How to create a dictionary from CSV using a loop function? - python-3.x

Question

I am trying to create several dictionaries out of a table of comments from a CSV with the following columns:
I need to create a dictionary for every row (hopefully using a loop so I don't have to create them all manually), where the dictionary keys are:
ID
ReviewType
Comment
However, I cannot figure out a fast way to do this. I tried creating a list of dictionaries using the following code:
# Import libraries
import csv
import json
import pprint
# Open file
reader = csv.DictReader(open('Comments.csv', 'rU'))
# Create list of dictionaries
dict_list = []
for line in reader:
dict_list.append(line)
pprint.pprint(dict_list)
However, now I do not know how to access the dictionaries or whether the key value pairs are matched properly since in the following image:
The ID, ReviewType and Comment do not seem to be showing as
dictionary keys
The Comment value seems to be showing as a list of half-sentences.
Is there any way to just create one dictionary for each row instead of a list of dictionaries?
Note: I did look at this question, however it didn't really help.

Answer 1

Here you go. I put the comment into an array
# Import libraries
import csv
import json
import pprint
# Open file
def readPerfReviewCSVToDict(csvPath):
reader = csv.DictReader(open(csvPath, 'rU'))
perfReviewsDictionary = []
for line in reader:
perfReviewsDictionary.append(line)
perfReviewsDictionaryWithCommentsSplit = []
for item in perfReviewsDictionary:
itemId = item["id"]
itemType = item["type"]
itemComment = item["comments"]
itemCommentDictionary = []
itemCommentDictionary = itemComment.split()
perfReviewsDictionaryWithCommentsSplit.append({'id':itemId, 'type':itemType, 'comments':itemCommentDictionary})
return perfReviewsDictionaryWithCommentsSplit
dict_list = readPerfReviewCSVToDict("test.csv")
pprint.pprint(dict_list)
The output is:
[{'comments': ['test', 'ape', 'dog'], 'id': '1', 'type': 'Test'},
{'comments': ['dog'], 'id': '2', 'type': 'Test'}]

Answer 2

Since you haven't given a reproducible example, with a sample DataFrame, I've created one for you
import pandas as pd
df = pd.DataFrame([[1, "Contractor", "Please post"], [2, "Developer", "a reproducible example"]])
df.columns = ['ID', 'ReviewType', 'Comment']
In your computer, instead of doing this, type:
df = pd.read_csv(file_path)
to read in the csv file as a pandas DataFrame.
Now I will create a list, called dictList which will be empty initially, I am going to populate it with a dictionary for each row in the DataFrame df
dictList = []
#Iterate over each row in df
for i in df.index:
#Creating an empty dictionary for each row
rowDict = {}
#Populating it
rowDict['ID'] = df.at[i, 'ID']
rowDict['ReviewType'] = df.at[i, 'ReviewType']
rowDict['Comment'] = df.at[i, 'Comment']
#Once I'm done populating it, I will append it to the list
dictList.append(rowDict)
#Go to the next row and repeat.
Now iterating over the list of dictionaries we have created for my example
for i in dictList:
print(i)
We get
{'ID': 1, 'ReviewType': 'Contractor', 'Comment': 'Please post'}
{'ID': 2, 'ReviewType': 'Developer', 'Comment': 'a reproducible example'}

Answer 3

Do you want this?
DICT = {}
for line in reader:
DICT[line['ID']] = line