Python import csv to list

问题:

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;

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

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

回答1:

Use the csv module (Python 2.x):

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\']]

If you need tuples:

import csv
with open(\'test.csv\', \'rb\') as f:
    reader = csv.reader(f)
    your_list = map(tuple, reader)

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

Python 3.x version (by @seokhoonlee below)

import csv

with open(\'file.csv\', \'r\') 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2:

Update for Python3:

import csv

with open(\'file.csv\', \'r\') 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3:

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
tuples = [[row[col] for col in df.columns] for row in df.to_dict(\'records\')]

The content of tuples 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4:

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(\",\")))

回答6:

Update for Python3:

import csv
from pprint import pprint

with open(\'text.csv\', newline=\'\') as file:
reader = csv.reader(file)
l = list(map(tuple, reader))
pprint(l)
[(\'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7:

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:

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9:

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

回答10:

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)