csv

问题 I am trying to read a csv file into SparkR (running Spark 2.0.0) - & trying to experiment with the newly added features. Using RStudio here. I am getting an error while "reading" the source file. My code: Sys.setenv(SPARK_HOME = "C:/spark-2.0.0-bin-hadoop2.6") library(SparkR, lib.loc = c(file.path(Sys.getenv("SPARK_HOME"), "R", "lib"))) sparkR.session(master = "local[*]", appName = "SparkR") df <- loadDF("F:/file.csv", "csv", header = "true") I get an error at at the loadDF function. The

问题 I am trying to read a csv file into SparkR (running Spark 2.0.0) - & trying to experiment with the newly added features. Using RStudio here. I am getting an error while "reading" the source file. My code: Sys.setenv(SPARK_HOME = "C:/spark-2.0.0-bin-hadoop2.6") library(SparkR, lib.loc = c(file.path(Sys.getenv("SPARK_HOME"), "R", "lib"))) sparkR.session(master = "local[*]", appName = "SparkR") df <- loadDF("F:/file.csv", "csv", header = "true") I get an error at at the loadDF function. The

问题 I am trying to wrap my head around ML and AI using TensorFlow. There is an example problem on the website which discusses the processing of .CSV data. The .CVS data is said to have been taken from the titanic and essentially contains categorical and numerical features that will be used to label a passenger as dead or alive. First of all, if anyone know or has any resources or references that discusses that example in more detail than is done on the TensorFlow website, please could you kindly

问题 I am trying to wrap my head around ML and AI using TensorFlow. There is an example problem on the website which discusses the processing of .CSV data. The .CVS data is said to have been taken from the titanic and essentially contains categorical and numerical features that will be used to label a passenger as dead or alive. First of all, if anyone know or has any resources or references that discusses that example in more detail than is done on the TensorFlow website, please could you kindly

问题 The csv module implements classes to read and write tabular data in CSV format. It allows programmers to say, “write this data in the format preferred by Excel,” or “read data from this file which was generated by Excel,” without knowing the precise details of the CSV format used by Excel. What if I want to know?? All kidding aside, I want to know specifically which attributes and settings would create the dialect csv.excel_tab Dialect.delimiter A one-character string used to separate fields.

问题 I have tried with api spark.read.csv to read compressed csv file with extension bz or gzip . It worked. But in source code I don't find any option parameter that we can declare the codec type. Even in this link, there is only setting for codec in writing side. Could anyone tell me or give the path to source code that showing how spark 2.x version deal with the compressed csv file. 回答1: All text-related data sources, including CSVDataSource, use Hadoop File API to deal with files (it was in

问题 I have tried with api spark.read.csv to read compressed csv file with extension bz or gzip . It worked. But in source code I don't find any option parameter that we can declare the codec type. Even in this link, there is only setting for codec in writing side. Could anyone tell me or give the path to source code that showing how spark 2.x version deal with the compressed csv file. 回答1: All text-related data sources, including CSVDataSource, use Hadoop File API to deal with files (it was in

问题 I have noticed that loading a CSV file using CSV.read is quite slow. For reference, I am attaching one example of time benchmark: using CSV, DataFrames file = download("https://github.com/foursquare/twofishes") @time CSV.read(file, DataFrame) Output: 9.450861 seconds (22.77 M allocations: 960.541 MiB, 5.48% gc time) 297 rows × 2 columns This is a random dataset, and a python alternate of such operation compiles in fraction of time compared to Julia. Since, julia is faster than python why is

问题 I have noticed that loading a CSV file using CSV.read is quite slow. For reference, I am attaching one example of time benchmark: using CSV, DataFrames file = download("https://github.com/foursquare/twofishes") @time CSV.read(file, DataFrame) Output: 9.450861 seconds (22.77 M allocations: 960.541 MiB, 5.48% gc time) 297 rows × 2 columns This is a random dataset, and a python alternate of such operation compiles in fraction of time compared to Julia. Since, julia is faster than python why is

问题 I need to write a pandas.Series object to a CSV file as a row, not as a column. Simply doing the_series.to_csv( 'file.csv' ) gives me a file like this: record_id,2013-02-07 column_a,7.0 column_b,5.0 column_c,6.0 What I need instead is this: record_id,column_a,column_b,column_c 2013-02-07,7.0,5.0,6.0 This needs to work with pandas 0.10, so using the_series.to_frame().transpose() is not an option. Is there a simple way to either transpose the Series, or otherwise get it written as a row? Thanks