Overview

Dataset statistics

Number of variables5
Number of observations68
Missing cells39
Missing cells (%)11.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory42.9 B

Variable types

Numeric1
Categorical1
Text3

Dataset

Description인천광역시 부평구 행정사현황입니다.(개업신고일, 영업소명칭,주소,전화 등)ex) 1,1999-07-02,최서홍사무소,길주로 497(청천동),032-504-7070
Author인천광역시 부평구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15028813&srcSe=7661IVAWM27C61E190

Alerts

연번 is highly overall correlated with 행정사 종류High correlation
행정사 종류 is highly overall correlated with 연번High correlation
행정사 종류 is highly imbalanced (88.9%)Imbalance
연번 has 1 (1.5%) missing valuesMissing
사무소 명칭 has 1 (1.5%) missing valuesMissing
사무소 주소 has 1 (1.5%) missing valuesMissing
사무소 연락처 has 36 (52.9%) missing valuesMissing

Reproduction

Analysis started2024-01-28 15:29:55.662358
Analysis finished2024-01-28 15:29:56.245317
Duration0.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct67
Distinct (%)100.0%
Missing1
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean34
Minimum1
Maximum67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size744.0 B
2024-01-29T00:29:56.560474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.3
Q117.5
median34
Q350.5
95-th percentile63.7
Maximum67
Range66
Interquartile range (IQR)33

Descriptive statistics

Standard deviation19.485037
Coefficient of variation (CV)0.57308932
Kurtosis-1.2
Mean34
Median Absolute Deviation (MAD)17
Skewness0
Sum2278
Variance379.66667
MonotonicityStrictly increasing
2024-01-29T00:29:56.665473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.5%
44 1
 
1.5%
50 1
 
1.5%
49 1
 
1.5%
48 1
 
1.5%
47 1
 
1.5%
46 1
 
1.5%
45 1
 
1.5%
43 1
 
1.5%
2 1
 
1.5%
Other values (57) 57
83.8%
ValueCountFrequency (%)
1 1
1.5%
2 1
1.5%
3 1
1.5%
4 1
1.5%
5 1
1.5%
6 1
1.5%
7 1
1.5%
8 1
1.5%
9 1
1.5%
10 1
1.5%
ValueCountFrequency (%)
67 1
1.5%
66 1
1.5%
65 1
1.5%
64 1
1.5%
63 1
1.5%
62 1
1.5%
61 1
1.5%
60 1
1.5%
59 1
1.5%
58 1
1.5%

행정사 종류
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size676.0 B
일반행정사
67 
<NA>
 
1

Length

Max length5
Median length5
Mean length4.9852941
Min length4

Unique

Unique1 ?
Unique (%)1.5%

Sample

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

Common Values

ValueCountFrequency (%)
일반행정사 67
98.5%
<NA> 1
 
1.5%

Length

2024-01-29T00:29:56.768985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T00:29:56.842184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반행정사 67
98.5%
na 1
 
1.5%

사무소 명칭
Text

MISSING 

Distinct66
Distinct (%)98.5%
Missing1
Missing (%)1.5%
Memory size676.0 B
2024-01-29T00:29:57.024118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length14
Mean length8.8208955
Min length3

Characters and Unicode

Total characters591
Distinct characters127
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique65 ?
Unique (%)97.0%

Sample

1st rowBH 행정사사무소
2nd rowHC 행정사사무소
3rd rowHORIZON(수평)행정사 사무소
4th rowJK행정사 김동후사무소
5th row가원행정사사무소
ValueCountFrequency (%)
행정사 18
 
15.8%
행정사사무소 13
 
11.4%
사무소 12
 
10.5%
가원행정사사무소 2
 
1.8%
인천 1
 
0.9%
유준 1
 
0.9%
윤 1
 
0.9%
이병수사무소 1
 
0.9%
플러스노무행정사 1
 
0.9%
이정일행정사 1
 
0.9%
Other values (63) 63
55.3%
2024-01-29T00:29:57.332654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 115
19.5%
정 71
12.0%
행 64
 
10.8%
무 54
 
9.1%
소 52
 
8.8%
47
 
8.0%
김 7
 
1.2%
이 6
 
1.0%
평 4
 
0.7%
원 4
 
0.7%
Other values (117) 167
28.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 529
89.5%
Space Separator 47
 
8.0%
Uppercase Letter 13
 
2.2%
Close Punctuation 1
 
0.2%
Open Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 115
21.7%
정 71
13.4%
행 64
12.1%
무 54
 
10.2%
소 52
 
9.8%
김 7
 
1.3%
이 6
 
1.1%
평 4
 
0.8%
원 4
 
0.8%
대 3
 
0.6%
Other values (104) 149
28.2%
Uppercase Letter
ValueCountFrequency (%)
H 3
23.1%
O 2
15.4%
K 1
 
7.7%
J 1
 
7.7%
N 1
 
7.7%
Z 1
 
7.7%
I 1
 
7.7%
R 1
 
7.7%
B 1
 
7.7%
C 1
 
7.7%
Space Separator
ValueCountFrequency (%)
47
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 529
89.5%
Common 49
 
8.3%
Latin 13
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 115
21.7%
정 71
13.4%
행 64
12.1%
무 54
 
10.2%
소 52
 
9.8%
김 7
 
1.3%
이 6
 
1.1%
평 4
 
0.8%
원 4
 
0.8%
대 3
 
0.6%
Other values (104) 149
28.2%
Latin
ValueCountFrequency (%)
H 3
23.1%
O 2
15.4%
K 1
 
7.7%
J 1
 
7.7%
N 1
 
7.7%
Z 1
 
7.7%
I 1
 
7.7%
R 1
 
7.7%
B 1
 
7.7%
C 1
 
7.7%
Common
ValueCountFrequency (%)
47
95.9%
) 1
 
2.0%
( 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 529
89.5%
ASCII 62
 
10.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 115
21.7%
정 71
13.4%
행 64
12.1%
무 54
 
10.2%
소 52
 
9.8%
김 7
 
1.3%
이 6
 
1.1%
평 4
 
0.8%
원 4
 
0.8%
대 3
 
0.6%
Other values (104) 149
28.2%
ASCII
ValueCountFrequency (%)
47
75.8%
H 3
 
4.8%
O 2
 
3.2%
K 1
 
1.6%
J 1
 
1.6%
) 1
 
1.6%
( 1
 
1.6%
N 1
 
1.6%
Z 1
 
1.6%
I 1
 
1.6%
Other values (3) 3
 
4.8%

사무소 주소
Text

MISSING 

Distinct61
Distinct (%)91.0%
Missing1
Missing (%)1.5%
Memory size676.0 B
2024-01-29T00:29:57.615436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length44
Mean length30.970149
Min length18

Characters and Unicode

Total characters2075
Distinct characters133
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)83.6%

Sample

1st row인천광역시 부평구 부흥북로 12, 카이저오피스텔 503호 (부평동)
2nd row인천광역시 부평구 부평문화로 14, 2층 202호 (부평동)
3rd row인천광역시 부평구 부평대로 32, 3층 (부평동)
4th row인천광역시 부평구 동수천로 13-1, 메트로시티 101동 1동 6층 602호 (부평동)
5th row인천광역시 부평구 부평대로 144 (부평동)
ValueCountFrequency (%)
인천광역시 67
 
16.4%
부평구 67
 
16.4%
부평동 38
 
9.3%
십정동 8
 
2.0%
부평대로 8
 
2.0%
장제로 5
 
1.2%
갈산동 5
 
1.2%
청천동 5
 
1.2%
주부토로 5
 
1.2%
2층 4
 
1.0%
Other values (154) 197
48.2%
2024-01-29T00:29:58.003085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
345
 
16.6%
부 128
 
6.2%
평 117
 
5.6%
동 84
 
4.0%
1 83
 
4.0%
천 78
 
3.8%
시 70
 
3.4%
인 69
 
3.3%
광 68
 
3.3%
구 67
 
3.2%
Other values (123) 966
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1202
57.9%
Space Separator 345
 
16.6%
Decimal Number 336
 
16.2%
Open Punctuation 65
 
3.1%
Close Punctuation 65
 
3.1%
Other Punctuation 45
 
2.2%
Dash Punctuation 12
 
0.6%
Uppercase Letter 5
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 128
 
10.6%
평 117
 
9.7%
동 84
 
7.0%
천 78
 
6.5%
시 70
 
5.8%
인 69
 
5.7%
광 68
 
5.7%
구 67
 
5.6%
역 67
 
5.6%
로 66
 
5.5%
Other values (105) 388
32.3%
Decimal Number
ValueCountFrequency (%)
1 83
24.7%
2 49
14.6%
0 45
13.4%
4 35
10.4%
6 31
 
9.2%
3 28
 
8.3%
5 22
 
6.5%
7 16
 
4.8%
8 15
 
4.5%
9 12
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
B 2
40.0%
U 2
40.0%
A 1
20.0%
Space Separator
ValueCountFrequency (%)
345
100.0%
Open Punctuation
ValueCountFrequency (%)
( 65
100.0%
Close Punctuation
ValueCountFrequency (%)
) 65
100.0%
Other Punctuation
ValueCountFrequency (%)
, 45
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1202
57.9%
Common 868
41.8%
Latin 5
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 128
 
10.6%
평 117
 
9.7%
동 84
 
7.0%
천 78
 
6.5%
시 70
 
5.8%
인 69
 
5.7%
광 68
 
5.7%
구 67
 
5.6%
역 67
 
5.6%
로 66
 
5.5%
Other values (105) 388
32.3%
Common
ValueCountFrequency (%)
345
39.7%
1 83
 
9.6%
( 65
 
7.5%
) 65
 
7.5%
2 49
 
5.6%
, 45
 
5.2%
0 45
 
5.2%
4 35
 
4.0%
6 31
 
3.6%
3 28
 
3.2%
Other values (5) 77
 
8.9%
Latin
ValueCountFrequency (%)
B 2
40.0%
U 2
40.0%
A 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1202
57.9%
ASCII 873
42.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
345
39.5%
1 83
 
9.5%
( 65
 
7.4%
) 65
 
7.4%
2 49
 
5.6%
, 45
 
5.2%
0 45
 
5.2%
4 35
 
4.0%
6 31
 
3.6%
3 28
 
3.2%
Other values (8) 82
 
9.4%
Hangul
ValueCountFrequency (%)
부 128
 
10.6%
평 117
 
9.7%
동 84
 
7.0%
천 78
 
6.5%
시 70
 
5.8%
인 69
 
5.7%
광 68
 
5.7%
구 67
 
5.6%
역 67
 
5.6%
로 66
 
5.5%
Other values (105) 388
32.3%

사무소 연락처
Text

MISSING 

Distinct30
Distinct (%)93.8%
Missing36
Missing (%)52.9%
Memory size676.0 B
2024-01-29T00:29:58.173944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.03125
Min length9

Characters and Unicode

Total characters385
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)87.5%

Sample

1st row031-967-7996
2nd row032-506-0028
3rd row032-506-0028
4th row032-361-3535
5th row032-523-9540
ValueCountFrequency (%)
032-519-1201 2
 
6.2%
032-506-0028 2
 
6.2%
032-508-2560 1
 
3.1%
032-513-6961 1
 
3.1%
1522-0423 1
 
3.1%
032-566-8666 1
 
3.1%
032-522-6312 1
 
3.1%
032-523-1621 1
 
3.1%
032-719-3637 1
 
3.1%
032-719-3637~8 1
 
3.1%
Other values (20) 20
62.5%
2024-01-29T00:29:58.445202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 63
16.4%
0 62
16.1%
2 58
15.1%
3 54
14.0%
5 34
8.8%
6 27
7.0%
1 26
6.8%
8 18
 
4.7%
9 15
 
3.9%
7 15
 
3.9%
Other values (2) 13
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 321
83.4%
Dash Punctuation 63
 
16.4%
Math Symbol 1
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 62
19.3%
2 58
18.1%
3 54
16.8%
5 34
10.6%
6 27
8.4%
1 26
8.1%
8 18
 
5.6%
9 15
 
4.7%
7 15
 
4.7%
4 12
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 63
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 385
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 63
16.4%
0 62
16.1%
2 58
15.1%
3 54
14.0%
5 34
8.8%
6 27
7.0%
1 26
6.8%
8 18
 
4.7%
9 15
 
3.9%
7 15
 
3.9%
Other values (2) 13
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 385
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 63
16.4%
0 62
16.1%
2 58
15.1%
3 54
14.0%
5 34
8.8%
6 27
7.0%
1 26
6.8%
8 18
 
4.7%
9 15
 
3.9%
7 15
 
3.9%
Other values (2) 13
 
3.4%

Interactions

2024-01-29T00:29:55.919369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-29T00:29:58.526149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번사무소 명칭사무소 주소사무소 연락처
연번1.0001.0000.8200.985
사무소 명칭1.0001.0001.0001.000
사무소 주소0.8201.0001.0001.000
사무소 연락처0.9851.0001.0001.000
2024-01-29T00:29:58.600023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번행정사 종류
연번1.0001.000
행정사 종류1.0001.000

Missing values

2024-01-29T00:29:56.031401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-29T00:29:56.105279image/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.
2024-01-29T00:29:56.191376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연번행정사 종류사무소 명칭사무소 주소사무소 연락처
01일반행정사BH 행정사사무소인천광역시 부평구 부흥북로 12, 카이저오피스텔 503호 (부평동)<NA>
12일반행정사HC 행정사사무소인천광역시 부평구 부평문화로 14, 2층 202호 (부평동)<NA>
23일반행정사HORIZON(수평)행정사 사무소인천광역시 부평구 부평대로 32, 3층 (부평동)<NA>
34일반행정사JK행정사 김동후사무소인천광역시 부평구 동수천로 13-1, 메트로시티 101동 1동 6층 602호 (부평동)031-967-7996
45일반행정사가원행정사사무소인천광역시 부평구 부평대로 144 (부평동)032-506-0028
56일반행정사가원행정사사무소인천광역시 부평구 부평대로 144 (부평동)032-506-0028
67일반행정사고스락행정사사무소인천광역시 부평구 백범로406번길 45, A동 201호 (십정동, 금모래팰리스)<NA>
78일반행정사공감 행정사 사무소인천광역시 부평구 동수북로 72, 2동 202호 (부평동, 청운하이츠빌)<NA>
89일반행정사권순범행정사사무소인천광역시 부평구 부평대로278번길 42 (갈산동)<NA>
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