The function clean_cr_cr() cleans a column containing Costa Rica foreigners ID number (CR) strings, and standardizes them in a given format. The function validate_cr_cr() validates either a single CR strings, a column of CR strings or a DataFrame of CR strings, returning True if the value is valid, and False otherwise.
clean_cr_cr()
validate_cr_cr()
True
False
CR 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 “122200569906”
compact
standard: CR strings with proper whitespace in the proper places. Note that in the case of CR, the compact format is the same as the standard one.
standard
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_cr_cr() and validate_cr_cr().
[1]:
import pandas as pd import numpy as np df = pd.DataFrame( { "cr": [ '122200569906', '12345678', 'BE 428759497', 'BE431150351', "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_cr_cr
By default, clean_cr_cr will clean cr strings and output them in the standard format with proper separators.
[2]:
from dataprep.clean import clean_cr_cr clean_cr_cr(df, column = "cr")
This section demonstrates the output parameter.
[3]:
clean_cr_cr(df, column = "cr", output_format="standard")
[4]:
clean_cr_cr(df, column = "cr", output_format="compact")
inplace
This deletes the given column from the returned DataFrame. A new column containing cleaned CR strings is added with a title in the format "{original title}_clean".
"{original title}_clean"
[5]:
clean_cr_cr(df, column="cr", inplace=True)
[6]:
clean_cr_cr(df, "cr", errors="coerce")
[7]:
clean_cr_cr(df, "cr", errors="ignore")
validate_cr_cr() returns True when the input is a valid CR. Otherwise it returns False.
The input of validate_cr_cr() 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_cr_cr() only returns the validation result for the specified column. If user doesn’t specify the column name, validate_cr_cr() returns the validation result for the whole DataFrame.
[8]:
from dataprep.clean import validate_cr_cr print(validate_cr_cr("122200569906")) print(validate_cr_cr("12345678")) print(validate_cr_cr('BE 428759497')) print(validate_cr_cr('BE431150351')) print(validate_cr_cr("004085616")) print(validate_cr_cr("hello")) print(validate_cr_cr(np.nan)) print(validate_cr_cr("NULL"))
True False False False False False False False
[9]:
validate_cr_cr(df["cr"])
0 True 1 False 2 False 3 False 4 False 5 False 6 False 7 False Name: cr, dtype: bool
[10]:
validate_cr_cr(df, column="cr")
[11]:
validate_cr_cr(df)
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