如何使用 CSV 文件的唯一值在 Python 中创建列表？
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How to create a list in Python with the unique values of a CSV file?
提问by Gravity M
I have CSV file that looks like the following,
我有如下所示的 CSV 文件，
1994, Category1, Something Happened 1
1994, Category2, Something Happened 2
1995, Category1, Something Happened 3
1996, Category3, Something Happened 4
1998, Category2, Something Happened 5
I want to create two lists,
我想创建两个列表，
Category = [Category1, Category2, Category3]
and
和
Year = [1994, 1995, 1996, 1998]
I want to omit the duplicates in the column. I am reading the file as following,
我想省略列中的重复项。我正在阅读文件如下，
DataCaptured = csv.reader(DataFile, delimiter=',')
DataCaptured.next()
and Looping through,
并循环通过，
for Column in DataCaptured:
采纳答案by dawg
You can do:
你可以做：
DataCaptured = csv.reader(DataFile, delimiter=',', skipinitialspace=True)
Category, Year = [], []
for row in DataCaptured:
if row[0] not in Year:
Year.append(row[0])
if row[1] not in Category:
Category.append(row[1])
print Category, Year
# ['Category1', 'Category2', 'Category3'] ['1994', '1995', '1996', '1998']
As stated in the comments, if order does not matter, using a set would be easier and faster:
如评论中所述，如果顺序无关紧要，则使用 set 会更容易、更快：
Category, Year = set(), set()
for row in DataCaptured:
Year.add(row[0])
Category.add(row[1])
回答by CT Zhu
A very concise way to do this is to use pandas, the benefits are: it has a faster CSV pharser; and it works in columns (so it only requires one df.apply(set)to get you there) :
一个非常简洁的方法是使用pandas，好处是：它有一个更快的 CSV 分析器；并且它在列中工作（所以它只需要一个df.apply(set)就能让你到达那里）：
In [244]:
#Suppose the CSV is named temp.csv
df=pd.read_csv('temp.csv',header=None)
df.apply(set)
Out[244]:
0 set([1994, 1995, 1996, 1998])
1 set([ Category2, Category3, Category1])
2 set([ Something Happened 4, Something Happene...
dtype: object
The downside is that it returns a pandas.Series, and to get access each list, you need to do something like list(df.apply(set)[0]).
缺点是它返回一个pandas.Series，并且要访问每个列表，您需要执行类似list(df.apply(set)[0]).
Edit
编辑
If the order has to be preserved, it can be also done very easily, for example:
如果必须保留顺序，也可以很容易地完成，例如：
for i, item in df.iteritems():
print item.unique()
item.unique()will return numpy.arrays, instead of lists.
item.unique()将返回numpy.arrays，而不是lists。
回答by rob_7cc
dawgpointed out one of the greatest tricks in Python: using set()to remove duplicates from a list. dawgshows how to build the unique list from scratch by adding each item to a set, which is perfect. But here's another equivalent way to do it, generating a list with duplicates and a list without duplicates using a list(set())approach:
dawg指出了 Python 中最伟大的技巧之一：使用set()从列表中删除重复项。 dawg展示了如何通过将每个项目添加到 a 来从头开始构建唯一列表set，这是完美的。但这是另一种等效的方法，使用一种list(set())方法生成一个有重复项的列表和一个没有重复项的列表：
import csv
in_str = [
'year, category, event',
'1994, Category1, Something Happened 1',
'1994, Category2, Something Happened 2',
'1995, Category1, Something Happened 3',
'1996, Category3, Something Happened 4',
'1998, Category2, Something Happened 5'
]
cdr = csv.DictReader(in_str, skipinitialspace=True)
col = []
for i in cdr:
col.append(i['category'])
# all items in the column...
print(col)
# only unique items in the column...
print(list(set(col)))
