问题:从CSV文件创建字典?

我正在尝试从csv文件创建字典。csv文件的第一列包含唯一键,第二列包含值。csv文件的每一行代表字典中的唯一键,值对。我尝试使用csv.DictReadercsv.DictWriter类,但是只能弄清楚如何为每一行生成一个新的字典。我要一部字典。这是我尝试使用的代码:

import csv

with open('coors.csv', mode='r') as infile:
    reader = csv.reader(infile)
    with open('coors_new.csv', mode='w') as outfile:
    writer = csv.writer(outfile)
    for rows in reader:
        k = rows[0]
        v = rows[1]
        mydict = {k:v for k, v in rows}
    print(mydict)

当我运行上面的代码时,我得到一个ValueError: too many values to unpack (expected 2)。如何从csv文件创建一个字典?谢谢。

I am trying to create a dictionary from a csv file. The first column of the csv file contains unique keys and the second column contains values. Each row of the csv file represents a unique key, value pair within the dictionary. I tried to use the csv.DictReader and csv.DictWriter classes, but I could only figure out how to generate a new dictionary for each row. I want one dictionary. Here is the code I am trying to use:

import csv

with open('coors.csv', mode='r') as infile:
    reader = csv.reader(infile)
    with open('coors_new.csv', mode='w') as outfile:
    writer = csv.writer(outfile)
    for rows in reader:
        k = rows[0]
        v = rows[1]
        mydict = {k:v for k, v in rows}
    print(mydict)

When I run the above code I get a ValueError: too many values to unpack (expected 2). How do I create one dictionary from a csv file? Thanks.


回答 0

我相信您正在寻找的语法如下:

import csv

with open('coors.csv', mode='r') as infile:
    reader = csv.reader(infile)
    with open('coors_new.csv', mode='w') as outfile:
        writer = csv.writer(outfile)
        mydict = {rows[0]:rows[1] for rows in reader}

或者,对于python <= 2.7.1,您需要:

mydict = dict((rows[0],rows[1]) for rows in reader)

I believe the syntax you were looking for is as follows:

import csv

with open('coors.csv', mode='r') as infile:
    reader = csv.reader(infile)
    with open('coors_new.csv', mode='w') as outfile:
        writer = csv.writer(outfile)
        mydict = {rows[0]:rows[1] for rows in reader}

Alternately, for python <= 2.7.1, you want:

mydict = dict((rows[0],rows[1]) for rows in reader)

回答 1

通过依次调用open和打开文件csv.DictReader

input_file = csv.DictReader(open("coors.csv"))

您可以通过遍历input_file遍历csv文件dict阅读器对象的行。

for row in input_file:
    print(row)

或仅访问第一行

dictobj = csv.DictReader(open('coors.csv')).next() 

更新 在python 3+版本中,此代码将有所变化:

reader = csv.DictReader(open('coors.csv'))
dictobj = next(reader) 

Open the file by calling open and then csv.DictReader.

input_file = csv.DictReader(open("coors.csv"))

You may iterate over the rows of the csv file dict reader object by iterating over input_file.

for row in input_file:
    print(row)

OR To access first line only

dictobj = csv.DictReader(open('coors.csv')).next() 

UPDATE In python 3+ versions, this code would change a little:

reader = csv.DictReader(open('coors.csv'))
dictobj = next(reader) 

回答 2

import csv
reader = csv.reader(open('filename.csv', 'r'))
d = {}
for row in reader:
   k, v = row
   d[k] = v
import csv
reader = csv.reader(open('filename.csv', 'r'))
d = {}
for row in reader:
   k, v = row
   d[k] = v

回答 3

这不是很好,但是使用熊猫的一线解决方案。

import pandas as pd
pd.read_csv('coors.csv', header=None, index_col=0, squeeze=True).to_dict()

如果要为索引指定dtype(如果由于bug而使用index_col参数,则无法在read_csv中指定该类型):

import pandas as pd
pd.read_csv('coors.csv', header=None, dtype={0: str}).set_index(0).squeeze().to_dict()

This isn’t elegant but a one line solution using pandas.

import pandas as pd
pd.read_csv('coors.csv', header=None, index_col=0, squeeze=True).to_dict()

If you want to specify dtype for your index (it can’t be specified in read_csv if you use the index_col argument because of a bug):

import pandas as pd
pd.read_csv('coors.csv', header=None, dtype={0: str}).set_index(0).squeeze().to_dict()

回答 4

您只需要将csv.reader转换为dict:

~ >> cat > 1.csv
key1, value1
key2, value2
key2, value22
key3, value3

~ >> cat > d.py
import csv
with open('1.csv') as f:
    d = dict(filter(None, csv.reader(f)))

print(d)

~ >> python d.py
{'key3': ' value3', 'key2': ' value22', 'key1': ' value1'}

You have to just convert csv.reader to dict:

~ >> cat > 1.csv
key1, value1
key2, value2
key2, value22
key3, value3

~ >> cat > d.py
import csv
with open('1.csv') as f:
    d = dict(filter(None, csv.reader(f)))

print(d)

~ >> python d.py
{'key3': ' value3', 'key2': ' value22', 'key1': ' value1'}

回答 5

您也可以为此使用numpy。

from numpy import loadtxt
key_value = loadtxt("filename.csv", delimiter=",")
mydict = { k:v for k,v in key_value }

You can also use numpy for this.

from numpy import loadtxt
key_value = loadtxt("filename.csv", delimiter=",")
mydict = { k:v for k,v in key_value }

回答 6

我建议添加if rows,以防文件末尾有空行

import csv
with open('coors.csv', mode='r') as infile:
    reader = csv.reader(infile)
    with open('coors_new.csv', mode='w') as outfile:
        writer = csv.writer(outfile)
        mydict = dict(row[:2] for row in reader if row)

I’d suggest adding if rows in case there is an empty line at the end of the file

import csv
with open('coors.csv', mode='r') as infile:
    reader = csv.reader(infile)
    with open('coors_new.csv', mode='w') as outfile:
        writer = csv.writer(outfile)
        mydict = dict(row[:2] for row in reader if row)

回答 7

一线解决方案

import pandas as pd

dict = {row[0] : row[1] for _, row in pd.read_csv("file.csv").iterrows()}

One-liner solution

import pandas as pd

dict = {row[0] : row[1] for _, row in pd.read_csv("file.csv").iterrows()}

回答 8

如果可以使用numpy包,则可以执行以下操作:

import numpy as np

lines = np.genfromtxt("coors.csv", delimiter=",", dtype=None)
my_dict = dict()
for i in range(len(lines)):
   my_dict[lines[i][0]] = lines[i][1]

If you are OK with using the numpy package, then you can do something like the following:

import numpy as np

lines = np.genfromtxt("coors.csv", delimiter=",", dtype=None)
my_dict = dict()
for i in range(len(lines)):
   my_dict[lines[i][0]] = lines[i][1]

回答 9

对于简单的csv文件,例如以下内容

id,col1,col2,col3
row1,r1c1,r1c2,r1c3
row2,r2c1,r2c2,r2c3
row3,r3c1,r3c2,r3c3
row4,r4c1,r4c2,r4c3

您可以仅使用内置功能将其转换为Python字典

with open(csv_file) as f:
    csv_list = [[val.strip() for val in r.split(",")] for r in f.readlines()]

(_, *header), *data = csv_list
csv_dict = {}
for row in data:
    key, *values = row   
    csv_dict[key] = {key: value for key, value in zip(header, values)}

这应该产生以下字典

{'row1': {'col1': 'r1c1', 'col2': 'r1c2', 'col3': 'r1c3'},
 'row2': {'col1': 'r2c1', 'col2': 'r2c2', 'col3': 'r2c3'},
 'row3': {'col1': 'r3c1', 'col2': 'r3c2', 'col3': 'r3c3'},
 'row4': {'col1': 'r4c1', 'col2': 'r4c2', 'col3': 'r4c3'}}

注意:Python字典具有唯一键,因此,如果csv文件重复ids,则应将每行追加到列表中。

for row in data:
    key, *values = row

    if key not in csv_dict:
            csv_dict[key] = []

    csv_dict[key].append({key: value for key, value in zip(header, values)})

For simple csv files, such as the following

id,col1,col2,col3
row1,r1c1,r1c2,r1c3
row2,r2c1,r2c2,r2c3
row3,r3c1,r3c2,r3c3
row4,r4c1,r4c2,r4c3

You can convert it to a Python dictionary using only built-ins

with open(csv_file) as f:
    csv_list = [[val.strip() for val in r.split(",")] for r in f.readlines()]

(_, *header), *data = csv_list
csv_dict = {}
for row in data:
    key, *values = row   
    csv_dict[key] = {key: value for key, value in zip(header, values)}

This should yield the following dictionary

{'row1': {'col1': 'r1c1', 'col2': 'r1c2', 'col3': 'r1c3'},
 'row2': {'col1': 'r2c1', 'col2': 'r2c2', 'col3': 'r2c3'},
 'row3': {'col1': 'r3c1', 'col2': 'r3c2', 'col3': 'r3c3'},
 'row4': {'col1': 'r4c1', 'col2': 'r4c2', 'col3': 'r4c3'}}

Note: Python dictionaries have unique keys, so if your csv file has duplicate ids you should append each row to a list.

for row in data:
    key, *values = row

    if key not in csv_dict:
            csv_dict[key] = []

    csv_dict[key].append({key: value for key, value in zip(header, values)})

回答 10

您可以使用它,这非常酷:

import dataconverters.commas as commas
filename = 'test.csv'
with open(filename) as f:
      records, metadata = commas.parse(f)
      for row in records:
            print 'this is row in dictionary:'+rowenter code here

You can use this, it is pretty cool:

import dataconverters.commas as commas
filename = 'test.csv'
with open(filename) as f:
      records, metadata = commas.parse(f)
      for row in records:
            print 'this is row in dictionary:'+rowenter code here

回答 11

已经发布了许多解决方案,我想为我的做出贡献,该解决方案适用于CSV文件中不同数量的列。它创建每列一个键的字典,每个键的值是一个列表,其中包含该列中的元素。

    input_file = csv.DictReader(open(path_to_csv_file))
    csv_dict = {elem: [] for elem in input_file.fieldnames}
    for row in input_file:
        for key in csv_dict.keys():
            csv_dict[key].append(row[key])

Many solutions have been posted and I’d like to contribute with mine, which works for a different number of columns in the CSV file. It creates a dictionary with one key per column, and the value for each key is a list with the elements in such column.

    input_file = csv.DictReader(open(path_to_csv_file))
    csv_dict = {elem: [] for elem in input_file.fieldnames}
    for row in input_file:
        for key in csv_dict.keys():
            csv_dict[key].append(row[key])

回答 12

例如,使用熊猫要容易得多。假设您拥有以下数据作为CSV并将其命名为test.txt/ test.csv(您知道CSV是一种文本文件)

a,b,c,d
1,2,3,4
5,6,7,8

现在正在使用熊猫

import pandas as pd
df = pd.read_csv("./text.txt")
df_to_doct = df.to_dict()

对于每一行,

df.to_dict(orient='records')

就是这样。

with pandas, it is much easier, for example. assuming you have the following data as CSV and let’s call it test.txt / test.csv (you know CSV is a sort of text file )

a,b,c,d
1,2,3,4
5,6,7,8

now using pandas

import pandas as pd
df = pd.read_csv("./text.txt")
df_to_doct = df.to_dict()

for each row, it would be

df.to_dict(orient='records')

and that’s it.


回答 13

尝试使用defaultdictDictReader

import csv
from collections import defaultdict
my_dict = defaultdict(list)

with open('filename.csv', 'r') as csv_file:
    csv_reader = csv.DictReader(csv_file)
    for line in csv_reader:
        for key, value in line.items():
            my_dict[key].append(value)

它返回:

{'key1':[value_1, value_2, value_3], 'key2': [value_a, value_b, value_c], 'Key3':[value_x, Value_y, Value_z]}

Try to use a defaultdict and DictReader.

import csv
from collections import defaultdict
my_dict = defaultdict(list)

with open('filename.csv', 'r') as csv_file:
    csv_reader = csv.DictReader(csv_file)
    for line in csv_reader:
        for key, value in line.items():
            my_dict[key].append(value)

It returns:

{'key1':[value_1, value_2, value_3], 'key2': [value_a, value_b, value_c], 'Key3':[value_x, Value_y, Value_z]}

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