Python将csv导入列表

问题:Python将csv导入列表

我有一个大约有2000条记录的CSV文件。

每个记录都有一个字符串和一个类别:

This is the first line,Line1
This is the second line,Line2
This is the third line,Line3

我需要将此文件读入如下列表:

data = [('This is the first line', 'Line1'),
        ('This is the second line', 'Line2'),
        ('This is the third line', 'Line3')]

如何使用Python将CSV导入到我需要的列表中?

I have a CSV file with about 2000 records.

Each record has a string, and a category to it:

This is the first line,Line1
This is the second line,Line2
This is the third line,Line3

I need to read this file into a list that looks like this:

data = [('This is the first line', 'Line1'),
        ('This is the second line', 'Line2'),
        ('This is the third line', 'Line3')]

How can import this CSV to the list I need using Python?


回答 0

使用csv模块

import csv

with open('file.csv', newline='') as f:
    reader = csv.reader(f)
    data = list(reader)

print(data)

输出:

[['This is the first line', 'Line1'], ['This is the second line', 'Line2'], ['This is the third line', 'Line3']]

如果您需要元组:

import csv

with open('file.csv', newline='') as f:
    reader = csv.reader(f)
    data = [tuple(row) for row in reader]

print(data)

输出:

[('This is the first line', 'Line1'), ('This is the second line', 'Line2'), ('This is the third line', 'Line3')]

旧的Python 2答案,也使用csv模块:

import csv
with open('file.csv', 'rb') as f:
    reader = csv.reader(f)
    your_list = list(reader)

print your_list
# [['This is the first line', 'Line1'],
#  ['This is the second line', 'Line2'],
#  ['This is the third line', 'Line3']]

Using the csv module:

import csv

with open('file.csv', newline='') as f:
    reader = csv.reader(f)
    data = list(reader)

print(data)

Output:

[['This is the first line', 'Line1'], ['This is the second line', 'Line2'], ['This is the third line', 'Line3']]

If you need tuples:

import csv

with open('file.csv', newline='') as f:
    reader = csv.reader(f)
    data = [tuple(row) for row in reader]

print(data)

Output:

[('This is the first line', 'Line1'), ('This is the second line', 'Line2'), ('This is the third line', 'Line3')]

Old Python 2 answer, also using the csv module:

import csv
with open('file.csv', 'rb') as f:
    reader = csv.reader(f)
    your_list = list(reader)

print your_list
# [['This is the first line', 'Line1'],
#  ['This is the second line', 'Line2'],
#  ['This is the third line', 'Line3']]

回答 1

已针对Python 3更新:

import csv

with open('file.csv', newline='') as f:
    reader = csv.reader(f)
    your_list = list(reader)

print(your_list)

输出:

[['This is the first line', 'Line1'], ['This is the second line', 'Line2'], ['This is the third line', 'Line3']]

Updated for Python 3:

import csv

with open('file.csv', newline='') as f:
    reader = csv.reader(f)
    your_list = list(reader)

print(your_list)

Output:

[['This is the first line', 'Line1'], ['This is the second line', 'Line2'], ['This is the third line', 'Line3']]

回答 2

熊猫非常擅长处理数据。这是一个如何使用它的示例:

import pandas as pd

# Read the CSV into a pandas data frame (df)
#   With a df you can do many things
#   most important: visualize data with Seaborn
df = pd.read_csv('filename.csv', delimiter=',')

# Or export it in many ways, e.g. a list of tuples
tuples = [tuple(x) for x in df.values]

# or export it as a list of dicts
dicts = df.to_dict().values()

一大优势是,熊猫自动处理标题行。

如果您还没有听说过Seaborn,建议您看看。

另请参阅:如何使用Python读写CSV文件?

熊猫#2

import pandas as pd

# Get data - reading the CSV file
import mpu.pd
df = mpu.pd.example_df()

# Convert
dicts = df.to_dict('records')

df的内容是:

     country   population population_time    EUR
0    Germany   82521653.0      2016-12-01   True
1     France   66991000.0      2017-01-01   True
2  Indonesia  255461700.0      2017-01-01  False
3    Ireland    4761865.0             NaT   True
4      Spain   46549045.0      2017-06-01   True
5    Vatican          NaN             NaT   True

字典的内容是

[{'country': 'Germany', 'population': 82521653.0, 'population_time': Timestamp('2016-12-01 00:00:00'), 'EUR': True},
 {'country': 'France', 'population': 66991000.0, 'population_time': Timestamp('2017-01-01 00:00:00'), 'EUR': True},
 {'country': 'Indonesia', 'population': 255461700.0, 'population_time': Timestamp('2017-01-01 00:00:00'), 'EUR': False},
 {'country': 'Ireland', 'population': 4761865.0, 'population_time': NaT, 'EUR': True},
 {'country': 'Spain', 'population': 46549045.0, 'population_time': Timestamp('2017-06-01 00:00:00'), 'EUR': True},
 {'country': 'Vatican', 'population': nan, 'population_time': NaT, 'EUR': True}]

熊猫#3

import pandas as pd

# Get data - reading the CSV file
import mpu.pd
df = mpu.pd.example_df()

# Convert
lists = [[row[col] for col in df.columns] for row in df.to_dict('records')]

的内容lists是:

[['Germany', 82521653.0, Timestamp('2016-12-01 00:00:00'), True],
 ['France', 66991000.0, Timestamp('2017-01-01 00:00:00'), True],
 ['Indonesia', 255461700.0, Timestamp('2017-01-01 00:00:00'), False],
 ['Ireland', 4761865.0, NaT, True],
 ['Spain', 46549045.0, Timestamp('2017-06-01 00:00:00'), True],
 ['Vatican', nan, NaT, True]]

Pandas is pretty good at dealing with data. Here is one example how to use it:

import pandas as pd

# Read the CSV into a pandas data frame (df)
#   With a df you can do many things
#   most important: visualize data with Seaborn
df = pd.read_csv('filename.csv', delimiter=',')

# Or export it in many ways, e.g. a list of tuples
tuples = [tuple(x) for x in df.values]

# or export it as a list of dicts
dicts = df.to_dict().values()

One big advantage is that pandas deals automatically with header rows.

If you haven’t heard of Seaborn, I recommend having a look at it.

See also: How do I read and write CSV files with Python?

Pandas #2

import pandas as pd

# Get data - reading the CSV file
import mpu.pd
df = mpu.pd.example_df()

# Convert
dicts = df.to_dict('records')

The content of df is:

     country   population population_time    EUR
0    Germany   82521653.0      2016-12-01   True
1     France   66991000.0      2017-01-01   True
2  Indonesia  255461700.0      2017-01-01  False
3    Ireland    4761865.0             NaT   True
4      Spain   46549045.0      2017-06-01   True
5    Vatican          NaN             NaT   True

The content of dicts is

[{'country': 'Germany', 'population': 82521653.0, 'population_time': Timestamp('2016-12-01 00:00:00'), 'EUR': True},
 {'country': 'France', 'population': 66991000.0, 'population_time': Timestamp('2017-01-01 00:00:00'), 'EUR': True},
 {'country': 'Indonesia', 'population': 255461700.0, 'population_time': Timestamp('2017-01-01 00:00:00'), 'EUR': False},
 {'country': 'Ireland', 'population': 4761865.0, 'population_time': NaT, 'EUR': True},
 {'country': 'Spain', 'population': 46549045.0, 'population_time': Timestamp('2017-06-01 00:00:00'), 'EUR': True},
 {'country': 'Vatican', 'population': nan, 'population_time': NaT, 'EUR': True}]

Pandas #3

import pandas as pd

# Get data - reading the CSV file
import mpu.pd
df = mpu.pd.example_df()

# Convert
lists = [[row[col] for col in df.columns] for row in df.to_dict('records')]

The content of lists is:

[['Germany', 82521653.0, Timestamp('2016-12-01 00:00:00'), True],
 ['France', 66991000.0, Timestamp('2017-01-01 00:00:00'), True],
 ['Indonesia', 255461700.0, Timestamp('2017-01-01 00:00:00'), False],
 ['Ireland', 4761865.0, NaT, True],
 ['Spain', 46549045.0, Timestamp('2017-06-01 00:00:00'), True],
 ['Vatican', nan, NaT, True]]

回答 3

Python3更新:

import csv
from pprint import pprint

with open('text.csv', newline='') as file:
    reader = csv.reader(file)
    res = list(map(tuple, reader))

pprint(res)

输出:

[('This is the first line', ' Line1'),
 ('This is the second line', ' Line2'),
 ('This is the third line', ' Line3')]

如果csvfile是文件对象,则应使用打开newline=''
CSV模组

Update for Python3:

import csv
from pprint import pprint

with open('text.csv', newline='') as file:
    reader = csv.reader(file)
    res = list(map(tuple, reader))

pprint(res)

Output:

[('This is the first line', ' Line1'),
 ('This is the second line', ' Line2'),
 ('This is the third line', ' Line3')]

If csvfile is a file object, it should be opened with newline=''.
csv module


回答 4

如果你相信有您的输入没有逗号,以外的其他类别分开,你可以逐行读取文件中的行分裂,,然后推结果List

也就是说,您似乎正在查看CSV文件,因此您可以考虑为其使用模块

If you are sure there are no commas in your input, other than to separate the category, you can read the file line by line and split on ,, then push the result to List

That said, it looks like you are looking at a CSV file, so you might consider using the modules for it


回答 5

result = []
for line in text.splitlines():
    result.append(tuple(line.split(",")))
result = []
for line in text.splitlines():
    result.append(tuple(line.split(",")))

回答 6

正如评论中已经说过的那样,您可以csv在python中使用该库。csv的意思是逗号分隔的值,这似乎与您的情况完全相同:标签和由逗号分隔的值。

作为类别和值类型,我宁愿使用字典类型而不是元组列表。

无论如何,在下面的代码中,我都会同时显示两种方式:d是字典,l是元组列表。

import csv

file_name = "test.txt"
try:
    csvfile = open(file_name, 'rt')
except:
    print("File not found")
csvReader = csv.reader(csvfile, delimiter=",")
d = dict()
l =  list()
for row in csvReader:
    d[row[1]] = row[0]
    l.append((row[0], row[1]))
print(d)
print(l)

As said already in the comments you can use the csv library in python. csv means comma separated values which seems exactly your case: a label and a value separated by a comma.

Being a category and value type I would rather use a dictionary type instead of a list of tuples.

Anyway in the code below I show both ways: d is the dictionary and l is the list of tuples.

import csv

file_name = "test.txt"
try:
    csvfile = open(file_name, 'rt')
except:
    print("File not found")
csvReader = csv.reader(csvfile, delimiter=",")
d = dict()
l =  list()
for row in csvReader:
    d[row[1]] = row[0]
    l.append((row[0], row[1]))
print(d)
print(l)

回答 7

一个简单的循环就足够了:

lines = []
with open('test.txt', 'r') as f:
    for line in f.readlines():
        l,name = line.strip().split(',')
        lines.append((l,name))

print lines

A simple loop would suffice:

lines = []
with open('test.txt', 'r') as f:
    for line in f.readlines():
        l,name = line.strip().split(',')
        lines.append((l,name))

print lines

回答 8

不幸的是,我发现没有一个现有的答案特别令人满意。

这是一个使用csv模块的简单,完整的Python 3解决方案。

import csv

with open('../resources/temp_in.csv', newline='') as f:
    reader = csv.reader(f, skipinitialspace=True)
    rows = list(reader)

print(rows)

注意skipinitialspace=True参数。这是必要的,因为不幸的是,OP的CSV在每个逗号后都包含空格。

输出:

[['This is the first line', 'Line1'], ['This is the second line', 'Line2'], ['This is the third line', 'Line3']]

Unfortunately I find none of the existing answers particularly satisfying.

Here is a straightforward and complete Python 3 solution, using the csv module.

import csv

with open('../resources/temp_in.csv', newline='') as f:
    reader = csv.reader(f, skipinitialspace=True)
    rows = list(reader)

print(rows)

Notice the skipinitialspace=True argument. This is necessary since, unfortunately, OP’s CSV contains whitespace after each comma.

Output:

[['This is the first line', 'Line1'], ['This is the second line', 'Line2'], ['This is the third line', 'Line3']]

回答 9

稍微扩展您的需求,并假设您不关心行的顺序,并希望将它们分组在类别下,则以下解决方案可能适用于您:

>>> fname = "lines.txt"
>>> from collections import defaultdict
>>> dct = defaultdict(list)
>>> with open(fname) as f:
...     for line in f:
...         text, cat = line.rstrip("\n").split(",", 1)
...         dct[cat].append(text)
...
>>> dct
defaultdict(<type 'list'>, {' CatA': ['This is the first line', 'This is the another line'], ' CatC': ['This is the third line'], ' CatB': ['This is the second line', 'This is the last line']})

这样,您可以在字典中键为类别下获得所有可用的相关行。

Extending your requirements a bit and assuming you do not care about the order of lines and want to get them grouped under categories, the following solution may work for you:

>>> fname = "lines.txt"
>>> from collections import defaultdict
>>> dct = defaultdict(list)
>>> with open(fname) as f:
...     for line in f:
...         text, cat = line.rstrip("\n").split(",", 1)
...         dct[cat].append(text)
...
>>> dct
defaultdict(<type 'list'>, {' CatA': ['This is the first line', 'This is the another line'], ' CatC': ['This is the third line'], ' CatB': ['This is the second line', 'This is the last line']})

This way you get all relevant lines available in the dictionary under key being the category.


回答 10

这是Python 3.x中最简单的将CSV导入多维数组的方法,它仅4行代码而无需导入任何内容!

#pull a CSV into a multidimensional array in 4 lines!

L=[]                            #Create an empty list for the main array
for line in open('log.txt'):    #Open the file and read all the lines
    x=line.rstrip()             #Strip the \n from each line
    L.append(x.split(','))      #Split each line into a list and add it to the
                                #Multidimensional array
print(L)

Here is the easiest way in Python 3.x to import a CSV to a multidimensional array, and its only 4 lines of code without importing anything!

#pull a CSV into a multidimensional array in 4 lines!

L=[]                            #Create an empty list for the main array
for line in open('log.txt'):    #Open the file and read all the lines
    x=line.rstrip()             #Strip the \n from each line
    L.append(x.split(','))      #Split each line into a list and add it to the
                                #Multidimensional array
print(L)

回答 11

接下来是一段代码,该代码使用csv模块,但使用第一行(即csv表的标头)将file.csv内容提取到字典列表中

import csv
def csv2dicts(filename):
  with open(filename, 'rb') as f:
    reader = csv.reader(f)
    lines = list(reader)
    if len(lines) < 2: return None
    names = lines[0]
    if len(names) < 1: return None
    dicts = []
    for values in lines[1:]:
      if len(values) != len(names): return None
      d = {}
      for i,_ in enumerate(names):
        d[names[i]] = values[i]
      dicts.append(d)
    return dicts
  return None

if __name__ == '__main__':
  your_list = csv2dicts('file.csv')
  print your_list

Next is a piece of code which uses csv module but extracts file.csv contents to a list of dicts using the first line which is a header of csv table

import csv
def csv2dicts(filename):
  with open(filename, 'rb') as f:
    reader = csv.reader(f)
    lines = list(reader)
    if len(lines) < 2: return None
    names = lines[0]
    if len(names) < 1: return None
    dicts = []
    for values in lines[1:]:
      if len(values) != len(names): return None
      d = {}
      for i,_ in enumerate(names):
        d[names[i]] = values[i]
      dicts.append(d)
    return dicts
  return None

if __name__ == '__main__':
  your_list = csv2dicts('file.csv')
  print your_list