大约有 3,000 项符合查询结果（耗时：0.0204秒） [XML]
cannot download, $GOPATH not set
I want to install json2csv using go get github.com/jehiah/json2csv but I receive this error:
15 Answers
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廉价共享存储解决方案1-drbd+ha+nfs - 更多技术 - 清泛网 - 专注C/C++及内核技术
...怎样呢？
这种方式有两个最重要的缺陷。由于Rsync是对文件进行操作，层次比价高，使用Rsync对文件夹进行同步，会占用大量的磁盘I/O和网络带宽。同时如果正在拷贝的文件在大量频繁的更改，Rsync将会变得无所适从而产生冲突...
How to create CSV Excel file C#? [closed]
I'm looking for a class for creating CSV Excel files.
13 Answers
13
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invalid byte sequence for encoding “UTF8”
... to help others and the search engines: this works for converting a Stripe CSV export with unreadable characters back into UTF-8: ` iconv -f ISO-8859-15 -t utf-8 customers.csv > customers-utf8.csv`
– sscarduzio
Nov 27 '17 at 21:49
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Import and Export Excel - What is the best library? [closed]
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I'm going to throw my hand in for flat csv files, if only because you've got the greatest control over the code. Just make sure that you read in the rows and process them one at a time (reading the document to the end and splitting will eat up all of your memory -...
How to use the CSV MIME-type?
In a web application I am working on, the user can click on a link to a CSV file. There is no header set for the mime-type, so the browser just renders it as text. I would like for this file to be sent as a .csv file, so the user can directly open it with calc, excel, gnumeric, etc.
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Read a zipped file as a pandas DataFrame
I'm trying to unzip a csv file and pass it into pandas so I can work on the file.
The code I have tried so far is:
5 Ans...
How to estimate how much memory a Pandas' DataFrame will need?
I have been wondering... If I am reading, say, a 400MB csv file into a pandas dataframe (using read_csv or read_table), is there any way to guesstimate how much memory this will need? Just trying to get a better feel of data frames and memory...
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Can iterators be reset in Python?
...y available.
As several answers rightly remarked, in the specific case of csv you can also .seek(0) the underlying file object (a rather special case). I'm not sure that's documented and guaranteed, though it does currently work; it would probably be worth considering only for truly huge csv file...
Create Pandas DataFrame from a string
...ringIO (python2) or io.StringIO (python3) and pass that to the pandas.read_csv function. E.g:
import sys
if sys.version_info[0] < 3:
from StringIO import StringIO
else:
from io import StringIO
import pandas as pd
TESTDATA = StringIO("""col1;col2;col3
1;4.4;99
2;4.5;200
3;4...