我有一个大约有2000条记录的CSV文件。
每条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
我需要把这个文件读入一个列表，看起来像这样:
data = [('This is the first line', 'Line1'),
('This is the second line', 'Line2'),
('This is the third line', 'Line3')]
如何使用Python将此CSV导入到我需要的列表?
当前回答
Python3的更新:
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是一个文件对象，它应该用newline= "打开。 csv模块
其他回答
如果确定输入中没有逗号，而不是分隔类别，则可以逐行读取文件并在，，上进行分割，然后将结果推入List
也就是说，看起来您正在查看一个CSV文件，因此您可以考虑为它使用模块
result = []
for line in text.splitlines():
result.append(tuple(line.split(",")))
这是Python 3中最简单的方法。x导入CSV到多维数组，它只有4行代码，没有导入任何东西!
#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)
Pandas非常擅长处理数据。下面是一个如何使用它的例子:
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()
一个很大的优势是pandas自动处理标题行。
如果你没有听说过Seaborn，我建议你去看看。
请参见: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的内容为:
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
词典的内容是
[{'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')]
列表的内容是:
[['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]]
Python3的更新:
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是一个文件对象，它应该用newline= "打开。 csv模块
