C#Top 37 C# Example Pages...

[".rwsl.t.t..yrys.fj.ysdryfy.ysss.r. Z0#CCST~~}T~~YF788mCC/BP89894F.BP89469466F3P8945F.B#BcBCCPG588G74G74F.B#CP588964FXCP7846565F55/B#CP95565FsCECEP557FXBCCP5576F,CCP766FbBCEXB(CCP55759563BXCEcCECE0BB0CC.CP5756FcBdB#B(B)B#B(BZBjBZCCK(XS}T~~}T~~","Dictionary."," In a city many buildings are found. To find a structure we can search the entire city, street by street. But this becomes slow.","With a dictionary,"," we can map a building to a location. We can map \"park\" to where it is found. Then to find the park, we can skip searching the whole city.","An example."," We add 4 keys with values to a Dictionary. Then we look inside it with the Visual Studio debugger. The Dictionary has pairs of keys and values. ","String, int: ","Dictionary is used with different elements. We specify its key type and its value type (string, int).","Output: ","The example program has no output. It does nothing useful. At least it does not crash.","ins","class","adsbygoogle","data-ad-client","ca-pub-4712093147740724","data-ad-slot","6227126509","data-ad-format","auto","ins","class","adsbygoogle","data-ad-client","ca-pub-4712093147740724","data-ad-slot","6227126509","data-ad-format","auto","Based on:"," .NET 4.7 (2017)\n\n","C# program that uses Add method","\n\nusing System;\nusing System.Collections.Generic;\n\nclass Program\n{\n static void Main()\n {\n ","Dictionary","<string, int> dictionary =\n new Dictionary<string, int>();\n\n dictionary.Add(","\"cat\"",", 2);\n dictionary.Add(","\"dog\"",", 1);\n dictionary.Add(","\"llama\"",", 0);\n dictionary.Add(","\"iguana\"",", -1);\n }\n}","Debugger."," Let us peek inside the Visual Studio debugger to examine the memory. The Dictionary instance is represented by a collection of key and value pairs. ","Note: ","It is fun (and perhaps helpful) to open and close the data structure elements. Learning often involves experimentation.","Tip: ","The pairs like (dog, 1) are the string representations of KeyValuePair instances. KeyValuePair is a struct.","ContainsKey."," This sees if a given string is present in a Dictionary. We use string keys here\u2014we look at more types of Dictionaries further on. ContainsKey returns true if the key is found. ","ContainsKey ","containskey","C# program that uses ContainsKey","\n\nusing System;\nusing System.Collections.Generic;\n\nclass Program\n{\n static void Main()\n {\n Dictionary<string, int> dictionary =\n new Dictionary<string, int>();\n\n dictionary.Add(","\"apple\"",", 1);\n dictionary.Add(","\"windows\"",", 5);","\n\n // See whether Dictionary contains this string.\n ","if (dictionary.","ContainsKey","(","\"apple\"","))\n {\n int value = dictionary[","\"apple\"","];\n Console.WriteLine(value);\n }","\n\n // See whether it contains this string.\n ","if (!dictionary.","ContainsKey","(","\"acorn\"","))\n {\n Console.WriteLine(false);\n }\n }\n}\n\n","Output","\n\n1\nFalse","TryGetValue."," This is often the most efficient look up method. As the name TryGetValue implies, it tests for the key. It then returns the value if it finds the key. ","TryGetValue ","trygetvalue","C# program that uses TryGetValue","\n\nusing System;\nusing System.Collections.Generic;\n\nclass Program\n{\n static void Main()\n {\n Dictionary<string, string> values =\n new Dictionary<string, string>();\n\n values.Add(","\"cat\"",", \"feline\");\n values.Add(","\"dog\"",", \"canine\");","\n // Use TryGetValue.\n ","string test;\n if (values.","TryGetValue","(","\"cat\"",", out test))"," // Returns true.\n ","{\n Console.WriteLine(test);"," // This is the value at cat.\n ","}\n if (values.","TryGetValue","(","\"bird\"",", out test))"," // Returns false.\n ","{\n Console.WriteLine(false);"," // Not reached.\n ","}\n }\n}\n\n","Output","\n\nfeline","TryGetValue, syntax 2."," We can declare the \"out\" variable directly in the method call. This can make the code easier to read\u2014the statements are combined, but have the same effects. ","C# program that uses inline out, TryGetValue","\n\nusing System.Collections.Generic;\n\nclass Program\n{\n static void Main()\n {\n var values = new Dictionary<string, string>();\n values.Add(","\"A\"",", \"uppercase letter A\");\n values.Add(","\"c\"",", \"lowercase letter C\");","\n\n // Use inline \"out string\" with TryGetValue.\n ","if (values.","TryGetValue","(","\"c\"",", ","out string"," description))\n {\n System.Console.WriteLine(description);\n }\n }\n}\n\n","Output","\n\nlowercase letter C","KeyValuePair."," When a collection that implements IDictionary (like Dictionary) is used in a foreach-loop, it returns an enumeration. A Dictionary will return KeyValuePair structs in a loop. ","KeyValuePair ","keyvaluepair","KeyNotFoundException."," This error happens when we access a nonexistent key. With Dictionary we must test keys for existence first, with ContainsKey or TryGetValue. ","KeyNotFoundException ","keynotfoundexception","Loop."," Here we loop over KeyValuePairs in a Dictionary. With collections like Dictionary, we must always know the value types. With each KeyValuePair, there is a Key member and Value member. ","Foreach ","foreach","String, int: ","The code creates a Dictionary with string keys and int values. The Dictionary stores animal counts.","Tip: ","In the foreach-loop, each KeyValuePair has two members, a string Key and an int Value.","C# program that uses foreach on Dictionary","\n\nusing System;\nusing System.Collections.Generic;\n\nclass Program\n{\n static void Main()\n {","\n // Example Dictionary again.\n ","Dictionary<string, int>"," d = new Dictionary<string, int>()\n {\n {","\"cat\"",", 2},\n {","\"dog\"",", 1},\n {","\"llama\"",", 0},\n {","\"iguana\"",", -1}\n };","\n // Loop over pairs with foreach.\n ","foreach"," (KeyValuePair<string, int> pair in d)\n {\n Console.WriteLine(","\"{0}, {1}\"",", pair.Key, pair.Value);\n }","\n // Use var keyword to enumerate dictionary.\n ","foreach"," (var pair in d)\n {\n Console.WriteLine(","\"{0}, {1}\"",", pair.Key, pair.Value);\n }\n }\n}\n\n","Output","\n\ncat, 2\ndog, 1\nllama, 0\niguana, -1\n\ncat, 2\ndog, 1\nllama, 0\niguana, -1","Var keyword."," The final loop in the above code uses var. This reduces the amount of typing required. And it may make code easier to read for humans (like us). ","Var: Dictionary ","var","Keys."," Here we use the Keys property. We then look through each key and look up the values. This method is slower but has the same results. ","KeyCollection: ","The Keys property returns a collection of type KeyCollection, not an actual List. We can convert it into a List.","C# program that gets Keys","\n\nusing System;\nusing System.Collections.Generic;\n\nclass Program\n{\n static void Main()\n {\n ","Dictionary<string, int>"," d = new Dictionary<string, int>()\n {\n {","\"cat\"",", 2},\n {","\"dog\"",", 1},\n {","\"llama\"",", 0},\n {","\"iguana\"",", -1}\n };","\n // Store keys in a List.\n ","List<string> list = new List<string>(d.","Keys",");","\n // Loop through list.\n ","foreach (string k in list)\n {\n Console.WriteLine(","\"{0}, {1}\"",", k, d[k]);\n }\n }\n}\n\n","Output","\n\ncat, 2\ndog, 1\nllama, 0\niguana, -1","Foreach, benchmark."," Here we compare loops. A foreach-loop on KeyValuePairs is faster than the looping over Keys and accessing values in the loop body. ","Tip: ","When possible, loop over the pairs in a Dictionary directly. Eliminating lookups will improve performance.","C# that benchmarks for each on Dictionary","\n\nusing System;\nusing System.Collections.Generic;\nusing System.Diagnostics;\n\nclass Program\n{\n static void Main()\n {\n var test = new ","Dictionary","<string, int>();\n test[","\"bird\"","] = 10;\n test[","\"frog\"","] = 20;\n test[","\"cat\"","] = 60;\n int sum = 0;\n const int _max = 1000000;","\n\n // Version 1: use foreach loop directly on Dictionary.\n ","var s1 = Stopwatch.StartNew();\n for (int i = 0; i < _max; i++)\n {\n ","foreach"," (var pair in test)\n {\n sum += pair.Value;\n }\n }\n s1.Stop();","\n\n // Version 2: use foreach loop on Keys, then access values.\n ","var s2 = Stopwatch.StartNew();\n for (int i = 0; i < _max; i++)\n {\n ","foreach"," (var key in test.Keys)\n {\n sum += test[key];\n }\n }\n s2.Stop();\n Console.WriteLine(s1.Elapsed.TotalMilliseconds);\n Console.WriteLine(s2.Elapsed.TotalMilliseconds);\n }\n}\n\n","Results","\n\n","28.117 ms"," Dictionary foreach (measured January 2017)\n","83.3468 ms"," Keys foreach (measured January 2017)","Sort."," A Dictionary cannot be directly sorted. But we can take its Keys and then sort those in a separate List collection. A query expression may also be used. ","Sort Dictionary ","sort-dictionary","Types."," Dictionary is a generic class. To use it, we must specify a type. This is a good feature. It means we can use an int key just as easily as a string key. ","Int: ","In this program, we see an example of a Dictionary with int keys. The values can also be any type.","C# that uses int keys","\n\nusing System;\nusing System.Collections.Generic;\n\nclass Program\n{\n static void Main()\n {","\n // Use a Dictionary with an int key.\n ","Dictionary<int, string> dict = new ","Dictionary","<int, string>();\n dict.Add(","100",", \"Bill\");\n dict.Add(","200",", \"Steve\");","\n // We can look up the int in the Dictionary.\n ","if (dict.ContainsKey(","200","))\n {\n Console.WriteLine(true);\n }\n }\n}\n\n","Output","\n\nTrue","LINQ."," Extension methods can be used with Dictionary. We use the ToDictionary method. This is an extension method on IEnumerable. It places keys and values into a new Dictionary. ","Lambda: ","The program uses lambda expressions. With these small functions, we specify a method directly as an argument.","Lambdas ","lambda","Here: ","The example uses ToDictionary, from System.Linq, on a string array. It creates a lookup table for the strings.","ToDictionary ","todictionary","C# that uses LINQ","\n\nusing System;\nusing System.Collections.Generic;\nusing System.Linq;\n\nclass Program\n{\n static void Main()\n {\n string[] arr = new string[]\n {\n ","\"One\"",",\n ","\"Two\"","\n };\n var dict = arr.","ToDictionary","(item => item, item => true);\n foreach (var pair in dict)\n {\n Console.WriteLine(\"{0}, {1}\",\n pair.Key,\n pair.Value);\n }\n }\n}\n\n","Output","\n\nOne, True\nTwo, True","ContainsValue."," This method lacks the constant-time look up speed of ContainsKey. It instead searches the entire collection. It is linear in complexity. ","ContainsValue ","containsvalue","Note: ","This example will loop through all elements in the Dictionary until it finds a match, or there are no more elements to check.","Speed: ","MSDN states that \"this method is an O(N) operation, where N is Count.\" It does not run in constant time.","C# that uses ContainsValue","\n\nusing System;\nusing System.Collections.Generic;\n\nclass Program\n{\n static void Main()\n {\n Dictionary<string, int> d = new Dictionary<string, int>();\n d.Add(\"cat\", ","1",");\n d.Add(\"dog\", ","2",");\n if (d.","ContainsValue","(1))\n {\n Console.WriteLine(true);"," // True.\n ","}\n }\n}\n\n","Output","\n\nTrue","Indexer."," We do not need to use Add to insert into a Dictionary. We can instead use the indexer, with the \"[\" and \"]\" brackets. This syntax also gets the value at the key. ","Caution: ","If we try to get a value at a key that doesn't exist, an exception is thrown.","Note: ","With the indexer, an exception is not thrown when we assign to a key that already has a value. But with Add, an exception is thrown.","C# that uses Dictionary indexer","\n\nusing System;\nusing System.Collections.Generic;\n\nclass Program\n{\n static void Main()\n {\n ","Dictionary","<int, int> dictionary = new Dictionary<int, int>();","\n\n // We can assign with the indexer.\n ","dictionary[1] = 2;\n dictionary[2] = 1;\n dictionary[1] = 3;"," // Reassign.\n\n // Read with the indexer.\n // ... An exception occurs if no element exists.\n ","Console.WriteLine(dictionary[1]);\n Console.WriteLine(dictionary[2]);\n }\n}\n\n","Output","\n\n3\n1","Clear."," We can erase all pairs with the Clear method. Or we can assign the Dictionary variable to null. This causes little difference in memory usage\u2014the entries are garbage-collected. ","Clear ","clear","Internally: ","We find that Clear calls Array.Clear, which is not managed code. Dictionary is implemented with arrays.","Array.Clear ","array-clear","Count."," This computes the total number of keys in a Dictionary. This is simpler than accessing the Keys property, or looping over the Dictionary to count it. ","Count ","count-dictionary","Remove."," We can eliminate an entry, not just by setting its value to null or string.Empty, but by also removing the key itself. With Remove, no remnants of the key-value pair are kept. ","Note: ","Running the code in Visual Studio, no exceptions are thrown. When we remove a key that doesn't exist, nothing happens.","However: ","Remove() throws System.ArgumentNullException with a null parameter. We cannot remove null.","C# that uses Remove","\n\nusing System;\nusing System.Collections.Generic;\n\nclass Program\n{\n static void Main()\n {\n Dictionary<string, int> d = new Dictionary<string, int>();\n d.Add(","\"cat\"",", 1);\n d.Add(","\"dog\"",", 2);\n\n d.","Remove","(","\"cat\"",");"," // Removes cat.\n ","d.","Remove","(","\"nothing\"",");"," // Doesn't remove anything.\n ","}\n}","Copy."," Dictionary provides a constructor that copies all values and keys into a new Dictionary instance. This constructor improves code reuse. It makes copying simpler. ","Copy Dictionary ","copy-dictionary","Return."," A Dictionary can be returned, or passed as an argument. The Dictionary type is defined as a class. It is always passed as a reference type. ","And: ","This means only 32-64 bits will be copied on the method invocation. The same principle applies when returning values.","Return ","return","List versus Dictionary."," I suggest almost always using Dictionary for lookups. In large collections, a List will become unusable for lookups. ","But: ","A Dictionary will still work well with large amounts of data. With Dictionary a program recovers from pathological, edge cases.","List ","list","Looping: ","It is faster to loop through elements in a List. If looping through elements is the most common operation, a List is superior.","Dictionary vs. List Loops ","dictionary-list-loop","Compare types."," Sometimes, an array or List can be used instead of a Dictionary. This influences performance. Any analysis depends on the program. ","Array vs. Dictionary ","array-dictionary-test","List vs. Dictionary ","dictionary-time","Composite keys."," We can sometimes use multiple variables in a key. We can create a special function that transforms those variables into a string, serializing them. ","Note: ","We can use the string \"1,2\" to mean the ints 1 and 2. This approach is not optimally fast.","Note 2: ","This is similar to how composite names in programming languages use a period: \"Type.Member\".","Field."," Sometimes it is useful to have a Dictionary at the class level, as a field. And if we have a static class, we should initialize the Dictionary at the class level. ","Note: ","Avoid the static constructor\u2014static constructors often carry performance penalties.","C# that uses Dictionary field","\n\nusing System;\nusing System.Collections.Generic;\n\nclass Program\n{\n static void Main()\n {\n Example e = new Example();\n Console.WriteLine(e.","GetValue","());\n }\n}\n\nclass Example\n{\n ","Dictionary","<int, int> _d = new Dictionary<int, int>()\n {\n {1, 1},\n {2, 3},\n {3, 5},\n {6, 10}\n };\n public int ","GetValue","()\n {\n return _d[2];"," // Example only.\n ","}\n}\n\n","Output","\n\n3","GetEnumerator."," With this method we can loop over a Dictionary with foreach. We call GetEnumerator() and then use a while-loop on the MoveNext method. ","GetEnumerator ","dictionary-getenumerator","IEqualityComparer."," Dictionary uses an IEqualityComparer to compute the hash code for its keys. We can implement this interface with a class. This can improve performance. ","IEqualityComparer ","iequalitycomparer","Optimization."," The Dictionary is well-optimized by the .NET Framework developers. They are smart people. But there are still techniques that influence performance. ","Optimization: Dictionary ","optimization","Binary format."," In serialization we write a Dictionary to the disk. Usually strings are less efficient than a binary format. But with binary we cannot easily read the file. ","Dictionary Binary File ","dictionary-binary","Equals."," Are two dictionaries equal? Do they have the same keys and values? With a special method we can check for equivalent data. ","Dictionary Equals ","dictionary-equals","Stop words."," With a Dictionary we can remove certain words from a string. Small words like \"and\" are sometimes called \"stop words.\" We can use a Dictionary to help eliminate these. ","Stopword Dictionary ","stopword-dictionary","Combine keys."," In this example we combine the keys of two dictionaries into another collection. A HashSet is used to union the two sets of keys. ","Combine Dictionary Keys ","combine-keys","Map."," The Dictionary is a map. A real map helps us find our destination, but a digital map directs us from a key to a value. In languages we often find a map type. ","Map ","map","Case-insensitive keys."," With a special StringComparer we can have case-insensitive key lookups on a dictionary. This reduces the need to call ToLower. It reduces string creation. ","Case, Dictionary ","case-insensitive-dictionary","Research."," A Dictionary is powerful, but it is not magic. It just uses a good algorithm. This requires a hash code. We take keys and use arithmetic to transform them into numbers. ","Locations: ","We then store them in locations based on those numbers in the Dictionary. We select a \"bucket\" based on the hash.","Finally: ","When we go to look up a key, we compute a new hash code and look for it in the Dictionary. Less searching is needed.","We try to reference items in a table directly by doing arithmetic operations to transform keys into table addresses.","Algorithms in C++ Third Edition","It is good"," that fast lookups are important. We just spent lots of time explaining how to do them. We looped over a Dictionary with KeyValuePair.","A review."," We checked key existence with ContainsKey and TryGetValue. Dictionary, like all hash tables, is fascinating. It is fast. It is useful in many places. ","ins","class","adsbygoogle","data-ad-client","ca-pub-4712093147740724","data-ad-slot","3679700504","data-ad-format","link","ins","class","adsbygoogle","data-ad-client","ca-pub-4712093147740724","data-ad-slot","6227126509","data-ad-format","auto","url(data:image/jpeg;base64,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)","url(data:image/jpeg;base64,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)","url(data:image/png;base64,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)"]

["url(data:image/png;base64,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)","url(data:image/png;base64,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)","url(data:image/png;base64,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)","url(data:image/png;base64,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)","url(data:image/png;base64,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)","url(data:image/png;base64,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)","url(data:image/jpeg;base64,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)","url(data:image/png;base64,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)","C","url(data:image/png;base64,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)","url(data:image/png;base64,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)","url(data:image/png;base64,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)","url(data:image/jpeg;base64,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)","url(data:image/png;base64,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)","url(data:image/png;base64,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)","url(data:image/jpeg;base64,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)","Q","url(data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAG4AAACCAgMAAADasxWRAAAADFBMVEX/6Nn///+VlZX29va5ksB5AAAASElEQVR4AWPACZj/f0DmjkqOSjKtQgcLyJccZiEE9SOmP8mXHEaBMBoIo4EwGggryJccvvVKKAogTnJUclRyVHLklgmjkqOSAALWyrsxEHLDAAAAAElFTkSuQmCC)","url(data:image/png;base64,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)","url(data:image/png;base64,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)","url(data:image/png;base64,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)","B","url(data:image/png;base64,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)","url(data:image/png;base64,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)","J","url(data:image/png;base64,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)","OU","url(data:image/png;base64,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)","IU","url(data:image/jpeg;base64,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)","url(data:image/jpeg;base64,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)","AU","url(data:image/png;base64,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)"]