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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 : November 17 2020, 11:55 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


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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")
Python -- How to apply split() function to an entire column in a dataframe

Python -- How to apply split() function to an entire column in a dataframe


By : fitraalfiananto
Date : March 29 2020, 07:55 AM
wish help you to fix your issue I'm working on a python assignment whereby I need to analyze a yelp dataset. The following are the columns of the dataset: , Insert a lambda function into your apply call:
code :
lambda x : len(x.split())
Split dataframe based on column values of another dataframe in python

Split dataframe based on column values of another dataframe in python


By : user3510692
Date : March 29 2020, 07:55 AM
this one helps. Convert the dataframe with the desired values into a list of tuples to be able to loop and filter through it
code :
tuples = [tuple(x) for x in df.values]
for mytuple in tuples:
    print(original_df[(original_df['Country'] == mytuple[0]) & (original_df['Type'] == mytuple[1])])
my_dfs = [df[(df['Country'] == mytuple[0]) & (df['Type'] == mytuple[1])] for mytuple in tuples]
for my_df in my_dfs:
    print(my_df)
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 python dataframe in to equal numbers based on the unique values of a column

split python dataframe in to equal numbers based on the unique values of a column


By : Frank Muñoz
Date : March 29 2020, 07:55 AM
hope this fix your issue My dataframe df looks something like this:
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