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Initializing Lookup<int, string>

By : sarahbusby
Date : October 18 2020, 11:12 AM
help you fix your problem Per MSDN documentation, there is no public constructor for the Lookup class: http://msdn.microsoft.com/en-us/library/bb460184.aspx
You can create an instance of a Lookup by calling ToLookup on an object that implements IEnumerable.
code :

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initializing char arrays in a way similar to initializing string literals

By : Janu Jan
Date : March 29 2020, 07:55 AM
I hope this helps . I might have found a way to do what i want though it isn't directly what I wanted, but it likely has the same effect.
First consider two following classes:
code :
template <size_t size>
class Cont{
  char charArray[size];
template <size_t size>
class ArrayToUse{
  Cont<size> container;
  inline ArrayToUse(const Cont<size+1> & input):container(reinterpret_cast<const Cont<size> &>(input)){}
const Cont<12> container={"hello world"};
ArrayToUse<11> temp(container);
char (&charArray)[11]=temp.container.charArray;

Initializing a lookup table in c++

By : Brasschaat
Date : March 29 2020, 07:55 AM
Does that help Since I cant seem to make the constructor for my conversion class static, I tried faking it but its giving me an error about unresolved externals: , Here is a simple solution that does the job.
code :
float ByteToFloat(int val)
    static const struct FloatConversions
        std::array<float, 256> ByteLUT;

            for (int i = 0; i < 256; i++)
                ByteLUT[i] = i / 255.0f;
    } conveter;

    return conveter.ByteLUT[val];

lookup string values in lookup table to populate second dataframe

By : Daniel Freiman
Date : March 29 2020, 07:55 AM
seems to work fine You can use map by Series:
code :
main_df['header_2'] = main_df['header_1'].map(lookup_df.set_index('header_1')['header_2'])
print (main_df)
  header_1        header_2
0  value_1  lookup_value_1
1  value_2  lookup_value_2
2  value_3  lookup_value_3
3  value_1  lookup_value_1
main_df['header_2'] = main_df['header_1'].map(lookup_df.set_index('header_1')['header_2']
print (main_df)
  header_1        header_2
0  value_1  lookup_value_1
1  value_2  lookup_value_2
2  value_3  lookup_value_3
3  value_1  lookup_value_1
#[400000 rows x 1 columns]
main_df = pd.concat([main_df]*100000).reset_index(drop=True)

In [139]: %timeit pd.merge(main_df, lookup_df, how='left', on=['header_1'])
10 loops, best of 3: 73.1 ms per loop

In [140]: %timeit main_df['header_1'].map(lookup_df.set_index('header_1')['header_2'])
10 loops, best of 3: 35.7 ms per loop

In [141]: %timeit main_df['header_1'].map(lookup_df.set_index('header_1')['header_2'].to_dict())
10 loops, best of 3: 35.1 ms per loop
print (lookup_df)
  header_1        header_2
0  value_1  lookup_value_1
1  value_2  lookup_value_2
2  value_3  lookup_value_3
3  value_1  lookup_value_4

#keep first value, default parameter keep='first'
lookup_df = lookup_df.drop_duplicates(['header_1'])
print (lookup_df)
  header_1        header_2
0  value_1  lookup_value_1
1  value_2  lookup_value_2
2  value_3  lookup_value_3

#keep last value
lookup_df1 = lookup_df.drop_duplicates(['header_1'], keep='last')
print (lookup_df1)
  header_1        header_2
0  value_1  lookup_value_1
1  value_2  lookup_value_2
2  value_3  lookup_value_3

Which lookup method is most efficient for simple integer to string lookup

By : user6922331
Date : March 29 2020, 07:55 AM
it should still fix some issue It's hard to beat a table lookup such as your lookup1() for clarity, concision, and speed. That's not to say, however, that other approaches might not compete at least on speed. For relative performance questions, you really need to benchmark.
If the maximum index number is large or if any of the index numbers are less than zero, or if you cannot rely on at least C99, then a direct array-based table lookup is problematic, but otherwise, gaps between the indexes are not a particular problem, including between the start of the array and the lowest index used. Consider this:
code :
#define INITIALIZER(x) [x] = #x,

const char *lookup4(int x) {
    static const char * const table[] = {
        // initializers have the form:
        // [MACRO] = "MACRO",

    const char *result = ((x < 0 | x >= (sizeof(table) / sizeof(table[0]))) 
        ? NULL 
        : table[x];

    return result ? result : "unknown";

lookup and replace part of a string for all cells in a column using text in cells for a separate table as the lookup cri

By : user2845984
Date : March 29 2020, 07:55 AM
Does that help It is slippery slope with addresses, but this can help I believe:
Initial data:
code :
DECLARE @tgt_address TABLE (ID INT IDENTITY(1,1),Name NVARCHAR(255),Address NVARCHAR(255));
INSERT INTO @tgt_address(Name,Address)VALUES
      ('John','123 State St Chester PA 25374')
     ,('Steve','798 Main Avn New York NY 21314')
     ,('Martha','981 West Ln Wilmington De 23142')
     ,('Mary','124 Main Street #2 Austin Tx 21432')
     ,('Timothy','25 Lark Thwy Maiami FL 12342')
DECLARE @Street_abbreviation TABLE (Commonly_used NVARCHAR(255),Abbreviation NVARCHAR(255));
INSERT INTO @Street_abbreviation(Commonly_used,Abbreviation)VALUES
SELECT t.Name,t.Address
    ,REPLACE(REPLACE(t.Address,'#','# '),' ' + a.Commonly_used + ' ',' ' + a.Abbreviation + ' ') AS [Result]
FROM @tgt_address t
INNER JOIN @Street_abbreviation a ON REPLACE(t.Address,'#','# ') LIKE '%[ ]' + a.Commonly_used + '[ ]%'
SELECT r.Name,r.Address,r.Result
    SELECT t.Name,t.Address
        ,REPLACE(REPLACE(t.Address,'#','# '),' ' + a.Commonly_used + ' ',' ' + a.Abbreviation + ' ') AS [Result]
        ,ROW_NUMBER()OVER(PARTITION BY t.Name,t.Address 
                        ORDER BY CASE WHEN REPLACE(t.Address,'#','# ') LIKE '%[ ]' + a.Commonly_used + '[ ]%' THEN 0 ELSE 1 END) AS [rn]
    FROM @tgt_address t
    CROSS JOIN @Street_abbreviation a
) r
WHERE r.rn = 1
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