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Trouble matching and setting values from multiple dataframes


Trouble matching and setting values from multiple dataframes

By : javad askari
Date : November 18 2020, 03:49 PM
I hope this helps you . I am trying to match the stop_id in stop_times.csv to the stop_id in stops.csv in order to copy over the stop_lat and stop_lon to their respective columns in stop_times.csv. , You can merge the two DataFrames:
code :
pd.merge(stops, st, on='stop_id')


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Capturing matching and non-matching values between two dataframes in R

Capturing matching and non-matching values between two dataframes in R


By : 68mustang
Date : March 29 2020, 07:55 AM
seems to work fine All the matching values are found with the intersect function, from the Set Operations. All the values in both variables are found with the union function. So the values that are in the union, but not in the intersect are non-matching.
code :
var1 <- LETTERS[1:5]
var2 <- LETTERS[4:8]
matched <- intersect(var1, var2)
all <-  union(var1, var2)
non.matched <- all[!all %in% matched]
Matching dataframes by id fails for some values

Matching dataframes by id fails for some values


By : Tianyi Zhang IRRI
Date : March 29 2020, 07:55 AM
will help you Much obliged @Sarina and @Jaap, your comments were correct and tolowersolved the issue for me.
Pandas merge 2 dataframes based on multiple matching column values

Pandas merge 2 dataframes based on multiple matching column values


By : DoubleCao
Date : March 29 2020, 07:55 AM
wish helps you You want to merge twice. The first merge focus on a1 in the left dataframe and a1 in the right dataframe. The second merge you switch focus to a2 from the left dataframe.
code :
df1.merge(
    df2,
    left_on=['seq', 'a1'],
    right_on=['seq', 'a1'],
    how='left'
).join(
    df1.merge(
        df2,
        left_on=['seq', 'a2'],
        right_on=['seq', 'a1'],
        how='left'
    )[['p1', 'p2']].rename(columns=dict(p1='p3', p2='p4'))
)

   seq      id           a1      a2    p1    p2    p3    p4
0   12  209981         None    None   NaN   NaN   NaN   NaN
1   12  209982        Funds    None  5.71  1.09   NaN   NaN
2   13  209983  Free_Income    None  2.53  2.64   NaN   NaN
3   13  209984  Free_Income    None  2.53  2.64   NaN   NaN
4   14  209985  Free_Income  Hybrid  4.53  2.25  1.89  2.45
Find matching values in two pandas dataframes and return a value from the matching row

Find matching values in two pandas dataframes and return a value from the matching row


By : user2321052
Date : March 29 2020, 07:55 AM
I hope this helps . You could do a left merge (left outer join). This will keep all of the observations in df1 while matching it with GEO_ID in df2:
code :
df1.merge(df2, left_on='CENSUS_ID', right_on='GEO_ID', how='left')
Add values to other values in another dataframe based on matching pair names in two columns of two dataframes in R

Add values to other values in another dataframe based on matching pair names in two columns of two dataframes in R


By : user3473768
Date : March 29 2020, 07:55 AM
Hope this helps I'm not sure if this is the most elegant way to accomplish this, but you can use tidyr::unite to create a new unique id based on ind_comp_a and ind_comp_b then base::merge using this id?
code :
library(tidyr)
#Create new_id column for merge
add_ida=tidyr::unite(Dataframe_A,new_id,ind_comp_a,ind_comp_b,remove=F)
add_idb=tidyr::unite(Dataframe_B,new_id,ind_comp_a,ind_comp_b,remove=F)

#Now do a left join to keep all Dataframe_A pairs that don't appear in Dataframe_B pairs
left = merge(add_ida,add_idb,by = 'new_id',all.x=T)
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