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Split dataframe into testing_df and validation_df


Split dataframe into testing_df and validation_df

By : UltimateC
Date : October 18 2020, 03:08 PM
will be helpful for those in need You need testing_df = df.iloc[:20000].
Think of iloc's arguments as referencing [rows, columns].
code :


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using split() to split values in an entire column in a python dataframe

using split() to split values in an entire column in a python dataframe


By : user2950601
Date : March 29 2020, 07:55 AM
wish help you to fix your issue You need to do the following, so call .str.split on the column and then .str[0] to access the first portion of the split string of interest:
code :
In [6]:

df['csuristem'].str.split('.').str[0]
Out[6]:
0    /gradoffice/index
1    /gradoffice/index
2    /gradoffice/index
3    /gradoffice/index
Name: csuristem, dtype: object
How to split/subset a dataframe in R, edit data according to split, then reform dataframe?

How to split/subset a dataframe in R, edit data according to split, then reform dataframe?


By : Mahendeka maiga
Date : March 29 2020, 07:55 AM
should help you out I have a large data set that is in a data frame. Here is a sample (there are also several columns of covariates that I have omitted for brevity): , We can use data.table
code :
library(data.table)
setDT(dfN)[order(id, week), c("event", "time") := list(+(1:.N==.N),
                      cumsum(c(1,diff(week))))  ,id]
dfN
#    id week event time
# 1:  1    5     0    1
# 2:  1    7     0    3
# 3:  1    8     0    4
# 4:  1    9     0    5
# 5:  1   10     0    6
# 6:  1   11     0    7
# 7:  1   14     0   10
# 8:  1   15     0   11
# 9:  1   16     0   12
#10:  1   17     0   13
#11:  1   18     1   14
#12:  2    3     0    1
#13:  2    5     0    3
#14:  2    6     0    4
#15:  2    7     0    5
#16:  2    9     0    7
#17:  2   10     0    8
#18:  2   11     0    9
#19:  2   14     0   12
#20:  2   15     0   13
#21:  2   16     0   14
#22:  2   17     0   15
#23:  2   18     0   16
#24:  2   20     0   18
#25:  2   22     1   20
setDT(dfN)[order(id, week), c("event", "time") := list(c(rep(0,.N-1), 1),
                      cumsum(c(1,diff(week))))  ,id]
dfN[, week:=factor(week, levels=1:37)]
dfN[, N:= 1:.N]


 res <- dcast(dfN, N~week, value.var="time", length, drop=FALSE)[,
    c("id", "event") := dfN[, c("id", "event"), with=FALSE]][]

 res[1:4]
 #   N 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 id event
#1: 1 0 0 0 0 1 0 0 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1     0
#2: 2 0 0 0 0 0 0 1 0 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1     0
#3: 3 0 0 0 0 0 0 0 1 0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1     0
#4: 4 0 0 0 0 0 0 0 0 1  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  0  1     0
dfN <- structure(list(id = c(1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 
 1L, 1L, 
1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L), 
week = c(5L, 7L, 8L, 9L, 10L, 11L, 14L, 15L, 16L, 17L, 18L, 
3L, 5L, 6L, 7L, 9L, 10L, 11L, 14L, 15L, 16L, 17L, 18L, 20L, 
22L)), .Names = c("id", "week"), row.names = c(NA, -25L), 
class = "data.frame")
SPARK DataFrame: How to efficiently split dataframe for each group based on same column values

SPARK DataFrame: How to efficiently split dataframe for each group based on same column values


By : Wesley
Date : March 29 2020, 07:55 AM
I wish this help you As noted in my comments, one potentially easy approach to this problem would be to use:
code :
df.write.partitionBy("hour").saveAsTable("myparquet")
Split a dataframe based on a column and write out the multiple split .txt files with specific names

Split a dataframe based on a column and write out the multiple split .txt files with specific names


By : user3700420
Date : March 29 2020, 07:55 AM
I hope this helps you . I don't believe this is very different from the OP's code but here it goes.
First, a test data set. I will use a copy of the built-in data set iris
code :
df <- iris
names(df)[5] <- "Pid_treatmentsum"
sptdf <- split(df, df$Pid_treatmentsum)
lapply(sptdf, function(DF){
  outfile <- as.character(unique(DF[["Pid_treatmentsum"]]))
  outfile <- paste0(outfile, ".txt")
  write.table(DF, 
              file = outfile,
              row.names = FALSE,
              quote = FALSE)
})
splitFun <- function(file, col = "Pid_treatmentsum", ...){
  X <- read.table(file, header = TRUE, ...)
  sptdf <- split(X, X[[col]])
  lapply(sptdf, function(DF){
    outfile <- as.character(unique(DF[[col]]))
    outfile <- paste0(outfile, ".txt")
    write.table(DF,
                file = outfile,
                row.names = FALSE,
                quote = FALSE)
  })
}


filenames <- list.files(pattern = "<a regular expression>")
lapply(filenames, splitFun)
Split Pandas Dataframe into Multiple Excel Sheets Based on Index Value in Dataframe

Split Pandas Dataframe into Multiple Excel Sheets Based on Index Value in Dataframe


By : Joe
Date : March 29 2020, 07:55 AM
I wish this helpful for you IIUC,
you can group by product and assign this as the sheet name, whilst assigning the data into the seet based on the aggregation.
code :
writer = pd.ExcelWriter('Report.xlsx')

for group, data in result2.groupby('Product'):
    data.to_excel(writer,group)
writer.save()
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