因为@User和@BiXiC请求帮助使用UTF-8，这里是@Matthew的解决方案的变体。(我不能发表评论，所以我来回答。)

import unicodecsv as csv
toCSV = [{'name':'bob','age':25,'weight':200},
         {'name':'jim','age':31,'weight':180}]
keys = toCSV[0].keys()
with open('people.csv', 'wb') as output_file:
    dict_writer = csv.DictWriter(output_file, keys)
    dict_writer.writeheader()
    dict_writer.writerows(toCSV)

import csv

with open('file_name.csv', 'w') as csv_file:
    writer = csv.writer(csv_file)
    writer.writerow(('colum1', 'colum2', 'colum3'))
    for key, value in dictionary.items():
        writer.writerow([key, value[0], value[1]])

这是将数据写入.csv文件的最简单方法

在python 3中，事情有点不同，但更简单，更不容易出错。告诉CSV你的文件应该使用utf8编码打开，这是一个好主意，因为它使数据更容易移植到其他人(假设你没有使用更严格的编码，如latin1)

import csv
toCSV = [{'name':'bob','age':25,'weight':200},
         {'name':'jim','age':31,'weight':180}]
with open('people.csv', 'w', encoding='utf8', newline='') as output_file:
    fc = csv.DictWriter(output_file, 
                        fieldnames=toCSV[0].keys(),

                       )
    fc.writeheader()
    fc.writerows(toCSV)

注意，python 3中的csv需要newline= "参数，否则在excel/opencalc中打开csv时会得到空行。

或者:我更喜欢使用pandas模块中的csv处理程序。我发现它对编码问题更宽容，并且pandas在加载文件时会自动将csv中的字符串数字转换为正确的类型(int,float等)。

import pandas
dataframe = pandas.read_csv(filepath)
list_of_dictionaries = dataframe.to_dict('records')
dataframe.to_csv(filepath)

注意:

pandas will take care of opening the file for you if you give it a path, and will default to utf8 in python3, and figure out headers too. a dataframe is not the same structure as what CSV gives you, so you add one line upon loading to get the same thing: dataframe.to_dict('records') pandas also makes it much easier to control the order of columns in your csv file. By default, they're alphabetical, but you can specify the column order. With vanilla csv module, you need to feed it an OrderedDict or they'll appear in a random order (if working in python < 3.5). See: Preserving column order in Python Pandas DataFrame for more.

import csv

to_csv = [
    {'name': 'bob', 'age': 25, 'weight': 200},
    {'name': 'jim', 'age': 31, 'weight': 180},
]

keys = to_csv[0].keys()

with open('people.csv', 'w', newline='') as output_file:
    dict_writer = csv.DictWriter(output_file, keys)
    dict_writer.writeheader()
    dict_writer.writerows(to_csv)

下面是另一个更通用的解决方案，假设你没有行列表(可能它们不适合内存)或头文件的副本(可能write_csv函数是通用的):

def gen_rows():
    yield OrderedDict(name='bob', age=25, weight=200)
    yield OrderedDict(name='jim', age=31, weight=180)

def write_csv():
    it = genrows()
    first_row = it.next()  # __next__ in py3
    with open("people.csv", "w") as outfile:
        wr = csv.DictWriter(outfile, fieldnames=list(first_row))
        wr.writeheader()
        wr.writerow(first_row)
        wr.writerows(it)

注意:此处使用的OrderedDict构造函数仅在python >3.4中保留顺序。如果顺序很重要，请使用OrderedDict([('name'， 'bob')，('age'，25)])形式。

熊猫的短期解决方案

import pandas as pd

list_of_dicts = [
    {'name': 'bob', 'age': 25, 'weight': 200},
    {'name': 'jim', 'age': 31, 'weight': 180},
]

df = pd.DataFrame(list_of_dicts) 
df.to_csv("names.csv", index=False)