Pandas df.to_csv(“Datei.csv" encode="utf-8") gibt noch trash Zeichen zum minus-Zeichen

Habe ich etwas gelesen über ein Python-2 Einschränkung mit Bezug auf Pandas' to_csv( ... etc ...). Habe ich es getroffen? Ich bin auf Python 2.7.3

Erweist sich das Papierkorb-Zeichen ≥ und - wenn-Sie erscheinen in strings. Abgesehen davon, dass der export ist perfekt.

df.to_csv("file.csv", encoding="utf-8") 

Gibt es Abhilfe?

df.head() ist diese:

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-Ausgabe ist diese

state,  Adults ≥49 yrs,   Adults 18−49 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

der ganze code ist:

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")