Skip to main content

Get repeated capture groups, search without having to fear Exceptions in any df/Series, convert results to appropriate dtypes, use fast Trie regex

Project description

Regex enhancements for Pandas DataFrame / Series

###Installation

pip install a-pandas-ex-regex-enhancements

###Usage

from a_pandas_ex_regex_enhancements import pd_add_regex_enhancements

pd_add_regex_enhancements()

import pandas as pd
The code above will add some methods to! You can use pandas like you did before, but you will have a couple of methods more:
  • pandas.DataFrame.ds_trie_regex_search / pandas.Series.ds_trie_regex_search

  • pandas.DataFrame.ds_trie_regex_sub / pandas.Series.ds_trie_regex_sub

  • pandas.DataFrame.ds_trie_regex_find_all / pandas.Series.ds_trie_regex_find_all

  • pandas.DataFrame.ds_regex_find_all / pandas.Series.ds_regex_find_all

  • pandas.DataFrame.ds_regex_find_all_special / pandas.Series.ds_regex_find_all_special

  • pandas.DataFrame.ds_regex_search / pandas.Series.ds_regex_search

  • pandas.DataFrame.ds_regex_sub / pandas.Series.ds_regex_sub

###How to use the new methods

pandas.DataFrame.ds_regex_find_all_special / pandas.Series.ds_regex_find_all_special*
    #Using this method, you can get each match from REPEATED CAPTURE GROUPS! (A very rare feature in regex engines)

    #Besides that, you will see the exact position of each group/match.



    df=pd.read_csv( "https://github.com/pandas-dev/pandas/raw/main/doc/data/titanic.csv")



    special=df.ds_regex_find_all_special(r'\b(Ma(\w)+)(\w+)\b', dtype_string=False)





                                                                           aa_start_0  ... aa_match_6

    aa_index aa_column aa_whole_match aa_whole_start aa_whole_end aa_group             ...

    7        Name      Master         9              15           0                 9  ...        NaN

                                                                  1                 9  ...        NaN

                                                                  2                11  ...        NaN

                                                                  3                14  ...        NaN

    10       Name      Marguerite     17             27           0                17  ...        NaN

                                                                               ...  ...        ...

    885      Name      Margaret       20             28           3                27  ...        NaN

    887      Name      Margaret       14             22           0                14  ...        NaN

                                                                  1                14  ...        NaN

                                                                  2                16  ...        NaN

                                                                  3                21  ...        NaN



    #If you use any common regex engine, you can't get the repeated capture groups, since every new result overwrites the old one:

    import re

    re.findall('(Ma(\w)+)', 'Margaret')

    Out[11]: [('Margaret', 't')]



    #Using this method you will get all repeated capture groups, they won't be overwritten!



    #Results for index 887

                                                                       aa_start_0  aa_start_1  aa_start_2  aa_start_3  aa_start_4  aa_start_5  aa_start_6  aa_stop_0  aa_stop_1  aa_stop_2  aa_stop_3  aa_stop_4  aa_stop_5  aa_stop_6 aa_match_0 aa_match_1 aa_match_2 aa_match_3 aa_match_4 aa_match_5 aa_match_6

    aa_column aa_whole_match aa_whole_start aa_whole_end aa_group

    Name      Margaret       14             22           0                 14        <NA>        <NA>        <NA>        <NA>        <NA>        <NA>         22       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>   Margaret       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>

                                                         1                 14        <NA>        <NA>        <NA>        <NA>        <NA>        <NA>         21       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>    Margare       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>

                                                         2                 16          17          18          19          20        <NA>        <NA>         17         18         19         20         21       <NA>       <NA>          r          g          a          r          e       <NA>       <NA>

                                                         3                 21        <NA>        <NA>        <NA>        <NA>        <NA>        <NA>         22       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>          t       <NA>       <NA>       <NA>       <NA>       <NA>       <NA>





    If you want to convert the results to the best available dtype, use:



    df.ds_regex_find_all_special(r'\b(Ma(\w)+)(\w+)\b', dtype_string=False)



    Out[3]:

                                                                            aa_start_0  ...  aa_match_6

    #aa_index aa_column aa_whole_match aa_whole_start aa_whole_end aa_group              ...

    7        Name      Master         9              15           0                  9  ...        <NA>

                                                                  1                  9  ...        <NA>

                                                                  2                 11  ...        <NA>

                                                                  3                 14  ...        <NA>

    10       Name      Marguerite     17             27           0                 17  ...        <NA>

                                                                                ...  ...         ...

    885      Name      Margaret       20             28           3                 27  ...        <NA>

    887      Name      Margaret       14             22           0                 14  ...        <NA>

                                                                  1                 14  ...        <NA>

                                                                  2                 16  ...        <NA>

                                                                  3                 21  ...        <NA>

    [764 rows x 21 columns]





    aa_start_0       uint8

    aa_start_1       Int64

    aa_start_2       Int64

    aa_start_3       Int64

    aa_start_4       Int64

    aa_start_5       Int64

    aa_start_6       Int64

    aa_stop_0        uint8

    aa_stop_1        Int64

    aa_stop_2        Int64

    aa_stop_3        Int64

    aa_stop_4        Int64

    aa_stop_5        Int64

    aa_stop_6        Int64

    aa_match_0    category

    aa_match_1    category

    aa_match_2    category

    aa_match_3    category

    aa_match_4    category

    aa_match_5    category

    aa_match_6    category



        Parameters:

            df: Union[pd.DataFrame, pd.Series]

            regular_expression: str

               Syntax from https://pypi.org/project/regex/

            flags:int

                You can use any flag that is available here: https://pypi.org/project/regex/

               (default  =regex.UNICODE)

            dtype_string:bool

                If True, it returns all results as a string

                If False, data types are converted to the best available

               (default  =True)

        Returns:

            Union[pd.Series, pd.DataFrame]
pandas.DataFrame.ds_regex_find_all / pandas.Series.ds_regex_find_all
    #Use regex.findall against a DataFrame/Series without having to fear any exception! You can get

    #the results as strings (dtype_string=True) or even as float, int, category (dtype_string=False) - Whatever

    #fits best!



    #Some examples



    df=pd.read_csv( "https://github.com/pandas-dev/pandas/raw/main/doc/data/titanic.csv")





    df.Name.ds_regex_find_all(regular_expression=r'(\bM\w+\b)\s+(\bW\w+\b)')

              result_0  result_1

    426 Name     Maria  Winfield

    472 Name      Mary     Worth

    862 Name  Margaret    Welles



    multilinetest=df.Name.map(lambda x: f'{x}\n' * 3) #Every name 3x in each cell



    multilinetest.ds_regex_find_all(regular_expression=r'^.*(\bM\w+\b)\s+(\bW\w+\b)', line_by_line=False)



    Out[3]:

              result_0  result_1

    58  Name    Mirium      West

    426 Name     Maria  Winfield

    472 Name      Mary     Worth

    862 Name  Margaret    Welles





    multilinetest.ds_regex_find_all(regular_expression=r'^.*(\bM\w+\b)\s+(\bW\w+\b)', line_by_line=True)

    Out[7]:

              result_0  result_1

    426 Name     Maria  Winfield

        Name     Maria  Winfield

        Name     Maria  Winfield

    472 Name      Mary     Worth

        Name      Mary     Worth

        Name      Mary     Worth

    862 Name  Margaret    Welles

        Name  Margaret    Welles

        Name  Margaret    Welles



    #By using line_by_line=True you can be sure that the regex engine will check every single line!



        Parameters:

            df: Union[pd.DataFrame, pd.Series]

            regular_expression: str

               Syntax from https://pypi.org/project/regex/

            flags:int

                You can use any flag that is available here: https://pypi.org/project/regex/

               (default  =regex.UNICODE)

            dtype_string:bool

                If True, it returns all results as a string

                If False, data types are converted to the best available

               (default  =True)

            line_by_line:bool

                If you want to split the line before searching. Useful, if you want to use ^....$ more than once.

               (default  =False)

        Returns:

            Union[pd.Series, pd.DataFrame]
pandas.DataFrame.ds_trie_regex_find_all / pandas.Series.ds_trie_regex_find_all
    #If you have a huge list of words you want to  search/sub/find_all on this list, you can try to use the Trie regex methods to get the job done faster

    #It is worth trying if:

    #1) your DataFrame/Series has a lot of text in each cell

    #2) you want to search for a lot of words in each cell

#

    #The more words you have, and the more text is in each cell, the faster it gets.

    #If you want to know more about, I recommend: https://stackoverflow.com/a/42789508/15096247



    Example:



    df=pd.read_csv( "https://github.com/pandas-dev/pandas/raw/main/doc/data/titanic.csv")

    allstrings=pd.DataFrame([[df.Name.to_string() *2] *2,[df.Name.to_string() *2] *2]) #lets create a little dataframe with a lot of text in each cell

    hugeregexlist=df.Name.str.extract(r'^\s*(\w+)').drop_duplicates()[0].to_list() #lets get all names (first word) in the titanic DataFrame

    #it should look like that: ['Braund',  'Cumings',  'Heikkinen',  'Futrelle',  'Allen',  'Moran',  'McCarthy',  'Palsson',  'Johnson',  'Nasser' ... ]

    %timeit allstrings.ds_trie_regex_find_all(hugeregexlist,add_left_to_regex=r'\b',add_right_to_regex=r'\b')



    776 ms ± 2.83 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)



    allstrings.ds_trie_regex_find_all(hugeregexlist, add_left_to_regex=r'\b', add_right_to_regex=r'\b')

    Out[6]:

        result_0 result_1 result_2  ... result_2133 result_2134 result_2135

    0 0   Braund   Harris  Cumings  ...    Johnston        Behr      Dooley

      1   Braund   Harris  Cumings  ...    Johnston        Behr      Dooley

    1 0   Braund   Harris  Cumings  ...    Johnston        Behr      Dooley

      1   Braund   Harris  Cumings  ...    Johnston        Behr      Dooley





    Let's compare with a regular regex search

    hugeregex=r"\b(?:" + "|".join([f'(?:{y})' for y in df.Name.str.extract(r'^\s*(\w+)').drop_duplicates()[0].to_list()]) + r")\b"  #let's create a regex from all names

    #it should look like this: '\\b(?:(?:Braund)|(?:Cumings)|(?:Heikkinen)|(?:Futrelle)|(?:Allen)|(?:Moran)|(?:McCarthy)|(?:Palsson)|(?:Johnson)|(?:Na...

    %timeit allstrings.ds_regex_find_all(hugeregex)



    945 ms ± 3.14 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)



    #Not bad, right? But it still can a lot better! Try it yourself!



    #Another good thing is that you can search in every cell, no matter what dtype it is.

    #There won't be thrown any exception, because everything is converted to string before performing any action.

    #If you pass "dtype_string=False", each column will be converted to the best available dtype after the actions have been completed



        Parameters:

            df: Union[pd.DataFrame, pd.Series]

            wordlist: list[str]

               All strings you are looking for

            add_left_to_regex: str

                if you want to add something before the generated Trie regex -> \b for example

                allstrings.ds_trie_regex_find_all(hugeregexlist,add_left_to_regex=r'\b',add_right_to_regex=r'\b')

               (default  = "")

            add_right_to_regex: str

                if you want to add something after the generated Trie regex -> \b for example

                allstrings.ds_trie_regex_find_all(hugeregexlist,add_left_to_regex=r'\b',add_right_to_regex=r'\b')

               (default  = "")

            flags:int

                You can use any flag that is available here: https://pypi.org/project/regex/

               (default  =regex.UNICODE)

            dtype_string:bool

                If True, it returns all results as a string

                If False, data types are converted to the best available

               (default  =True)

            line_by_line:bool

                If you want to split the line before searching. Useful, if you want to use ^....$ more than once.

               (default  =False)

        Returns:

            Union[pd.Series, pd.DataFrame]
pandas.DataFrame.ds_regex_sub / pandas.Series.ds_regex_sub
    #Use regex.sub against a DataFrame/Series without having to fear any exception! You can get

    #the results as strings (dtype_string=True) or even as float, int, category (dtype_string=False) - Whatever

    #fits best!

#

    #Some examples:

    df=pd.read_csv( "https://github.com/pandas-dev/pandas/raw/main/doc/data/titanic.csv")





         PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked

    0              1         0       3  ...   7.2500   NaN         S

    1              2         1       1  ...  71.2833   C85         C

    2              3         1       3  ...   7.9250   NaN         S

    3              4         1       1  ...  53.1000  C123         S

    4              5         0       3  ...   8.0500   NaN         S

    ..           ...       ...     ...  ...      ...   ...       ...

    886          887         0       2  ...  13.0000   NaN         S

    887          888         1       1  ...  30.0000   B42         S

    888          889         0       3  ...  23.4500   NaN         S

    889          890         1       1  ...  30.0000  C148         C

    890          891         0       3  ...   7.7500   NaN         Q

    [891 rows x 12 columns]





    subst=df.ds_regex_sub(regular_expression=r'^\b8\d(\d)\b', replace=r'\g<1>00000',dtype_string=False)



    Out[5]:

         PassengerId  Survived  Pclass  ...     Fare Cabin Embarked

    0              1         0       3  ...   7.2500  <NA>        S

    1              2         1       1  ...  71.2833   C85        C

    2              3         1       3  ...   7.9250  <NA>        S

    3              4         1       1  ...  53.1000  C123        S

    4              5         0       3  ...   8.0500  <NA>        S

    ..           ...       ...     ...  ...      ...   ...      ...

    886       700000         0       2  ...  13.0000  <NA>        S

    887       800000         1       1  ...  30.0000   B42        S

    888       900000         0       3  ...  23.4500  <NA>        S

    889            0         1       1  ...  30.0000  C148        C

    890       100000         0       3  ...   7.7500  <NA>        Q

    [891 rows x 12 columns]





    subst.dtypes

    Out[8]:

    PassengerId      uint32

    Survived          uint8

    Pclass            uint8

    Name             string

    Sex            category

    Age              object

    SibSp             uint8

    Parch             uint8

    Ticket           object

    Fare            float64

    Cabin          category

    Embarked       category



    #As you can see, the numbers that we have substituted have been converted to int



    #Let's do something like math.floor in a very unconventional way :)



    df.Fare

    Out[16]:

    0       7.2500

    1      71.2833

    2       7.9250

    3      53.1000

    4       8.0500

            ...

    886    13.0000

    887    30.0000

    888    23.4500

    889    30.0000

    890     7.7500

    Name: Fare, Length: 891, dtype: float64



    Fareint=df.Fare.ds_regex_sub(r'(\d+)\.\d+$', r'\g<1>',dtype_string=False)



    0       7

    1      71

    2       7

    3      53

    4       8

    ..    ...

    886    13

    887    30

    888    23

    889    30

    890     7



    Fareint.dtypes

    Out[18]:

    Fare    uint16

    #You should not use this method if there are other ways to convert float to int.

    #It serves best for data cleaning, at least that's what I am using it for.



        Parameters:

            df: Union[pd.DataFrame, pd.Series]

            regular_expression: str

               Syntax from https://pypi.org/project/regex/

            replace: str

               the replacement you want to use (groups are allowed)

            flags:int

                You can use any flag that is available here: https://pypi.org/project/regex/

               (default  =regex.UNICODE)

            dtype_string:bool

                If True, it returns all results as a string

                If False, data types are converted to the best available

               (default  =True)

            line_by_line:bool

                If you want to split the line before searching. Useful, if you want to use ^....$ more than once.

               (default  =False)

        Returns:

            Union[pd.Series, pd.DataFrame]
pandas.DataFrame.ds_trie_regex_sub / pandas.Series.ds_trie_regex_sub
    #Check out the docs of df.trie_regex_find_all() for detailed information



    #Some examples with DataFrames / Series

    df=pd.read_csv( "https://github.com/pandas-dev/pandas/raw/main/doc/data/titanic.csv")

    #hugeregexlist = ['Braund',  'Cumings',  'Heikkinen',  'Futrelle',  'Allen',  'Moran',  'McCarthy',  'Palsson',  'Johnson',  'Nasser' ... ]



    df.Name.ds_trie_regex_sub(hugeregexlist, 'HANS',add_left_to_regex=r'^\b',add_right_to_regex=r'\b')



    Out[16]:

                                                     Name

    0                               HANS, Mr. Owen Harris

    1    HANS, Mrs. John Bradley (Florence Briggs Thayer)

    2                                   HANS, Miss. Laina

    3            HANS, Mrs. Jacques Heath (Lily May Peel)

    4                             HANS, Mr. William Henry

    ..                                                ...

    886                                 HANS, Rev. Juozas

    887                        HANS, Miss. Margaret Edith

    888              HANS, Miss. Catherine Helen "Carrie"

    889                             HANS, Mr. Karl Howell

    890                                 HANS, Mr. Patrick

    [891 rows x 1 columns]





    allstrings.ds_trie_regex_search(hugeregexlist,line_by_line=True)



    Out[25]:

          result_0

    0 0     Braund

      0    Cumings

      0  Heikkinen

      0   Futrelle

      0      Allen

    ..         ...

    1 1   Montvila

      1     Graham

      1   Johnston

      1       Behr

      1     Dooley

    [7124 rows x 1 columns]







        Parameters:

            df: Union[pd.DataFrame, pd.Series]

            wordlist: list[str]

               All strings you are looking for

            replace: str

               the replacement you want to use (groups are allowed)

            add_left_to_regex: str

                if you want to add something before the generated Trie regex -> \b for example

                allstrings.ds_trie_regex_find_all(hugeregexlist,add_left_to_regex=r'\b',add_right_to_regex=r'\b')

               (default  = "")

            add_right_to_regex: str

                if you want to add something after the generated Trie regex -> \b for example

                allstrings.ds_trie_regex_find_all(hugeregexlist,add_left_to_regex=r'\b',add_right_to_regex=r'\b')

               (default  = "")

            flags:int

                You can use any flag that is available here: https://pypi.org/project/regex/

               (default  =regex.UNICODE)

            dtype_string:bool

                If True, it returns all results as a string

                If False, data types are converted to the best available

               (default  =True)

            line_by_line:bool

                If you want to split the line before searching. Useful, if you want to use ^....$ more than once.

               (default  =False)

        Returns:

            Union[pd.Series, pd.DataFrame]
pandas.DataFrame.ds_regex_search / pandas.Series.ds_regex_search
    #Use regex.search against a DataFrame/Series without having to fear any exception! You can get

    #the results as strings (dtype_string=True) or even as float, int, category (dtype_string=False) - Whatever

    #fits best!

#

    #Some examples



    df=pd.read_csv( "https://github.com/pandas-dev/pandas/raw/main/doc/data/titanic.csv")



    multilinetest=df.Name.map(lambda x: f'{x}\n' * 3) #Every name 3x in each cell to test line_by_line



    #using line_by_line=False

    multilinetest.ds_regex_search(regular_expression=r'^.*(\bM\w+\b)\s+(\bW\w+\b)', line_by_line=False, flags=re.IGNORECASE)

    Out[13]:

                                                 result_0

    58  Name           West, Miss. Constance Mirium\nWest

        Name                                       Mirium

        Name                                         West

    426 Name   Clarke, Mrs. Charles V (Ada Maria Winfield

        Name                                        Maria

        Name                                     Winfield

    472 Name       West, Mrs. Edwy Arthur (Ada Mary Worth

        Name                                         Mary

        Name                                        Worth

    862 Name  Swift, Mrs. Frederick Joel (Margaret Welles

        Name                                     Margaret

        Name                                       Welles



    #using line_by_line=True

    multilinetest.ds_regex_search(regular_expression=r'^.*(\bM\w+\b)\s+(\bW\w+\b)', line_by_line=True, flags=re.IGNORECASE)

    Out[19]:

                                                 result_0

    426 Name   Clarke, Mrs. Charles V (Ada Maria Winfield

        Name                                        Maria

        Name                                     Winfield

        Name   Clarke, Mrs. Charles V (Ada Maria Winfield

        Name                                        Maria

        Name                                     Winfield

        Name   Clarke, Mrs. Charles V (Ada Maria Winfield

        Name                                        Maria

        Name                                     Winfield

    472 Name       West, Mrs. Edwy Arthur (Ada Mary Worth

        Name                                         Mary

        Name                                        Worth

        Name       West, Mrs. Edwy Arthur (Ada Mary Worth

        Name                                         Mary

        Name                                        Worth

        Name       West, Mrs. Edwy Arthur (Ada Mary Worth

        Name                                         Mary

        Name                                        Worth

    862 Name  Swift, Mrs. Frederick Joel (Margaret Welles

        Name                                     Margaret

        Name                                       Welles

        Name  Swift, Mrs. Frederick Joel (Margaret Welles

        Name                                     Margaret

        Name                                       Welles

        Name  Swift, Mrs. Frederick Joel (Margaret Welles

        Name                                     Margaret

        Name                                       Welles



    #Now, we get a match for each line!



        Parameters:

            df: Union[pd.DataFrame, pd.Series]

            regular_expression: str

               Syntax from https://pypi.org/project/regex/

            flags:int

                You can use any flag that is available here: https://pypi.org/project/regex/

               (default  =regex.UNICODE)

            dtype_string:bool

                If True, it returns all results as a string

                If False, data types are converted to the best available

               (default  =True)

            line_by_line:bool

                If you want to split the line before searching. Useful, if you want to use ^....$ more than once.

               (default  =False)

        Returns:

            Union[pd.Series, pd.DataFrame]
pandas.DataFrame.ds_regex_sub / pandas.Series.ds_regex_sub
    #Use regex.sub against a DataFrame/Series without having to fear any exception! You can get

    #the results as strings (dtype_string=True) or even as float, int, category (dtype_string=False) - Whatever

    #fits best!

#

    #Some examples

    df=pd.read_csv( "https://github.com/pandas-dev/pandas/raw/main/doc/data/titanic.csv")





         PassengerId  Survived  Pclass  ...     Fare Cabin  Embarked

    0              1         0       3  ...   7.2500   NaN         S

    1              2         1       1  ...  71.2833   C85         C

    2              3         1       3  ...   7.9250   NaN         S

    3              4         1       1  ...  53.1000  C123         S

    4              5         0       3  ...   8.0500   NaN         S

    ..           ...       ...     ...  ...      ...   ...       ...

    886          887         0       2  ...  13.0000   NaN         S

    887          888         1       1  ...  30.0000   B42         S

    888          889         0       3  ...  23.4500   NaN         S

    889          890         1       1  ...  30.0000  C148         C

    890          891         0       3  ...   7.7500   NaN         Q

    [891 rows x 12 columns]





    subst=df.ds_regex_sub(regular_expression=r'^\b8\d(\d)\b', replace=r'\g<1>00000',dtype_string=False)



    Out[5]:

         PassengerId  Survived  Pclass  ...     Fare Cabin Embarked

    0              1         0       3  ...   7.2500  <NA>        S

    1              2         1       1  ...  71.2833   C85        C

    2              3         1       3  ...   7.9250  <NA>        S

    3              4         1       1  ...  53.1000  C123        S

    4              5         0       3  ...   8.0500  <NA>        S

    ..           ...       ...     ...  ...      ...   ...      ...

    886       700000         0       2  ...  13.0000  <NA>        S

    887       800000         1       1  ...  30.0000   B42        S

    888       900000         0       3  ...  23.4500  <NA>        S

    889            0         1       1  ...  30.0000  C148        C

    890       100000         0       3  ...   7.7500  <NA>        Q

    [891 rows x 12 columns]





    subst.dtypes

    Out[8]:

    PassengerId      uint32

    Survived          uint8

    Pclass            uint8

    Name             string

    Sex            category

    Age              object

    SibSp             uint8

    Parch             uint8

    Ticket           object

    Fare            float64

    Cabin          category

    Embarked       category



    #As you can see, the numbers that we have substituted have been converted to int



    #Let's do something like math.floor in a very unconventional way :)



    df.Fare

    Out[16]:

    0       7.2500

    1      71.2833

    2       7.9250

    3      53.1000

    4       8.0500

            ...

    886    13.0000

    887    30.0000

    888    23.4500

    889    30.0000

    890     7.7500

    Name: Fare, Length: 891, dtype: float64



    Fareint=df.Fare.ds_regex_sub(r'(\d+)\.\d+$', r'\g<1>',dtype_string=False)



    0       7

    1      71

    2       7

    3      53

    4       8

    ..    ...

    886    13

    887    30

    888    23

    889    30

    890     7



    Fareint.dtypes

    Out[18]:

    Fare    uint16

    #You should not use this method if there are other ways to convert float to int.

    #It serves best for data cleaning, at least that's what I am using it for.



        Parameters:

            df: Union[pd.DataFrame, pd.Series]

            regular_expression: str

               Syntax from https://pypi.org/project/regex/

            replace: str

               the replacement you want to use (groups are allowed)

            flags:int

                You can use any flag that is available here: https://pypi.org/project/regex/

               (default  =regex.UNICODE)

            dtype_string:bool

                If True, it returns all results as a string

                If False, data types are converted to the best available

               (default  =True)

            line_by_line:bool

                If you want to split the line before searching. Useful, if you want to use ^....$ more than once.

               (default  =False)

        Returns:

            Union[pd.Series, pd.DataFrame]

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

a_pandas_ex_regex_enhancements-0.11.tar.gz (21.1 kB view details)

Uploaded Source

Built Distribution

File details

Details for the file a_pandas_ex_regex_enhancements-0.11.tar.gz.

File metadata

File hashes

Hashes for a_pandas_ex_regex_enhancements-0.11.tar.gz
Algorithm Hash digest
SHA256 a95cbc12a362de7aa10ac88cf8602c00282d8779c174f19d0aa241af8f0be342
MD5 c685c1016a007ea2395a624c0b352c78
BLAKE2b-256 ef84b81b4f0c9a50e90546bfcf6160fa99d58f4ed576049f126c36ff2636b6a9

See more details on using hashes here.

Provenance

File details

Details for the file a_pandas_ex_regex_enhancements-0.11-py3-none-any.whl.

File metadata

File hashes

Hashes for a_pandas_ex_regex_enhancements-0.11-py3-none-any.whl
Algorithm Hash digest
SHA256 a928a9f68165c1182fd1a5f7d714e4b209e2856819a0fdc35eb3af6c46186b1c
MD5 2506bd948d828f34369a805d303a5756
BLAKE2b-256 bf290e0a05ebcf90e2d8fad010e406becb1cadab8b379b49cf6c1188915b6e35

See more details on using hashes here.

Provenance

Supported by

AWS AWS Cloud computing and Security Sponsor Datadog Datadog Monitoring Fastly Fastly CDN Google Google Download Analytics Microsoft Microsoft PSF Sponsor Pingdom Pingdom Monitoring Sentry Sentry Error logging StatusPage StatusPage Status page