If you are working with CSV data and want a solution with a smaller footprint than pandas, you can try my package, littletable. Can be pip-installed, or just dropped in as a single .py file with your own code, so very portable and suitable for serverless apps.

Reading CSV data is as simple as calling csv_import:

data = """\
1,"A towel,",1.0
42," it says, ",2.0
1337,is about the most ,-1
0,massively useful thing ,123
-2,an interstellar hitchhiker can have.,3"""

import littletable as lt
tbl = lt.Table().csv_import(data, fieldnames="number1,words,number2".split(','))
tbl.present()

Prints:

  Number1   Words                                  Number2  
 ────────────────────────────────────────────────────────── 
  1         A towel,                               1.0      
  42         it says,                              2.0      
  1337      is about the most                      -1       
  0         massively useful thing                 123      
  -2        an interstellar hitchhiker can have.   3    

(littletable uses the rich module for presenting Tables.)

littletable doesn't automatically try to convert numeric data, so a numeric transform function is needed for the numeric columns.

def get_numeric(s):
    try:
        return int(s)
    except ValueError:
        try:
            return float(s)
        except ValueError:
            return s

tbl = lt.Table().csv_import(
    data,
    fieldnames="number1,words,number2".split(','),
    transforms={}.fromkeys("number1 number2".split(), get_numeric)
)
tbl.present()

This gives:

  Number1   Words                                  Number2  
 ────────────────────────────────────────────────────────── 
        1   A towel,                                   1.0  
       42    it says,                                  2.0  
     1337   is about the most                           -1  
        0   massively useful thing                     123  
       -2   an interstellar hitchhiker can have.         3  

The numeric columns are right-justified instead of left-justified.

littletable also has other ORM-ish features, such as indexing, joining, pivoting, and full-text search. Here is a table of statistics on the numeric columns:

tbl.stats("number1 number2".split()).present()

  Name       Mean   Min    Max   Variance              Std_Dev   Count   Missing  
 ──────────────────────────────────────────────────────────────────────────────── 
  number1   275.6    -2   1337   352390.3    593.6247130974249       5         0  
  number2    25.6    -1    123     2966.8   54.468339427597755       5         0  

or transposed:

tbl.stats("number1 number2".split(), by_field=False).present()

  Stat                 Number1              Number2 
 ─────────────────────────────────────────────────── 
  mean                   275.6                 25.6
  min                       -2                   -1
  max                     1337                  123
  variance            352390.3               2966.8
  std_dev    593.6247130974249   54.468339427597755
  count                      5                    5
  missing                    0                    0

Other formats can be output too, such as Markdown:

print(tbl.stats("number1 number2".split(), by_field=False).as_markdown())

| stat | number1 | number2 |
|---|---:|---:|
| mean | 275.6 | 25.6 |
| min | -2 | -1 |
| max | 1337 | 123 |
| variance | 352390.3 | 2966.8 |
| std_dev | 593.6247130974249 | 54.468339427597755 |
| count | 5 | 5 |
| missing | 0 | 0 |

Which would render from Markdown as

Lastly, here is a text search on the words for any entry with the word "hitchhiker":

tbl.create_search_index("words")
for match in tbl.search.words("hitchhiker"):
    print(match)

Prints:

   namespace(number1=-2, words='an interstellar hitchhiker can have.', number2=3)

Use pandas:

import pandas as pd

fruits = ["apple", "banana", "grape", "orange"]
vegetables = ["onion", "garlic", "carrot", "cabbage"]
spices = ["salt", "pepper", "paprika", "cumin"]
types = ["Fruits", "Vegetables", "Spices"]

df = pd.DataFrame( list(zip(fruits,vegetables,spices)), columns = types) 
df.to_csv(r'file_name.csv', index = False)

Result:

Fruits,Vegetables,Spices
apple,onion,salt
banana,garlic,pepper
grape,carrot,paprika
orange,cabbage,cumin

If you really want to use the csv module there are multiple errors in your code you need to fix, you need to pass the fieldnames to csv.DictWriter then write them and the length of the empty string will be 1 so csv.DictWriter will never be True so nothing will be changed:

with open('in.csv', 'r') as infile, open('Fixed.csv', 'w') as outfile:
    r = csv.DictReader(infile)
    w = csv.DictWriter(outfile, fieldnames=r.fieldnames)
    w.writeheader()
    for row in r:
        for k, v in row.items():
            if v == "''":
                row[k] = "0"
        w.writerow(row)

To change the original file it would actually be easier just use str.replace and open the file without the csv module, to change the original file you can use fileinput with inplace=True:

import fileinput
import sys

for line in fileinput.input("in.csv",inplace=True):
    sys.stdout.write(line.replace("''","0"))

Output:

One,Two,Three,Four
1,0,0,1
0,0,1,0
1,0,1,0
0,0,0,1

Or using a NamedTemporaryFile with shutil.move, writing to the tempfile the replacing the original file with the updated file using move:

from tempfile import NamedTemporaryFile

from shutil import move

with open('TestCSV.csv', 'r') as infile, NamedTemporaryFile(dir=".", delete=False) as  outfile:
    for line in infile:
        outfile.write(line.replace("''", "0"))

move(outfile.name,'TestCSV.csv')

Or if you want a new file just do the same using file.write:

with open('TestCSV.csv','r') as infile,open('Fixed.csv','w') as outfile:
     for line in infile:
         outfile.write(line.replace("''","0"))

You only have either an empty string or a "1" in each line so it is simpler just to replace the empty strings.

You could also do it quite easily with pandas using a Dataframe and df.replace if you intend to actually create a df with your data:

df = pd.read_csv("in.csv")
df.replace("''","0",inplace=True)
print(df)

  One Two Three Four
0   1   0     0    1
1   0   0     1    0
2   1   0     1    0
3   0   0     0    1
# work on df
.......
# save results to csv
df.to_csv("fixed.csv",index=False)

Output:

One,Two,Three,Four
1,0,0,1
0,0,1,0
1,0,1,0
0,0,0,1