注意：这个问题涵盖为什么脚本太慢了。但是，如果您更喜欢那些想要改进某些内容的人，可以查看my post on CodeReview which aims to improve the performance。
我正在开发一个破解纯文本文件（.lst）的项目。
文件名（fileName）的名称很重要，因为我将提取node（例如 abessijn ）和component（例如WR-PEA） ）从他们到数据帧。例子：
abessijn.WR-P-E-A.lst
A-bom.WR-P-E-A.lst
acroniem.WR-P-E-C.lst
acroniem.WR-P-E-G.lst
adapter.WR-P-E-A.lst
adapter.WR-P-E-C.lst
adapter.WR-P-E-G.lst
每个文件由一行或多行组成。每行包含一个句子（在<sentence>标签内）。示例（abessijn.WR-P-E-A.lst）
/home/nobackup/SONAR/COMPACT/WR-P-E-A/WR-P-E-A0000364.data.ids.xml: <sentence>Vooral mijn abessijn ruikt heerlijk kruidig .. : ) )</sentence>
/home/nobackup/SONAR/COMPACT/WR-P-E-A/WR-P-E-A0000364.data.ids.xml: <sentence>Mijn abessijn denkt daar heel anders over .. : ) ) Maar mijn kinderen richt ik ook niet af , zit niet in mijn bloed .</sentence>
从每一行中我提取句子，对其进行一些小修改，然后将其称为sentence。接下来是一个名为leftContext的元素，它将node（例如 abessijn ）与其来自的句子之间的分割的第一部分。最后，从leftContext开始，我获得了node中的sentence之前的单词，或leftContext中最右边的单词（有一些限制，例如a的选项）用连字符形成的化合物）。例如：
ID | filename | node | component | precedingWord | leftContext | sentence
---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
1 adapter.WR-P-P-F.lst adapter WR-P-P-F aanpassingseenheid Een aanpassingseenheid ( Een aanpassingseenheid ( adapter ) ,
2 adapter.WR-P-P-F.lst adapter WR-P-P-F toestel Het toestel ( Het toestel ( adapter ) draagt zorg voor de overbrenging van gegevens
3 adapter.WR-P-P-F.lst adapter WR-P-P-F de de aansluiting tussen de sensor en de de aansluiting tussen de sensor en de adapter ,
4 airbag.WS-U-E-A.lst airbag WS-U-E-A den ja voor den ja voor den airbag op te pompen eh :p
5 airbag.WS-U-E-A.lst airbag WS-U-E-A ne Dobby , als ze valt heeft ze dan wel al ne Dobby , als ze valt heeft ze dan wel al ne airbag hee
该数据框导出为dataset.csv。
之后，我的项目的目的就在眼前：我创建了一个频率表，考虑了node和precedingWord。从变量我定义neuter和non_neuter，例如（在Python中）
neuter = ["het", "Het"]
non_neuter = ["de","De"]
和其他类别unspecified。当precedingWord是列表中的项目时，将其分配给变量。频率表输出示例：
node | neuter | nonNeuter | unspecified
-------------------------------------------------
A-bom 0 4 2
acroniem 3 0 2
act 3 2 1
频率列表导出为frequency.csv。
我从R开始，考虑到稍后我会对频率进行一些统计分析。我当前的R脚本（也可用paste）：
# ---
# STEP 0: Preparations
start_time <- Sys.time()
## 1. Set working directory in R
setwd("")
## 2. Load required library/libraries
library(dplyr)
library(mclm)
library(stringi)
## 3. Create directory where we'll save our dataset(s)
dir.create("../R/dataset", showWarnings = FALSE)
# ---
# STEP 1: Loop through files, get data from the filename
## 1. Create first dataframe, based on filename of all files
files <- list.files(pattern="*.lst", full.names=T, recursive=FALSE)
d <- data.frame(fileName = unname(sapply(files, basename)), stringsAsFactors = FALSE)
## 2. Create additional columns (word & component) based on filename
d$node <- sub("\\..+", "", d$fileName, perl=TRUE)
d$node <- tolower(d$node)
d$component <- gsub("^[^\\.]+\\.|\\.lst$", "", d$fileName, perl=TRUE)
# ---
# STEP 2: Loop through files again, but now also through its contents
# In other words: get the sentences
## 1. Create second set which is an rbind of multiple frames
## One two-column data.frame per file
## First column is fileName, second column is data from each file
e <- do.call(rbind, lapply(files, function(x) {
data.frame(fileName = x, sentence = readLines(x, encoding="UTF-8"), stringsAsFactors = FALSE)
}))
## 2. Clean fileName
e$fileName <- sub("^\\.\\/", "", e$fileName, perl=TRUE)
## 3. Get the sentence and clean
e$sentence <- gsub(".*?<sentence>(.*?)</sentence>", "\\1", e$sentence, perl=TRUE)
e$sentence <- tolower(e$sentence)
# Remove floating space before/after punctuation
e$sentence <- gsub("\\s(?:(?=[.,:;?!) ])|(?<=\\( ))", "\\1", e$sentence, perl=TRUE)
# Add space after triple dots ...
e$sentence <- gsub("\\.{3}(?=[^\\s])", "... ", e$sentence, perl=TRUE)
# Transform HTML entities into characters
# It is unfortunate that there's no easier way to do this
# E.g. Python provides the HTML package which can unescape (decode) HTML
# characters
e$sentence <- gsub("'", "'", e$sentence, perl=TRUE)
e$sentence <- gsub("&", "&", e$sentence, perl=TRUE)
# Avoid R from wrongly interpreting ", so replace by single quotes
e$sentence <- gsub(""|\"", "'", e$sentence, perl=TRUE)
# Get rid of some characters we can't use such as ³ and ¾
e$sentence <- gsub("[^[:graph:]\\s]", "", e$sentence, perl=TRUE)
# ---
# STEP 3:
# Create final dataframe
## 1. Merge d and e by common column name fileName
df <- merge(d, e, by="fileName", all=TRUE)
## 2. Make sure that only those sentences in which df$node is present in df$sentence are taken into account
matchFunction <- function(x, y) any(x == y)
matchedFrame <- with(df, mapply(matchFunction, node, stri_split_regex(sentence, "[ :?.,]")))
df <- df[matchedFrame, ]
## 3. Create leftContext based on the split of the word and the sentence
# Use paste0 to make sure we are looking for the node, not a compound
# node can only be preceded by a space, but can be followed by punctuation as well
contexts <- strsplit(df$sentence, paste0("(^| )", df$node, "( |[!\",.:;?})\\]])"), perl=TRUE)
df$leftContext <- sapply(contexts, `[`, 1)
## 4. Get the word preceding the node
df$precedingWord <- gsub("^.*\\b(?<!-)(\\w+(?:-\\w+)*)[^\\w]*$","\\1", df$leftContext, perl=TRUE)
## 5. Improve readability by sorting columns
df <- df[c("fileName", "component", "precedingWord", "node", "leftContext", "sentence")]
## 6. Write dataset to dataset dir
write.dataset(df,"../R/dataset/r-dataset.csv")
# ---
# STEP 4:
# Create dataset with frequencies
## 1. Define neuter and nonNeuter classes
neuter <- c("het")
non.neuter<- c("de")
## 2. Mutate df to fit into usable frame
freq <- mutate(df, gender = ifelse(!df$precedingWord %in% c(neuter, non.neuter), "unspecified",
ifelse(df$precedingWord %in% neuter, "neuter", "non_neuter")))
## 3. Transform into table, but still usable as data frame (i.e. matrix)
## Also add column name "node"
freqTable <- table(freq$node, freq$gender) %>%
as.data.frame.matrix %>%
mutate(node = row.names(.))
## 4. Small adjustements
freqTable <- freqTable[,c(4,1:3)]
## 5. Write dataset to dataset dir
write.dataset(freqTable,"../R/dataset/r-frequencies.csv")
diff <- Sys.time() - start_time # calculate difference
print(diff) # print in nice format
然而，由于我使用的是大数据集（16,500个文件，所有文件都有多行），因此它需要很长时间。在我的系统上，整个过程大约需要一个小时四分之一。我心里想，那里应该有一种可以更快地完成这项工作的语言，所以我去教自己一些Python，并在这里问了很多问题。
最后，我提出了以下脚本（paste）。
import os, pandas as pd, numpy as np, regex as re
from glob import glob
from datetime import datetime
from html import unescape
start_time = datetime.now()
# Create empty dataframe with correct column names
columnNames = ["fileName", "component", "precedingWord", "node", "leftContext", "sentence" ]
df = pd.DataFrame(data=np.zeros((0,len(columnNames))), columns=columnNames)
# Create correct path where to fetch files
subdir = "rawdata"
path = os.path.abspath(os.path.join(os.getcwd(), os.pardir, subdir))
# "Cache" regex
# See http://stackoverflow.com/q/452104/1150683
p_filename = re.compile(r"[./\\]")
p_sentence = re.compile(r"<sentence>(.*?)</sentence>")
p_typography = re.compile(r" (?:(?=[.,:;?!) ])|(?<=\( ))")
p_non_graph = re.compile(r"[^\x21-\x7E\s]")
p_quote = re.compile(r"\"")
p_ellipsis = re.compile(r"\.{3}(?=[^ ])")
p_last_word = re.compile(r"^.*\b(?<!-)(\w+(?:-\w+)*)[^\w]*$", re.U)
# Loop files in folder
for file in glob(path+"\\*.lst"):
with open(file, encoding="utf-8") as f:
[n, c] = p_filename.split(file.lower())[-3:-1]
fn = ".".join([n, c])
for line in f:
s = p_sentence.search(unescape(line)).group(1)
s = s.lower()
s = p_typography.sub("", s)
s = p_non_graph.sub("", s)
s = p_quote.sub("'", s)
s = p_ellipsis.sub("... ", s)
if n in re.split(r"[ :?.,]", s):
lc = re.split(r"(^| )" + n + "( |[!\",.:;?})\]])", s)[0]
pw = p_last_word.sub("\\1", lc)
df = df.append([dict(fileName=fn, component=c,
precedingWord=pw, node=n,
leftContext=lc, sentence=s)])
continue
# Reset indices
df.reset_index(drop=True, inplace=True)
# Export dataset
df.to_csv("dataset/py-dataset.csv", sep="\t", encoding="utf-8")
# Let's make a frequency list
# Create new dataframe
# Define neuter and non_neuter
neuter = ["het"]
non_neuter = ["de"]
# Create crosstab
df.loc[df.precedingWord.isin(neuter), "gender"] = "neuter"
df.loc[df.precedingWord.isin(non_neuter), "gender"] = "non_neuter"
df.loc[df.precedingWord.isin(neuter + non_neuter)==0, "gender"] = "rest"
freqDf = pd.crosstab(df.node, df.gender)
freqDf.to_csv("dataset/py-frequencies.csv", sep="\t", encoding="utf-8")
# How long has the script been running?
time_difference = datetime.now() - start_time
print("Time difference of", time_difference)
在确保两个脚本的输出相同后，我想我会把它们放到测试中。
我在Windows 10 64 bit上运行，配备四核处理器和8 GB Ram。对于R，我使用的是RGui 64位3.2.2，Python在版本 3.4.3 （Anaconda）上运行，并在Spyder中执行。请注意，我在32位运行Python，因为我希望将来使用nltk module，并且不鼓励用户使用64位。
我发现R在大约55分钟内完成。但是Python已经连续运行了两个小时，我可以在变量资源管理器中看到它只在business.wr-p-p-g.lst（文件按字母顺序排序）。 这是waaaaayyyy慢！
所以我做的是做一个测试用例，看看两个脚本如何用更小的数据集执行。我拿了大约100个文件（而不是16,500个）并运行了脚本。再一次，R更快。 R在大约2秒内完成，Python在17秒内完成！
看到Python的目标是让一切顺利，我感到困惑。我读Python很快（而且R很慢），所以我哪里出错了？问题是什么？ Python在阅读文件和行或执行正则表达式时是否较慢？或者R是否更适合处理数据帧而不能被熊猫打败？ 或是我的代码，只是非常优化，Python应该确实是胜利者吗？
我的问题是：在这种情况下，为什么Python比R慢，而且 - 如果可能的话 - 我们如何改进Python以发光？
每个愿意尝试使用任何一个脚本的人都可以下载我使用的测试数据here。下载文件时请与我联系。
答案 0 :(得分：3)
你做的最可怕的低效事情是在循环中调用DataFrame.append方法，即
df = pandas.DataFrame(...)
for file in files:
...
for line in file:
...
df = df.append(...)
NumPy数据结构在设计时考虑了函数式编程，因此这种操作并不意味着以迭代命令式方式使用，因为调用不会就地更改数据框，但会创建一个新的数据框，导致时间和内存复杂性的巨大增加。如果您确实想要使用数据框，请在list中累积行并将其传递给DataFrame构造函数，例如
pre_df = []
for file in files:
...
for line in file:
...
pre_df.append(processed_line)
df = pandas.DataFrame(pre_df, ...)
这是最简单的方法，因为它会对您拥有的代码进行最小的更改。但更好（和计算上更漂亮）的方法是弄清楚如何懒洋洋地生成数据集。这可以通过将工作流分成离散函数（在函数式编程风格的意义上）并使用惰性生成器表达式和/或imap，ifilter高阶函数来组合它们来轻松实现。然后，您可以使用生成的生成器来构建数据框，例如
df = pandas.DataFrame.from_records(processed_lines_generator, columns=column_names, ...)
至于在一次运行中阅读多个文件，您可能需要阅读this。
P.S。
如果您遇到性能问题，则应在尝试优化代码之前对其进行分析。