How do I convert this list of dictionaries to a csv file?

I have a list of dictionaries that looks something like this:

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

What should I do to convert this to a csv file that looks something like this:

name,age,weight
bob,25,200
jim,31,180

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)

In python 3 things are a little different, but way simpler and less error prone. It's a good idea to tell the CSV your file should be opened with utf8 encoding, as it makes that data more portable to others (assuming you aren't using a more restrictive encoding, like 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)

• Note that csv in python 3 needs the newline='' parameter, otherwise you get blank lines in your CSV when opening in excel/opencalc.

Alternatively: I prefer use to the csv handler in the pandas module. I find it is more tolerant of encoding issues, and pandas will automatically convert string numbers in CSVs into the correct type (int,float,etc) when loading the file.

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

Note:

• 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.

this is when you have one dictionary list:

import csv
with open('names.csv', 'w') as csvfile:
    fieldnames = ['first_name', 'last_name']
    writer = csv.DictWriter(csvfile, fieldnames=fieldnames)
    writer.writeheader()
    writer.writerow({'first_name': 'Baked', 'last_name': 'Beans'})

Because @User and @BiXiC asked for help with UTF-8 here a variation of the solution by @Matthew. (I'm not allowed to comment, so I'm answering.)

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)

Here is another, more general solution assuming you don't have a list of rows (maybe they don't fit in memory) or a copy of the headers (maybe the write_csv function is generic):

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)

Note: the OrderedDict constructor used here only preserves order in python >3.4. If order is important, use the OrderedDict([('name', 'bob'),('age',25)]) form.