Czech Birth Numbers

Introduction

The function clean_cz_rc() cleans a column containing Czech birth number (RC) strings, and standardizes them in a given format. The function validate_cz_rc() validates either a single RC strings, a column of RC strings or a DataFrame of RC strings, returning True if the value is valid, and False otherwise.

RC strings can be converted to the following formats via the output_format parameter:

  • compact: only number strings without any seperators or whitespace, like “7103192745”

  • standard: RC strings with proper whitespace in the proper places, like “710319/2745”

Invalid parsing is handled with the errors parameter:

  • coerce (default): invalid parsing will be set to NaN

  • ignore: invalid parsing will return the input

  • raise: invalid parsing will raise an exception

The following sections demonstrate the functionality of clean_cz_rc() and validate_cz_rc().

An example dataset containing RC strings

[1]:
import pandas as pd
import numpy as np
df = pd.DataFrame(
    {
        "rc": [
            "7103192745",
            "7103192746",
            "51824753556",
            "51 824 753 556",
            "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",
            "(staples center) 1111 S Figueroa St, Los Angeles",
            "hello",
        ]
    }
)
df
[1]:
rc address
0 7103192745 123 Pine Ave.
1 7103192746 main st
2 51824753556 1234 west main heights 57033
3 51 824 753 556 apt 1 789 s maple rd manhattan
4 hello robie house, 789 north main street
5 NaN (staples center) 1111 S Figueroa St, Los Angeles
6 NULL hello

1. Default clean_cz_rc

By default, clean_cz_rc will clean rc strings and output them in the standard format with proper separators.

[2]:
from dataprep.clean import clean_cz_rc
clean_cz_rc(df, column = "rc")
[2]:
rc address rc_clean
0 7103192745 123 Pine Ave. 710319/2745
1 7103192746 main st NaN
2 51824753556 1234 west main heights 57033 NaN
3 51 824 753 556 apt 1 789 s maple rd manhattan NaN
4 hello robie house, 789 north main street NaN
5 NaN (staples center) 1111 S Figueroa St, Los Angeles NaN
6 NULL hello NaN

2. Output formats

This section demonstrates the output parameter.

standard (default)

[3]:
clean_cz_rc(df, column = "rc", output_format="standard")
[3]:
rc address rc_clean
0 7103192745 123 Pine Ave. 710319/2745
1 7103192746 main st NaN
2 51824753556 1234 west main heights 57033 NaN
3 51 824 753 556 apt 1 789 s maple rd manhattan NaN
4 hello robie house, 789 north main street NaN
5 NaN (staples center) 1111 S Figueroa St, Los Angeles NaN
6 NULL hello NaN

compact

[4]:
clean_cz_rc(df, column = "rc", output_format="compact")
[4]:
rc address rc_clean
0 7103192745 123 Pine Ave. 7103192745
1 7103192746 main st NaN
2 51824753556 1234 west main heights 57033 NaN
3 51 824 753 556 apt 1 789 s maple rd manhattan NaN
4 hello robie house, 789 north main street NaN
5 NaN (staples center) 1111 S Figueroa St, Los Angeles NaN
6 NULL hello NaN

3. inplace parameter

This deletes the given column from the returned DataFrame. A new column containing cleaned RC strings is added with a title in the format "{original title}_clean".

[5]:
clean_cz_rc(df, column="rc", inplace=True)
[5]:
rc_clean address
0 710319/2745 123 Pine Ave.
1 NaN main st
2 NaN 1234 west main heights 57033
3 NaN apt 1 789 s maple rd manhattan
4 NaN robie house, 789 north main street
5 NaN (staples center) 1111 S Figueroa St, Los Angeles
6 NaN hello

4. errors parameter

coerce (default)

[6]:
clean_cz_rc(df, "rc", errors="coerce")
[6]:
rc address rc_clean
0 7103192745 123 Pine Ave. 710319/2745
1 7103192746 main st NaN
2 51824753556 1234 west main heights 57033 NaN
3 51 824 753 556 apt 1 789 s maple rd manhattan NaN
4 hello robie house, 789 north main street NaN
5 NaN (staples center) 1111 S Figueroa St, Los Angeles NaN
6 NULL hello NaN

ignore

[7]:
clean_cz_rc(df, "rc", errors="ignore")
[7]:
rc address rc_clean
0 7103192745 123 Pine Ave. 710319/2745
1 7103192746 main st 7103192746
2 51824753556 1234 west main heights 57033 51824753556
3 51 824 753 556 apt 1 789 s maple rd manhattan 51 824 753 556
4 hello robie house, 789 north main street hello
5 NaN (staples center) 1111 S Figueroa St, Los Angeles NaN
6 NULL hello NaN

4. validate_cz_rc()

validate_cz_rc() returns True when the input is a valid RC. Otherwise it returns False.

The input of validate_cz_rc() 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_cz_rc() only returns the validation result for the specified column. If user doesn’t specify the column name, validate_cz_rc() returns the validation result for the whole DataFrame.

[8]:
from dataprep.clean import validate_cz_rc
print(validate_cz_rc("7103192745"))
print(validate_cz_rc("7103192746"))
print(validate_cz_rc("51824753556"))
print(validate_cz_rc("51 824 753 556"))
print(validate_cz_rc("hello"))
print(validate_cz_rc(np.nan))
print(validate_cz_rc("NULL"))
True
False
False
False
False
False
False

Series

[9]:
validate_cz_rc(df["rc"])
[9]:
0     True
1    False
2    False
3    False
4    False
5    False
6    False
Name: rc, dtype: bool

DataFrame + Specify Column

[10]:
validate_cz_rc(df, column="rc")
[10]:
0     True
1    False
2    False
3    False
4    False
5    False
6    False
Name: rc, dtype: bool

Only DataFrame

[11]:
validate_cz_rc(df)
[11]:
rc address
0 True False
1 False False
2 False False
3 False False
4 False False
5 False False
6 False False
[ ]: