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MariaDB - embed function to automatically sum columns and store result?


MariaDB - embed function to automatically sum columns and store result?

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MariaDB - embed function to automatically sum columns and store result?
Tag : development , By : Killercode
Date : November 24 2020, 03:01 PM

To fix this issue it is possible to store a function IN the table to automatically sum a group of columns and store the result in a final column? , Yes, it is possible. But is it worth it? It is simple enough to do
SELECT ...
    appleCount + pearCount + bananaCount AS totalFruit
    ...

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how can i store the result of the selected columns in my sql query in a variable in vb.net


Tag : mysql , By : dbarbot
Date : March 29 2020, 07:55 AM
I hope this helps you .
            ''declare variables    v1,v2,v3
            Try
                            cn.Open()
                            Dim query As String
                            Dim fname As String
                            Dim reader As MySqlDataReader
                            query = "Select emp_id,emp_fname,emp_lname,empmname,position,branch from dtrsystem.tblemployee where emp_id = '" & Class1.empid & "' "
                            sql = New MySqlCommand(query, cn)
                            reader = sql.ExecuteReader
                while reader.read

                v1=reader(0) 'emp id
                v2=reader(1) 'emp fname
                v3=reader(2) '  emp lname
    ''etc

                End While
                            cn.Close()
                        Catch ex As Exception

                        End Try

multiply certain dataframe columns by other columns and store the result


Tag : python , By : Alpinfish
Date : March 29 2020, 07:55 AM
To fix the issue you can do I have a data frame called stockData a very cut down version cab be seen below. , you could use a for loop to accomplish that.
#column names selection (it assume that the columns base name has a fixed length and starts by BBG)
In [54]: columns_prefix = set([col[0:14] for col in df.columns if col.startswith("BBG")])

In [55]: columns_prefix
Out[55]: {'BBG.XLON.BTA.S', 'BBG.XLON.VOD.S'}

In [56]: for pre in columns_prefix:
   ....:     df[pre+"_EUR"] =df[pre+"_FX"]*df[pre+"_LAST"]
   ....:

In [57]: df
Out[57]:
         date  BBG.XLON.BTA.S_LAST  BBG.XLON.BTA.S_FX  BBG.XLON.VOD.S_LAST  \
0  30/01/2008               257.25             1.3370                176.9
1  31/01/2008               259.00             1.3375                175.3
2  01/02/2008               264.50             1.3277                181.3
3  04/02/2008               262.00             1.3307                177.9

   BBG.XLON.VOD.S_FX  BBG.XLON.BTA.S_EUR  BBG.XLON.VOD.S_EUR
0             1.3370           343.94325           236.51530
1             1.3375           346.41250           234.46375
2             1.3277           351.17665           240.71201
3             1.3307           348.64340           236.73153

include in result set columns the 5 returned columns in a User Defined Table Function


Tag : development , By : ezzze
Date : March 29 2020, 07:55 AM
it should still fix some issue I have a UDTF that will always return 1 row of 6 columns The UDTF has one parameter , Got it working
SELECT T1.AQABVN, T1.AQA8TX, 
       (SELECT COUNT(*) FROM fffff T4 WHERE T4.BDABVN = T1.AQABVN) AS SACCMS,
        t2.status, t2.used, t2.created, t2.signon, t2.exclude 
FROM  
  zzzzz T1 
    join 
  table(SMLFQA.UF_XAJKUPR(T1.AQABVN)) t2 on T1.AQABVN = t2.p2User

mariaDB sum function Get decimal result


Tag : development , By : micate
Date : March 29 2020, 07:55 AM
seems to work fine You can cast it into a DECIMAL:
SELECT CAST(sum(cost) AS DECIMAL(32,4)) as ucost FROM uout WHERE id='aaaa'

Compare corresponding columns with each other and store the result in a new column


Tag : python , By : Pitmairen
Date : March 29 2020, 07:55 AM
this will help The problem is that the coefficient to use when building the new compx column does not depend only on the columns position. In fact in each row it is reset to its maximum of 50 after each 0 value and is half of previous one after a non 0 value. Those resetable series are hard to vectorize in pandas, especially in rows. Here I would build a companion dataframe holding only those coefficients, and use directly the numpy underlying arrays to compute them as efficiently as possible. Code could be:
# transpose the dataframe to process columns instead of rows
coeff = df.T

# compute the coefficients
for name, s in coeff.items():
    top = 100              # start at 100
    r = []
    for i, v in enumerate(s):
        if v == 0:         # reset to 100 on a 0 value
            top=100
        else:
            top = top/2    # else half the previous value
        r.append(top)
    coeff.loc[:, name] = r # set the whole column in one operation

# transpose back to have a companion dataframe for df
coeff = coeff.T

# build a new column from 2 consecutive ones, using the coeff dataframe
def build_comp(col1, col2, i):
    df['comp{}'.format(i)] = np.where(df[col1] == 0, np.nan,
                                      np.where(df[col2] == 0, 100,
                                               df[col2]/df[col1]*coeff[col1]
                                               +coeff[col1]))

old = df.columns[0]          # store name of first column

# Ok, enumerate all the columns (except first one)
for i, col in enumerate(df.columns[1:], 1):
    build_comp(old, col, i)
    old = col                # keep current column name for next iteration

# special processing for last comp column
df['comp{}'.format(i+1)] = np.where(df[col] == 0, np.nan, 100)
date     2019-04-25 15:08:23  2019-04-25 16:14:14  2019-04-25 16:29:05  2019-04-25 16:36:32
rule_id
50402                      0                    0                    9                    0
51121                      0                    1                    0                    0
51147                      0                    1                    0                    0
51183                      2                    0                    0                    0
51283                      0                   12                    9                    6
51684                      0                    1                    0                    0
52035                      0                    4                    3                    2
date     2019-04-25 15:08:23  2019-04-25 16:14:14  2019-04-25 16:29:05  2019-04-25 16:36:32  comp1  comp2       comp3  comp4
rule_id
50402                      0                    0                    9                    0    NaN    NaN  100.000000    NaN
51121                      0                    1                    0                    0    NaN  100.0         NaN    NaN
51147                      0                    1                    0                    0    NaN  100.0         NaN    NaN
51183                      2                    0                    0                    0  100.0    NaN         NaN    NaN
51283                      0                   12                    9                    6    NaN   87.5   41.666667  100.0
51684                      0                    1                    0                    0    NaN  100.0         NaN    NaN
52035                      0                    4                    3                    2    NaN   87.5   41.666667  100.0
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