a-pandas-ex-fuzz 0.12

Intuitive way of using fuzz matching in pandas

Intuitive way of using fuzz matching in pandas

Updates

05.10.2022 - Added compare rows

Installation

#Try it first like this: 
#rapidfuzz is a lot faster than fuzzywuzzy, but I had some problems installing it, #even with Visual C++ 2019 redistributable installed   a-pandas-ex-fuzz will try to import this module first
pip install a-pandas-ex-plode-tool
pip install a-pandas-ex-df-to-string
pip install rapidfuzz #https://github.com/maxbachmann/RapidFuzz
pip install --no-deps a-pandas-ex-fuzz

#if rapidfuzz does not work, use:
pip install a-pandas-ex-plode-tool
pip install a-pandas-ex-df-to-string
pip install fuzzywuzzy 
pip install --no-deps a-pandas-ex-fuzz


 #Or if you want to try to install everything:
 pip install a-pandas-ex-fuzz

Compare values in column against each other: pandas.Series.s_fuzz_all_values_in_col()

from a_pandas_ex_fuzz import pd_add_fuzzy_matching
pd_add_fuzzy_matching() #adds three new methods to pd.   
import pandas as pd


df = pd.read_csv(
        "https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv"
    )  
df11 = df.Name.s_fuzz_all_values_in_column(
    limit=5, merge_with_series=True, partial_full_weighted="weighted"
)
df22 = df.Name.s_fuzz_all_values_in_column(
    limit=2, merge_with_series=False, partial_full_weighted="full"
)
df33 = df.Name.s_fuzz_all_values_in_column(
    limit=1, merge_with_series=True, partial_full_weighted="partial"
)

df22

    0  Braund...     70.833333          477    Cann, ...     63.829787
1  Angle,...     55.445545          518    Astor,...     53.061224
2  Sinkko...     79.069767          747    Honkan...     77.272727
3  Futrel...     77.142857          137    Potter...     52.873563
4  Gilles...     84.615385          722    Saunde...     77.777778
5  Bracke...     77.777778          221    Scanla...     76.470588
6  O'Brie...     65.116279          552    Maisne...     58.536585
7  Goodwi...     68.852459          386    Palsso...     67.857143
8  Rosblo...     62.068966          254    Hockin...      59.52381
9  Nasser...     74.074074          122    Astor,...     58.536585
  fuzz_index_1
0         37
1        700
2        216
3        879
4         12
5        468
6        464
7        374
8        774
9        700

    Parameters:
        df: [pd.Series]
        limit: int
            How many results do you want to have?
            Each result will have 3 columns [string, match, position in column]
            (default=5)
        partial_full_weighted: str
            weighted = fuzz.WRatio
            full = fuzz.ratio
            partial = fuzz.partial_ratio
            (default="weighted")
        merge_with_series: str
            (default=True)
    Returns:
        pd.DataFrame

Compare values in column against list: pandas.Series.s_fuzz_from_list()

from a_pandas_ex_fuzz import pd_add_fuzzy_matching
pd_add_fuzzy_matching() #adds three new methods to pd.   
import pandas as pd   

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

df111 = df.Name.s_fuzz_from_list(
    list_to_compare=["Johannes", "Paulo", "Kevin"],
    limit=2,
    merge_with_series=True,
    partial_full_weighted="partial",
)
df222 = df.Name.s_fuzz_from_list(
    list_to_compare=["John", "Johannes", "Paulo", "Kevin"],
    limit=3,
    merge_with_series=False,
    partial_full_weighted="full",
)
df333 = df.Name.s_fuzz_from_list(
    list_to_compare=["Maria", "Anna"],
    limit=1,
    merge_with_series=False,
    partial_full_weighted="partial",
)
df333
        fuzz_string_0 fuzz_match_0 fuzz_index_0
0           Maria         60.0            0
1           Maria    44.444444            0
2            Anna         75.0            1
3           Maria         40.0            0
4           Maria         40.0            0
..            ...          ...          ...
886         Maria         40.0            0
887         Maria         80.0            0
888         Maria         60.0            0
889         Maria         40.0            0
890         Maria         60.0            0
[891 rows x 3 columns]

    Parameters:
        df: [pd.Series]
        list_to_compare: list
            The strings you want to be compared
        limit: int
            How many results do you want to have?
            Each result will have 3 columns [string, match, position in column]
            (default=5)
        partial_full_weighted: str
            weighted = fuzz.WRatio
            full = fuzz.ratio
            partial = fuzz.partial_ratio
            (default="weighted")
        merge_with_series: str
            (default=True)
    Returns:
        pd.DataFrame

Compare values in column against list: pandas.Series.s_fuzz_one_word()

from a_pandas_ex_fuzz import pd_add_fuzzy_matching
pd_add_fuzzy_matching() #adds three new methods to pd.   
import pandas as pd   

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

df1 = df.Name.s_fuzz_one_word(
word_to_search="Karolina", partial_full_weighted="weighted"
)
df2 = df.Name.s_fuzz_one_word(word_to_search="Karolina", partial_full_weighted="full")
df3 = df.Name.s_fuzz_one_word(
    word_to_search="Karolina", partial_full_weighted="partial"
)
df1
                                                  Name fuzz_string_0  \
0                              Braund, Mr. Owen Harris      Karolina
1  Cumings, Mrs. John Bradley (Florence Briggs Thayer)      Karolina
2                               Heikkinen, Miss. Laina      Karolina
3         Futrelle, Mrs. Jacques Heath (Lily May Peel)      Karolina
4                             Allen, Mr. William Henry      Karolina
5                                     Moran, Mr. James      Karolina
6                              McCarthy, Mr. Timothy J      Karolina
7                       Palsson, Master. Gosta Leonard      Karolina
8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)      Karolina
9                  Nasser, Mrs. Nicholas (Adele Achem)      Karolina
   fuzz_match_0
0     41.538462
1     33.750000
2     60.000000
3     33.750000
4     42.750000
5     30.000000
6     27.692308
7     45.000000
8     45.600000
9     42.750000

df2
                                                  Name fuzz_string_0  \
0                              Braund, Mr. Owen Harris      Karolina
1  Cumings, Mrs. John Bradley (Florence Briggs Thayer)      Karolina
2                               Heikkinen, Miss. Laina      Karolina
3         Futrelle, Mrs. Jacques Heath (Lily May Peel)      Karolina
4                             Allen, Mr. William Henry      Karolina
5                                     Moran, Mr. James      Karolina
6                              McCarthy, Mr. Timothy J      Karolina
7                       Palsson, Master. Gosta Leonard      Karolina
8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)      Karolina
9                  Nasser, Mrs. Nicholas (Adele Achem)      Karolina
   fuzz_match_0
0     32.258065
1     17.241379
2     33.333333
3     15.686275
4     31.250000
5     25.000000
6     19.354839
7     31.578947
8     21.428571
9     23.809524

df3
                                                  Name fuzz_string_0  \
0                              Braund, Mr. Owen Harris      Karolina
1  Cumings, Mrs. John Bradley (Florence Briggs Thayer)      Karolina
2                               Heikkinen, Miss. Laina      Karolina
3         Futrelle, Mrs. Jacques Heath (Lily May Peel)      Karolina
4                             Allen, Mr. William Henry      Karolina
5                                     Moran, Mr. James      Karolina
6                              McCarthy, Mr. Timothy J      Karolina
7                       Palsson, Master. Gosta Leonard      Karolina
8    Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)      Karolina
9                  Nasser, Mrs. Nicholas (Adele Achem)      Karolina
   fuzz_match_0
0     46.153846
1     37.500000
2     66.666667
3     37.500000
4     46.153846
5     33.333333
6     30.769231
7     50.000000
8     50.000000
9     40.000000

    Parameters:
        df: [pd.Series]
        word_to_search: str
        partial_full_weighted: str
            weighted = fuzz.WRatio
            full = fuzz.ratio
            partial = fuzz.partial_ratio
            (default="weighted")
    Returns:
        pd.DataFrame

pandas.Series.ds_fuzz_compare_row_to_others/ pandas.DataFrame.ds_fuzz_compare_row_to_others

    from a_pandas_ex_fuzz import pd_add_fuzzy_matching
    pd_add_fuzzy_matching()
    import pandas as pd
    df = pd.read_csv("https://raw.githubusercontent.com/pandas-dev/pandas/main/doc/data/titanic.csv")
    df.ds_fuzz_compare_row_to_others(2,loc_or_iloc='iloc', partial_full_weighted='full', sort_values=True)    
    
    
    Out[4]:   
    
         PassengerId  Survived  Pclass  ... Cabin Embarked  aa_fuzz_match
    2              3         1       3  ...   NaN        S     100.000000
    216          217         1       3  ...   NaN        S      90.816327
    816          817         0       3  ...   NaN        S      88.118812
    382          383         0       3  ...   NaN        S      83.769634
    400          401         1       3  ...   NaN        S      83.769634
    ..           ...       ...     ...  ...   ...      ...            ...
    745          746         0       1  ...   B22        S      54.450262
    556          557         1       1  ...   A16        C      53.744493
    581          582         1       1  ...   C68        C      53.456221
    669          670         1       1  ...  C126        S      52.132701
    307          308         1       1  ...   C65        C      51.612903
    [891 rows x 13 columns]