminate this overhead by forcing the packer to use the appropriate type, which will save it from running the auto-detection routine. Another option is to explicitly specify the value type. The library provides 2 auxiliary classes for this, Map and Bin. Check the "Custom types" section below for details.

Examples:

// detect str/bin type and pack PHP 64-bit floats (doubles) to MP 32-bit floats
$packer = new Packer(PackOptions::DETECT_STR_BIN | PackOptions::FORCE_FLOAT32);

// these will throw MessagePack\Exception\InvalidOptionException
$packer = new Packer(PackOptions::FORCE_STR | PackOptions::FORCE_BIN);
$packer = new Packer(PackOptions::FORCE_FLOAT32 | PackOptions::FORCE_FLOAT64);

Unpacking

To unpack data you can either use an instance of a BufferUnpacker:

$unpacker = new BufferUnpacker();

$unpacker->reset($packed);
$value = $unpacker->unpack();

or call a static method on the MessagePack class:

$value = MessagePack::unpack($packed);

If the packed data is received in chunks (e.g. when reading from a stream), use the tryUnpack method, which attempts to unpack data and returns an array of unpacked messages (if any) instead of throwing an InsufficientDataException:

while ($chunk = ...) {
    $unpacker->append($chunk);
    if ($messages = $unpacker->tryUnpack()) {
        return $messages;
    }
}

If you want to unpack from a specific position in a buffer, use seek:

$unpacker->seek(42); // set position equal to 42 bytes
$unpacker->seek(-8); // set position to 8 bytes before the end of the buffer

To skip bytes from the current position, use skip:

$unpacker->skip(10); // set position to 10 bytes ahead of the current position

To get the number of remaining (unread) bytes in the buffer:

$unreadBytesCount = $unpacker->getRemainingCount();

To check whether the buffer has unread data:

$hasUnreadBytes = $unpacker->hasRemaining();

If needed, you can remove already read data from the buffer by calling:

$releasedBytesCount = $unpacker->release();

With the read method you can read raw (packed) data:

$packedData = $unpacker->read(2); // read 2 bytes

Besides the above methods BufferUnpacker provides type-specific unpacking methods, namely:

$unpacker->unpackNil();   // PHP null
$unpacker->unpackBool();  // PHP bool
$unpacker->unpackInt();   // PHP int
$unpacker->unpackFloat(); // PHP float
$unpacker->unpackStr();   // PHP UTF-8 string
$unpacker->unpackBin();   // PHP binary string
$unpacker->unpackArray(); // PHP sequential array
$unpacker->unpackMap();   // PHP associative array
$unpacker->unpackExt();   // PHP MessagePack\Type\Ext object

Unpacking options

The BufferUnpacker object supports a number of bitmask-based options for fine-tuning the unpacking process (defaults are in bold):

NameDescription
BIGINT_AS_STRConverts overflowed integers to strings [1]
BIGINT_AS_GMPConverts overflowed integers to GMP objects [2]
BIGINT_AS_DECConverts overflowed integers to Decimal\Decimal objects [3]

1. The binary MessagePack format has unsigned 64-bit as its largest integer data type, but PHP does not support such integers, which means that an overflow can occur during unpacking.

2. Make sure the GMP extension is enabled.

3. Make sure the Decimal extension is enabled.

Examples:

$packedUint64 = "\xcf"."\xff\xff\xff\xff"."\xff\xff\xff\xff";

$unpacker = new BufferUnpacker($packedUint64);
var_dump($unpacker->unpack()); // string(20) "18446744073709551615"

$unpacker = new BufferUnpacker($packedUint64, UnpackOptions::BIGINT_AS_GMP);
var_dump($unpacker->unpack()); // object(GMP) {...}

$unpacker = new BufferUnpacker($packedUint64, UnpackOptions::BIGINT_AS_DEC);
var_dump($unpacker->unpack()); // object(Decimal\Decimal) {...}

Custom types

In addition to the basic types, the library provides functionality to serialize and deserialize arbitrary types. This can be done in several ways, depending on your use case. Let's take a look at them.

Type objects

If you need to serialize an instance of one of your classes into one of the basic MessagePack types, the best way to do this is to implement the CanBePacked interface in the class. A good example of such a class is the Map type class that comes with the library. This type is useful when you want to explicitly specify that a given PHP array should be packed as a MessagePack map without triggering an automatic type detection routine:

$packer = new Packer();

$packedMap = $packer->pack(new Map([1, 2, 3]));
$packedArray = $packer->pack([1, 2, 3]);

More type examples can be found in the src/Type directory.

Type transformers

As with type objects, type transformers are only responsible for serializing values. They should be used when you need to serialize a value that does not implement the CanBePacked interface. Examples of such values could be instances of built-in or third-party classes that you don't own, or non-objects such as resources.

A transformer class must implement the CanPack interface. To use a transformer, it must first be registered in the packer. Here is an example of how to serialize PHP streams into the MessagePack bin format type using one of the supplied transformers, StreamTransformer:

$packer = new Packer(null, [new StreamTransformer()]);

$packedBin = $packer->pack(fopen('/path/to/file', 'r+'));

More type transformer examples can be found in the src/TypeTransformer directory.

Extensions

In contrast to the cases described above, extensions are intended to handle extension types and are responsible for both serialization and deserialization of values (types).

An extension class must implement the Extension interface. To use an extension, it must first be registered in the packer and the unpacker.

The MessagePack specification divides extension types into two groups: predefined and application-specific. Currently, there is only one predefined type in the specification, Timestamp.

Timestamp

The Timestamp extension type is a predefined type. Support for this type in the library is done through the TimestampExtension class. This class is responsible for handling Timestamp objects, which represent the number of seconds and optional adjustment in nanoseconds:

$timestampExtension = new TimestampExtension();

$packer = new Packer();
$packer = $packer->extendWith($timestampExtension);

$unpacker = new BufferUnpacker();
$unpacker = $unpacker->extendWith($timestampExtension);

$packedTimestamp = $packer->pack(Timestamp::now());
$timestamp = $unpacker->reset($packedTimestamp)->unpack();

$seconds = $timestamp->getSeconds();
$nanoseconds = $timestamp->getNanoseconds();

When using the MessagePack class, the Timestamp extension is already registered:

$packedTimestamp = MessagePack::pack(Timestamp::now());
$timestamp = MessagePack::unpack($packedTimestamp);

Application-specific extensions

In addition, the format can be extended with your own types. For example, to make the built-in PHP DateTime objects first-class citizens in your code, you can create a corresponding extension, as shown in the example. Please note, that custom extensions have to be registered with a unique extension ID (an integer from 0 to 127).

More extension examples can be found in the examples/MessagePack directory.

To learn more about how extension types can be useful, check out this article.

Exceptions

If an error occurs during packing/unpacking, a PackingFailedException or an UnpackingFailedException will be thrown, respectively. In addition, an InsufficientDataException can be thrown during unpacking.

An InvalidOptionException will be thrown in case an invalid option (or a combination of mutually exclusive options) is used.

Tests

Run tests as follows:

vendor/bin/phpunit

Also, if you already have Docker installed, you can run the tests in a docker container. First, create a container:

./dockerfile.sh | docker build -t msgpack -

The command above will create a container named msgpack with PHP 8.1 runtime. You may change the default runtime by defining the PHP_IMAGE environment variable:

PHP_IMAGE='php:8.0-cli' ./dockerfile.sh | docker build -t msgpack -

See a list of various images here.

Then run the unit tests:

docker run --rm -v $PWD:/msgpack -w /msgpack msgpack

Fuzzing

To ensure that the unpacking works correctly with malformed/semi-malformed data, you can use a testing technique called Fuzzing. The library ships with a help file (target) for PHP-Fuzzer and can be used as follows:

php-fuzzer fuzz tests/fuzz_buffer_unpacker.php

Performance

To check performance, run:

php -n -dzend_extension=opcache.so \
-dpcre.jit=1 -dopcache.enable=1 -dopcache.enable_cli=1 \
tests/bench.php

Example output

Filter: MessagePack\Tests\Perf\Filter\ListFilter
Rounds: 3
Iterations: 100000

=============================================
Test/Target            Packer  BufferUnpacker
---------------------------------------------
nil .................. 0.0030 ........ 0.0139
false ................ 0.0037 ........ 0.0144
true ................. 0.0040 ........ 0.0137
7-bit uint #1 ........ 0.0052 ........ 0.0120
7-bit uint #2 ........ 0.0059 ........ 0.0114
7-bit uint #3 ........ 0.0061 ........ 0.0119
5-bit sint #1 ........ 0.0067 ........ 0.0126
5-bit sint #2 ........ 0.0064 ........ 0.0132
5-bit sint #3 ........ 0.0066 ........ 0.0135
8-bit uint #1 ........ 0.0078 ........ 0.0200
8-bit uint #2 ........ 0.0077 ........ 0.0212
8-bit uint #3 ........ 0.0086 ........ 0.0203
16-bit uint #1 ....... 0.0111 ........ 0.0271
16-bit uint #2 ....... 0.0115 ........ 0.0260
16-bit uint #3 ....... 0.0103 ........ 0.0273
32-bit uint #1 ....... 0.0116 ........ 0.0326
32-bit uint #2 ....... 0.0118 ........ 0.0332
32-bit uint #3 ....... 0.0127 ........ 0.0325
64-bit uint #1 ....... 0.0140 ........ 0.0277
64-bit uint #2 ....... 0.0134 ........ 0.0294
64-bit uint #3 ....... 0.0134 ........ 0.0281
8-bit int #1 ......... 0.0086 ........ 0.0241
8-bit int #2 ......... 0.0089 ........ 0.0225
8-bit int #3 ......... 0.0085 ........ 0.0229
16-bit int #1 ........ 0.0118 ........ 0.0280
16-bit int #2 ........ 0.0121 ........ 0.0270
16-bit int #3 ........ 0.0109 ........ 0.0274
32-bit int #1 ........ 0.0128 ........ 0.0346
32-bit int #2 ........ 0.0118 ........ 0.0339
32-bit int #3 ........ 0.0135 ........ 0.0368
64-bit int #1 ........ 0.0138 ........ 0.0276
64-bit int #2 ........ 0.0132 ........ 0.0286
64-bit int #3 ........ 0.0137 ........ 0.0274
64-bit int #4 ........ 0.0180 ........ 0.0285
64-bit float #1 ...... 0.0134 ........ 0.0284
64-bit float #2 ...... 0.0125 ........ 0.0275
64-bit float #3 ...... 0.0126 ........ 0.0283
fix string #1 ........ 0.0035 ........ 0.0133
fix string #2 ........ 0.0094 ........ 0.0216
fix string #3 ........ 0.0094 ........ 0.0222
fix string #4 ........ 0.0091 ........ 0.0241
8-bit string #1 ...... 0.0122 ........ 0.0301
8-bit string #2 ...... 0.0118 ........ 0.0304
8-bit string #3 ...... 0.0119 ........ 0.0315
16-bit string #1 ..... 0.0150 ........ 0.0388
16-bit string #2 ..... 0.1545 ........ 0.1665
32-bit string ........ 0.1570 ........ 0.1756
wide char string #1 .. 0.0091 ........ 0.0236
wide char string #2 .. 0.0122 ........ 0.0313
8-bit binary #1 ...... 0.0100 ........ 0.0302
8-bit binary #2 ...... 0.0123 ........ 0.0324
8-bit binary #3 ...... 0.0126 ........ 0.0327
16-bit binary ........ 0.0168 ........ 0.0372
32-bit binary ........ 0.1588 ........ 0.1754
fix array #1 ......... 0.0042 ........ 0.0131
fix array #2 ......... 0.0294 ........ 0.0367
fix array #3 ......... 0.0412 ........ 0.0472
16-bit array #1 ...... 0.1378 ........ 0.1596
16-bit array #2 ........... S ............. S
32-bit array .............. S ............. S
complex array ........ 0.1865 ........ 0.2283
fix map #1 ........... 0.0725 ........ 0.1048
fix map #2 ........... 0.0319 ........ 0.0405
fix map #3 ........... 0.0356 ........ 0.0665
fix map #4 ........... 0.0465 ........ 0.0497
16-bit map #1 ........ 0.2540 ........ 0.3028
16-bit map #2 ............. S ............. S
32-bit map ................ S ............. S
complex map .......... 0.2372 ........ 0.2710
fixext 1 ............. 0.0283 ........ 0.0358
fixext 2 ............. 0.0291 ........ 0.0371
fixext 4 ............. 0.0302 ........ 0.0355
fixext 8 ............. 0.0288 ........ 0.0384
fixext 16 ............ 0.0293 ........ 0.0359
8-bit ext ............ 0.0302 ........ 0.0439
16-bit ext ........... 0.0334 ........ 0.0499
32-bit ext ........... 0.1845 ........ 0.1888
32-bit timestamp #1 .. 0.0337 ........ 0.0547
32-bit timestamp #2 .. 0.0335 ........ 0.0560
64-bit timestamp #1 .. 0.0371 ........ 0.0575
64-bit timestamp #2 .. 0.0374 ........ 0.0542
64-bit timestamp #3 .. 0.0356 ........ 0.0533
96-bit timestamp #1 .. 0.0362 ........ 0.0699
96-bit timestamp #2 .. 0.0381 ........ 0.0701
96-bit timestamp #3 .. 0.0367 ........ 0.0687
=============================================
Total                  2.7618          4.0820
Skipped                     4               4
Failed                      0               0
Ignored                     0               0

With JIT:

php -n -dzend_extension=opcache.so \
-dpcre.jit=1 -dopcache.jit_buffer_size=64M -dopcache.jit=tracing -dopcache.enable=1 -dopcache.enable_cli=1 \
tests/bench.php

Example output

Filter: MessagePack\Tests\Perf\Filter\ListFilter
Rounds: 3
Iterations: 100000

=============================================
Test/Target            Packer  BufferUnpacker
---------------------------------------------
nil .................. 0.0005 ........ 0.0054
false ................ 0.0004 ........ 0.0059
true ................. 0.0004 ........ 0.0059
7-bit uint #1 ........ 0.0010 ........ 0.0047
7-bit uint #2 ........ 0.0010 ........ 0.0046
7-bit uint #3 ........ 0.0010 ........ 0.0046
5-bit sint #1 ........ 0.0025 ........ 0.0046
5-bit sint #2 ........ 0.0023 ........ 0.0046
5-bit sint #3 ........ 0.0024 ........ 0.0045
8-bit uint #1 ........ 0.0043 ........ 0.0081
8-bit uint #2 ........ 0.0043 ........ 0.0079
8-bit uint #3 ........ 0.0041 ........ 0.0080
16-bit uint #1 ....... 0.0064 ........ 0.0095
16-bit uint #2 ....... 0.0064 ........ 0.0091
16-bit uint #3 ....... 0.0064 ........ 0.0094
32-bit uint #1 ....... 0.0085 ........ 0.0114
32-bit uint #2 ....... 0.0077 ........ 0.0122
32-bit uint #3 ....... 0.0077 ........ 0.0120
64-bit uint #1 ....... 0.0085 ........ 0.0159
64-bit uint #2 ....... 0.0086 ........ 0.0157
64-bit uint #3 ....... 0.0086 ........ 0.0158
8-bit int #1 ......... 0.0042 ........ 0.0080
8-bit int #2 ......... 0.0042 ........ 0.0080
8-bit int #3 ......... 0.0042 ........ 0.0081
16-bit int #1 ........ 0.0065 ........ 0.0095
16-bit int #2 ........ 0.0065 ........ 0.0090
16-bit int #3 ........ 0.0056 ........ 0.0085
32-bit int #1 ........ 0.0067 ........ 0.0107
32-bit int #2 ........ 0.0066 ........ 0.0106
32-bit int #3 ........ 0.0063 ........ 0.0104
64-bit int #1 ........ 0.0072 ........ 0.0162
64-bit int #2 ........ 0.0073 ........ 0.0174
64-bit int #3 ........ 0.0072 ........ 0.0164
64-bit int #4 ........ 0.0077 ........ 0.0161
64-bit float #1 ...... 0.0053 ........ 0.0135
64-bit float #2 ...... 0.0053 ........ 0.0135
64-bit float #3 ...... 0.0052 ........ 0.0135
fix string #1 ....... -0.0002 ........ 0.0044
fix string #2 ........ 0.0035 ........ 0.0067
fix string #3 ........ 0.0035 ........ 0.0077
fix string #4 ........ 0.0033 ........ 0.0078
8-bit string #1 ...... 0.0059 ........ 0.0110
8-bit string #2 ...... 0.0063 ........ 0.0121
8-bit string #3 ...... 0.0064 ........ 0.0124
16-bit string #1 ..... 0.0099 ........ 0.0146
16-bit string #2 ..... 0.1522 ........ 0.1474
32-bit string ........ 0.1511 ........ 0.1483
wide char string #1 .. 0.0039 ........ 0.0084
wide char string #2 .. 0.0073 ........ 0.0123
8-bit binary #1 ...... 0.0040 ........ 0.0112
8-bit binary #2 ...... 0.0075 ........ 0.0123
8-bit binary #3 ...... 0.0077 ........ 0.0129
16-bit binary ........ 0.0096 ........ 0.0145
32-bit binary ........ 0.1535 ........ 0.1479
fix array #1 ......... 0.0008 ........ 0.0061
fix array #2 ......... 0.0121 ........ 0.0165
fix array #3 ......... 0.0193 ........ 0.0222
16-bit array #1 ...... 0.0607 ........ 0.0479
16-bit array #2 ........... S ............. S
32-bit array .............. S ............. S
complex array ........ 0.0749 ........ 0.0824
fix map #1 ........... 0.0329 ........ 0.0431
fix map #2 ........... 0.0161 ........ 0.0189
fix map #3 ........... 0.0205 ........ 0.0262
fix map #4 ........... 0.0252 ........ 0.0205
16-bit map #1 ........ 0.1016 ........ 0.0927
16-bit map #2 ............. S ............. S
32-bit map ................ S ............. S
complex map .......... 0.1096 ........ 0.1030
fixext 1 ............. 0.0157 ........ 0.0161
fixext 2 ............. 0.0175 ........ 0.0183
fixext 4 ............. 0.0156 ........ 0.0185
fixext 8 ............. 0.0163 ........ 0.0184
fixext 16 ............ 0.0164 ........ 0.0182
8-bit ext ............ 0.0158 ........ 0.0207
16-bit ext ........... 0.0203 ........ 0.0219
32-bit ext ........... 0.1614 ........ 0.1539
32-bit timestamp #1 .. 0.0195 ........ 0.0249
32-bit timestamp #2 .. 0.0188 ........ 0.0260
64-bit timestamp #1 .. 0.0207 ........ 0.0281
64-bit timestamp #2 .. 0.0212 ........ 0.0291
64-bit timestamp #3 .. 0.0207 ........ 0.0295
96-bit timestamp #1 .. 0.0222 ........ 0.0358
96-bit timestamp #2 .. 0.0228 ........ 0.0353
96-bit timestamp #3 .. 0.0210 ........ 0.0319
=============================================
Total                  1.6432          1.9674
Skipped                     4               4
Failed                      0               0
Ignored                     0               0

You may change default benchmark settings by defining the following environment variables:

NameDefault
MP_BENCH_TARGETSpure_p,pure_u, see a list of available targets
MP_BENCH_ITERATIONS100_000
MP_BENCH_DURATIONnot set
MP_BENCH_ROUNDS3
MP_BENCH_TESTS-@slow, see a list of available tests

For example:

export MP_BENCH_TARGETS=pure_p
export MP_BENCH_ITERATIONS=1000000
export MP_BENCH_ROUNDS=5
# a comma separated list of test names
export MP_BENCH_TESTS='complex array, complex map'
# or a group name
# export MP_BENCH_TESTS='-@slow' // @pecl_comp
# or a regexp
# export MP_BENCH_TESTS='/complex (array|map)/'

Another example, benchmarking both the library and the PECL extension:

MP_BENCH_TARGETS=pure_p,pure_u,pecl_p,pecl_u \
php -n -dextension=msgpack.so -dzend_extension=opcache.so \
-dpcre.jit=1 -dopcache.enable=1 -dopcache.enable_cli=1 \
tests/bench.php

Example output

Filter: MessagePack\Tests\Perf\Filter\ListFilter
Rounds: 3
Iterations: 100000

===========================================================================
Test/Target            Packer  BufferUnpacker  msgpack_pack  msgpack_unpack
---------------------------------------------------------------------------
nil .................. 0.0031 ........ 0.0141 ...... 0.0055 ........ 0.0064
false ................ 0.0039 ........ 0.0154 ...... 0.0056 ........ 0.0053
true ................. 0.0038 ........ 0.0139 ...... 0.0056 ........ 0.0044
7-bit uint #1 ........ 0.0061 ........ 0.0110 ...... 0.0059 ........ 0.0046
7-bit uint #2 ........ 0.0065 ........ 0.0119 ...... 0.0042 ........ 0.0029
7-bit uint #3 ........ 0.0054 ........ 0.0117 ...... 0.0045 ........ 0.0025
5-bit sint #1 ........ 0.0047 ........ 0.0103 ...... 0.0038 ........ 0.0022
5-bit sint #2 ........ 0.0048 ........ 0.0117 ...... 0.0038 ........ 0.0022
5-bit sint #3 ........ 0.0046 ........ 0.0102 ...... 0.0038 ........ 0.0023
8-bit uint #1 ........ 0.0063 ........ 0.0174 ...... 0.0039 ........ 0.0031
8-bit uint #2 ........ 0.0063 ........ 0.0167 ...... 0.0040 ........ 0.0029
8-bit uint #3 ........ 0.0063 ........ 0.0168 ...... 0.0039 ........ 0.0030
16-bit uint #1 ....... 0.0092 ........ 0.0222 ...... 0.0049 ........ 0.0030
16-bit uint #2 ....... 0.0096 ........ 0.0227 ...... 0.0042 ........ 0.0046
16-bit uint #3 ....... 0.0123 ........ 0.0274 ...... 0.0059 ........ 0.0051
32-bit uint #1 ....... 0.0136 ........ 0.0331 ...... 0.0060 ........ 0.0048
32-bit uint #2 ....... 0.0130 ........ 0.0336 ...... 0.0070 ........ 0.0048
32-bit uint #3 ....... 0.0127 ........ 0.0329 ...... 0.0051 ........ 0.0048
64-bit uint #1 ....... 0.0126 ........ 0.0268 ...... 0.0055 ........ 0.0049
64-bit uint #2 ....... 0.0135 ........ 0.0281 ...... 0.0052 ........ 0.0046
64-bit uint #3 ....... 0.0131 ........ 0.0274 ...... 0.0069 ........ 0.0044
8-bit int #1 ......... 0.0077 ........ 0.0236 ...... 0.0058 ........ 0.0044
8-bit int #2 ......... 0.0087 ........ 0.0244 ...... 0.0058 ........ 0.0048
8-bit int #3 ......... 0.0084 ........ 0.0241 ...... 0.0055 ........ 0.0049
16-bit int #1 ........ 0.0112 ........ 0.0271 ...... 0.0048 ........ 0.0045
16-bit int #2 ........ 0.0124 ........ 0.0292 ...... 0.0057 ........ 0.0049
16-bit int #3 ........ 0.0118 ........ 0.0270 ...... 0.0058 ........ 0.0050
32-bit int #1 ........ 0.0137 ........ 0.0366 ...... 0.0058 ........ 0.0051
32-bit int #2 ........ 0.0133 ........ 0.0366 ...... 0.0056 ........ 0.0049
32-bit int #3 ........ 0.0129 ........ 0.0350 ...... 0.0052 ........ 0.0048
64-bit int #1 ........ 0.0145 ........ 0.0254 ...... 0.0034 ........ 0.0025
64-bit int #2 ........ 0.0097 ........ 0.0214 ...... 0.0034 ........ 0.0025
64-bit int #3 ........ 0.0096 ........ 0.0287 ...... 0.0059 ........ 0.0050
64-bit int #4 ........ 0.0143 ........ 0.0277 ...... 0.0059 ........ 0.0046
64-bit float #1 ...... 0.0134 ........ 0.0281 ...... 0.0057 ........ 0.0052
64-bit float #2 ...... 0.0141 ........ 0.0281 ...... 0.0057 ........ 0.0050
64-bit float #3 ...... 0.0144 ........ 0.0282 ...... 0.0057 ........ 0.0050
fix string #1 ........ 0.0036 ........ 0.0143 ...... 0.0066 ........ 0.0053
fix string #2 ........ 0.0107 ........ 0.0222 ...... 0.0065 ........ 0.0068
fix string #3 ........ 0.0116 ........ 0.0245 ...... 0.0063 ........ 0.0069
fix string #4 ........ 0.0105 ........ 0.0253 ...... 0.0083 ........ 0.0077
8-bit string #1 ...... 0.0126 ........ 0.0318 ...... 0.0075 ........ 0.0088
8-bit string #2 ...... 0.0121 ........ 0.0295 ...... 0.0076 ........ 0.0086
8-bit string #3 ...... 0.0125 ........ 0.0293 ...... 0.0130 ........ 0.0093
16-bit string #1 ..... 0.0159 ........ 0.0368 ...... 0.0117 ........ 0.0086
16-bit string #2 ..... 0.1547 ........ 0.1686 ...... 0.1516 ........ 0.1373
32-bit string ........ 0.1558 ........ 0.1729 ...... 0.1511 ........ 0.1396
wide char string #1 .. 0.0098 ........ 0.0237 ...... 0.0066 ........ 0.0065
wide char string #2 .. 0.0128 ........ 0.0291 ...... 0.0061 ........ 0.0082
8-bit binary #1 ........... I ............. I ........... F ............. I
8-bit binary #2 ........... I ............. I ........... F ............. I
8-bit binary #3 ........... I ............. I ........... F ............. I
16-bit binary ............. I ............. I ........... F ............. I
32-bit binary ............. I ............. I ........... F ............. I
fix array #1 ......... 0.0040 ........ 0.0129 ...... 0.0120 ........ 0.0058
fix array #2 ......... 0.0279 ........ 0.0390 ...... 0.0143 ........ 0.0165
fix array #3 ......... 0.0415 ........ 0.0463 ...... 0.0162 ........ 0.0187
16-bit array #1 ...... 0.1349 ........ 0.1628 ...... 0.0334 ........ 0.0341
16-bit array #2 ........... S ............. S ........... S ............. S
32-bit array .............. S ............. S ........... S ............. S
complex array ............. I ............. I ........... F ............. F
fix map #1 ................ I ............. I ........... F ............. I
fix map #2 ........... 0.0345 ........ 0.0391 ...... 0.0143 ........ 0.0168
fix map #3 ................ I ............. I ........... F ............. I
fix map #4 ........... 0.0459 ........ 0.0473 ...... 0.0151 ........ 0.0163
16-bit map #1 ........ 0.2518 ........ 0.2962 ...... 0.0400 ........ 0.0490
16-bit map #2 ............. S ............. S ........... S ............. S
32-bit map ................ S ............. S ........... S ............. S
complex map .......... 0.2380 ........ 0.2682 ...... 0.0545 ........ 0.0579
fixext 1 .................. I ............. I ........... F ............. F
fixext 2 .................. I ............. I ........... F ............. F
fixext 4 .................. I ............. I ........... F ............. F
fixext 8 .................. I ............. I ........... F ............. F
fixext 16 ................. I ............. I ........... F ............. F
8-bit ext ................. I ............. I ........... F ............. F
16-bit ext ................ I ............. I ........... F ............. F
32-bit ext ................ I ............. I ........... F ............. F
32-bit timestamp #1 ....... I ............. I ........... F ............. F
32-bit timestamp #2 ....... I ............. I ........... F ............. F
64-bit timestamp #1 ....... I ............. I ........... F ............. F
64-bit timestamp #2 ....... I ............. I ........... F ............. F
64-bit timestamp #3 ....... I ............. I ........... F ............. F
96-bit timestamp #1 ....... I ............. I ........... F ............. F
96-bit timestamp #2 ....... I ............. I ........... F ............. F
96-bit timestamp #3 ....... I ............. I ........... F ............. F
===========================================================================
Total                  1.5625          2.3866        0.7735          0.7243
Skipped                     4               4             4               4
Failed                      0               0            24              17
Ignored                    24              24             0               7

With JIT:

MP_BENCH_TARGETS=pure_p,pure_u,pecl_p,pecl_u \
php -n -dextension=msgpack.so -dzend_extension=opcache.so \
-dpcre.jit=1 -dopcache.jit_buffer_size=64M -dopcache.jit=tracing -dopcache.enable=1 -dopcache.enable_cli=1 \
tests/bench.php

Example output

Filter: MessagePack\Tests\Perf\Filter\ListFilter
Rounds: 3
Iterations: 100000

===========================================================================
Test/Target            Packer  BufferUnpacker  msgpack_pack  msgpack_unpack
---------------------------------------------------------------------------
nil .................. 0.0001 ........ 0.0052 ...... 0.0053 ........ 0.0042
false ................ 0.0007 ........ 0.0060 ...... 0.0057 ........ 0.0043
true ................. 0.0008 ........ 0.0060 ...... 0.0056 ........ 0.0041
7-bit uint #1 ........ 0.0031 ........ 0.0046 ...... 0.0062 ........ 0.0041
7-bit uint #2 ........ 0.0021 ........ 0.0043 ...... 0.0062 ........ 0.0041
7-bit uint #3 ........ 0.0022 ........ 0.0044 ...... 0.0061 ........ 0.0040
5-bit sint #1 ........ 0.0030 ........ 0.0048 ...... 0.0062 ........ 0.0040
5-bit sint #2 ........ 0.0032 ........ 0.0046 ...... 0.0062 ........ 0.0040
5-bit sint #3 ........ 0.0031 ........ 0.0046 ...... 0.0062 ........ 0.0040
8-bit uint #1 ........ 0.0054 ........ 0.0079 ...... 0.0062 ........ 0.0050
8-bit uint #2 ........ 0.0051 ........ 0.0079 ...... 0.0064 ........ 0.0044
8-bit uint #3 ........ 0.0051 ........ 0.0082 ...... 0.0062 ........ 0.0044
16-bit uint #1 ....... 0.0077 ........ 0.0094 ...... 0.0065 ........ 0.0045
16-bit uint #2 ....... 0.0077 ........ 0.0094 ...... 0.0063 ........ 0.0045
16-bit uint #3 ....... 0.0077 ........ 0.0095 ...... 0.0064 ........ 0.0047
32-bit uint #1 ....... 0.0088 ........ 0.0119 ...... 0.0063 ........ 0.0043
32-bit uint #2 ....... 0.0089 ........ 0.0117 ...... 0.0062 ........ 0.0039
32-bit uint #3 ....... 0.0089 ........ 0.0118 ...... 0.0063 ........ 0.0044
64-bit uint #1 ....... 0.0097 ........ 0.0155 ...... 0.0063 ........ 0.0045
64-bit uint #2 ....... 0.0095 ........ 0.0153 ...... 0.0061 ........ 0.0045
64-bit uint #3 ....... 0.0096 ........ 0.0156 ...... 0.0063 ........ 0.0047
8-bit int #1 ......... 0.0053 ........ 0.0083 ...... 0.0062 ........ 0.0044
8-bit int #2 ......... 0.0052 ........ 0.0080 ...... 0.0062 ........ 0.0044
8-bit int #3 ......... 0.0052 ........ 0.0080 ...... 0.0062 ........ 0.0043
16-bit int #1 ........ 0.0089 ........ 0.0097 ...... 0.0069 ........ 0.0046
16-bit int #2 ........ 0.0075 ........ 0.0093 ...... 0.0063 ........ 0.0043
16-bit int #3 ........ 0.0075 ........ 0.0094 ...... 0.0062 ........ 0.0046
32-bit int #1 ........ 0.0086 ........ 0.0122 ...... 0.0063 ........ 0.0044
32-bit int #2 ........ 0.0087 ........ 0.0120 ...... 0.0066 ........ 0.0046
32-bit int #3 ........ 0.0086 ........ 0.0121 ...... 0.0060 ........ 0.0044
64-bit int #1 ........ 0.0096 ........ 0.0149 ...... 0.0060 ........ 0.0045
64-bit int #2 ........ 0.0096 ........ 0.0157 ...... 0.0062 ........ 0.0044
64-bit int #3 ........ 0.0096 ........ 0.0160 ...... 0.0063 ........ 0.0046
64-bit int #4 ........ 0.0097 ........ 0.0157 ...... 0.0061 ........ 0.0044
64-bit float #1 ...... 0.0079 ........ 0.0153 ...... 0.0056 ........ 0.0044
64-bit float #2 ...... 0.0079 ........ 0.0152 ...... 0.0057 ........ 0.0045
64-bit float #3 ...... 0.0079 ........ 0.0155 ...... 0.0057 ........ 0.0044
fix string #1 ........ 0.0010 ........ 0.0045 ...... 0.0071 ........ 0.0044
fix string #2 ........ 0.0048 ........ 0.0075 ...... 0.0070 ........ 0.0060
fix string #3 ........ 0.0048 ........ 0.0086 ...... 0.0068 ........ 0.0060
fix string #4 ........ 0.0050 ........ 0.0088 ...... 0.0070 ........ 0.0059
8-bit string #1 ...... 0.0081 ........ 0.0129 ...... 0.0069 ........ 0.0062
8-bit string #2 ...... 0.0086 ........ 0.0128 ...... 0.0069 ........ 0.0065
8-bit string #3 ...... 0.0086 ........ 0.0126 ...... 0.0115 ........ 0.0065
16-bit string #1 ..... 0.0105 ........ 0.0137 ...... 0.0128 ........ 0.0068
16-bit string #2 ..... 0.1510 ........ 0.1486 ...... 0.1526 ........ 0.1391
32-bit string ........ 0.1517 ........ 0.1475 ...... 0.1504 ........ 0.1370
wide char string #1 .. 0.0044 ........ 0.0085 ...... 0.0067 ........ 0.0057
wide char string #2 .. 0.0081 ........ 0.0125 ...... 0.0069 ........ 0.0063
8-bit binary #1 ........... I ............. I ........... F ............. I
8-bit binary #2 ........... I ............. I ........... F ............. I
8-bit binary #3 ........... I ............. I ........... F ............. I
16-bit binary ............. I ............. I ........... F ............. I
32-bit binary ............. I ............. I ........... F ............. I
fix array #1 ......... 0.0014 ........ 0.0059 ...... 0.0132 ........ 0.0055
fix array #2 ......... 0.0146 ........ 0.0156 ...... 0.0155 ........ 0.0148
fix array #3 ......... 0.0211 ........ 0.0229 ...... 0.0179 ........ 0.0180
16-bit array #1 ...... 0.0673 ........ 0.0498 ...... 0.0343 ........ 0.0388
16-bit array #2 ........... S ............. S ........... S ............. S
32-bit array .............. S ............. S ........... S ............. S
complex array ............. I ............. I ........... F ............. F
fix map #1 ................ I ............. I ........... F ............. I
fix map #2 ........... 0.0148 ........ 0.0180 ...... 0.0156 ........ 0.0179
fix map #3 ................ I ............. I ........... F ............. I
fix map #4 ........... 0.0252 ........ 0.0201 ...... 0.0214 ........ 0.0167
16-bit map #1 ........ 0.1027 ........ 0.0836 ...... 0.0388 ........ 0.0510
16-bit map #2 ............. S ............. S ........... S ............. S
32-bit map ................ S ............. S ........... S ............. S
complex map .......... 0.1104 ........ 0.1010 ...... 0.0556 ........ 0.0602
fixext 1 .................. I ............. I ........... F ............. F
fixext 2 .................. I ............. I ........... F ............. F
fixext 4 .................. I ............. I ........... F ............. F
fixext 8 .................. I ............. I ........... F ............. F
fixext 16 ................. I ............. I ........... F ............. F
8-bit ext ................. I ............. I ........... F ............. F
16-bit ext ................ I ............. I ........... F ............. F
32-bit ext ................ I ............. I ........... F ............. F
32-bit timestamp #1 ....... I ............. I ........... F ............. F
32-bit timestamp #2 ....... I ............. I ........... F ............. F
64-bit timestamp #1 ....... I ............. I ........... F ............. F
64-bit timestamp #2 ....... I ............. I ........... F ............. F
64-bit timestamp #3 ....... I ............. I ........... F ............. F
96-bit timestamp #1 ....... I ............. I ........... F ............. F
96-bit timestamp #2 ....... I ............. I ........... F ............. F
96-bit timestamp #3 ....... I ............. I ........... F ............. F
===========================================================================
Total                  0.9642          1.0909        0.8224          0.7213
Skipped                     4               4             4               4
Failed                      0               0            24              17
Ignored                    24              24             0               7

Note that the msgpack extension (v2.1.2) doesn't support ext, bin and UTF-8 str types.

License

The library is released under the MIT License. See the bundled LICENSE file for details.

Author: rybakit
Source Code: https://github.com/rybakit/msgpack.php
License: MIT License

#php 

Treebender: A Symbolic Natural Language Parsing Library for Rust

Treebender

A symbolic natural language parsing library for Rust, inspired by HDPSG.

What is this?

This is a library for parsing natural or constructed languages into syntax trees and feature structures. There's no machine learning or probabilistic models, everything is hand-crafted and deterministic.

You can find out more about the motivations of this project in this blog post.

But what are you using it for?

I'm using this to parse a constructed language for my upcoming xenolinguistics game, Themengi.

Motivation

Using a simple 80-line grammar, introduced in the tutorial below, we can parse a simple subset of English, checking reflexive pronoun binding, case, and number agreement.

$ cargo run --bin cli examples/reflexives.fgr
> she likes himself
Parsed 0 trees

> her likes herself
Parsed 0 trees

> she like herself
Parsed 0 trees

> she likes herself
Parsed 1 tree
(0..3: S
  (0..1: N (0..1: she))
  (1..2: TV (1..2: likes))
  (2..3: N (2..3: herself)))
[
  child-2: [
    case: acc
    pron: ref
    needs_pron: #0 she
    num: sg
    child-0: [ word: herself ]
  ]
  child-1: [
    tense: nonpast
    child-0: [ word: likes ]
    num: #1 sg
  ]
  child-0: [
    child-0: [ word: she ]
    case: nom
    pron: #0
    num: #1
  ]
]

Low resource language? Low problem! No need to train on gigabytes of text, just write a grammar using your brain. Let's hypothesize that in American Sign Language, topicalized nouns (expressed with raised eyebrows) must appear first in the sentence. We can write a small grammar (18 lines), and plug in some sentences:

$ cargo run --bin cli examples/asl-wordorder.fgr -n
> boy sit
Parsed 1 tree
(0..2: S
  (0..1: NP ((0..1: N (0..1: boy))))
  (1..2: IV (1..2: sit)))

> boy throw ball
Parsed 1 tree
(0..3: S
  (0..1: NP ((0..1: N (0..1: boy))))
  (1..2: TV (1..2: throw))
  (2..3: NP ((2..3: N (2..3: ball)))))

> ball nm-raised-eyebrows boy throw
Parsed 1 tree
(0..4: S
  (0..2: NP
    (0..1: N (0..1: ball))
    (1..2: Topic (1..2: nm-raised-eyebrows)))
  (2..3: NP ((2..3: N (2..3: boy))))
  (3..4: TV (3..4: throw)))

> boy throw ball nm-raised-eyebrows
Parsed 0 trees

Tutorial

As an example, let's say we want to build a parser for English reflexive pronouns (himself, herself, themselves, themself, itself). We'll also support number ("He likes X" v.s. "They like X") and simple embedded clauses ("He said that they like X").

Grammar files are written in a custom language, similar to BNF, called Feature GRammar (.fgr). There's a VSCode syntax highlighting extension for these files available as fgr-syntax.

We'll start by defining our lexicon. The lexicon is the set of terminal symbols (symbols in the actual input) that the grammar will match. Terminal symbols must start with a lowercase letter, and non-terminal symbols must start with an uppercase letter.

// pronouns
N -> he
N -> him
N -> himself
N -> she
N -> her
N -> herself
N -> they
N -> them
N -> themselves
N -> themself

// names, lowercase as they are terminals
N -> mary
N -> sue
N -> takeshi
N -> robert

// complementizer
Comp -> that

// verbs -- intransitive, transitive, and clausal
IV -> falls
IV -> fall
IV -> fell

TV -> likes
TV -> like
TV -> liked

CV -> says
CV -> say
CV -> said

Next, we can add our sentence rules (they must be added at the top, as the first rule in the file is assumed to be the top-level rule):

// sentence rules
S -> N IV
S -> N TV N
S -> N CV Comp S

// ... previous lexicon ...

Assuming this file is saved as examples/no-features.fgr (which it is :wink:), we can test this file with the built-in CLI:

$ cargo run --bin cli examples/no-features.fgr
> he falls
Parsed 1 tree
(0..2: S
  (0..1: N (0..1: he))
  (1..2: IV (1..2: falls)))
[
  child-1: [ child-0: [ word: falls ] ]
  child-0: [ child-0: [ word: he ] ]
]

> he falls her
Parsed 0 trees

> he likes her
Parsed 1 tree
(0..3: S
  (0..1: N (0..1: he))
  (1..2: TV (1..2: likes))
  (2..3: N (2..3: her)))
[
  child-2: [ child-0: [ word: her ] ]
  child-1: [ child-0: [ word: likes ] ]
  child-0: [ child-0: [ word: he ] ]
]

> he likes
Parsed 0 trees

> he said that he likes her
Parsed 1 tree
(0..6: S
  (0..1: N (0..1: he))
  (1..2: CV (1..2: said))
  (2..3: Comp (2..3: that))
  (3..6: S
    (3..4: N (3..4: he))
    (4..5: TV (4..5: likes))
    (5..6: N (5..6: her))))
[
  child-0: [ child-0: [ word: he ] ]
  child-2: [ child-0: [ word: that ] ]
  child-1: [ child-0: [ word: said ] ]
  child-3: [
    child-2: [ child-0: [ word: her ] ]
    child-1: [ child-0: [ word: likes ] ]
    child-0: [ child-0: [ word: he ] ]
  ]
]

> he said that he
Parsed 0 trees

This grammar already parses some correct sentences, and blocks some trivially incorrect ones. However, it doesn't care about number, case, or reflexives right now:

> she likes himself  // unbound reflexive pronoun
Parsed 1 tree
(0..3: S
  (0..1: N (0..1: she))
  (1..2: TV (1..2: likes))
  (2..3: N (2..3: himself)))
[
  child-0: [ child-0: [ word: she ] ]
  child-2: [ child-0: [ word: himself ] ]
  child-1: [ child-0: [ word: likes ] ]
]

> him like her  // incorrect case on the subject pronoun, should be nominative
                // (he) instead of accusative (him)
Parsed 1 tree
(0..3: S
  (0..1: N (0..1: him))
  (1..2: TV (1..2: like))
  (2..3: N (2..3: her)))
[
  child-0: [ child-0: [ word: him ] ]
  child-1: [ child-0: [ word: like ] ]
  child-2: [ child-0: [ word: her ] ]
]

> he like her  // incorrect verb number agreement
Parsed 1 tree
(0..3: S
  (0..1: N (0..1: he))
  (1..2: TV (1..2: like))
  (2..3: N (2..3: her)))
[
  child-2: [ child-0: [ word: her ] ]
  child-1: [ child-0: [ word: like ] ]
  child-0: [ child-0: [ word: he ] ]
]

To fix this, we need to add features to our lexicon, and restrict the sentence rules based on features.

Features are added with square brackets, and are key: value pairs separated by commas. **top** is a special feature value, which basically means "unspecified" -- we'll come back to it later. Features that are unspecified are also assumed to have a **top** value, but sometimes explicitly stating top is more clear.

/// Pronouns
// The added features are:
// * num: sg or pl, whether this noun wants a singular verb (likes) or
//   a plural verb (like). note this is grammatical number, so for example
//   singular they takes plural agreement ("they like X", not *"they likes X")
// * case: nom or acc, whether this noun is nominative or accusative case.
//   nominative case goes in the subject, and accusative in the object.
//   e.g., "he fell" and "she likes him", not *"him fell" and *"her likes he"
// * pron: he, she, they, or ref -- what type of pronoun this is
// * needs_pron: whether this is a reflexive that needs to bind to another
//   pronoun.
N[ num: sg, case: nom, pron: he ]                    -> he
N[ num: sg, case: acc, pron: he ]                    -> him
N[ num: sg, case: acc, pron: ref, needs_pron: he ]   -> himself
N[ num: sg, case: nom, pron: she ]                   -> she
N[ num: sg, case: acc, pron: she ]                   -> her
N[ num: sg, case: acc, pron: ref, needs_pron: she]   -> herself
N[ num: pl, case: nom, pron: they ]                  -> they
N[ num: pl, case: acc, pron: they ]                  -> them
N[ num: pl, case: acc, pron: ref, needs_pron: they ] -> themselves
N[ num: sg, case: acc, pron: ref, needs_pron: they ] -> themself

// Names
// The added features are:
// * num: sg, as people are singular ("mary likes her" / *"mary like her")
// * case: **top**, as names can be both subjects and objects
//   ("mary likes her" / "she likes mary")
// * pron: whichever pronoun the person uses for reflexive agreement
//   mary    pron: she  => mary likes herself
//   sue     pron: they => sue likes themself
//   takeshi pron: he   => takeshi likes himself
N[ num: sg, case: **top**, pron: she ]  -> mary
N[ num: sg, case: **top**, pron: they ] -> sue
N[ num: sg, case: **top**, pron: he ]   -> takeshi
N[ num: sg, case: **top**, pron: he ]   -> robert

// Complementizer doesn't need features
Comp -> that

// Verbs -- intransitive, transitive, and clausal
// The added features are:
// * num: sg, pl, or **top** -- to match the noun numbers.
//   **top** will match either sg or pl, as past-tense verbs in English
//   don't agree in number: "he fell" and "they fell" are both fine
// * tense: past or nonpast -- this won't be used for agreement, but will be
//   copied into the final feature structure, and the client code could do
//   something with it
IV[ num:      sg, tense: nonpast ] -> falls
IV[ num:      pl, tense: nonpast ] -> fall
IV[ num: **top**, tense: past ]    -> fell

TV[ num:      sg, tense: nonpast ] -> likes
TV[ num:      pl, tense: nonpast ] -> like
TV[ num: **top**, tense: past ]    -> liked

CV[ num:      sg, tense: nonpast ] -> says
CV[ num:      pl, tense: nonpast ] -> say
CV[ num: **top**, tense: past ]    -> said

Now that our lexicon is updated with features, we can update our sentence rules to constrain parsing based on those features. This uses two new features, tags and unification. Tags allow features to be associated between nodes in a rule, and unification controls how those features are compatible. The rules for unification are:

  1. A string feature can unify with a string feature with the same value
  2. A top feature can unify with anything, and the nodes are merged
  3. A complex feature ([ ... ] structure) is recursively unified with another complex feature.

If unification fails anywhere, the parse is aborted and the tree is discarded. This allows the programmer to discard trees if features don't match.

// Sentence rules
// Intransitive verb:
// * Subject must be nominative case
// * Subject and verb must agree in number (copied through #1)
S -> N[ case: nom, num: #1 ] IV[ num: #1 ]
// Transitive verb:
// * Subject must be nominative case
// * Subject and verb must agree in number (copied through #2)
// * If there's a reflexive in the object position, make sure its `needs_pron`
//   feature matches the subject's `pron` feature. If the object isn't a
//   reflexive, then its `needs_pron` feature will implicitly be `**top**`, so
//   will unify with anything.
S -> N[ case: nom, pron: #1, num: #2 ] TV[ num: #2 ] N[ case: acc, needs_pron: #1 ]
// Clausal verb:
// * Subject must be nominative case
// * Subject and verb must agree in number (copied through #1)
// * Reflexives can't cross clause boundaries (*"He said that she likes himself"),
//   so we can ignore reflexives and delegate to inner clause rule
S -> N[ case: nom, num: #1 ] CV[ num: #1 ] Comp S

Now that we have this augmented grammar (available as examples/reflexives.fgr), we can try it out and see that it rejects illicit sentences that were previously accepted, while still accepting valid ones:

> he fell
Parsed 1 tree
(0..2: S
  (0..1: N (0..1: he))
  (1..2: IV (1..2: fell)))
[
  child-1: [
    child-0: [ word: fell ]
    num: #0 sg
    tense: past
  ]
  child-0: [
    pron: he
    case: nom
    num: #0
    child-0: [ word: he ]
  ]
]

> he like him
Parsed 0 trees

> he likes himself
Parsed 1 tree
(0..3: S
  (0..1: N (0..1: he))
  (1..2: TV (1..2: likes))
  (2..3: N (2..3: himself)))
[
  child-1: [
    num: #0 sg
    child-0: [ word: likes ]
    tense: nonpast
  ]
  child-2: [
    needs_pron: #1 he
    num: sg
    child-0: [ word: himself ]
    pron: ref
    case: acc
  ]
  child-0: [
    child-0: [ word: he ]
    pron: #1
    num: #0
    case: nom
  ]
]

> he likes herself
Parsed 0 trees

> mary likes herself
Parsed 1 tree
(0..3: S
  (0..1: N (0..1: mary))
  (1..2: TV (1..2: likes))
  (2..3: N (2..3: herself)))
[
  child-0: [
    pron: #0 she
    num: #1 sg
    case: nom
    child-0: [ word: mary ]
  ]
  child-1: [
    tense: nonpast
    child-0: [ word: likes ]
    num: #1
  ]
  child-2: [
    child-0: [ word: herself ]
    num: sg
    pron: ref
    case: acc
    needs_pron: #0
  ]
]

> mary likes themself
Parsed 0 trees

> sue likes themself
Parsed 1 tree
(0..3: S
  (0..1: N (0..1: sue))
  (1..2: TV (1..2: likes))
  (2..3: N (2..3: themself)))
[
  child-0: [
    pron: #0 they
    child-0: [ word: sue ]
    case: nom
    num: #1 sg
  ]
  child-1: [
    tense: nonpast
    num: #1
    child-0: [ word: likes ]
  ]
  child-2: [
    needs_pron: #0
    case: acc
    pron: ref
    child-0: [ word: themself ]
    num: sg
  ]
]

> sue likes himself
Parsed 0 trees

If this is interesting to you and you want to learn more, you can check out my blog series, the excellent textbook Syntactic Theory: A Formal Introduction (2nd ed.), and the DELPH-IN project, whose work on the LKB inspired this simplified version.

Using from code

I need to write this section in more detail, but if you're comfortable with Rust, I suggest looking through the codebase. It's not perfect, it started as one of my first Rust projects (after migrating through F# -> TypeScript -> C in search of the right performance/ergonomics tradeoff), and it could use more tests, but overall it's not too bad.

Basically, the processing pipeline is:

  1. Make a Grammar struct
  • Grammar is defined in rules.rs.
  • The easiest way to make a Grammar is Grammar::parse_from_file, which is mostly a hand-written recusive descent parser in parse_grammar.rs. Yes, I recognize the irony here.
  1. It takes input (in Grammar::parse, which does everything for you, or Grammar::parse_chart, which just does the chart)
  2. The input is first chart-parsed in earley.rs
  3. Then, a forest is built from the chart, in forest.rs, using an algorithm I found in a very useful blog series I forget the URL for, because the algorithms in the academic literature for this are... weird.
  4. Finally, the feature unification is used to prune the forest down to only valid trees. It would be more efficient to do this during parsing, but meh.

The most interesting thing you can do via code and not via the CLI is probably getting at the raw feature DAG, as that would let you do things like pronoun coreference. The DAG code is in featurestructure.rs, and should be fairly approachable -- there's a lot of Rust ceremony around Rc<RefCell<...>> because using an arena allocation crate seemed too harlike overkill, but that is somewhat mitigated by the NodeRef type alias. Hit me up at https://vgel.me/contact if you need help with anything here!

Download Details:
Author: vgel
Source Code: https://github.com/vgel/treebender
License: MIT License

#rust  #machinelearning 

August  Larson

August Larson

1660147320

Top 14 Ways to Filter Pandas Dataframes Easily

Whenever we work with data of any sort, we need a clear picture of the kind of data that we are dealing with. For most of the data out there, which may contain thousands or even millions of entries with a wide variety of information, it’s really impossible to make sense of that data without any tool to present the data in a short and readable format.

Most of the time we need to go through the data, manipulate it, and visualize it for getting insights. Well, there is a great library which goes by the name pandas which provides us with that capability. The most frequent Data manipulation operation is Data Filtering. It is very similar to the WHERE clause in SQL or you must have used a filter in MS Excel for selecting specific rows based on some conditions.

pandas is a powerful, flexible and open source data analysis/manipulation tool which is essentially a python package that provides speed, flexibility and expressive data structures crafted to work with “relational” or “labelled” data in an intuitive and easy manner. It is one of the most popular libraries to perform real-world data analysis in Python.

pandas is built on top of the NumPy library which aims to integrate well with the scientific computing environment and numerous other 3rd party libraries. It has two primary data structures namely Series (1D) and Dataframes(2D), which in most real-world use cases is the type of data that is being dealt with in many sectors of finance, scientific computing, engineering and statistics.

Let’s Start Filtering Data With the Help of Pandas Dataframe

Installing pandas

!pip install pandas

Importing the Pandas library, reading our sample data file and assigning it to “df” DataFrame

import pandas as pd
df = pd.read_csv(r"C:\Users\rajam\Desktop\sample_data.csv")

Let’s check out our dataframe:

print(df.head())

Sample_data

Sample_data

Now that we have our DataFrame, we will be applying various methods to filter it.

Method – 1: Filtering DataFrame by column value

We have a column named “Total_Sales” in our DataFrame and we want to filter out all the sales value which is greater than 300.

#Filter a DataFrame for a single column value with a given condition
 
greater_than = df[df['Total_Sales'] > 300]
print(greater_than.head())

Sample_data with sales > 300

Sales with Greater than 300

Method – 2: Filtering DataFrame based on multiple conditions

Here we are filtering all the values whose “Total_Sales” value is greater than 300 and also where the “Units” is greater than 20. We will have to use the python operator “&” which performs a bitwise AND operation in order to display the corresponding result.

#Filter a DataFrame with multiple conditions
 
filter_sales_units = df[(df['Total_Sales'] > 300) & (df["Units"] > 20)]
print(Filter_sales_units.head())

Image 3

Filter on Sales and Units

Method – 3: Filtering DataFrame based on Date value

If we want to filter our data frame based on a certain date value, for example here we are trying to get all the results based on a particular date, in our case the results after the date ’03/10/21′.

#Filter a DataFrame based on specific date
 
date_filter = df[df['Date'] > '03/10/21']
print(date_filter.head())

Image 1

Filter on Date

Method – 4: Filtering DataFrame based on Date value with multiple conditions

Here we are getting all the results for our Date operation evaluating multiple dates.

#Filter a DataFrame with multiple conditions our Date value
 
date_filter2 = df[(df['Date'] >= '3/25/2021') & (df['Date'] <'8/17/2021')]
print(date_filter2.head())

Image 2

Filter on a date with multiple conditions

Method – 5: Filtering DataFrame based on a specific string

Here we are selecting a column called ‘Region’ and getting all the rows that are from the region ‘East’, thus filtering based on a specific string value.

#Filter a DataFrame to a specific string
 
east = df[df['Region'] == 'East']
print(east.head())

Image 6

Filter based on a specific string

Method – 6: Filtering DataFrame based on a specific index value in a string

Here we are selecting a column called ‘Region’ and getting all the rows which has the letter ‘E’ as the first character i.e at index 0 in the specified column results.

#Filter a DataFrame to show rows starting with a specfic letter
 
starting_with_e = df[df['Region'].str[0]== 'E']
print(starting_with_e.head())

Image 7

Filter based on a specific letter

Method – 7: Filtering DataFrame based on a list of values

Here we are filtering rows in the column ‘Region’ which contains the values ‘West’ as well as ‘East’ and display the combined result. Two methods can be used to perform this filtering namely using a pipe | operator with the corresponding desired set of values with the below syntax OR we can use the .isin() function to filter for the values in a given column, which in our case is the ‘Region’, and provide the list of the desired set of values inside it as a list.

#Filter a DataFrame rows based on list of values
 
#Method 1:
east_west = df[(df['Region'] == 'West') | (df['Region'] == 'East')]
print(east_west)
 
#Method 2:
east_west_1 = df[df['Region'].isin(['West', 'East'])]
print(east_west_1.head())

Image 9

Output of Method -2

Method – 8: Filtering DataFrame rows based on specific values using RegEx

Here we want all the values in the column ‘Region’, which ends with ‘th’ in their string value and display them. In other words, we want our results to show the values of ‘North‘ and ‘South‘ and ignore ‘East’ and ‘West’. The method .str.contains() with the specified values along with the $ RegEx pattern can be used to get the desired results.

For more information please check the Regex Documentation

#Filtering the DataFrame rows using regular expressions(REGEX)
 
regex_df = df[df['Region'].str.contains('th$')]
print(regex_df.head())

Image 10

Filter based on REGEX

Method – 9: Filtering DataFrame to check for null

Here, we’ll check for null and not null values in all the columns with the help of isnull() function.

#Filtering to check for null and not null values in all columns
 
df_null = df[df.isnull().any(axis=1)]
print(df_null.head())

Image 12

Filter based on NULL or NOT null values

Method – 10: Filtering DataFrame to check for null values in a specific column.

#Filtering to check for null values if any in the 'Units' column
 
units_df = df[df['Units'].isnull()]
print(units_df.head())

Image 13

Finding null values on specific columns

Method – 11: Filtering DataFrame to check for not null values in specific columns

#Filtering to check for not null values in the 'Units' column
 
df_not_null = df[df['Units'].notnull()]
print(df_not_null.head())

Image 14

Finding not-null values on specific columns

Method – 12: Filtering DataFrame using query() with a condition

#Using query function in pandas
 
df_query = df.query('Total_Sales > 300')
print(df_query.head())

Image 17

Filtering values with Query Function

Method – 13: Filtering DataFrame using query() with multiple conditions

#Using query function with multiple conditions in pandas
 
df_query_1 = df.query('Total_Sales > 300 and Units <18')
print(df_query_1.head())

Image 18

Filtering multiple columns with Query Function

Method – 14: Filtering our DataFrame using the loc and iloc functions.

#Creating a sample DataFrame for illustrations
 
import numpy as np
data = pd.DataFrame({"col1" : np.arange(1, 20 ,2)}, index=[19, 18 ,8, 6, 0, 1, 2, 3, 4, 5])
print(data)

Image 19

sample_data

Explanation: iloc considers rows based on the position of the given index, so that it takes only integers as values.

For more information please check out Pandas Documentation

#Filter with iloc
 
data.iloc[0 : 5]

Image 20

Filter using iloc

Explanation: loc considers rows based on index labels

#Filter with loc
 
data.loc[0 : 5]

Image 21

Filter using loc

You might be thinking about why the loc function returns 6 rows instead of 5 rows. This is because loc does not produce output based on index position. It considers labels of index only which can be an alphabet as well and includes both starting and endpoint.

Conclusion

So, these were some of the most common filtering methods used in pandas. There are many other filtering methods that could be used, but these are some of the most common.

Link: https://www.askpython.com/python-modules/pandas/filter-pandas-dataframe

#pandas #python #datafame

Thierry  Perret

Thierry Perret

1660017761

14 Meilleures Façons De Filtrer Facilement Les Dataframes Pandas

Chaque fois que nous travaillons avec des données de toutes sortes, nous avons besoin d'une image claire du type de données avec lesquelles nous traitons. Pour la plupart des données disponibles, qui peuvent contenir des milliers, voire des millions d'entrées avec une grande variété d'informations, il est vraiment impossible de donner un sens à ces données sans aucun outil pour présenter les données dans un format court et lisible.

La plupart du temps, nous devons parcourir les données, les manipuler et les visualiser pour obtenir des informations. Eh bien, il existe une excellente bibliothèque qui porte le nom de pandas et qui nous offre cette capacité. L'opération de manipulation de données la plus fréquente est le filtrage de données. Il est très similaire à la clause WHERE dans SQL ou vous devez avoir utilisé un filtre dans MS Excel pour sélectionner des lignes spécifiques en fonction de certaines conditions.

pandas est un outil d'analyse/manipulation de données puissant, flexible et open source qui est essentiellement unpackage pythonqui offre vitesse, flexibilité et structures de données expressives conçues pour fonctionner avec des données « relationnelles » ou « étiquetées » de manière intuitive et simple. C'est l'une des bibliothèques les plus populairespour effectuer une analyse de données du monde réel en Python.

pandas est construit au-dessus de la bibliothèque NumPy qui vise à bien s'intégrer à l'environnement informatique scientifique et à de nombreuses autres bibliothèques tierces. Il comporte deux structures de données principales, à savoir Series (1D) et Dataframes (2D) , qui, dans la plupart des cas d'utilisation réels, correspondent au type de données traitées dans de nombreux secteurs de la finance, du calcul scientifique, de l'ingénierie et des statistiques.

Commençons à filtrer les données à l'aide de Pandas Dataframe

Installer des pandas

!pip install pandas

Importation de la bibliothèque Pandas, lecture de notre exemple de fichier de données et affectation à "df" DataFrame

import pandas as pd
df = pd.read_csv(r"C:\Users\rajam\Desktop\sample_data.csv")

Voyons notre dataframe :

print(df.head())

Sample_data

Sample_data

Maintenant que nous avons notre DataFrame, nous allons appliquer différentes méthodes pour le filtrer.

Méthode – 1 : Filtrage de DataFrame par valeur de colonne

Nous avons une colonne nommée "Total_Sales" dans notre DataFrame et nous voulons filtrer toute la valeur des ventes supérieure à 300.

#Filter a DataFrame for a single column value with a given condition
 
greater_than = df[df['Total_Sales'] > 300]
print(greater_than.head())

Sample_data avec des ventes > 300

Ventes avec plus de 300

Méthode – 2 : Filtrage de DataFrame basé sur plusieurs conditions

Ici, nous filtrons toutes les valeurs dont la valeur "Total_Sales" est supérieure à 300 et également où les "Unités" sont supérieures à 20. Nous devrons utiliser l'opérateur python "&" qui effectue une opération ET au niveau du bit afin d'afficher le résultat correspondant.

#Filter a DataFrame with multiple conditions
 
filter_sales_units = df[(df['Total_Sales'] > 300) & (df["Units"] > 20)]
print(Filter_sales_units.head())

Image 3

Filtrer sur les ventes et les unités

Méthode - 3 : Filtrage de DataFrame basé sur la valeur Date

Si nous voulons filtrer notre trame de données en fonction d'une certaine valeur de date, par exemple ici nous essayons d'obtenir tous les résultats en fonction d'une date particulière, dans notre cas les résultats après la date '03/10/21'.

#Filter a DataFrame based on specific date
 
date_filter = df[df['Date'] > '03/10/21']
print(date_filter.head())

Image 1

Filtrer par date

Méthode - 4 : Filtrage de DataFrame en fonction de la valeur Date avec plusieurs conditions

Ici, nous obtenons tous les résultats de notre opération Date évaluant plusieurs dates .

#Filter a DataFrame with multiple conditions our Date value
 
date_filter2 = df[(df['Date'] >= '3/25/2021') & (df['Date'] <'8/17/2021')]
print(date_filter2.head())

Image 2

Filtrer sur une date avec plusieurs conditions

Méthode - 5 : Filtrage de DataFrame en fonction d'une chaîne spécifique

Ici, nous sélectionnons une colonne appelée 'Region' et obtenons toutes les lignes qui proviennent de la région 'East', filtrant ainsi en fonction d'une valeur de chaîne spécifique .

#Filter a DataFrame to a specific string
 
east = df[df['Region'] == 'East']
print(east.head())

Image 6

Filtre basé sur une chaîne spécifique

Méthode - 6 : Filtrage de DataFrame en fonction d'une valeur d'index spécifique dans une chaîne

Ici, nous sélectionnons une colonne appelée 'Region' et obtenons toutes les lignes qui ont la lettre 'E' comme premier caractère, c'est-à-dire à l'index 0 dans les résultats de colonne spécifiés.

#Filter a DataFrame to show rows starting with a specfic letter
 
starting_with_e = df[df['Region'].str[0]== 'E']
print(starting_with_e.head())

Image 7

Filtre basé sur une lettre spécifique

Méthode - 7 : Filtrage de DataFrame basé sur une liste de valeurs

Ici, nous filtrons les lignes dans la colonne « Région » qui contient les valeurs « Ouest » ainsi que « Est » et affichons le résultat combiné. Deux méthodes peuvent être utilisées pour effectuer ce filtrage à savoir l'utilisation d'un tube | opérateur avec l'ensemble de valeurs souhaité correspondant avec la syntaxe ci-dessous OU nous pouvons utiliser la fonction .isin() pour filtrer les valeurs dans une colonne donnée, qui dans notre cas est la 'Région', et fournir la liste de l'ensemble souhaité de valeurs à l'intérieur sous forme de liste.

#Filter a DataFrame rows based on list of values
 
#Method 1:
east_west = df[(df['Region'] == 'West') | (df['Region'] == 'East')]
print(east_west)
 
#Method 2:
east_west_1 = df[df['Region'].isin(['West', 'East'])]
print(east_west_1.head())

Image 9

Sortie de la méthode -2

Méthode - 8: Filtrage des lignes DataFrame en fonction de valeurs spécifiques à l'aide de RegEx

Ici, nous voulons toutes les valeurs de la colonne 'Region' , qui se termine par 'th' dans leur valeur de chaîne et les afficher. En d'autres termes, nous voulons que nos résultats montrent les valeurs de « Nord » et « Sud » et ignorent « Est » et « Ouest » . La méthode .str.contains() avec les valeurs spécifiées avec le modèle $ RegEx peut être utilisée pour obtenir les résultats souhaités.

Pour plus d'informations, veuillez consulter la documentation Regex

#Filtering the DataFrame rows using regular expressions(REGEX)
 
regex_df = df[df['Region'].str.contains('th$')]
print(regex_df.head())

Image 10

Filtre basé sur REGEX

Méthode - 9: Filtrage de DataFrame pour vérifier null

Ici, nous allons vérifier les valeurs nulles et non nulles dans toutes les colonnes à l'aide de la fonction isnull() .

#Filtering to check for null and not null values in all columns
 
df_null = df[df.isnull().any(axis=1)]
print(df_null.head())

Image 12

Filtre basé sur les valeurs NULL ou NOT null

Méthode - 10 : Filtrage de DataFrame pour vérifier les valeurs nulles dans une colonne spécifique.

#Filtering to check for null values if any in the 'Units' column
 
units_df = df[df['Units'].isnull()]
print(units_df.head())

Image 13

Recherche de valeurs nulles sur des colonnes spécifiques

Méthode - 11 : Filtrage de DataFrame pour vérifier les valeurs non nulles dans des colonnes spécifiques

#Filtering to check for not null values in the 'Units' column
 
df_not_null = df[df['Units'].notnull()]
print(df_not_null.head())

Image 14

Recherche de valeurs non nulles sur des colonnes spécifiques

Méthode - 12: Filtrage de DataFrame à l'aide query()d'une condition

#Using query function in pandas
 
df_query = df.query('Total_Sales > 300')
print(df_query.head())

Image 17

Filtrer les valeurs avec Queryla fonction

Méthode - 13: Filtrage de DataFrame à l'aide query()de plusieurs conditions

#Using query function with multiple conditions in pandas
 
df_query_1 = df.query('Total_Sales > 300 and Units <18')
print(df_query_1.head())

Image 18

Filtrer plusieurs colonnes avec QueryFunction

Méthode – 14 : Filtrage de notre DataFrame à l'aide des fonctions locet iloc.

#Creating a sample DataFrame for illustrations
 
import numpy as np
data = pd.DataFrame({"col1" : np.arange(1, 20 ,2)}, index=[19, 18 ,8, 6, 0, 1, 2, 3, 4, 5])
print(data)

Image 19

sample_data

Explication : iloc considère les lignes en fonction de la position de l'index donné, de sorte qu'il ne prend que des entiers comme valeurs.

Pour plus d'informations, veuillez consulter la documentation de Pandas

#Filter with iloc
 
data.iloc[0 : 5]

Image 20

Filtrer en utilisantiloc

Explication : loc considère les lignes en fonction des étiquettes d'index

#Filter with loc
 
data.loc[0 : 5]

Image 21

Filtrer en utilisantloc

Vous vous demandez peut-être pourquoi la locfonction renvoie 6 lignes au lieu de 5 lignes. En effet , ne produit pas de sortie basée sur la position de l'index. Il ne prend en compte que les étiquettes d'index qui peuvent également être un alphabet et incluent à la fois le point de départ et le point final. loc 

Conclusion

Donc, ce sont quelques-unes des méthodes de filtrage les plus couramment utilisées dans les pandas. Il existe de nombreuses autres méthodes de filtrage qui pourraient être utilisées, mais celles-ci sont parmi les plus courantes.

Lien : https://www.askpython.com/python-modules/pandas/filter-pandas-dataframe

#pandas #python #datafame

田辺  亮介

田辺 亮介

1660032308

輕鬆過濾 Pandas 數據框的 14 種方法

每當我們處理任何類型的數據時,我們都需要清楚地了解我們正在處理的數據類型。對於那裡的大多數數據,其中可能包含數千甚至數百萬個包含各種信息的條目,如果沒有任何工具以簡短易讀的格式呈現數據,就真的不可能理解這些數據。

大多數時候,我們需要瀏覽數據、操作數據並將其可視化以獲得洞察力。嗯,有一個很棒的庫,它的名字叫 pandas,它為我們提供了這種能力。最常見的數據操作操作是數據過濾。它與 SQL 中的 WHERE 子句非常相似,或者您必須在 MS Excel 中使用過濾器來根據某些條件選擇特定行。

pandas是一個強大、靈活和開源的數據分析/操作工具,它本質上是一個python 包,提供速度、靈活性和富有表現力的數據結構,以直觀和簡單的方式處理關係”或“標記它是在 Python 中執行實際數據分析的最流行的庫

pandas建立在 NumPy 庫之上,旨在與科學計算環境和眾多其他第三方庫很好地集成。它有兩個主要數據結構,即Series (1D)Dataframes(2D),在大多數實際用例中,這是金融、科學計算、工程和統計等許多領域正在處理的數據類型。

讓我們開始在 Pandas Dataframe 的幫助下過濾數據

安裝熊貓

!pip install pandas

導入 Pandas 庫,讀取我們的示例數據文件並將其分配給“df” DataFrame

import pandas as pd
df = pd.read_csv(r"C:\Users\rajam\Desktop\sample_data.csv")

讓我們看看我們的數據框

print(df.head())

樣本數據

樣本數據

現在我們有了 DataFrame,我們將應用各種方法來過濾它。

方法 - 1:按列值過濾 DataFrame

我們的 DataFrame 中有一個名為“Total_Sales”的列,我們想要過濾掉所有大於 300 的銷售額。

#Filter a DataFrame for a single column value with a given condition
 
greater_than = df[df['Total_Sales'] > 300]
print(greater_than.head())

銷售額 > 300 的 Sample_data

銷售額超過 300

Method – 2 : Filtering DataFrame based on multiple conditions

在這裡,我們過濾“Total_Sales”值大於 300 以及“Units”大於 20 的所有值。我們將不得不使用執行按位與操作的 python 運算符“&”以顯示相應的結果。

#Filter a DataFrame with multiple conditions
 
filter_sales_units = df[(df['Total_Sales'] > 300) & (df["Units"] > 20)]
print(Filter_sales_units.head())

圖 3

篩選銷售額和單位

方法 – 3:根據日期值過濾 DataFrame

如果我們想根據某個日期值過濾我們的數據框,例如這裡我們試圖獲取基於特定日期的所有結果,在我們的例子中是日期 '03/10/21' 之後的結果。

#Filter a DataFrame based on specific date
 
date_filter = df[df['Date'] > '03/10/21']
print(date_filter.head())

圖 1

按日期過濾

方法四:基於Date值多條件過濾DataFrame

在這裡,我們得到了評估多個日期的 Date 操作的所有結果。

#Filter a DataFrame with multiple conditions our Date value
 
date_filter2 = df[(df['Date'] >= '3/25/2021') & (df['Date'] <'8/17/2021')]
print(date_filter2.head())

圖 2

篩選具有多個條件的日期

方法五:根據特定字符串過濾DataFrame

在這裡,我們選擇一個名為“Region”的列並獲取來自“East”區域的所有行,從而根據特定的字符串值進行過濾。

#Filter a DataFrame to a specific string
 
east = df[df['Region'] == 'East']
print(east.head())

圖 6

根據特定字符串過濾

方法6:根據字符串中的特定索引值過濾 DataFrame

在這裡,我們選擇一個名為“Region”的列,並獲取所有以字母“E”作為第一個字符的行,即指定列結果中索引 0 處的所有行。

#Filter a DataFrame to show rows starting with a specfic letter
 
starting_with_e = df[df['Region'].str[0]== 'E']
print(starting_with_e.head())

圖 7

根據特定字母過濾

方法7:根據值列表過濾 DataFrame

在這裡,我們過濾包含值“West”和“East”的“Region”列中的行,並顯示組合結果。可以使用兩種方法來執行此過濾,即使用管道 | 具有相應所需值集的運算符具有以下語法,或者我們可以使用.isin()函數過濾給定列中的值,在我們的例子中是“區域”,並提供所需集的列表它裡面的值作為一個列表。

#Filter a DataFrame rows based on list of values
 
#Method 1:
east_west = df[(df['Region'] == 'West') | (df['Region'] == 'East')]
print(east_west)
 
#Method 2:
east_west_1 = df[df['Region'].isin(['West', 'East'])]
print(east_west_1.head())

圖 9

方法-2的輸出

方法 – 8:使用 RegEx 根據特定值過濾 DataFrame 行

在這裡,我們想要列 'Region' 中的所有值,並在其字符串值中以 'th'結尾並顯示它們。換句話說,我們希望我們的結果顯示 'Nor th ' 和 'Sout th ' 的值並忽略 'East' 和 'West'。具有指定值的方法.str.contains()以及$ RegEx 模式可用於獲得所需的結果。

有關更多信息,請查看正則表達式文檔

#Filtering the DataFrame rows using regular expressions(REGEX)
 
regex_df = df[df['Region'].str.contains('th$')]
print(regex_df.head())

圖 10

基於 REGEX 的過濾器

方法9:過濾 DataFrame 以檢查null

在這裡,我們將在isnull() 函數的幫助下檢查所有列中的空值和非空值。

#Filtering to check for null and not null values in all columns
 
df_null = df[df.isnull().any(axis=1)]
print(df_null.head())

圖 12

基於 NULL 或 NOT 空值過濾

方法 - 10:過濾 DataFrame 以檢查特定列中的空值。

#Filtering to check for null values if any in the 'Units' column
 
units_df = df[df['Units'].isnull()]
print(units_df.head())

圖 13

在特定列上查找空值

方法 – 11:過濾 DataFrame 以檢查特定列中的非 空值

#Filtering to check for not null values in the 'Units' column
 
df_not_null = df[df['Units'].notnull()]
print(df_not_null.head())

圖 14

在特定列上查找非空值

Method – 12: Filtering DataFrame using query()with a condition

#Using query function in pandas
 
df_query = df.query('Total_Sales > 300')
print(df_query.head())

圖 17

Query使用函數過濾值

Method – 13: Filtering DataFrame using query()with multiple conditions

#Using query function with multiple conditions in pandas
 
df_query_1 = df.query('Total_Sales > 300 and Units <18')
print(df_query_1.head())

圖 18

Query使用函數過濾多列

方法 –loc 14:使用和iloc函數過濾我們的 DataFrame 。

#Creating a sample DataFrame for illustrations
 
import numpy as np
data = pd.DataFrame({"col1" : np.arange(1, 20 ,2)}, index=[19, 18 ,8, 6, 0, 1, 2, 3, 4, 5])
print(data)

圖 19

樣本數據

解釋iloc 根據給定索引的位置考慮行,因此它僅將整數作為值。

有關更多信息,請查看Pandas 文檔

#Filter with iloc
 
data.iloc[0 : 5]

圖 20

過濾使用iloc

說明loc 考慮基於索引標籤的行

#Filter with loc
 
data.loc[0 : 5]

圖 21

過濾使用loc

您可能正在思考為什麼loc函數返回 6 行而不是 5 行。這是因為不會根據索引位置產生輸出。它只考慮索引標籤,它也可以是字母表,包括起點和終點。 loc 

結論

因此,這些是 pandas 中最常用的一些過濾方法。還有許多其他過濾方法可以使用,但這些是最常見的一些。

鏈接:https ://www.askpython.com/python-modules/pandas/filter-pandas-dataframe

#pandas #python #datafame