从CSV文件创建字典？

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.

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)

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) 

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

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()

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'}

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 }

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)

One-liner solution

import pandas as pd

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

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]

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)})

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

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])

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.

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]}