How to change the numbers in a string in a value of a dictionary?

user_16945470 0
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I have the following dictionary given and a list of keys. What I want to do this is that if these keys are in dictionary and has any numbers in the their value (which are strings). I want to change those numbers into '#' characters.

dict:

{"name":"Jone","age":"40 years","phone":"88777444"}

keys:

["age","phone"]

output:

{"name":"Jone","age":"## years","phone":"########"}

so far I have been able to grab those numbers but dont know how to change them in the dictionary:

my progress:

def convert(input, keys):
    for k in range(len(keys)):
        if keys[k] in input:
            for el in input[keys[k]]:
                if el.isdigit():
                    print(el)

As you can see I am using python. If you use different language a hint towards the right direction will be great.

dictionary algorithm python string
6 Answers

This is essentially the same as an answer that's already been given but takes an approach that is more easily adapted to having, say, a list of dictionaries:

import re

d = {"name":"Jone","age":"40 years","phone":"88777444"}
keys = ["age","phone"]

for k, v in d.items():
    if k in keys:
        d[k] = re.sub('\d', '#', v)
print(d)

Output:

{'name': 'Jone', 'age': '## years', 'phone': '########'}
user_175803810

I'd recommend a regular expression.

import re

data = {"name":"Jone","age":"40 years","phone":"88777444"}
keys = ["age","phone"]

for key in keys:
    if key in data:
        # replace every key with a new string where every number 
        # ('[0-9]') is substituted by a '#'
        data[key] = re.sub('[0-9]', '#', data[key])

# {'name': 'Jone', 'age': '## years', 'phone': '########'}
print(data)
user_158730430

Here is the one line solution using dictionary comprehension.

d = {k: re.sub("\d", "#", d[k]) if k in keys else v for k, v in d.items()}
user_171441690

Solution without re:

d = {"name": "Jone", "age": "40 years", "phone": "88777444"}
keys = ["age", "phone"]

for k in d.keys() & keys:
    d[k] = "".join("#" if c in "0123456789" else c for c in d[k])

print(d)

Prints:

{'name': 'Jone', 'age': '## years', 'phone': '########'}
user_100359850

I saw 2 methods, based on re and if ... else ..., and I'm adding one here based on str.translate.

The re methods can be optimized a bit by pre-compiling the regex, but still the str.maketrans method is a bit faster (see performances at bottom).

import re
from string import digits


digits_re = re.compile(r"\d")

trans_dict = dict.fromkeys(digits, "#")
trans_table = str.maketrans(trans_dict)


def obfuscate_dict_tr(d):
    return {k: v.translate(trans_table) for k, v in d.items()}

def obfuscate_dict_re(d):
    return {k: digits_re.sub("#", v) for k, v in d.items()}

def obfuscate_dict_cond(d):
    return {k: "".join("#" if c in digits else c for c in v) for k, v in d.items()}

To try on a large dictionary I'll use the faker library:

In [ ]: from faker import Faker
   ...:
   ...: fake = Faker()
   ...: num_keys = 10_000
   ...: d = {fake.name(): fake.address() for _ in range(num_keys)}

Let's first check that all methods are equivalent:

In [ ]: d1 = obfuscate_dict_tr(d)
   ...: d2 = obfuscate_dict_re(d)
   ...: d3 = obfuscate_dict_cond(d)
   ...:
   ...: assert d1 == d2
   ...: assert d2 == d3

And then a performance comparison

In [ ]: %timeit obfuscate_dict_tr(d)
   ...: %timeit obfuscate_dict_re(d)
   ...: %timeit obfuscate_dict_cond(d)
14.1 ms ± 216 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
16 ms ± 255 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
28.7 ms ± 217 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
user_136364070 
import re
input_={"name":"Jone",
       "age":"40 years",
       "phone":"88777444"}
key=["age","phone"]

def convert(input_, keys):
    for k in range(len(keys)):
        if keys[k] in input:
            input_[keys[k]] = re.sub(r'\d', "#", input_[keys[k]])
    return input_

print(convert(input_,key))
user_193210770
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