对于 Python / Pandas，我发现 df.to_csv(fname) 函数的速度大约为每分钟 ~1 百万行。有时我可以通过以下方法提高运行效率达到 7 倍：
def df2csv(df,fname,myformats=[],sep=','):
"""
# function is faster than to_csv
# 7 times faster for numbers if formats are specified,
# 2 times faster for strings.
# Note - be careful. It doesn't add quotes and doesn't check
# for quotes or separators inside elements
# We've seen output time going down from 45 min to 6 min
# on a simple numeric 4-col dataframe with 45 million rows.
"""
if len(df.columns) <= 0:
return
Nd = len(df.columns)
Nd_1 = Nd - 1
formats = myformats[:] # take a copy to modify it
Nf = len(formats)
# make sure we have formats for all columns
if Nf < Nd:
for ii in range(Nf,Nd):
coltype = df[df.columns[ii]].dtype
ff = '%s'
if coltype == np.int64:
ff = '%d'
elif coltype == np.float64:
ff = '%f'
formats.append(ff)
fh=open(fname,'w')
fh.write(','.join(df.columns) + '\n')
for row in df.itertuples(index=False):
ss = ''
for ii in xrange(Nd):
ss += formats[ii] % row[ii]
if ii < Nd_1:
ss += sep
fh.write(ss+'\n')
fh.close()
aa=DataFrame({'A':range(1000000)})
aa['B'] = aa.A + 1.0
aa['C'] = aa.A + 2.0
aa['D'] = aa.A + 3.0
timeit -r1 -n1 aa.to_csv('junk1') # 52.9 sec
timeit -r1 -n1 df2csv(aa,'junk3',myformats=['%d','%.1f','%.1f','%.1f']) # 7.5 sec
注意：性能提升取决于数据类型。但至少在我的测试中，to_csv() 总是比未优化的 Python 表现得慢得多。
如果我有一个 4500 万行的 CSV 文件，则：
aa = read_csv(infile) # 1.5 min
aa.to_csv(outfile) # 45 min
df2csv(aa,...) # ~6 min
问题：
What are the ways to make the output even faster?
What's wrong with to_csv() ? Why is it soooo slow ?
注意：我的测试是在 Linux 服务器的本地驱动器上使用 pandas 0.9.1 进行的。