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 thecsv.DictReader
andcsv.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 using 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 }
Assuming you have a CSV of this structure:
"a","b"
1,2
3,4
5,6
And you want the output to be:
[{'a': '1', ' "b"': '2'}, {'a': '3', ' "b"': '4'}, {'a': '5', ' "b"': '6'}]
A zip function (not yet mentioned) is simple and quite helpful.
def read_csv(filename):
with open(filename) as f:
file_data=csv.reader(f)
headers=next(file_data)
return [dict(zip(headers,i)) for i in file_data]
If you prefer pandas, it can also do this quite nicely:
import pandas as pd
def read_csv(filename):
return pd.read_csv(filename).to_dict('records')
One-liner solution
import pandas as pd
dict = {row[0] : row[1] for _, row in pd.read_csv("file.csv").iterrows()}
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)})
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)
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]
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.
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
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]}
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])
If you have:
Do this:
mydict = {y[0]: y[1] for y in [x.split(",") for x in open('file.csv').read().split('\n') if x]}
It uses list comprehension to split lines and the last "if x" is used to ignore blank line (usually at the end) which is then unpacked into a dict using dictionary comprehension.
here is an approach for CSV to Dict:
import pandas
data = pandas.read_csv('coors.csv')
the_dictionary_name = {row.k: row.v for (index, row) in data.iterrows()}
The question derailed us from the correct solution... which requires taking a step back and asking if we chose the correct format to store dictionary data? For a dictionary, a CSV file is a lossy format that silently casts all numeric values to string values... so the correct answer would be IMO to save it to JSON in the first place.
And then simply:
import json
my_dict = json.load(open('my_file.json', 'r'))
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