Pandas Regex Extract to New Column If Condition

I am trying to run a script that will look at the value in a cell along a specific column ("Name") and check to see if it contains a particular string. If the cell contains the string, the script will look at the value contained in the "Value" cell and either copy the value into the "New_Value" column and append a string or regex extract a portion of the value and append a string into the new column.

The issue is with the regex extraction of the phone numbers. If I remove the regex portion, the script will copy the cell value into the New_Value cell, with the appended [PHONE] string. When I include the regex extraction, it doesn't copy anything over, like it didn't see a match to the regex. The weird thing is that if I alter the code so the regex just returns a True/False based on a Match, it returns a True for a match.

Am I somehow screwing up regex extraction? Any help is appreciated, thank you.

My code

import pandas as pd
import numpy as np
import re

df = pd.read_csv("path_to_file")

df.loc[df["Name"].str.contains("Number"), "New_Value"] = (
    df["Value"].str.extract(r"(\d{10,13})") + "[PHONE]"
)
df.loc[df["Name"].str.contains("Animal"), "New_Value"] = (
    df["Value"] + "[PET]"
)
df.loc[df["Name"].str.contains("Company"), "New_Value"] = (
    df["Value"] + "[COMPANY]"
)
df.loc[df["Name"].str.contains("Contact"), "New_Value"] = (
    df["Value"] + "[NAME]"
)

Sample Data

| Name     | Value             |
| -------- | ---------------   |
| Number   | +639892943187     |
| Company  | Ebert_LLC         |
| Animal   | Snake             |
| Contact  | Michal_Ashburner  |
| Number   | 668949201578      |
| Company  | Hoppe_LLC         |
| Animal   | European_badger   |
| Contact  | Marlee_Gofford    |

Desired result

| Name     | Value             | New_Value              |
| -------- | ---------------   | ----------------------
| Number   | +639892943187     | 639892943187[PHONE]    |
| Company  | Ebert_LLC         | Ebert_LLC[COMPANY]     |
| Animal   | Snake             | Snake[PET]             |
| Contact  | Michal_Ashburner  | Michal_Ashburner[NAME] |
| Number   | 668949201578      | 668949201578[PHONE]    |
| Company  | Hoppe_LLC         | Hoppe_LLC[COMPANY]     |
| Animal   | European_badger   | European_badger[PET    |
| Contact  | Marlee_Gofford    | Marlee_Gofford[NAME]   |

This should do the job:

df.loc[df.Name == 'Number', 'New_Value'] = df.Value.replace('+', '', regex=False) + '[PHONE]'
df.loc[df.Name == 'Animal', 'New_Value'] = df.Value + '[PET]'
df.loc[df.Name == 'Company', 'New_Value'] = df.Value + '[COMPANY]'
df.loc[df.Name == 'Contact', 'New_Value'] = df.Value + '[NAME]'

Note that the .loc() can be utilised to create new columns/rows in a DataFrame.