python - 将分块文件读入数据帧

Question

我是 pandas/r 的新手，我不太确定如何将这些数据读入 pandas 或 r 进行分析。

目前，我在想我可以使用 readr 的 read_chunkwise 或 pandas 的 chunksize，但这可能不是我需要的。这真的可以通过 for 循环或使用 purr 遍历所有元素轻松解决吗？

数据:

wine/name: 1981 Château de Beaucastel Châteauneuf-du-Pape
wine/wineId: 18856 
wine/variant: Red Rhone Blend 
wine/year: 1981 
review/points: 96   
review/time: 1160179200   
review/userId: 1 
review/userName: Eric 
review/text: Olive, horse sweat, dirty saddle, and smoke. This actually got quite a bit more spicy and expressive with significant aeration. This was a little dry on the palate first but filled out considerably in time, lovely, loaded with tapenade, leather, dry and powerful, very black olive, meaty. This improved considerably the longer it was open. A terrific bottle of 1981, 96+ and improving. This may well be my favorite vintage of Beau except for perhaps the 1990.

wine/name: 1995 Château Pichon-Longueville Baron 
wine/wineId: 3495 wine/variant: Red Bordeaux Blend 
wine/year: 1995 
review/points: 93 
review/time: 1063929600 
review/userId: 1 
review/userName: Eric 
review/text: A remarkably floral nose with violet and chambord. On the palate this is super sweet and pure with a long, somewhat searing finish. My notes are very terse, but this was a lovely wine.

目前，这是我的函数，但我遇到了一个错误:

>

 convertchunkfile <- function(df){   for(i in 1:length(df)){
>     #While the length of any line is not 0, process it with the following loop
>     while(nchar(df[[i]]) != 0){
>       case_when(
>         
>         #When data at x index == wine/name, then extract the data after that clause
>         #Wine Name parsing
>         cleandf$WineName[[i]] <- df[i] == str_sub(df[1],0, 10) ~ str_trim(substr(df[1], 11, nchar(df[1]))),
>         #Wine ID parsing
>         cleandf$WineID[[i]] <- df[i] == str_sub(df[2],0,11) ~ str_trim(substr(df[2], 13, nchar(df[1])))
>         #same format for other attributes
>       )
>     }   
>    }
>  } 

Error in cleandf$BeerName[[i]] <- df[i] == str_sub(df[1], 0, 10) ~ str_trim(substr(df[1],  : 
  more elements supplied than there are to replace

编辑:

在解决了一些问题之后，我认为这可能是最好的解决方案，借鉴了@hereismyname 的解决方案:

#Use Bash's iconv to force convert the file in OS X
iconv -c -t UTF-8 cellartracker-clean.txt > cellartracker-iconv.txt

#Check number of lines within the file
wc -l cellartracker-iconv.txt
20259950 cellartracker-iconv.txt

#Verify new encoding of the file
file -I cellartracker-clean.txt


ReadEmAndWeep <- function(file, chunk_size) {
  f <- function(chunk, pos) {
    data_frame(text = chunk) %>%
      filter(text != "") %>%
      separate(text, c("var", "value"), ":", extra = "merge") %>%
      mutate(
        chunk_id = rep(1:(nrow(.) / 9), each = 9),
        value = trimws(value)
      ) %>%
      spread(var, value)
  }

  read_lines_chunked(file, DataFrameCallback$new(f), chunk_size = chunk_size)
}

#Final Function call to read in the file
dataframe <- ReadEmAndWeep(file, chunk_size = 100000)

Answer 1

这是一个在 R 中相当惯用的方法:

library(readr)
library(tidyr)
library(dplyr)

out <- data_frame(text = read_lines(the_text)) %>%
  filter(text != "") %>% 
  separate(text, c("var", "value"), ":", extra = "merge") %>% 
  mutate(
    chunk_id = rep(1:(nrow(.) / 9), each = 9),
    value    = trimws(value)
  ) %>% 
  spread(var, value)