Overview

Dataset statistics

Number of variables14
Number of observations227
Missing cells227
Missing cells (%)7.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory25.6 KiB
Average record size in memory115.6 B

Variable types

Text6
Numeric2
Categorical5
Unsupported1

Dataset

Description키값,기관코드,유형1,유형2,대표기관명,전체기관명,최하위기관명,대표전화번호,새우편번호,주소,도로명주소,행정시,행정구,행정동
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-12977/S/1/datasetView.do

Alerts

유형1 has constant value ""Constant
대표기관명 has constant value ""Constant
유형2 is highly overall correlated with 새우편번호 and 1 other fieldsHigh correlation
행정시 is highly overall correlated with 새우편번호 and 1 other fieldsHigh correlation
기관코드 is highly overall correlated with 행정구High correlation
새우편번호 is highly overall correlated with 유형2 and 2 other fieldsHigh correlation
행정구 is highly overall correlated with 기관코드 and 1 other fieldsHigh correlation
유형2 is highly imbalanced (93.9%)Imbalance
행정시 is highly imbalanced (95.9%)Imbalance
주소 has 227 (100.0%) missing valuesMissing
키값 has unique valuesUnique
기관코드 has unique valuesUnique
전체기관명 has unique valuesUnique
최하위기관명 has unique valuesUnique
대표전화번호 has unique valuesUnique
도로명주소 has unique valuesUnique
주소 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-11 07:36:39.911056
Analysis finished2023-12-11 07:36:41.576709
Duration1.67 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

키값
Text

UNIQUE 

Distinct227
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-11T16:36:41.796928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters3178
Distinct characters18
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique227 ?
Unique (%)100.0%

Sample

1st rowBE_LiST27-0164
2nd rowBE_LiST27-0165
3rd rowBE_LiST27-0166
4th rowBE_LiST27-0167
5th rowBE_LiST27-0168
ValueCountFrequency (%)
be_list27-0164 1
 
0.4%
be_list27-0161 1
 
0.4%
be_list27-0076 1
 
0.4%
be_list27-0065 1
 
0.4%
be_list27-0066 1
 
0.4%
be_list27-0067 1
 
0.4%
be_list27-0068 1
 
0.4%
be_list27-0069 1
 
0.4%
be_list27-0070 1
 
0.4%
be_list27-0071 1
 
0.4%
Other values (217) 217
95.6%
2023-12-11T16:36:42.254694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 377
11.9%
2 306
9.6%
7 270
8.5%
B 227
 
7.1%
T 227
 
7.1%
E 227
 
7.1%
- 227
 
7.1%
S 227
 
7.1%
i 227
 
7.1%
L 227
 
7.1%
Other values (8) 636
20.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1362
42.9%
Uppercase Letter 1135
35.7%
Dash Punctuation 227
 
7.1%
Lowercase Letter 227
 
7.1%
Connector Punctuation 227
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 377
27.7%
2 306
22.5%
7 270
19.8%
1 153
11.2%
6 43
 
3.2%
4 43
 
3.2%
3 43
 
3.2%
5 43
 
3.2%
8 42
 
3.1%
9 42
 
3.1%
Uppercase Letter
ValueCountFrequency (%)
B 227
20.0%
T 227
20.0%
E 227
20.0%
S 227
20.0%
L 227
20.0%
Dash Punctuation
ValueCountFrequency (%)
- 227
100.0%
Lowercase Letter
ValueCountFrequency (%)
i 227
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 227
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1816
57.1%
Latin 1362
42.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 377
20.8%
2 306
16.9%
7 270
14.9%
- 227
12.5%
_ 227
12.5%
1 153
8.4%
6 43
 
2.4%
4 43
 
2.4%
3 43
 
2.4%
5 43
 
2.4%
Other values (2) 84
 
4.6%
Latin
ValueCountFrequency (%)
B 227
16.7%
T 227
16.7%
E 227
16.7%
S 227
16.7%
i 227
16.7%
L 227
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3178
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 377
11.9%
2 306
9.6%
7 270
8.5%
B 227
 
7.1%
T 227
 
7.1%
E 227
 
7.1%
- 227
 
7.1%
S 227
 
7.1%
i 227
 
7.1%
L 227
 
7.1%
Other values (8) 636
20.0%

기관코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct227
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1710865.7
Minimum1710375
Maximum1714600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-11T16:36:42.448929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1710375
5-th percentile1710409.7
Q11710517
median1710619
Q31710725.5
95-th percentile1714569.4
Maximum1714600
Range4225
Interquartile range (IQR)208.5

Descriptive statistics

Standard deviation998.15452
Coefficient of variation (CV)0.00058342072
Kurtosis10.091636
Mean1710865.7
Median Absolute Deviation (MAD)106
Skewness3.4319273
Sum3.8836652 × 108
Variance996312.45
MonotonicityNot monotonic
2023-12-11T16:36:42.636983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1710674 1
 
0.4%
1710713 1
 
0.4%
1710714 1
 
0.4%
1710710 1
 
0.4%
1710531 1
 
0.4%
1710539 1
 
0.4%
1710542 1
 
0.4%
1710540 1
 
0.4%
1710541 1
 
0.4%
1710544 1
 
0.4%
Other values (217) 217
95.6%
ValueCountFrequency (%)
1710375 1
0.4%
1710387 1
0.4%
1710397 1
0.4%
1710398 1
0.4%
1710399 1
0.4%
1710400 1
0.4%
1710401 1
0.4%
1710402 1
0.4%
1710404 1
0.4%
1710405 1
0.4%
ValueCountFrequency (%)
1714600 1
0.4%
1714599 1
0.4%
1714598 1
0.4%
1714597 1
0.4%
1714596 1
0.4%
1714595 1
0.4%
1714594 1
0.4%
1714585 1
0.4%
1714581 1
0.4%
1714572 1
0.4%

유형1
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
우정
227 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row우정
2nd row우정
3rd row우정
4th row우정
5th row우정

Common Values

ValueCountFrequency (%)
우정 227
100.0%

Length

2023-12-11T16:36:42.815999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T16:36:42.914817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
우정 227
100.0%

유형2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
우정_우체국
224 
우정_우정청
 
1
우정_물류센터
 
1
우정_집중국
 
1

Length

Max length7
Median length6
Mean length6.0044053
Min length6

Unique

Unique3 ?
Unique (%)1.3%

Sample

1st row우정_우체국
2nd row우정_우체국
3rd row우정_우체국
4th row우정_우체국
5th row우정_우체국

Common Values

ValueCountFrequency (%)
우정_우체국 224
98.7%
우정_우정청 1
 
0.4%
우정_물류센터 1
 
0.4%
우정_집중국 1
 
0.4%

Length

2023-12-11T16:36:43.013761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T16:36:43.126873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
우정_우체국 224
98.7%
우정_우정청 1
 
0.4%
우정_물류센터 1
 
0.4%
우정_집중국 1
 
0.4%

대표기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
미래부
227 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row미래부
2nd row미래부
3rd row미래부
4th row미래부
5th row미래부

Common Values

ValueCountFrequency (%)
미래부 227
100.0%

Length

2023-12-11T16:36:43.277321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T16:36:43.365282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미래부 227
100.0%

전체기관명
Text

UNIQUE 

Distinct227
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-11T16:36:43.528578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length42
Mean length38.154185
Min length22

Characters and Unicode

Total characters8661
Distinct characters190
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique227 ?
Unique (%)100.0%

Sample

1st row미래창조과학부 우정사업본부 서울지방우정청 서울송파우체국 서울삼전동우체국
2nd row미래창조과학부 우정사업본부 서울지방우정청 서울송파우체국 서울송파1동우체국
3rd row미래창조과학부 우정사업본부 서울지방우정청 서울송파우체국 서울오금동우체국
4th row미래창조과학부 우정사업본부 서울지방우정청 서울송파우체국 서울오륜동우체국
5th row미래창조과학부 우정사업본부 서울지방우정청 서울송파우체국 서울잠실2동우체국
ValueCountFrequency (%)
미래창조과학부 227
20.5%
서울지방우정청 227
20.5%
우정사업본부 227
20.5%
서울송파우체국 16
 
1.4%
서울강남우체국 15
 
1.4%
광화문우체국 13
 
1.2%
서울도봉우체국 13
 
1.2%
서울광진우체국 13
 
1.2%
서울금천우체국 12
 
1.1%
서울용산우체국 12
 
1.1%
Other values (219) 334
30.1%
2023-12-11T16:36:43.862027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
우 884
 
10.2%
882
 
10.2%
서 631
 
7.3%
울 599
 
6.9%
정 461
 
5.3%
부 459
 
5.3%
국 435
 
5.0%
체 426
 
4.9%
학 238
 
2.7%
방 237
 
2.7%
Other values (180) 3409
39.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7733
89.3%
Space Separator 882
 
10.2%
Decimal Number 46
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
우 884
 
11.4%
서 631
 
8.2%
울 599
 
7.7%
정 461
 
6.0%
부 459
 
5.9%
국 435
 
5.6%
체 426
 
5.5%
학 238
 
3.1%
방 237
 
3.1%
지 237
 
3.1%
Other values (171) 3126
40.4%
Decimal Number
ValueCountFrequency (%)
1 14
30.4%
2 10
21.7%
3 10
21.7%
4 4
 
8.7%
5 4
 
8.7%
6 2
 
4.3%
0 1
 
2.2%
7 1
 
2.2%
Space Separator
ValueCountFrequency (%)
882
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7733
89.3%
Common 928
 
10.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
우 884
 
11.4%
서 631
 
8.2%
울 599
 
7.7%
정 461
 
6.0%
부 459
 
5.9%
국 435
 
5.6%
체 426
 
5.5%
학 238
 
3.1%
방 237
 
3.1%
지 237
 
3.1%
Other values (171) 3126
40.4%
Common
ValueCountFrequency (%)
882
95.0%
1 14
 
1.5%
2 10
 
1.1%
3 10
 
1.1%
4 4
 
0.4%
5 4
 
0.4%
6 2
 
0.2%
0 1
 
0.1%
7 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7733
89.3%
ASCII 928
 
10.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
우 884
 
11.4%
서 631
 
8.2%
울 599
 
7.7%
정 461
 
6.0%
부 459
 
5.9%
국 435
 
5.6%
체 426
 
5.5%
학 238
 
3.1%
방 237
 
3.1%
지 237
 
3.1%
Other values (171) 3126
40.4%
ASCII
ValueCountFrequency (%)
882
95.0%
1 14
 
1.5%
2 10
 
1.1%
3 10
 
1.1%
4 4
 
0.4%
5 4
 
0.4%
6 2
 
0.2%
0 1
 
0.1%
7 1
 
0.1%

최하위기관명
Text

UNIQUE 

Distinct227
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-11T16:36:44.098747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.246696
Min length5

Characters and Unicode

Total characters1872
Distinct characters187
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique227 ?
Unique (%)100.0%

Sample

1st row서울삼전동우체국
2nd row서울송파1동우체국
3rd row서울오금동우체국
4th row서울오륜동우체국
5th row서울잠실2동우체국
ValueCountFrequency (%)
서울삼전동우체국 1
 
0.4%
서울마천동우체국 1
 
0.4%
서울건국대학교우체국 1
 
0.4%
서울염창동우체국 1
 
0.4%
서울화곡4동우체국 1
 
0.4%
화곡우체국 1
 
0.4%
서울관악우체국 1
 
0.4%
서울난곡우체국 1
 
0.4%
서울대학교우체국 1
 
0.4%
서울봉천동우체국 1
 
0.4%
Other values (217) 217
95.6%
2023-12-11T16:36:44.498752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
국 233
12.4%
우 229
12.2%
체 224
12.0%
서 219
 
11.7%
울 211
 
11.3%
동 146
 
7.8%
1 14
 
0.7%
대 14
 
0.7%
로 13
 
0.7%
신 13
 
0.7%
Other values (177) 556
29.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1826
97.5%
Decimal Number 46
 
2.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 233
12.8%
우 229
12.5%
체 224
12.3%
서 219
12.0%
울 211
11.6%
동 146
 
8.0%
대 14
 
0.8%
로 13
 
0.7%
신 13
 
0.7%
원 13
 
0.7%
Other values (169) 511
28.0%
Decimal Number
ValueCountFrequency (%)
1 14
30.4%
3 10
21.7%
2 10
21.7%
4 4
 
8.7%
5 4
 
8.7%
6 2
 
4.3%
0 1
 
2.2%
7 1
 
2.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1826
97.5%
Common 46
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 233
12.8%
우 229
12.5%
체 224
12.3%
서 219
12.0%
울 211
11.6%
동 146
 
8.0%
대 14
 
0.8%
로 13
 
0.7%
신 13
 
0.7%
원 13
 
0.7%
Other values (169) 511
28.0%
Common
ValueCountFrequency (%)
1 14
30.4%
3 10
21.7%
2 10
21.7%
4 4
 
8.7%
5 4
 
8.7%
6 2
 
4.3%
0 1
 
2.2%
7 1
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1826
97.5%
ASCII 46
 
2.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
국 233
12.8%
우 229
12.5%
체 224
12.3%
서 219
12.0%
울 211
11.6%
동 146
 
8.0%
대 14
 
0.8%
로 13
 
0.7%
신 13
 
0.7%
원 13
 
0.7%
Other values (169) 511
28.0%
ASCII
ValueCountFrequency (%)
1 14
30.4%
3 10
21.7%
2 10
21.7%
4 4
 
8.7%
5 4
 
8.7%
6 2
 
4.3%
0 1
 
2.2%
7 1
 
2.2%

대표전화번호
Text

UNIQUE 

Distinct227
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-11T16:36:44.774793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length11.242291
Min length11

Characters and Unicode

Total characters2552
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique227 ?
Unique (%)100.0%

Sample

1st row02-417-0800
2nd row02-422-0205
3rd row02-443-0200
4th row02-404-0014
5th row02-423-2004
ValueCountFrequency (%)
02-417-0800 1
 
0.4%
02-407-6661 1
 
0.4%
02-452-3260 1
 
0.4%
02-3665-0591 1
 
0.4%
02-2653-0004 1
 
0.4%
02-2602-0014 1
 
0.4%
02-885-0002 1
 
0.4%
02-863-3415 1
 
0.4%
02-889-0205 1
 
0.4%
02-878-2005 1
 
0.4%
Other values (217) 217
95.6%
2023-12-11T16:36:45.180494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 655
25.7%
- 454
17.8%
2 453
17.8%
4 174
 
6.8%
5 170
 
6.7%
3 138
 
5.4%
1 117
 
4.6%
6 110
 
4.3%
7 103
 
4.0%
9 93
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2098
82.2%
Dash Punctuation 454
 
17.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 655
31.2%
2 453
21.6%
4 174
 
8.3%
5 170
 
8.1%
3 138
 
6.6%
1 117
 
5.6%
6 110
 
5.2%
7 103
 
4.9%
9 93
 
4.4%
8 85
 
4.1%
Dash Punctuation
ValueCountFrequency (%)
- 454
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2552
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 655
25.7%
- 454
17.8%
2 453
17.8%
4 174
 
6.8%
5 170
 
6.7%
3 138
 
5.4%
1 117
 
4.6%
6 110
 
4.3%
7 103
 
4.0%
9 93
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2552
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 655
25.7%
- 454
17.8%
2 453
17.8%
4 174
 
6.8%
5 170
 
6.7%
3 138
 
5.4%
1 117
 
4.6%
6 110
 
4.3%
7 103
 
4.0%
9 93
 
3.6%

새우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct226
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4950.7048
Minimum1039
Maximum22381
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-11T16:36:45.369833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1039
5-th percentile1437.4
Q13083.5
median4715
Q36689
95-th percentile8521.4
Maximum22381
Range21342
Interquartile range (IQR)3605.5

Descriptive statistics

Standard deviation2468.2729
Coefficient of variation (CV)0.49857
Kurtosis9.4635121
Mean4950.7048
Median Absolute Deviation (MAD)1697
Skewness1.558127
Sum1123810
Variance6092371.2
MonotonicityNot monotonic
2023-12-11T16:36:45.519681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3187 2
 
0.9%
5584 1
 
0.4%
4780 1
 
0.4%
7721 1
 
0.4%
8755 1
 
0.4%
8771 1
 
0.4%
8826 1
 
0.4%
8727 1
 
0.4%
8814 1
 
0.4%
8750 1
 
0.4%
Other values (216) 216
95.2%
ValueCountFrequency (%)
1039 1
0.4%
1054 1
0.4%
1145 1
0.4%
1158 1
0.4%
1181 1
0.4%
1205 1
0.4%
1230 1
0.4%
1304 1
0.4%
1322 1
0.4%
1334 1
0.4%
ValueCountFrequency (%)
22381 1
0.4%
8826 1
0.4%
8814 1
0.4%
8771 1
0.4%
8755 1
0.4%
8750 1
0.4%
8738 1
0.4%
8727 1
0.4%
8632 1
0.4%
8622 1
0.4%

주소
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing227
Missing (%)100.0%
Memory size2.1 KiB

도로명주소
Text

UNIQUE 

Distinct227
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-11T16:36:45.920389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length45
Median length34
Mean length23.625551
Min length13

Characters and Unicode

Total characters5363
Distinct characters229
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique227 ?
Unique (%)100.0%

Sample

1st row서울특별시 송파구 백제고분로 256 (삼전동 72-4)
2nd row서울특별시 송파구 백제고분로 42길 5 (송파1동 113-2 여성문화회관 1층)
3rd row서울특별시 송파구 동남로 300 (오금동 129-3)
4th row서울특별시 송파구 양재대로 1218 (오륜동 89-13)
5th row서울특별시 송파구 올림픽로 107 (잠실동 19-7)
ValueCountFrequency (%)
서울특별시 226
 
20.1%
송파구 16
 
1.4%
강남구 15
 
1.3%
종로구 14
 
1.2%
용산구 12
 
1.1%
영등포구 12
 
1.1%
노원구 12
 
1.1%
중구 11
 
1.0%
동대문구 10
 
0.9%
서대문구 9
 
0.8%
Other values (558) 787
70.0%
2023-12-11T16:36:46.401321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
897
 
16.7%
서 266
 
5.0%
로 255
 
4.8%
구 243
 
4.5%
시 231
 
4.3%
울 229
 
4.3%
별 226
 
4.2%
특 226
 
4.2%
동 192
 
3.6%
1 180
 
3.4%
Other values (219) 2418
45.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3233
60.3%
Space Separator 897
 
16.7%
Decimal Number 882
 
16.4%
Open Punctuation 152
 
2.8%
Close Punctuation 152
 
2.8%
Dash Punctuation 47
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 266
 
8.2%
로 255
 
7.9%
구 243
 
7.5%
시 231
 
7.1%
울 229
 
7.1%
별 226
 
7.0%
특 226
 
7.0%
동 192
 
5.9%
대 63
 
1.9%
길 56
 
1.7%
Other values (205) 1246
38.5%
Decimal Number
ValueCountFrequency (%)
1 180
20.4%
2 138
15.6%
3 100
11.3%
5 77
8.7%
0 71
 
8.0%
4 70
 
7.9%
7 67
 
7.6%
6 66
 
7.5%
8 59
 
6.7%
9 54
 
6.1%
Space Separator
ValueCountFrequency (%)
897
100.0%
Open Punctuation
ValueCountFrequency (%)
( 152
100.0%
Close Punctuation
ValueCountFrequency (%)
) 152
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 47
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3233
60.3%
Common 2130
39.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 266
 
8.2%
로 255
 
7.9%
구 243
 
7.5%
시 231
 
7.1%
울 229
 
7.1%
별 226
 
7.0%
특 226
 
7.0%
동 192
 
5.9%
대 63
 
1.9%
길 56
 
1.7%
Other values (205) 1246
38.5%
Common
ValueCountFrequency (%)
897
42.1%
1 180
 
8.5%
( 152
 
7.1%
) 152
 
7.1%
2 138
 
6.5%
3 100
 
4.7%
5 77
 
3.6%
0 71
 
3.3%
4 70
 
3.3%
7 67
 
3.1%
Other values (4) 226
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3233
60.3%
ASCII 2130
39.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
897
42.1%
1 180
 
8.5%
( 152
 
7.1%
) 152
 
7.1%
2 138
 
6.5%
3 100
 
4.7%
5 77
 
3.6%
0 71
 
3.3%
4 70
 
3.3%
7 67
 
3.1%
Other values (4) 226
 
10.6%
Hangul
ValueCountFrequency (%)
서 266
 
8.2%
로 255
 
7.9%
구 243
 
7.5%
시 231
 
7.1%
울 229
 
7.1%
별 226
 
7.0%
특 226
 
7.0%
동 192
 
5.9%
대 63
 
1.9%
길 56
 
1.7%
Other values (205) 1246
38.5%

행정시
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
서울특별시
226 
인천광역시
 
1

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row서울특별시
2nd row서울특별시
3rd row서울특별시
4th row서울특별시
5th row서울특별시

Common Values

ValueCountFrequency (%)
서울특별시 226
99.6%
인천광역시 1
 
0.4%

Length

2023-12-11T16:36:46.542697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T16:36:46.982943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 226
99.6%
인천광역시 1
 
0.4%

행정구
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
송파구
16 
강남구
 
15
종로구
 
14
영등포구
 
12
용산구
 
12
Other values (20)
158 

Length

Max length4
Median length3
Mean length3.0881057
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row송파구
2nd row송파구
3rd row송파구
4th row송파구
5th row송파구

Common Values

ValueCountFrequency (%)
송파구 16
 
7.0%
강남구 15
 
6.6%
종로구 14
 
6.2%
영등포구 12
 
5.3%
용산구 12
 
5.3%
노원구 12
 
5.3%
중구 11
 
4.8%
동대문구 10
 
4.4%
서대문구 9
 
4.0%
서초구 9
 
4.0%
Other values (15) 107
47.1%

Length

2023-12-11T16:36:47.099388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
송파구 16
 
7.0%
강남구 15
 
6.6%
종로구 14
 
6.2%
영등포구 12
 
5.3%
용산구 12
 
5.3%
노원구 12
 
5.3%
중구 11
 
4.8%
동대문구 10
 
4.4%
마포구 9
 
4.0%
성북구 9
 
4.0%
Other values (15) 107
47.1%

행정동
Text

Distinct180
Distinct (%)79.3%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-11T16:36:47.415540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length4
Mean length3.8502203
Min length2

Characters and Unicode

Total characters874
Distinct characters156
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique148 ?
Unique (%)65.2%

Sample

1st row삼전동
2nd row송파1동
3rd row오금동
4th row오륜동
5th row잠실2동
ValueCountFrequency (%)
용신동 7
 
3.1%
여의동 5
 
2.2%
종로1.2.3.4가동 4
 
1.8%
삼선동 3
 
1.3%
한강로동 3
 
1.3%
역삼1동 3
 
1.3%
충현동 3
 
1.3%
신촌동 3
 
1.3%
상계1동 2
 
0.9%
자양2동 2
 
0.9%
Other values (170) 192
84.6%
2023-12-11T16:36:47.949469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 227
26.0%
1 53
 
6.1%
2 48
 
5.5%
3 26
 
3.0%
신 22
 
2.5%
. 18
 
2.1%
로 17
 
1.9%
삼 14
 
1.6%
성 13
 
1.5%
가 13
 
1.5%
Other values (146) 423
48.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 707
80.9%
Decimal Number 149
 
17.0%
Other Punctuation 18
 
2.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 227
32.1%
신 22
 
3.1%
로 17
 
2.4%
삼 14
 
2.0%
성 13
 
1.8%
가 13
 
1.8%
계 11
 
1.6%
상 10
 
1.4%
현 9
 
1.3%
양 9
 
1.3%
Other values (136) 362
51.2%
Decimal Number
ValueCountFrequency (%)
1 53
35.6%
2 48
32.2%
3 26
17.4%
4 9
 
6.0%
5 5
 
3.4%
6 4
 
2.7%
7 2
 
1.3%
0 1
 
0.7%
8 1
 
0.7%
Other Punctuation
ValueCountFrequency (%)
. 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 707
80.9%
Common 167
 
19.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 227
32.1%
신 22
 
3.1%
로 17
 
2.4%
삼 14
 
2.0%
성 13
 
1.8%
가 13
 
1.8%
계 11
 
1.6%
상 10
 
1.4%
현 9
 
1.3%
양 9
 
1.3%
Other values (136) 362
51.2%
Common
ValueCountFrequency (%)
1 53
31.7%
2 48
28.7%
3 26
15.6%
. 18
 
10.8%
4 9
 
5.4%
5 5
 
3.0%
6 4
 
2.4%
7 2
 
1.2%
0 1
 
0.6%
8 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 707
80.9%
ASCII 167
 
19.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 227
32.1%
신 22
 
3.1%
로 17
 
2.4%
삼 14
 
2.0%
성 13
 
1.8%
가 13
 
1.8%
계 11
 
1.6%
상 10
 
1.4%
현 9
 
1.3%
양 9
 
1.3%
Other values (136) 362
51.2%
ASCII
ValueCountFrequency (%)
1 53
31.7%
2 48
28.7%
3 26
15.6%
. 18
 
10.8%
4 9
 
5.4%
5 5
 
3.0%
6 4
 
2.4%
7 2
 
1.2%
0 1
 
0.6%
8 1
 
0.6%

Interactions

2023-12-11T16:36:40.957193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:36:40.773067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:36:41.060871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T16:36:40.854377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T16:36:48.066767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기관코드유형2새우편번호행정시행정구
기관코드1.0000.7390.4430.2420.908
유형20.7391.0000.6431.0000.000
새우편번호0.4430.6431.0001.0000.983
행정시0.2421.0001.0001.0000.000
행정구0.9080.0000.9830.0001.000
2023-12-11T16:36:48.187451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정구유형2행정시
행정구1.0000.0000.000
유형20.0001.0000.996
행정시0.0000.9961.000
2023-12-11T16:36:48.288095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기관코드새우편번호유형2행정시행정구
기관코드1.000-0.0570.3900.3930.733
새우편번호-0.0571.0000.5720.9930.797
유형20.3900.5721.0000.9960.000
행정시0.3930.9930.9961.0000.000
행정구0.7330.7970.0000.0001.000

Missing values

2023-12-11T16:36:41.219655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T16:36:41.480345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

키값기관코드유형1유형2대표기관명전체기관명최하위기관명대표전화번호새우편번호주소도로명주소행정시행정구행정동
0BE_LiST27-01641710674우정우정_우체국미래부미래창조과학부 우정사업본부 서울지방우정청 서울송파우체국 서울삼전동우체국서울삼전동우체국02-417-08005584<NA>서울특별시 송파구 백제고분로 256 (삼전동 72-4)서울특별시송파구삼전동
1BE_LiST27-01651710669우정우정_우체국미래부미래창조과학부 우정사업본부 서울지방우정청 서울송파우체국 서울송파1동우체국서울송파1동우체국02-422-02055666<NA>서울특별시 송파구 백제고분로 42길 5 (송파1동 113-2 여성문화회관 1층)서울특별시송파구송파1동
2BE_LiST27-01661710679우정우정_우체국미래부미래창조과학부 우정사업본부 서울지방우정청 서울송파우체국 서울오금동우체국서울오금동우체국02-443-02005739<NA>서울특별시 송파구 동남로 300 (오금동 129-3)서울특별시송파구오금동
3BE_LiST27-01671710675우정우정_우체국미래부미래창조과학부 우정사업본부 서울지방우정청 서울송파우체국 서울오륜동우체국서울오륜동우체국02-404-00145649<NA>서울특별시 송파구 양재대로 1218 (오륜동 89-13)서울특별시송파구오륜동
4BE_LiST27-01681710681우정우정_우체국미래부미래창조과학부 우정사업본부 서울지방우정청 서울송파우체국 서울잠실2동우체국서울잠실2동우체국02-423-20045501<NA>서울특별시 송파구 올림픽로 107 (잠실동 19-7)서울특별시송파구잠실2동
5BE_LiST27-01691710682우정우정_우체국미래부미래창조과학부 우정사업본부 서울지방우정청 서울송파우체국 서울잠실3동우체국서울잠실3동우체국02-414-22005503<NA>서울특별시 송파구 잠실로 51-33 (잠실3동 27-12)서울특별시송파구잠실3동
6BE_LiST27-01701710671우정우정_우체국미래부미래창조과학부 우정사업본부 서울지방우정청 서울송파우체국 서울잠실4동우체국서울잠실4동우체국02-422-00035507<NA>서울특별시 송파구 신천로 133-1 (신천동 20-5)서울특별시송파구잠실6동
7BE_LiST27-01711710667우정우정_우체국미래부미래창조과학부 우정사업본부 서울지방우정청 서울송파우체국 서울잠실우체국서울잠실우체국02-417-20055552<NA>서울특별시 송파구 오금로 90 (신천동 29-1)서울특별시송파구잠실6동
8BE_LiST27-01721710677우정우정_우체국미래부미래창조과학부 우정사업본부 서울지방우정청 서울송파우체국 서울풍납동우체국서울풍납동우체국02-471-01555520<NA>서울특별시 송파구 올림픽로 575 (풍납동 497)서울특별시송파구풍납1동
9BE_LiST27-01731710684우정우정_우체국미래부미래창조과학부 우정사업본부 서울지방우정청 서울양천우체국서울양천우체국02-2646-00047988<NA>서울특별시 양천구 목동서로 117 (목동 905-16)서울특별시양천구목5동
키값기관코드유형1유형2대표기관명전체기관명최하위기관명대표전화번호새우편번호주소도로명주소행정시행정구행정동
217BE_LiST27-00281710446우정우정_우체국미래부미래창조과학부 우정사업본부 서울지방우정청 서대문우체국 경찰청우체국경찰청우체국02-393-20013739<NA>서울특별시 서대문구 통일로 97 (미근동 209)서울특별시서대문구충현동
218BE_LiST27-00291710438우정우정_우체국미래부미래창조과학부 우정사업본부 서울지방우정청 서대문우체국 서울모래내우체국서울모래내우체국02-373-20053690<NA>서울특별시 서대문구 거북골로160 (북가좌동 424-29)서울특별시서대문구북가좌2동
219BE_LiST27-00301710439우정우정_우체국미래부미래창조과학부 우정사업본부 서울지방우정청 서대문우체국 서울신촌우체국서울신촌우체국02-362-02053766<NA>서울특별시 서대문구 신촌로161 (대현동 90-101)서울특별시서대문구신촌동
220BE_LiST27-00311710443우정우정_우체국미래부미래창조과학부 우정사업본부 서울지방우정청 서대문우체국 서울연세대학교우체국서울연세대학교우체국02-390-02053722<NA>서울특별시 서대문구 연세로50 (신촌동 134)서울특별시서대문구신촌동
221BE_LiST27-00321710441우정우정_우체국미래부미래창조과학부 우정사업본부 서울지방우정청 서대문우체국 서울연희동우체국서울연희동우체국02-324-20053698<NA>서울특별시 서대문구 증가로12 (연희동 90-15)서울특별시서대문구연희동
222BE_LiST27-00331710445우정우정_우체국미래부미래창조과학부 우정사업본부 서울지방우정청 서대문우체국 서울이화여자대학교우체국서울이화여자대학교우체국02-363-02053760<NA>서울특별시 서대문구 이화여대길 52 (대현동 11-1)서울특별시서대문구충현동
223BE_LiST27-00341710442우정우정_우체국미래부미래창조과학부 우정사업본부 서울지방우정청 서대문우체국 서울충정로우체국서울충정로우체국02-362-00043735<NA>서울특별시 서대문구 통일로127 (충정로2가 8-2)서울특별시서대문구충현동
224BE_LiST27-00351710440우정우정_우체국미래부미래창조과학부 우정사업본부 서울지방우정청 서대문우체국 서울홍제동우체국서울홍제동우체국02-379-42053646<NA>서울특별시 서대문구 통일로419 (홍제동 158-45)서울특별시서대문구홍제1동
225BE_LiST27-00361710580우정우정_우체국미래부미래창조과학부 우정사업본부 서울지방우정청 서울강남우체국서울강남우체국02-2040-40006336<NA>서울특별시 강남구 개포로 619 (개포동)서울특별시강남구일원2동
226BE_LiST27-00371710597우정우정_우체국미래부미래창조과학부 우정사업본부 서울지방우정청 서울강남우체국 서울개포1동우체국서울개포1동우체국02-575-02056325<NA>서울특별시 강남구 선릉로 36 (개포동)서울특별시강남구개포2동