Mask Based Data Profiling, for Data Quality Assessment
Bytefreq-rs is to be used as a lightweight tool for data quality profiling. 6point6 have developed a more mature and complex tool built in Scala and Spark, that is capable of profiling datasets with billions of records. This is not currently open-sourced but will be in the future.
Bytefreq-rs implements a mask based data profiling technique that is one of the most efficient methods for doing data quality assessment on new unknown datasets you receive.
A "Mask" is the output of a function that generalises a string of data into a pattern, the mask, which greatly reduces the cardinality of the original values. This cardinality reduction allows you to inspect vast quantities of data quickly in a field or column, helping you to discover outliers and data quality issues in your dataset. Examples of each pattern help to validate what you can expect when you come to use the data in a use case. bytefreq-rs is a refactor of the original bytefreq tool found here: https://github.com/minkymorgan/bytefreq.
I highly suggest you pre-parse complex csv using a decent parser, and pass clean pipe delimited values to this program. Also - this program expects a header for tabular data. (note: If there are ragged columns, this will probably error presently).
- Produces two report formats: Data Profiling, and Byte Frequency reports
- Supports both complex nested JSON and Delimited tabular data formats
- Offers modern masks: "HU: HighGrain Unicode", and "LU: LowGrain Unicode"
- Supports well known ASCII "HighGrain" and "LowGrain" masks
- Produces human readable Frequency counts of the patterns/masks in your data.
- Reports a true random example of a mask, using Reservoir Sampling.
- Handles complex json nesting, including unrolling arrays.
- Byte frequency reports supports Unicode, as well as control characts like LF / CR
To help you understand how masks work, bytefreq-rs provides examples of high grain and low grain masks, which can be optionally utilized within the tool. These examples are shown in the table below:
| Raw Data | High Grain Example | Low Grain Example |
|---|---|---|
| SW1 1AA | AA9 9AA | A9 9A |
| 01/01/2023 | 99/99/9999 | 9/9/9 |
Bytefreq-rs is released under the GNU General Public License v3.0. See the LICENSE file for more information.
To use bytefreq-rs, install rust, clone the repo, and compile the Rust program, and check it delivers the help information.
$ cargo clean
$ cargo build --release
$ ./target/release/bytefreq-rs --help
Bytefreq Data Profiler 1.0
Andrew Morgan <[email protected]>
Edward Jones <[email protected]>
A command-line tool to generate data profiling reports based on various masking strategies.
USAGE:
bytefreq-rs [OPTIONS]
OPTIONS:
-a, --remove-array-numbers <REMOVE_ARRAY_NUMBERS>
Remove array numbers when set to true [default: false]
-d, --delimiter <DELIMITER>
Sets the delimiter used to separate fields in input tabular data.
Default: '|' (pipe character) [default: |]
-f, --format <FORMAT>
Sets the format of the input data:
'json' - JSON data (each line should contain a JSON object)
'tabular' - Tabular data (first line should be the header) [default: tabular]
-g, --grain <GRAIN>
Sets the grain type for masking:
'H' - High grain (A for uppercase letters, a for lowercase letters, 9 for digits)
'L' - Low grain (repeated pattern characters will be compressed to one)
'U' - Unicode (uses Unicode general categories for masking
'LU'- Low grain Unicode (repeated pattern classes compressed to one
) [default: LU]
-h, --help
Print help information
-p, --pathdepth <PATHDEPTH>
Sets the depth for JSON paths (applicable for JSON data only). [default: 2]
-r, --report <REPORT>
Sets the type of report to generate:
'DQ' - Data Quality (default)
'CP' - Character Profiling [default: DQ]
-V, --version
Print version information
- Process a tabular data file with default options (Unicode grain, '|' delimiter):
$ cat testdata/test1.pip | ./target/release/bytefreq-rs- Process a JSON data file with low grain masking:
$ cat testdata/test2.json | ./target/release/bytefreq-rs -f "json" -g "L"- Process a tabular data file with a custom delimiter and high grain masking:
$ cat testdata/test3.tsv | ./target/release/bytefreq-rs -d "\t" -g "H"This report provides an analysis of the post code field in a filtered 100k record CSV dataset obtained from Companies House (https://www.gov.uk/guidance/companies-house-data-products) that can be found in the testdata folder. The data has been examined to detect patterns of characters in the postcode field, including low grain Unicode characters.
The dataset contains information on the postcodes of various company registration addresses. A total of 100k rows of data were examined, and several patterns of characters in the postcode field were detected. The report shows a count of the fields that contain low grain Unicode characters, along with the patterns detected and example data.
The table below provides a summary of the fields detected by the tool along with the number of occurrences and patterns of characters detected. The column column shows the name of the field, count shows the number of occurrences of the pattern, pattern shows the pattern detected, and example provides an example of the data in the field.
The analysis of the postcode field shows that the most common pattern is A9 9A, which occurred in 88252 rows. This pattern indicates a UK postcode where the first part consists of a letter followed by a digit, and the second part consists of a digit followed by two letters.
The second most common pattern is A9A 9A, which occurred in 7347 rows. This pattern indicates a UK postcode where the first part consists of a letter followed by a digit and another letter, and the second part consists of a digit followed by two letters.
The following examples of poor quality data masks were identified:
- A9: 3 occurences, where only the first few characters of the postcodes were entered e.g. IP20
- 9: 3 ocurrences, where only numerics were entered e.g. 8022
- A_A9 9A: 1 occurence, where alphanumerics are identified in the postcode field e.g. L;N9 6NE
To generate a similar report for a tabular file with low grain Unicode characters, the bytefreq-rs tool can be used with the appropriate flags. The output can be redirected to a file or piped to other commands for further analysis. In this example the Postcode field is specifically grepped.
For the UK Chargepoints dataset, the following command can be used:
cat testdata/BasicCompanyData-2021-02-01-part6_6_100k.pip | ./target/release/bytefreq-rs -g "LU" | grep RegAddress.PostCode | column -t -s $'\t'col_00009_RegAddress.PostCode 88252 A9 9A BA2 2EL
col_00009_RegAddress.PostCode 7347 A9A 9A EC4V 4BE
col_00009_RegAddress.PostCode 4367 _
col_00009_RegAddress.PostCode 12 A9A NE349PE
col_00009_RegAddress.PostCode 3 A9 IP20
col_00009_RegAddress.PostCode 3 9 8022
col_00009_RegAddress.PostCode 3 9 9 19 904
col_00009_RegAddress.PostCode 2 A9 A BA14 HHD
col_00009_RegAddress.PostCode 2 A9 9A. BR7 5HF.
col_00009_RegAddress.PostCode 2 A9 9 A SW18 4 UH
col_00009_RegAddress.PostCode 1 A9A9A EC1V2NX
col_00009_RegAddress.PostCode 1 A 9 BLOCK 3
col_00009_RegAddress.PostCode 1 A9A 9 A EC1V 1 NR
col_00009_RegAddress.PostCode 1 A 9A CRO 9XP
col_00009_RegAddress.PostCode 1 A_A9 9A L;N9 6NE
col_00009_RegAddress.PostCode 1 9A A 2L ONE
This table shows an analysis of the ChargeDeviceLocation.Address.County field of the UK Chargepoints JSON dataset, that was taken from https://chargepoints.dft.gov.uk/api/retrieve/registry/format/json.
The dataset contains information on the count of the fields that contain low grain Unicode characters, along with the patterns detected and example data.
The table provides a summary of the fields detected by the tool along with the number of occurrences and patterns of characters detected. The column column shows the name of the field, count shows the number of occurrences of the pattern, pattern shows the pattern detected, and example provides an example of the data in the field.
The ChargeDeviceLocation.Address.County field was examined, and several patterns of characters were detected. The most common pattern was two uppercase letters (Aa), which was found in 10,088 occurrences and included examples such as "Coventry". The second most common pattern was two uppercase letters separated by a space (Aa Aa), which was found in 9,191 occurrences and included examples such as "Greater London".
The following examples of poor quality data masks were identified:
- 9: 14 occurrences due to numerics being entered
- a: 4 occurrences due to null being entered
- Aa9 9Aa: 1 occurence, due to postcodes being entered
These examples demonstrate the power and the speed of this technique for profiling
To generate a similar report for a JSON file with low grain Unicode characters, the bytefreq-rs tool can be used with the appropriate flags. For this dataset the -a flag was used to unroll arrays. The output can be redirected to a file or piped to other commands for further analysis. This example shows the County column being grepped.
For the UK Chargepoints dataset, the following command can be used:
cat testdata/chargepointsUK.json | ./target/release/bytefreq-rs -f json -g "LU" -a "true" |grep Address.County | column -t -s $'\t'col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 10088 "Aa" "Coventry"
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 9191 "Aa Aa" "Greater London"
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 2761 "Aa a Aa" "Tyne and Wear"
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 2313 "Aa Aa a Aa" "London Borough of Sutton"
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 1955 "A" "NA"
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 1087 "Aa Aa a Aa a Aa" "London Borough of Hammersmith and Fulham"
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 358 "Aa Aa a Aa Aa" "London Borough of Waltham Forest"
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 192 "Aa Aa Aa" "Liverpool City Council"
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 180 "Aa a Aa Aa" "Richmond upon Thames Council"
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 112 "Aa " "London "
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 111 "Aa _ Aa" "Dumfries & Galloway"
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 82 "Aa." "Notts."
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 69 "Aa _ Aa Aa" "Hammersmith & Fulham Council"
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 57 "Aa Aa Aa Aa" "London Borough Of Southwark"
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 23 "Aa a Aa Aa Aa" "Bath and North East Somerset"
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 21 "Aa Aa " "West Midlands "
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 20 "A Aa Aa" "LB Tower Hamlets"
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 17 "a" "flintshire"
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 14 "9" "0"
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 11 "Aa a-Aa Aa" "Na h-Eileanan Siar"
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 7 "Aa-a-Aa" "Stockton-on-Tees"
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 4 "Aa-Aa" "Inverness-Shire"
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 4 " Aa" " Newport"
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 4 a null
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 3 "Aa, Aa Aa" "Yorkshire, North Riding"
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 3 "Aa Aa a Aa " "London Borough of Wandsworth "
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 2 "Aa, Aa a Aa" "Bournemouth, Christchurch and Poole"
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 2 "Aa a Aa," "Tyne and Wear,"
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 2 "Aa a Aa, Aa" "Vale of Glamorgan, The"
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 1 "Aa9 9Aa" "Me10 2La"
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 1 "Aa-a" "Inverness-shire"
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 1 "a Aa" "west Yorkshire"
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 1 "a " "kent "
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 1 "_Aa Aa" "`West Midlands"
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 1 "Aa _ Aa " "Tyne & Wear "
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 1 "A_A" "N/A"
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 1 "A9 9A" "SG1 1EP"
col_00009_ChargeDevice[].ChargeDeviceLocation.Address.County 1 "Aa _Aa_" "Glamorgan (Morgannwg)"
This is an example report generated by the bytefreq-rs tool for a geojson file.
The tool examined a total of 164,816 rows of data and detected several patterns of characters in the fields of the data. The report shows a count of the fields that contain low grain Unicode characters, along with the patterns detected and example data.
The table provides a summary of the fields detected by the tool along with the number of occurrences and patterns of characters detected. The column column shows the name of the field, count shows the number of occurrences of the pattern, pattern shows the pattern detected, and example provides an example of the data in the field.
Examples of common patterns include most of the coordinate field all collapsing to 9.9 as you would expect for a coordinate. There are two examples of a this field collapsing to A, indicating non-numeric characters and consequent data quality issues.
The Character Profiling indicates a total of 164816 Line Feeds which matches the total record number, this technique can be extremely useuful for trouble shooting erroneous files within data pipelines.
To generate a similar report for a JSON file with low grain Unicode characters, the bytefreq-rs tool can be used with the appropriate flags. The output can be redirected to a file or piped to other commands for further analysis.
cat testdata/source.geojson* | ./target/release/bytefreq-rs --format "json" -g "LU" |grep -v hash | column -t -s $'\t'Data Profiling Report: 20230413 10:15:22
Examined rows: 164816
FieldsPerLine:
column count pattern example
-------------------------------- -------- -------- --------------------------------
col_00011_properties.unit 164816 " ""
col_00012_type 164816 "Aa" "Feature"
col_00002_geometry.type 164816 "Aa" "Point"
col_00009_properties.region 164816 " ""
col_00003_properties.city 164816 " ""
col_00006_properties.id 164816 " ""
col_00010_properties.street 164816 "a" "奈摩郷"
col_00004_properties.district 164816 " ""
col_00007_properties.number 161668 "9-9" "452-1"
col_00007_properties.number 2784 "a9-9" "ﾛ2403-1"
col_00007_properties.number 177 "9a-9" "85ﾆ-1"
col_00007_properties.number 155 "9a9-9" "826第2-1"
col_00007_properties.number 17 "a-9" "又-1"
col_00007_properties.number 12 "a9a9-9" "又1161第2-1"
col_00007_properties.number 3 "a9a-9" "又1176ｲ-1"
col_00008_properties.postcode 164816 " ""
col_00000_geometry.coordinates[0] 164814 9.9 129.686892
col_00000_geometry.coordinates[0] 2 "A" "WRONG"
col_00001_geometry.coordinates[1] 164814 9.9 32.866601
col_00001_geometry.coordinates[1] 2 "A" "ERROR"
Output on the same JSON file showing Unicode Statistics using Character Profiling
cat testdata/source.geojson* | ./target/release/bytefreq-rs -f json -r CP -g "LU" |grep -v hash | column -t -s $'\t' char count description name
-------- -------- --------------- ---------------
\u{a} 164816 \n LF - Line Feed
\u{22} 8240808 \" QUOTATION MARK
\u{2c} 1977792 , COMMA
\u{2d} 164816 - HYPHEN-MINUS
\u{2e} 329628 . FULL STOP
\u{30} 390967 0 DIGIT ZERO
\u{31} 782647 1 DIGIT ONE
\u{32} 643395 2 DIGIT TWO
\u{33} 666322 3 DIGIT THREE
\u{34} 383220 4 DIGIT FOUR
\u{35} 388767 5 DIGIT FIVE
\u{36} 416052 6 DIGIT SIX
\u{37} 442232 7 DIGIT SEVEN
\u{38} 454692 8 DIGIT EIGHT
\u{39} 532556 9 DIGIT NINE
\u{3a} 2307424 : COLON
\u{45} 3 E LATIN CAPITAL LETTER E
\u{46} 164816 F LATIN CAPITAL LETTER F
\u{47} 1 G LATIN CAPITAL LETTER G
\u{4e} 1 N LATIN CAPITAL LETTER N
\u{4f} 4 O LATIN CAPITAL LETTER O
\u{50} 164816 P LATIN CAPITAL LETTER P
\u{52} 10 R LATIN CAPITAL LETTER R
\u{57} 1 W LATIN CAPITAL LETTER W
\u{5b} 164816 [ LEFT SQUARE BRACKET
\u{5d} 164816 ] RIGHT SQUARE BRACKET
\u{61} 659143 a LATIN SMALL LETTER A
\u{62} 329746 b LATIN SMALL LETTER B
\u{63} 823358 c LATIN SMALL LETTER C
\u{64} 824138 d LATIN SMALL LETTER D
\u{65} 2472247 e LATIN SMALL LETTER E
\u{66} 164417 f LATIN SMALL LETTER F
\u{67} 329632 g LATIN SMALL LETTER G
\u{68} 329632 h LATIN SMALL LETTER H
\u{69} 1483344 i LATIN SMALL LETTER I
\u{6d} 329632 m LATIN SMALL LETTER M
\u{6e} 824080 n LATIN SMALL LETTER N
\u{6f} 1318528 o LATIN SMALL LETTER O
\u{70} 824080 p LATIN SMALL LETTER P
\u{72} 1483344 r LATIN SMALL LETTER R
\u{73} 988896 s LATIN SMALL LETTER S
\u{74} 2307424 t LATIN SMALL LETTER T
\u{75} 494448 u LATIN SMALL LETTER U
\u{79} 659264 y LATIN SMALL LETTER Y
\u{7b} 494448 { LEFT CURLY BRACKET
\u{7d} 494448 } RIGHT CURLY BRACKET
\u{3005} 2133 々 IDEOGRAPHIC ITERATION MARK
\u{304b} 262 か HIRAGANA LETTER KA
\u{304c} 681 が HIRAGANA LETTER GA
\u{304d} 262 き HIRAGANA LETTER KI
\u{304f} 80 く HIRAGANA LETTER KU
\u{3053} 22 こ HIRAGANA LETTER KO
\u{3055} 102 さ HIRAGANA LETTER SA
\u{3058} 136 じ HIRAGANA LETTER ZI
\u{3064} 204 つ HIRAGANA LETTER TU
\u{3068} 104 と HIRAGANA LETTER TO
\u{306a} 216 な HIRAGANA LETTER NA
\u{306e} 4134 の HIRAGANA LETTER NO
\u{3073} 112 び HIRAGANA LETTER BI
\u{307e} 112 ま HIRAGANA LETTER MA
\u{307f} 156 み HIRAGANA LETTER MI
\u{3082} 52 も HIRAGANA LETTER MO
\u{3089} 80 ら HIRAGANA LETTER RA
\u{30a4} 256 イ KATAKANA LETTER I
\u{30a6} 13 ウ KATAKANA LETTER U
\u{30a8} 27 エ KATAKANA LETTER E
\u{30b1} 1045 ケ KATAKANA LETTER KE
\u{30b5} 89 サ KATAKANA LETTER SA
\u{30b7} 89 シ KATAKANA LETTER SI
\u{30b9} 26 ス KATAKANA LETTER SU
\u{30c0} 152 ダ KATAKANA LETTER DA
\u{30c1} 1 チ KATAKANA LETTER TI
\u{30c3} 21 ッ KATAKANA LETTER SMALL TU
\u{30c4} 280 ツ KATAKANA LETTER TU
\u{30c6} 13 テ KATAKANA LETTER TE
\u{30c8} 27 ト KATAKANA LETTER TO
\u{30c9} 241 ド KATAKANA LETTER DO
\u{30ca} 5 ナ KATAKANA LETTER NA
\u{30cc} 429 ヌ KATAKANA LETTER NU
\u{30cd} 27 ネ KATAKANA LETTER NE
\u{30ce} 2675 ノ KATAKANA LETTER NO
\u{30cf} 13 ハ KATAKANA LETTER HA
\u{30dc} 13 ボ KATAKANA LETTER BO
\u{30de} 5 マ KATAKANA LETTER MA
\u{30df} 27 ミ KATAKANA LETTER MI
\u{30e4} 152 ヤ KATAKANA LETTER YA
\u{30e9} 152 ラ KATAKANA LETTER RA
\u{30ea} 6 リ KATAKANA LETTER RI
\u{30ed} 20 ロ KATAKANA LETTER RO
\u{30f3} 192 ン KATAKANA LETTER N
\u{30f6} 1 ヶ KATAKANA LETTER SMALL KE
\u{30fc} 94 ー KATAKANA-HIRAGANA PROLONGED SOUND MARK
\u{4e00} 5566 一 CJK UNIFIED IDEOGRAPH-4E00
\u{4e01} 18433 丁 CJK UNIFIED IDEOGRAPH-4E01
\u{4e03} 229 七 CJK UNIFIED IDEOGRAPH-4E03
\u{4e07} 117 万 CJK UNIFIED IDEOGRAPH-4E07
\u{4e09} 6214 三 CJK UNIFIED IDEOGRAPH-4E09
\u{4e0a} 5505 上 CJK UNIFIED IDEOGRAPH-4E0A
\u{4e0b} 3712 下 CJK UNIFIED IDEOGRAPH-4E0B
\u{4e0e} 171 与 CJK UNIFIED IDEOGRAPH-4E0E
\u{4e16} 475 世 CJK UNIFIED IDEOGRAPH-4E16
\u{4e18} 480 丘 CJK UNIFIED IDEOGRAPH-4E18
\u{4e19} 1625 丙 CJK UNIFIED IDEOGRAPH-4E19
\u{4e26} 981 並 CJK UNIFIED IDEOGRAPH-4E26
\u{4e2d} 5178 中 CJK UNIFIED IDEOGRAPH-4E2D
\u{4e32} 522 串 CJK UNIFIED IDEOGRAPH-4E32
\u{4e38} 500 丸 CJK UNIFIED IDEOGRAPH-4E38
\u{4e3b} 148 主 CJK UNIFIED IDEOGRAPH-4E3B
\u{4e45} 5508 久 CJK UNIFIED IDEOGRAPH-4E45
\u{4e4b} 1701 之 CJK UNIFIED IDEOGRAPH-4E4B
\u{4e57} 6 乗 CJK UNIFIED IDEOGRAPH-4E57
\u{4e59} 1687 乙 CJK UNIFIED IDEOGRAPH-4E59
\u{4e71} 58 乱 CJK UNIFIED IDEOGRAPH-4E71
\u{4e7e} 109 乾 CJK UNIFIED IDEOGRAPH-4E7E
\u{4e80} 175 亀 CJK UNIFIED IDEOGRAPH-4E80
\u{4e8c} 7137 二 CJK UNIFIED IDEOGRAPH-4E8C
\u{4e94} 599 五 CJK UNIFIED IDEOGRAPH-4E94
\u{4e95} 1570 井 CJK UNIFIED IDEOGRAPH-4E95
\u{4eac} 225 京 CJK UNIFIED IDEOGRAPH-4EAC
\u{4eba} 107 人 CJK UNIFIED IDEOGRAPH-4EBA
\u{4ec1} 91 仁 CJK UNIFIED IDEOGRAPH-4EC1
\u{4eca} 1291 今 CJK UNIFIED IDEOGRAPH-4ECA
\u{4ecf} 89 仏 CJK UNIFIED IDEOGRAPH-4ECF
\u{4ed9} 184 仙 CJK UNIFIED IDEOGRAPH-4ED9
\u{4ee3} 1185 代 CJK UNIFIED IDEOGRAPH-4EE3
\u{4ee5} 267 以 CJK UNIFIED IDEOGRAPH-4EE5
\u{4ef2} 71 仲 CJK UNIFIED IDEOGRAPH-4EF2
\u{4f0a} 330 伊 CJK UNIFIED IDEOGRAPH-4F0A
\u{4f1a} 50 会 CJK UNIFIED IDEOGRAPH-4F1A
\u{4f3c} 136 似 CJK UNIFIED IDEOGRAPH-4F3C
\u{4f4f} 98 住 CJK UNIFIED IDEOGRAPH-4F4F
\u{4f50} 3222 佐 CJK UNIFIED IDEOGRAPH-4F50
\u{4fdd} 1889 保 CJK UNIFIED IDEOGRAPH-4FDD
\u{4ff5} 171 俵 CJK UNIFIED IDEOGRAPH-4FF5
\u{5009} 590 倉 CJK UNIFIED IDEOGRAPH-5009
\u{5143} 1168 元 CJK UNIFIED IDEOGRAPH-5143
\u{5148} 22 先 CJK UNIFIED IDEOGRAPH-5148
\u{5149} 115 光 CJK UNIFIED IDEOGRAPH-5149
\u{514d} 7700 免 CJK UNIFIED IDEOGRAPH-514D
\u{5165} 108 入 CJK UNIFIED IDEOGRAPH-5165
\u{516b} 387 八 CJK UNIFIED IDEOGRAPH-516B
\u{516d} 290 六 CJK UNIFIED IDEOGRAPH-516D
\u{5185} 3274 内 CJK UNIFIED IDEOGRAPH-5185
\u{5186} 7 円 CJK UNIFIED IDEOGRAPH-5186
\u{51a8} 81 冨 CJK UNIFIED IDEOGRAPH-51A8
\u{51b6} 11 冶 CJK UNIFIED IDEOGRAPH-51B6
\u{51fa} 991 出 CJK UNIFIED IDEOGRAPH-51FA
\u{5206} 768 分 CJK UNIFIED IDEOGRAPH-5206
\u{5207} 291 切 CJK UNIFIED IDEOGRAPH-5207
\u{5208} 154 刈 CJK UNIFIED IDEOGRAPH-5208
\u{5225} 26 別 CJK UNIFIED IDEOGRAPH-5225
\u{524d} 742 前 CJK UNIFIED IDEOGRAPH-524D
\u{526f} 114 副 CJK UNIFIED IDEOGRAPH-526F
\u{52a0} 1302 加 CJK UNIFIED IDEOGRAPH-52A0
\u{52a9} 62 助 CJK UNIFIED IDEOGRAPH-52A9
\u{52d2} 55 勒 CJK UNIFIED IDEOGRAPH-52D2
\u{52dd} 139 勝 CJK UNIFIED IDEOGRAPH-52DD
\u{52e2} 6 勢 CJK UNIFIED IDEOGRAPH-52E2
\u{5316} 484 化 CJK UNIFIED IDEOGRAPH-5316
\u{5317} 2329 北 CJK UNIFIED IDEOGRAPH-5317
\u{533a} 100 区 CJK UNIFIED IDEOGRAPH-533A
\u{5341} 80 十 CJK UNIFIED IDEOGRAPH-5341
\u{5343} 1692 千 CJK UNIFIED IDEOGRAPH-5343
\u{5354} 154 協 CJK UNIFIED IDEOGRAPH-5354
\u{5357} 1367 南 CJK UNIFIED IDEOGRAPH-5357
\u{535a} 36 博 CJK UNIFIED IDEOGRAPH-535A
\u{5378} 66 卸 CJK UNIFIED IDEOGRAPH-5378
\u{539a} 10 厚 CJK UNIFIED IDEOGRAPH-539A
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\u{826f} 3459 良 CJK UNIFIED IDEOGRAPH-826F
\u{8292} 291 芒 CJK UNIFIED IDEOGRAPH-8292
\u{82b1} 260 花 CJK UNIFIED IDEOGRAPH-82B1
\u{82b3} 29 芳 CJK UNIFIED IDEOGRAPH-82B3
\u{82d7} 376 苗 CJK UNIFIED IDEOGRAPH-82D7
\u{82e5} 591 若 CJK UNIFIED IDEOGRAPH-82E5
\u{8302} 785 茂 CJK UNIFIED IDEOGRAPH-8302
\u{8349} 132 草 CJK UNIFIED IDEOGRAPH-8349
\u{8352} 1146 荒 CJK UNIFIED IDEOGRAPH-8352
\u{8377} 39 荷 CJK UNIFIED IDEOGRAPH-8377
\u{837b} 59 荻 CJK UNIFIED IDEOGRAPH-837B
\u{83c5} 93 菅 CJK UNIFIED IDEOGRAPH-83C5
\u{83f0} 42 菰 CJK UNIFIED IDEOGRAPH-83F0
\u{8429} 597 萩 CJK UNIFIED IDEOGRAPH-8429
\u{8449} 523 葉 CJK UNIFIED IDEOGRAPH-8449
\u{84b2} 234 蒲 CJK UNIFIED IDEOGRAPH-84B2
\u{8535} 281 蔵 CJK UNIFIED IDEOGRAPH-8535
\u{85e4} 273 藤 CJK UNIFIED IDEOGRAPH-85E4
\u{8679} 72 虹 CJK UNIFIED IDEOGRAPH-8679
\u{868a} 630 蚊 CJK UNIFIED IDEOGRAPH-868A
\u{86ce} 11 蛎 CJK UNIFIED IDEOGRAPH-86CE
\u{86e4} 38 蛤 CJK UNIFIED IDEOGRAPH-86E4
\u{86ed} 65 蛭 CJK UNIFIED IDEOGRAPH-86ED
\u{8823} 286 蠣 CJK UNIFIED IDEOGRAPH-8823
\u{884c} 243 行 CJK UNIFIED IDEOGRAPH-884C
\u{897f} 5274 西 CJK UNIFIED IDEOGRAPH-897F
\u{898b} 5822 見 CJK UNIFIED IDEOGRAPH-898B
\u{899a} 28 覚 CJK UNIFIED IDEOGRAPH-899A
\u{89aa} 89 親 CJK UNIFIED IDEOGRAPH-89AA
\u{89d2} 26 角 CJK UNIFIED IDEOGRAPH-89D2
\u{89e6} 957 触 CJK UNIFIED IDEOGRAPH-89E6
\u{8a08} 212 計 CJK UNIFIED IDEOGRAPH-8A08
\u{8a2a} 866 訪 CJK UNIFIED IDEOGRAPH-8A2A
\u{8abf} 643 調 CJK UNIFIED IDEOGRAPH-8ABF
\u{8acf} 866 諏 CJK UNIFIED IDEOGRAPH-8ACF
\u{8c37} 753 谷 CJK UNIFIED IDEOGRAPH-8C37
\u{8c46} 487 豆 CJK UNIFIED IDEOGRAPH-8C46
\u{8c4a} 40 豊 CJK UNIFIED IDEOGRAPH-8C4A
\u{8c9d} 879 貝 CJK UNIFIED IDEOGRAPH-8C9D
\u{8cc0} 614 賀 CJK UNIFIED IDEOGRAPH-8CC0
\u{8cd1} 12 賑 CJK UNIFIED IDEOGRAPH-8CD1
\u{8d64} 1011 赤 CJK UNIFIED IDEOGRAPH-8D64
\u{8d8a} 2259 越 CJK UNIFIED IDEOGRAPH-8D8A
\u{8def} 625 路 CJK UNIFIED IDEOGRAPH-8DEF
\u{8e0a} 65 踊 CJK UNIFIED IDEOGRAPH-8E0A
\u{8f9b} 71 辛 CJK UNIFIED IDEOGRAPH-8F9B
\u{8fba} 408 辺 CJK UNIFIED IDEOGRAPH-8FBA
\u{8fbb} 31 辻 CJK UNIFIED IDEOGRAPH-8FBB
\u{8fbc} 76 込 CJK UNIFIED IDEOGRAPH-8FBC
\u{8fce} 1116 迎 CJK UNIFIED IDEOGRAPH-8FCE
\u{8feb} 52 迫 CJK UNIFIED IDEOGRAPH-8FEB
\u{901a} 146 通 CJK UNIFIED IDEOGRAPH-901A
\u{9053} 931 道 CJK UNIFIED IDEOGRAPH-9053
\u{90ce} 29 郎 CJK UNIFIED IDEOGRAPH-90CE
\u{90e8} 711 部 CJK UNIFIED IDEOGRAPH-90E8
\u{90f7} 19310 郷 CJK UNIFIED IDEOGRAPH-90F7
\u{90fd} 268 都 CJK UNIFIED IDEOGRAPH-90FD
\u{91cc} 4016 里 CJK UNIFIED IDEOGRAPH-91CC
\u{91cd} 1403 重 CJK UNIFIED IDEOGRAPH-91CD
\u{91ce} 7179 野 CJK UNIFIED IDEOGRAPH-91CE
\u{91d1} 342 金 CJK UNIFIED IDEOGRAPH-91D1
\u{91dc} 101 釜 CJK UNIFIED IDEOGRAPH-91DC
\u{91dd} 2531 針 CJK UNIFIED IDEOGRAPH-91DD
\u{9262} 190 鉢 CJK UNIFIED IDEOGRAPH-9262
\u{9280} 7 銀 CJK UNIFIED IDEOGRAPH-9280
\u{9285} 20 銅 CJK UNIFIED IDEOGRAPH-9285
\u{92ad} 33 銭 CJK UNIFIED IDEOGRAPH-92AD
\u{9326} 75 錦 CJK UNIFIED IDEOGRAPH-9326
\u{935b} 11 鍛 CJK UNIFIED IDEOGRAPH-935B
\u{938c} 29 鎌 CJK UNIFIED IDEOGRAPH-938C
\u{93e1} 503 鏡 CJK UNIFIED IDEOGRAPH-93E1
\u{9577} 2137 長 CJK UNIFIED IDEOGRAPH-9577
\u{9580} 298 門 CJK UNIFIED IDEOGRAPH-9580
\u{9593} 1211 間 CJK UNIFIED IDEOGRAPH-9593
\u{95a2} 38 関 CJK UNIFIED IDEOGRAPH-95A2
\u{963f} 155 阿 CJK UNIFIED IDEOGRAPH-963F
\u{9663} 586 陣 CJK UNIFIED IDEOGRAPH-9663
\u{9670} 153 陰 CJK UNIFIED IDEOGRAPH-9670
\u{9678} 162 陸 CJK UNIFIED IDEOGRAPH-9678
\u{967d} 45 陽 CJK UNIFIED IDEOGRAPH-967D
\u{96c4} 14 雄 CJK UNIFIED IDEOGRAPH-96C4
\u{96f2} 291 雲 CJK UNIFIED IDEOGRAPH-96F2
\u{970a} 100 霊 CJK UNIFIED IDEOGRAPH-970A
\u{9752} 887 青 CJK UNIFIED IDEOGRAPH-9752
\u{9762} 140 面 CJK UNIFIED IDEOGRAPH-9762
\u{97f3} 353 音 CJK UNIFIED IDEOGRAPH-97F3
\u{9808} 1781 須 CJK UNIFIED IDEOGRAPH-9808
\u{982d} 161 頭 CJK UNIFIED IDEOGRAPH-982D
\u{98a8} 978 風 CJK UNIFIED IDEOGRAPH-98A8
\u{98ef} 355 飯 CJK UNIFIED IDEOGRAPH-98EF
\u{98fd} 29 飽 CJK UNIFIED IDEOGRAPH-98FD
\u{9928} 20 館 CJK UNIFIED IDEOGRAPH-9928
\u{9996} 251 首 CJK UNIFIED IDEOGRAPH-9996
\u{9999} 713 香 CJK UNIFIED IDEOGRAPH-9999
\u{99ac} 778 馬 CJK UNIFIED IDEOGRAPH-99AC
\u{99c4} 132 駄 CJK UNIFIED IDEOGRAPH-99C4
\u{9ad8} 4322 高 CJK UNIFIED IDEOGRAPH-9AD8
\u{9aea} 729 髪 CJK UNIFIED IDEOGRAPH-9AEA
\u{9b3c} 197 鬼 CJK UNIFIED IDEOGRAPH-9B3C
\u{9b41} 22 魁 CJK UNIFIED IDEOGRAPH-9B41
\u{9b5a} 79 魚 CJK UNIFIED IDEOGRAPH-9B5A
\u{9bdb} 146 鯛 CJK UNIFIED IDEOGRAPH-9BDB
\u{9ce5} 49 鳥 CJK UNIFIED IDEOGRAPH-9CE5
\u{9cf4} 354 鳴 CJK UNIFIED IDEOGRAPH-9CF4
\u{9d28} 53 鴨 CJK UNIFIED IDEOGRAPH-9D28
\u{9d5c} 143 鵜 CJK UNIFIED IDEOGRAPH-9D5C
\u{9db4} 89 鶴 CJK UNIFIED IDEOGRAPH-9DB4
\u{9df2} 213 鷲 CJK UNIFIED IDEOGRAPH-9DF2
\u{9e7f} 1906 鹿 CJK UNIFIED IDEOGRAPH-9E7F
\u{9ea6} 37 麦 CJK UNIFIED IDEOGRAPH-9EA6
\u{9eb9} 6 麹 CJK UNIFIED IDEOGRAPH-9EB9
\u{9ed2} 2009 黒 CJK UNIFIED IDEOGRAPH-9ED2
\u{9f8d} 214 龍 CJK UNIFIED IDEOGRAPH-9F8D
\u{ff72} 165 ｲ HALFWIDTH KATAKANA LETTER I
\u{ff84} 1 ﾄ HALFWIDTH KATAKANA LETTER TO
\u{ff86} 7 ﾆ HALFWIDTH KATAKANA LETTER NI
\u{ff8a} 20 ﾊ HALFWIDTH KATAKANA LETTER HA
\u{ff8d} 1 ﾍ HALFWIDTH KATAKANA LETTER HE
\u{ff8e} 3 ﾎ HALFWIDTH KATAKANA LETTER HO
\u{ff9b} 181 ﾛ HALFWIDTH KATAKANA LETTER RO
--------END OF REPORT--------