Record Equivalence Discoverer based on
String Grouper
(Red String Grouper) is a python package that finds similarities between
rows/records of a table with multiple fields.
It is an extension of
String Grouper, a library that
makes finding groups of similar strings within one or two lists of strings
easy — and fast.
pip install red_string_grouper
How do we achieve matching for multiple fields and priorities?
Import the function record_linkage()
from red_string_grouper
and specify
the fields of the table over which the comparisons will be made.
import pandas as pd
import numpy as np
from red_string_grouper import record_linkage, field, field_pair
matches = record_linkage(data_frames, fields_2b_matched_fuzzily,
fields_2b_matched_exactly=None,
hierarchical=True, **kwargs)
This is a function that combines similarity-matching results of several fields of one
or two DataFrames (data_frames
) and returns them in another DataFrame (matches
).
Examples are given below.
Parameter | Status | Description |
---|---|---|
data_frames |
Required | Either a pandas.DataFrame or a list of two pandas.DataFrame s of strings which are the tables over which the comparisons will be made. |
fields_2b_matched_fuzzily |
Required | List of tuples. When data_frames is a DataFrame , each tuple must be a triple: (〈field name〉, 〈weight〉, 〈field_kwargs〉) which is best input using the provided auxiliary function: field() in the following way: field(<field name>, weight=<weight>, **kwargs) . 〈field name〉 is a string denoting the name of a field in data_frame whose values are to be matched. When data_frames is a list of two DataFrame s, then each tuple must be a quadruple:(〈field name 1〉, 〈field name 2〉, 〈weight〉, 〈field_kwargs〉) which is best input using the provided auxiliary function: field_pair() in the following way: field_pair(<field name1>, <field name2>, weight=<weight>, **kwargs) . 〈field name1〉 is a string denoting the name of a field in data_frames[0] . Similarly, field name2〉 is a string denoting the name of a field in data_frames[1] whose values are to be matched with those of 〈field name1〉. 〈weight〉 is a number (default: 1.0) that defines the relative importance of the field (or field-pair) to other fields (or field-pairs) -- the field's (or field-pair's) contribution to the mean similarity will be weighted by this number. 〈weighted mean similarity score〉 = (Σfield 〈weight〉field × 〈similarity〉field) / (Σfield〈weight〉field), where Σfield means "sum over fields". 〈field_kwargs〉 is a dict capturing any keyword arguments to be passed to StringGrouper for this field. Any StringGrouper keyword arguments specified for the **kwargs of the field() (or field_pair() ) function will be captured by 〈field_kwargs〉. |
fields_2b_matched_exactly |
Optional | List of tuples. When data_frames is a DataFrame , each tuple must be a pair: (〈field name〉, 〈weight〉). 〈field name〉 is the name of a field in data_frame which is to be matched exactly. The auxiliary function: field() may be used to enter each tuple in the following way: field(<field name>, weight=<weight>, **kwargs) . When data_frames is a list of two DataFrame s, then each tuple must be a triple:(〈field name 1〉, 〈field name 2〉, 〈weight〉) which is best input using the provided auxiliary function: field_pair() in the following way: field_pair(<field name1>, <field name2>, weight=<weight>) . 〈field name1〉 is a string denoting the name of a field in data_frames[0] . Similarly, field name2〉 is a string denoting the name of a field in data_frames[1] whose values are to be matched with those of 〈field name1〉. 〈weight〉 has the same meaning as in parameter fields_2b_matched_fuzzily . Defaults to None . |
hierarchical |
Optional | bool . Determines if the output DataFrame will have a hierarchical column-structure (True ) or not (False ). Defaults to True . |
**kwargs |
Optional | Any string_grouper keyword arguments may be specified here. These will apply to all fields (or field-pairs) in fields_2b_matched_fuzzily . However, any keyword arguments already given in fields_2b_matched_fuzzily take precedence over those specified here. |
import pandas as pd
import numpy as np
from red_string_grouper import record_linkage, field
Here's some sample data:
file = 'data/us-cities-real-estate-sample-zenrows.csv'
df = pd.read_csv(file, dtype = str, keep_default_na=False) # all strings
dfna = pd.read_csv(file, dtype = str, keep_default_na=True) # has null-values
Note that the data has been read into memory as strings (dtype=str
), since
red_string_grouper
is based on string comparison.
The dataset is a table of 10 000 records:
len(df)
10000
Let us examine the data to determine which fields are good to compare (that is, we will use only columns without null or NaN data). At the same time, we will also check how many unique values each field has and its maximum string-length (to inform our choice of its n-gram size):
column_info = pd.concat(
[
df.nunique().rename('#unique'),
dfna.isna().any().rename('has null?'),
df.applymap(len).max().rename('max strlen')
],
axis=1
).rename_axis("column").reset_index()
column_info[
(column_info['#unique'] > 1) & (~column_info['has null?'])
]
column | #unique | has null? | max strlen | |
---|---|---|---|---|
0 | zpid | 10000 | False | 10 |
1 | id | 10000 | False | 10 |
3 | imgSrc | 9940 | False | 231 |
5 | detailUrl | 10000 | False | 121 |
7 | statusText | 24 | False | 26 |
11 | address | 10000 | False | 72 |
14 | addressState | 51 | False | 2 |
15 | addressZipcode | 6446 | False | 5 |
16 | isUndisclosedAddress | 2 | False | 5 |
22 | isZillowOwned | 2 | False | 5 |
31 | has3DModel | 2 | False | 5 |
32 | hasVideo | 2 | False | 5 |
38 | isFeaturedListing | 2 | False | 5 |
39 | list | 2 | False | 5 |
Thus we may set field 'zpid'
as the index, since it has exactly the same number
of unique values as the number of rows. zpid
will thus be used to identify
each row in the matching results.
df.set_index('zpid', inplace=True)
record_linkage()
:There is more than one way to achieve the same matching result. But some ways are faster than others, depending on the data.
record_linkage()
calls with and without grouping on a test field having a varying number of unique values in a 10 000-row DataFrameNote that those fields that have very few unique values distributed among a
large number of rows, such as 'hasVideo' (2 unique values) and 'addressState'
(51 unique values), can be specified as "fields that are to be matched exactly"
(that is, in parameter fields_2b_matched_exactly
) which can lead to a significant
performance boost.
Behind the scenes, this allows record_linkage()
to avoid using cosine-
similarity matching on these fields (which is time-consuming, since many
matches are likely to be found), and instead group the data by these fields.
In this way, cosine-similarity matching can be performed only on the other
fields (in parameter fields_2b_matched_fuzzily
) for each group.
On the other hand, grouping by 'addressZipcode' (which has 6446 unique values) degrades performance, since the groups by this field are so many.
To illustrate, the following call took ≈ 5 minutes to run:
matches = record_linkage(
df,
fields_2b_matched_fuzzily=[
field('statusText'),
field('address')
],
fields_2b_matched_exactly=[
field('addressZipcode', weight=2),
field('addressState', weight=4),
field('hasVideo')
]
)
whereas, the following call (which produces the same result) took ≈8 seconds to run:
matches = record_linkage(
df,
fields_2b_matched_fuzzily=[
field('statusText'),
field('address'),
field('addressZipcode', weight=2, min_similarity=0.9999)
],
fields_2b_matched_exactly=[
field('addressState', weight=4),
field('hasVideo')
]
)
Note that in the above calls, unspecified options, such as weight
and string_grouper keyword arguments
) such as min_similarity
and ngram_size
take up their default values. The default value for weight
is 1.0
, while the default values for the string_grouper
keyword arguments can be found at this link.
Let's display the results:
matches
Exactly Matched Fields | Fuzzily Matched Fields | ||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|
addressState | hasVideo | statusText | address | addressZipcode | |||||||||
Weighted Mean Similarity Score | left | similarity | right | left | similarity | right | left | similarity | right | ||||
left_zpid | right_zpid | ||||||||||||
2077667803 | 2077679643 | 1.000000 | WY | false | Lot / Land for sale | 1.0 | Lot / Land for sale | Jlda Minor Sub Division LOT C, Buffalo, WY 82834 | 1.000000 | Jlda Minor Subdivision LOT C, Buffalo, WY 82834 | 82834 | 1.0 | 82834 |
2075244057 | 2075358943 | 0.997100 | OH | false | Lot / Land for sale | 1.0 | Lot / Land for sale | 0 Township Road 118, Kimbolton, OH 43749 | 0.973904 | Township Road 118, Kimbolton, OH 43749 | 43749 | 1.0 | 43749 |
2077676622 | 2077676809 | 0.993867 | ND | false | Lot / Land for sale | 1.0 | Lot / Land for sale | 4 55th St SE, Christine, ND 58015 | 0.944802 | 2 55th St SE, Christine, ND 58015 | 58015 | 1.0 | 58015 |
2077093064 | 2078843498 | 0.993328 | SD | false | Lot / Land for sale | 1.0 | Lot / Land for sale | 17 Sidney Park Rd, Custer, SD 57730 | 0.939948 | Sidney Park Rd, Custer, SD 57730 | 57730 | 1.0 | 57730 |
150690392 | 2076123604 | 0.992909 | NJ | false | Lot / Land for sale | 1.0 | Lot / Land for sale | 5 Windsor Ln, Gladstone, NJ 07934 | 0.936180 | 0 Windsor Ln, Gladstone, NJ 07934 | 7934 | 1.0 | 7934 |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
2070837516 | 2072047318 | 0.978032 | HI | false | New construction | 1.0 | New construction | D12C Plan, Kaikoi at Hoopili | 0.802290 | D12B Plan, Kaikoi at Hoopili | 96706 | 1.0 | 96706 |
305578084 | 90035758 | 0.977991 | MO | false | Condo for sale | 1.0 | Condo for sale | 210 N 17th St UNIT 203, Saint Louis, MO 63103 | 0.801920 | 210 N 17th St UNIT 1202, Saint Louis, MO 63103 | 63103 | 1.0 | 63103 |
2071195670 | 88086529 | 0.977983 | MI | false | Condo for sale | 1.0 | Condo for sale | 6533 E Jefferson Ave APT 426, Detroit, MI 48207 | 0.801844 | 6533 E Jefferson Ave APT 102E, Detroit, MI 48207 | 48207 | 1.0 | 48207 |
247263033 | 247263136 | 0.977941 | IA | false | New construction | 1.0 | New construction | 1 University Way #511, Iowa City, IA 52246 | 0.801474 | 1 University Way #503, Iowa City, IA 52246 | 52246 | 1.0 | 52246 |
2083656138 | 2083656146 | 0.977873 | IN | false | Condo for sale | 1.0 | Condo for sale | 3789 S Anderson Dr, Terre Haute, IN 47803 | 0.800855 | 3776 S Anderson Dr, Terre Haute, IN 47803 | 47803 | 1.0 | 47803 |
94 rows × 12 columns
The results above can be obtained in yet another way. However, as mentioned above, it can take much longer to compute in cases where some fuzzily matched fields have very few uniques values.
The following call took ≈3 minutes 30 seconds to run:
record_linkage(
df,
fields_2b_matched_fuzzily=[
field('statusText'),
field('address'),
field('addressZipcode', weight=2, min_similarity=0.9999),
field('addressState', weight=4, ngram_size=2, min_similarity=0.9999),
field('hasVideo', min_similarity=0.9999)
]
)
statusText | address | addressZipcode | hasVideo | addressState | |||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Weighted Mean Similarity Score | left | similarity | right | left | similarity | right | left | similarity | right | left | similarity | right | left | similarity | right | ||
left_zpid | right_zpid | ||||||||||||||||
2077667803 | 2077679643 | 1.000000 | Lot / Land for sale | 1.0 | Lot / Land for sale | Jlda Minor Sub Division LOT C, Buffalo, WY 82834 | 1.000000 | Jlda Minor Subdivision LOT C, Buffalo, WY 82834 | 82834 | 1.0 | 82834 | false | 1.0 | false | WY | 1.0 | WY |
2075244057 | 2075358943 | 0.997100 | Lot / Land for sale | 1.0 | Lot / Land for sale | 0 Township Road 118, Kimbolton, OH 43749 | 0.973904 | Township Road 118, Kimbolton, OH 43749 | 43749 | 1.0 | 43749 | false | 1.0 | false | OH | 1.0 | OH |
2077676622 | 2077676809 | 0.993867 | Lot / Land for sale | 1.0 | Lot / Land for sale | 4 55th St SE, Christine, ND 58015 | 0.944802 | 2 55th St SE, Christine, ND 58015 | 58015 | 1.0 | 58015 | false | 1.0 | false | ND | 1.0 | ND |
2077093064 | 2078843498 | 0.993328 | Lot / Land for sale | 1.0 | Lot / Land for sale | 17 Sidney Park Rd, Custer, SD 57730 | 0.939948 | Sidney Park Rd, Custer, SD 57730 | 57730 | 1.0 | 57730 | false | 1.0 | false | SD | 1.0 | SD |
150690392 | 2076123604 | 0.992909 | Lot / Land for sale | 1.0 | Lot / Land for sale | 5 Windsor Ln, Gladstone, NJ 07934 | 0.936180 | 0 Windsor Ln, Gladstone, NJ 07934 | 7934 | 1.0 | 7934 | false | 1.0 | false | NJ | 1.0 | NJ |
... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... | ... |
2070837516 | 2072047318 | 0.978032 | New construction | 1.0 | New construction | D12C Plan, Kaikoi at Hoopili | 0.802290 | D12B Plan, Kaikoi at Hoopili | 96706 | 1.0 | 96706 | false | 1.0 | false | HI | 1.0 | HI |
305578084 | 90035758 | 0.977991 | Condo for sale | 1.0 | Condo for sale | 210 N 17th St UNIT 203, Saint Louis, MO 63103 | 0.801920 | 210 N 17th St UNIT 1202, Saint Louis, MO 63103 | 63103 | 1.0 | 63103 | false | 1.0 | false | MO | 1.0 | MO |
2071195670 | 88086529 | 0.977983 | Condo for sale | 1.0 | Condo for sale | 6533 E Jefferson Ave APT 426, Detroit, MI 48207 | 0.801844 | 6533 E Jefferson Ave APT 102E, Detroit, MI 48207 | 48207 | 1.0 | 48207 | false | 1.0 | false | MI | 1.0 | MI |
247263033 | 247263136 | 0.977941 | New construction | 1.0 | New construction | 1 University Way #511, Iowa City, IA 52246 | 0.801474 | 1 University Way #503, Iowa City, IA 52246 | 52246 | 1.0 | 52246 | false | 1.0 | false | IA | 1.0 | IA |
2083656138 | 2083656146 | 0.977873 | Condo for sale | 1.0 | Condo for sale | 3789 S Anderson Dr, Terre Haute, IN 47803 | 0.800855 | 3776 S Anderson Dr, Terre Haute, IN 47803 | 47803 | 1.0 | 47803 | false | 1.0 | false | IN | 1.0 | IN |
94 rows × 16 columns
One may choose to remove the field-values and output single-level column-
headings by setting hierarchical to False
:
record_linkage(
df,
fields_2b_matched_fuzzily=[
field('statusText'),
field('address'),
field('addressZipcode', weight=2, min_similarity=0.9999),
field('addressState', weight=4, ngram_size=2, min_similarity=0.9999),
field('hasVideo', min_similarity=0.9999)
],
hierarchical=False
)
Weighted Mean Similarity Score | statusText | address | addressZipcode | hasVideo | addressState | ||
---|---|---|---|---|---|---|---|
left_zpid | right_zpid | ||||||
2077667803 | 2077679643 | 1.000000 | 1.0 | 1.000000 | 1.0 | 1.0 | 1.0 |
2075244057 | 2075358943 | 0.997100 | 1.0 | 0.973904 | 1.0 | 1.0 | 1.0 |
2077676622 | 2077676809 | 0.993867 | 1.0 | 0.944802 | 1.0 | 1.0 | 1.0 |
2077093064 | 2078843498 | 0.993328 | 1.0 | 0.939948 | 1.0 | 1.0 | 1.0 |
150690392 | 2076123604 | 0.992909 | 1.0 | 0.936180 | 1.0 | 1.0 | 1.0 |
... | ... | ... | ... | ... | ... | ... | ... |
2070837516 | 2072047318 | 0.978032 | 1.0 | 0.802290 | 1.0 | 1.0 | 1.0 |
305578084 | 90035758 | 0.977991 | 1.0 | 0.801920 | 1.0 | 1.0 | 1.0 |
2071195670 | 88086529 | 0.977983 | 1.0 | 0.801844 | 1.0 | 1.0 | 1.0 |
247263033 | 247263136 | 0.977941 | 1.0 | 0.801474 | 1.0 | 1.0 | 1.0 |
2083656138 | 2083656146 | 0.977873 | 1.0 | 0.800855 | 1.0 | 1.0 | 1.0 |
94 rows × 6 columns
record_linkage()
vs the number n_blocks[1]
of blocks into which the right matrix-operand of the dataset (663 000 strings from sec__edgar_company_info.csv) was split before performing the string comparison. As shown in the legend, each plot corresponds to the number n_blocks[0]
of blocks into which the left matrix-operand was split.String comparison, as implemented by string_grouper
, is essentially matrix
multiplication. A DataFrame of strings is converted (tokenized) into a
matrix. Then that matrix is multiplied by itself (or another) transposed.
Here is an illustration of multiplication of two matrices D and MT:
It turns out that when the matrix (or DataFrame) is very large, the computer
proceeds quite slowly with the multiplication (apparently due to the RAM being
too full). Some computers give up with an OverflowError
.
To circumvent this issue, red_string_grouper
allows to divide the DataFrame
into smaller chunks (or blocks) and multiply the chunks one pair at a time
instead to get the same result:
But surprise ... the run-time of the process is sometimes drastically reduced
as a result. For example, the speed-up of the following call is about 500%
(here, the DataFrame is divided into 200 blocks on the right operand, that is,
1 block on the left × 200 on the right) compared to the same call with no
splitting [n_blocks=(1, 1)
, the default, which is equivalent to match_strings
call of string_grouper
(versions 0.5.0 and earlier)]:
# 663000 records:
companies = pd.read_csv('data/sec__edgar_company_info.csv')
# the following call produces the same result as
# string_grouper using
# match_strings(companies['Company Name'])
# but is more than 3 times faster!
record_linkage(
companies,
fields_2b_matched_fuzzily=[field('Company Name')],
n_blocks=(1, 200)
)
Further exploration of the block number space (see plot above) has revealed that for any fixed number of right blocks, the run-time gets longer the larger the number of left blocks specified. For this reason, it is recommended not to split the left matrix.
So what are the optimum block number values for any given DataFrame? That is anyone's guess, and the answer may vary from computer to computer.
We however encourage the user to make judicious use of the n_blocks
parameter to boost performance of record_linkage()
.