Python Pandas df.to_csv("file.csv" encode="utf-8") 仍然为减号提供垃圾字符
声明：本页面是StackOverFlow热门问题的中英对照翻译，遵循CC BY-SA 4.0协议，如果您需要使用它，必须同样遵循CC BY-SA许可，注明原文地址和作者信息，同时你必须将它归于原作者（不是我）：StackOverFlow
原文地址: http://stackoverflow.com/questions/25788037/
Warning: these are provided under cc-by-sa 4.0 license. You are free to use/share it, But you must attribute it to the original authors (not me):
StackOverFlow
Pandas df.to_csv("file.csv" encode="utf-8") still gives trash characters for minus sign
提问by Maggie
I've read something about a Python 2 limitation with respect to Pandas' to_csv( ... etc ...). Have I hit it? I'm on Python 2.7.3
我已经阅读了一些关于 Pandas 的 to_csv( ... etc ...) 的 Python 2 限制。我打中了吗？我使用的是 Python 2.7.3
This turns out trash characters for ≥ and - when they appear in strings. Aside from that the export is perfect.
当 ≥ 和 - 出现在字符串中时，结果是垃圾字符。除此之外，出口是完美的。
df.to_csv("file.csv", encoding="utf-8")
Is there any workaround?
有什么解决方法吗？
df.head() is this:
df.head() 是这样的：
demography Adults ≥49 yrs Adults 18?49 yrs at high risk|| \
state
Alabama 32.7 38.6
Alaska 31.2 33.2
Arizona 22.9 38.8
Arkansas 31.2 34.0
California 29.8 38.8
csv output is this
csv输出是这个
state, Adults a‰￥49 yrs, Adults 18a?'49 yrs at high risk||
0, Alabama, 32.7, 38.6
1, Alaska, 31.2, 33.2
2, Arizona, 22.9, 38.8
3, Arkansas,31.2, 34
4, California,29.8, 38.8
the whole code is this:
整个代码是这样的：
import pandas
import xlrd
import csv
import json
df = pandas.DataFrame()
dy = pandas.DataFrame()
# first merge all this xls together
workbook = xlrd.open_workbook('csv_merger/vaccoverage.xls')
worksheets = workbook.sheet_names()
for i in range(3,len(worksheets)):
dy = pandas.io.excel.read_excel(workbook, i, engine='xlrd', index=None)
i = i+1
df = df.append(dy)
df.index.name = "index"
df.columns = ['demography', 'area','state', 'month', 'rate', 'moe']
#Then just grab month = 'May'
may_mask = df['month'] == "May"
may_df = (df[may_mask])
#then delete some columns we dont need
may_df = may_df.drop('area', 1)
may_df = may_df.drop('month', 1)
may_df = may_df.drop('moe', 1)
print may_df.dtypes #uh oh, it sees 'rate' as type 'object', not 'float'. Better change that.
may_df = may_df.convert_objects('rate', convert_numeric=True)
print may_df.dtypes #that's better
res = may_df.pivot_table('rate', 'state', 'demography')
print res.head()
#and this is going to spit out an array of Objects, each Object a state containing its demographics
res.reset_index().to_json("thejson.json", orient='records')
#and a .csv for good measure
res.reset_index().to_csv("thecsv.csv", orient='records', encoding="utf-8")
回答by Mark Tolonen
Your "bad" output is UTF-8 displayed as CP1252.
您的“坏”输出是 UTF-8，显示为 CP1252。
On Windows, many editors assume the default ANSI encoding (CP1252 on US Windows) instead of UTF-8 if there is no byte order mark (BOM) character at the start of the file. While a BOM is meaningless to the UTF-8 encoding, its UTF-8-encoded presence serves as a signature for some programs. For example, Microsoft Office's Excel requires it even on non-Windows OSes. Try:
在 Windows 上，如果文件开头没有字节顺序标记 (BOM) 字符，则许多编辑器假定默认的 ANSI 编码（美国 Windows 上的 CP1252）而不是 UTF-8。虽然 BOM 对 UTF-8 编码毫无意义，但它的 UTF-8 编码存在可用作某些程序的签名。例如，即使在非 Windows 操作系统上，Microsoft Office 的 Excel 也需要它。尝试：
df.to_csv('file.csv',encoding='utf-8-sig')
That encoder will add the BOM.
该编码器将添加 BOM。
