The function clean_in_aadhaar() cleans a column containing Indian digital resident personal identity number (Aadhaar) strings, and standardizes them in a given format. The function validate_in_aadhaar() validates either a single Aadhaar strings, a column of Aadhaar strings or a DataFrame of Aadhaar strings, returning True if the value is valid, and False otherwise.
clean_in_aadhaar()
validate_in_aadhaar()
True
False
Aadhaar strings can be converted to the following formats via the output_format parameter:
output_format
compact: only number strings without any seperators or whitespace, like “234123412346”
compact
standard: Aadhaar strings with proper whitespace in the proper places, like “2341 2341 2346”
standard
mask: mask the first 8 digits as per MeitY guidelines for securing identity information and Sensitive personal data, like “XXXX XXXX 2346”.
mask
Invalid parsing is handled with the errors parameter:
errors
coerce (default): invalid parsing will be set to NaN
coerce
ignore: invalid parsing will return the input
ignore
raise: invalid parsing will raise an exception
raise
The following sections demonstrate the functionality of clean_in_aadhaar() and validate_in_aadhaar().
[1]:
import pandas as pd import numpy as np df = pd.DataFrame( { "aadhaar": [ "234123412346", "643343121", "999 999 999", "004085616", "002 724 334", "hello", np.nan, "NULL", ], "address": [ "123 Pine Ave.", "main st", "1234 west main heights 57033", "apt 1 789 s maple rd manhattan", "robie house, 789 north main street", "1111 S Figueroa St, Los Angeles, CA 90015", "(staples center) 1111 S Figueroa St, Los Angeles", "hello", ] } ) df
clean_in_aadhaar
By default, clean_in_aadhaar will clean aadhaar strings and output them in the standard format with proper separators.
[2]:
from dataprep.clean import clean_in_aadhaar clean_in_aadhaar(df, column = "aadhaar")
This section demonstrates the output parameter.
[3]:
clean_in_aadhaar(df, column = "aadhaar", output_format="standard")
[4]:
clean_in_aadhaar(df, column = "aadhaar", output_format="compact")
[5]:
clean_in_aadhaar(df, column = "aadhaar", output_format="mask")
inplace
This deletes the given column from the returned DataFrame. A new column containing cleaned Aadhaar strings is added with a title in the format "{original title}_clean".
"{original title}_clean"
[6]:
clean_in_aadhaar(df, column="aadhaar", inplace=True)
[7]:
clean_in_aadhaar(df, "aadhaar", errors="coerce")
[8]:
clean_in_aadhaar(df, "aadhaar", errors="ignore")
validate_in_aadhaar() returns True when the input is a valid Aadhaar. Otherwise it returns False.
The input of validate_in_aadhaar() can be a string, a Pandas DataSeries, a Dask DataSeries, a Pandas DataFrame and a dask DataFrame.
When the input is a string, a Pandas DataSeries or a Dask DataSeries, user doesn’t need to specify a column name to be validated.
When the input is a Pandas DataFrame or a dask DataFrame, user can both specify or not specify a column name to be validated. If user specify the column name, validate_in_aadhaar() only returns the validation result for the specified column. If user doesn’t specify the column name, validate_in_aadhaar() returns the validation result for the whole DataFrame.
[9]:
from dataprep.clean import validate_in_aadhaar print(validate_in_aadhaar("234123412346")) print(validate_in_aadhaar("643343121")) print(validate_in_aadhaar("999 999 999")) print(validate_in_aadhaar("51824753556")) print(validate_in_aadhaar("004085616")) print(validate_in_aadhaar("hello")) print(validate_in_aadhaar(np.nan)) print(validate_in_aadhaar("NULL"))
True False False False False False False False
[10]:
validate_in_aadhaar(df["aadhaar"])
0 True 1 False 2 False 3 False 4 False 5 False 6 False 7 False Name: aadhaar, dtype: bool
[11]:
validate_in_aadhaar(df, column="aadhaar")
[12]:
validate_in_aadhaar(df)
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