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Using VLOOKUP with merge in Python


Using VLOOKUP with merge in Python

By : user3042819
Date : November 29 2020, 12:01 PM
this will help Using pandas.Series.map:
code :
df2 = df2.drop_duplicates().set_index('username')
df1['community'] = df1['username'].map(df2['community'])
print(df1)
  username   hour  totalCount  community
0     lowi  00:00          12        NaN
1    klark  00:00           0        0.0
2    sturi  00:00           2        2.0
3     nukr  00:00          10        NaN
4    irore  00:00           2        2.0


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Merge Two different Data Frame (like VLOOKUP)

Merge Two different Data Frame (like VLOOKUP)


By : Beemo
Date : March 29 2020, 07:55 AM
I wish did fix the issue. You don't need merge here, as you can do this with two applications of match directly:
code :
df_Edges$Source <- df_Nodes$Id[match(df_Edges$Source, df_Nodes$Label)]
df_Edges$Target <- df_Nodes$Id[match(df_Edges$Target, df_Nodes$Label)]
df_Edges
##   Source Target     Type Weight
## 1     73     NA Directed      1
## 2     73    244 Directed      1
## 3     73     73 Directed      1
## 4     73    168 Directed      1
## 5     73    148 Directed      1
## 6     73    216 Directed      1
Using Merge in R to replicate Vlookup

Using Merge in R to replicate Vlookup


By : Digital Hacker
Date : March 29 2020, 07:55 AM
To fix the issue you can do This kind of helped: How to do vlookup in R , A dplyr solution.
code :
library(dplyr)
dbase <- data.frame(machine_number = c("10","20","30","10","10","50"),
                second_attribute=c("a","b","c","c","a","d"))
mbase <- data.frame(machine_number = c("10","20","30","40","50","60","70","80","90","100"),
                    rate=c(22,22,25,17,15,15,55,12,15,19))

left_join(dbase, mbase, by = "machine_number") 

  machine_number second_attribute rate
1             10                a   22
2             20                b   22
3             30                c   25
4             10                c   22
5             10                a   22
6             50                d   15
VLOOKUP to merge two tables into one

VLOOKUP to merge two tables into one


By : Achi_054
Date : March 29 2020, 07:55 AM
wish helps you So, vlookup will work fine, I would think about how to control the list of unique values using a dropdown...
But here is an example, updated to deal with missing prices:
Vlookup csv merge using batch file

Vlookup csv merge using batch file


By : user2671069
Date : March 29 2020, 07:55 AM
hop of those help? Your sample data doesn't include any spaces in col1 of Groups.txt,
so it's unclear why you do the string replacement !filename: =_!
code :
@echo off
setlocal EnableDelayedExpansion

rem Load File1 as lookup table:
for /F "tokens=1,2 delims=," %%a in (Reviewer.txt) do (
set "filename=%%~a"
set "col1_2[!filename: =_!]=%%~b"
 )

rem Process File2:
for /F "tokens=1,2* delims=," %%a in (Groups.txt) do (
    set "filename=%%a"
    for /F %%f in ("!filename: =_!") do if defined col1_2[%%f] (
        echo %%a,!col1_2[%%f]!,%%b
    ) else (
        echo NoMatchingFilename,%%b
    )
)
> SO_55736556.cmd
HFM_E_AHSM_FinRpt_Coor,Test,xABC
HFM_E_AHSM_FinRpt_Coor,Test,xDEF
HFM_E_AHSM_FinRpt_Coor,Test,xGHI
Python VLookup get output without merge

Python VLookup get output without merge


By : user3164209
Date : March 29 2020, 07:55 AM
Does that help You can do the same merge and call the MAKE column from the merge output using df.loc[]:
code :
new_df = df1.merge(df2,on='MODEL',how='left').loc[:,['MAKE']]
#pd.merge(df1, df2[['MODEL', 'MAKE']], on='MODEL', how='left').loc[:,['MAKE']]
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