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How to merge first 12 columns?


By : العز طبعي
Date : October 18 2020, 06:10 AM
will help you With GNU sed for \s/\S shorthand for space/non-space and -E to enable EREs:
code :
$ sed -E 's/\s+(\S+)\s+(\S+)$/,\1,\2/' file
Somename of someone,1234,7894
Even some more name,2345,5343
Even more of the same,6572,6456
I am a customer,1324,7894
I am another customer,5612,3657
Also I am a customer and I am number Three,9631,7411
And I am number four and not the latest one in list,8529,9369
And here I am,4567,9876
$ sed 's/[[:space:]]*\([^[:space:]]\{1,\}\)[[:space:]]*\([^[:space:]]\{1,\}\)$/,\1,\2/' file
Somename of someone,1234,7894
Even some more name,2345,5343
Even more of the same,6572,6456
I am a customer,1324,7894
I am another customer,5612,3657
Also I am a customer and I am number Three,9631,7411
And I am number four and not the latest one in list,8529,9369
And here I am,4567,9876
$ awk -v OFS=',' '{x=$(NF-1) OFS $NF; sub(/([[:space:]]+[^[:space:]]+){2}$/,""); print $0, x}' file
Somename of someone,1234,7894
Even some more name,2345,5343
Even more of the same,6572,6456
I am a customer,1324,7894
I am another customer,5612,3657
Also I am a customer and I am number Three,9631,7411
And I am number four and not the latest one in list,8529,9369
And here I am,4567,9876


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How can I use merge() in R to merge data frames on columns?


By : Mardaneus
Date : March 29 2020, 07:55 AM
help you fix your problem I have an empty data frame called "weatherDataFinal" that I've initialized with the following columns: "visibility", "cloudCover", "pressure", "windSpeed" and "windBearing"
code :
d1 <- read.table(text="visibility,cloudCover,pressure
10,0.03,1014.06", header=TRUE, stringsAsFactors=FALSE, sep=",")

d2 <- read.table(text="windSpeed,windBearing
16.27,280", header=TRUE, stringsAsFactors=FALSE, sep=",")

d3 <- read.table(text="visibility,pressure
2.97,1010.04", header=TRUE, stringsAsFactors=FALSE, sep=",")

dplyr::rbind_list(d1, d2, d3)
## Source: local data frame [3 x 5]
## 
##   visibility cloudCover pressure windSpeed windBearing
##        (dbl)      (dbl)    (dbl)     (dbl)       (int)
## 1      10.00       0.03  1014.06        NA          NA
## 2         NA         NA       NA     16.27         280
## 3       2.97         NA  1010.04        NA          NA

R - reduce with merge and more than 2 suffixes (or: how to merge multiple dataframes and keep track of columns)


By : Bin JIANG
Date : March 29 2020, 07:55 AM
will be helpful for those in need This seems to be easier with a for loop as the Reduce or reduce (purrr) at a time takes only two datasets, so we can't have more than two suffixes in the merge.
Here, we created a vector of suffixes ('sfx'). Initialize an output dataset with the first list element. Then loop through the sequence of 'list.df' and do a sequential merge with the 'res' and the next element of list.df while updating the 'res' in each step
code :
sfx <- c(".df1", ".df2", ".df3", ".df4")
res <- list.df[[1]]
for(i in head(seq_along(list.df), -1)) {

 res <- merge(res, list.df[[i+1]], all = TRUE, 
                 suffixes = sfx[i:(i+1)], by = c("Name", "Color"))
  }

res
#    Name  Color Freq.df1 Freq.df2 Freq.df3 Freq.df4
#1  apple  green        4       NA        8        2
#2  apple    red        1        2        9        1
#3 banana yellow        3        3        7       NA
#4   plum purple        8        1       NA        6

pandas merge: merge two dataframes on same column but keep different columns


By : Alejandro
Date : March 29 2020, 07:55 AM
I wish this help you I have two pandas dataFrames that share one common column name. I would like to merge on the common column name but keep all the different columns from the second dataFrame where there's a match on the common column name. Here's an example of the two dataFrames: , If you have
setup
code :
z=io.StringIO("""    A      B      C     D     E      F      G     H
    value2 value2   value2 value2 value2 value2  value2 value2
    value3 value3   value3 value3 value3 value3  value3 value3
    value value   value value value value  value value""")

df = pd.read_table(z, delim_whitespace=True)

z2=io.StringIO("""      A     I     J     K     L
    value value value value value
    value2 value2 value2 value2 value2
    value3 value3 value3 value3 value3""")

df2=pd.read_table(z2,delim_whitespace=True)
pd.merge(df,df2, on="A",right_index=True, left_index=True)


    A       B       C       D       E       F       G       H       I       J       K       L
0   value   value   value   value   value   value   value   value   value   value   value   value
1   value   value   value   value   value   value   value   value   value   value   value   value
2   value   value   value   value   value   value   value   value   value   value   value   value
pd.merge(df.set_index("A"),df2.set_index("A"), right_index=True, left_index=True).reset_index()

    A       B       C       D       E       F       G       H      I        J       K       L
0   value2  value2  value2  value2  value2  value2  value2  value2  value2  value2  value2  value2
1   value3  value3  value3  value3  value3  value3  value3  value3  value3  value3  value3  value3
2   value   value   value   value   value   value   value   value   value   value   value   value

Merge Pandas DataFrame using apply() to only merge on partial match in two columns


By : user2688971
Date : March 29 2020, 07:55 AM
it helps some times I need to merge two pandas DataFrames but not only on exact column values, but also on approximate ones. , Here is one way from merge_asof
code :
pd.merge_asof(df,df2,left_on='col2',right_on='col2b',tolerance = 1,direction ='nearest').dropna()
Out[7]: 
  col1  col2 col1a  col2b
0    a     3    aa    3.0
1    b     4    bb    4.0
2    c    66    cc   67.0

merge columns: merge on column has truncated digits


By : user3701934
Date : March 29 2020, 07:55 AM
Any of those help You can use regexpr and regmatches to truncate the ID_key from dataframe_b to those found in dataframe_b and then use merge.
code :
dataframe_b$ID_key_tr  <- regmatches(dataframe_b$ID_key, regexpr(paste0("^", unique(dataframe_a$ID_key), collapse = "|"),  dataframe_b$ID_key))
dataframe_b
#       ID_key ID_key_tr
#1 10020000000      1002
#2 10020000000      1002
#3 10020000000      1002
#4 10020000000      1002
#5 32410001001  32410001
#6 32410001001  32410001
#7 32410001001  32410001

merge(dataframe_a, dataframe_b, by.x="ID_key", by.y="ID_key_tr")
dataframe_a <- data.frame(ID_key=c("1002","1002","1002","1002","32410001","32410001","32410001"))
dataframe_b <- data.frame(ID_key=c("10020000000","10020000000","10020000000","10020000000","32410001001","32410001001","32410001001"))
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