Overview

Dataset statistics

Number of variables13
Number of observations83
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.0 KiB
Average record size in memory110.6 B

Variable types

Categorical7
Numeric4
Text2

Dataset

Description공간정보의 구축 및 관리 등에 관한 법률 제44조에 따른 측량업 등록정보인천시 관내 지적측량업, 공공측량업, 일반측량업 현황
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15075111&srcSe=7661IVAWM27C61E190

Alerts

등록기관 has constant value ""Constant
시도_시군구 has constant value ""Constant
영업여부 has constant value ""Constant
본점/지점 has constant value ""Constant
기술자 수 is highly overall correlated with 장비 수 and 2 other fieldsHigh correlation
장비 수 is highly overall correlated with 기술자 수 and 1 other fieldsHigh correlation
업종 is highly overall correlated with 기술자 수 and 2 other fieldsHigh correlation
개인/법인 is highly overall correlated with 기술자 수 and 1 other fieldsHigh correlation
대표자 수 is highly imbalanced (62.6%)Imbalance
업등록번호 has unique valuesUnique
임원 수 has 51 (61.4%) zerosZeros

Reproduction

Analysis started2024-01-28 06:44:22.542393
Analysis finished2024-01-28 06:44:24.430686
Duration1.89 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

등록기관
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size796.0 B
지자체
83 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지자체 83
100.0%

Length

2024-01-28T15:44:24.489744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T15:44:24.570814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지자체 83
100.0%

시도_시군구
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size796.0 B
인천광역시
83 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인천광역시 83
100.0%

Length

2024-01-28T15:44:24.644755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T15:44:24.719937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 83
100.0%

업종
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size796.0 B
일반측량
49 
공공측량
24 
지적측량
10 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row지적측량
2nd row일반측량
3rd row공공측량
4th row일반측량
5th row일반측량

Common Values

ValueCountFrequency (%)
일반측량 49
59.0%
공공측량 24
28.9%
지적측량 10
 
12.0%

Length

2024-01-28T15:44:24.796128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T15:44:24.884163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반측량 49
59.0%
공공측량 24
28.9%
지적측량 10
 
12.0%

업체일련번호
Real number (ℝ)

Distinct75
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2840.2771
Minimum155
Maximum7128
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size879.0 B
2024-01-28T15:44:24.981667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum155
5-th percentile328
Q1363.5
median416
Q35639
95-th percentile6885.7
Maximum7128
Range6973
Interquartile range (IQR)5275.5

Descriptive statistics

Standard deviation2786.5138
Coefficient of variation (CV)0.98107109
Kurtosis-1.7720754
Mean2840.2771
Median Absolute Deviation (MAD)91
Skewness0.32127646
Sum235743
Variance7764658.9
MonotonicityNot monotonic
2024-01-28T15:44:25.083479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
325 3
 
3.6%
417 2
 
2.4%
328 2
 
2.4%
329 2
 
2.4%
332 2
 
2.4%
330 2
 
2.4%
396 2
 
2.4%
404 1
 
1.2%
356 1
 
1.2%
375 1
 
1.2%
Other values (65) 65
78.3%
ValueCountFrequency (%)
155 1
 
1.2%
325 3
3.6%
328 2
2.4%
329 2
2.4%
330 2
2.4%
332 2
2.4%
333 1
 
1.2%
335 1
 
1.2%
340 1
 
1.2%
349 1
 
1.2%
ValueCountFrequency (%)
7128 1
1.2%
7057 1
1.2%
7027 1
1.2%
6983 1
1.2%
6893 1
1.2%
6820 1
1.2%
6762 1
1.2%
6719 1
1.2%
6673 1
1.2%
6572 1
1.2%

업등록번호
Text

UNIQUE 

Distinct83
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size796.0 B
2024-01-28T15:44:25.303710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length9
Min length9

Characters and Unicode

Total characters747
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

Unique83 ?
Unique (%)100.0%

Sample

1st row02-000394
2nd row04-005011
3rd row03-001340
4th row04-004978
5th row04-004957
ValueCountFrequency (%)
02-000394 1
 
1.2%
04-002940 1
 
1.2%
04-000081 1
 
1.2%
04-000095 1
 
1.2%
03-000035 1
 
1.2%
04-001579 1
 
1.2%
03-000219 1
 
1.2%
02-000034 1
 
1.2%
02-000030 1
 
1.2%
03-000383 1
 
1.2%
Other values (73) 73
88.0%
2024-01-28T15:44:25.628755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 351
47.0%
4 84
 
11.2%
- 83
 
11.1%
3 64
 
8.6%
2 35
 
4.7%
9 29
 
3.9%
1 25
 
3.3%
8 24
 
3.2%
5 19
 
2.5%
7 17
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 664
88.9%
Dash Punctuation 83
 
11.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 351
52.9%
4 84
 
12.7%
3 64
 
9.6%
2 35
 
5.3%
9 29
 
4.4%
1 25
 
3.8%
8 24
 
3.6%
5 19
 
2.9%
7 17
 
2.6%
6 16
 
2.4%
Dash Punctuation
ValueCountFrequency (%)
- 83
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 747
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 351
47.0%
4 84
 
11.2%
- 83
 
11.1%
3 64
 
8.6%
2 35
 
4.7%
9 29
 
3.9%
1 25
 
3.3%
8 24
 
3.2%
5 19
 
2.5%
7 17
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 747
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 351
47.0%
4 84
 
11.2%
- 83
 
11.1%
3 64
 
8.6%
2 35
 
4.7%
9 29
 
3.9%
1 25
 
3.3%
8 24
 
3.2%
5 19
 
2.5%
7 17
 
2.3%

측량업체명
Text

Distinct75
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Memory size796.0 B
2024-01-28T15:44:25.835085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length8.9879518
Min length4

Characters and Unicode

Total characters746
Distinct characters134
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)81.9%

Sample

1st row송하지적측량공사
2nd row주식회사신진지적지아이에스
3rd row주식회사삼조이앤씨
4th row강화공간측량사무소
5th row그린토목설계
ValueCountFrequency (%)
주식회사 9
 
9.7%
주식회사삼조이앤씨 3
 
3.2%
주식회사신진지적지아이에스 2
 
2.2%
주식회사경원지적엔지니어링 2
 
2.2%
한라이엔씨 2
 
2.2%
지오서베이주식회사 2
 
2.2%
세종측량설계기술단 2
 
2.2%
명지공간개발(주 2
 
2.2%
청수엔지니어링 1
 
1.1%
주)정방종합엔지니어링 1
 
1.1%
Other values (67) 67
72.0%
2024-01-28T15:44:26.145663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 48
 
6.4%
사 43
 
5.8%
지 39
 
5.2%
) 29
 
3.9%
( 29
 
3.9%
설 28
 
3.8%
계 26
 
3.5%
측 25
 
3.4%
량 24
 
3.2%
엔 24
 
3.2%
Other values (124) 431
57.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 675
90.5%
Close Punctuation 29
 
3.9%
Open Punctuation 29
 
3.9%
Space Separator 10
 
1.3%
Uppercase Letter 2
 
0.3%
Other Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 48
 
7.1%
사 43
 
6.4%
지 39
 
5.8%
설 28
 
4.1%
계 26
 
3.9%
측 25
 
3.7%
량 24
 
3.6%
엔 24
 
3.6%
니 21
 
3.1%
이 20
 
3.0%
Other values (118) 377
55.9%
Uppercase Letter
ValueCountFrequency (%)
B 1
50.0%
H 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Space Separator
ValueCountFrequency (%)
10
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 676
90.6%
Common 68
 
9.1%
Latin 2
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 48
 
7.1%
사 43
 
6.4%
지 39
 
5.8%
설 28
 
4.1%
계 26
 
3.8%
측 25
 
3.7%
량 24
 
3.6%
엔 24
 
3.6%
니 21
 
3.1%
이 20
 
3.0%
Other values (119) 378
55.9%
Common
ValueCountFrequency (%)
) 29
42.6%
( 29
42.6%
10
 
14.7%
Latin
ValueCountFrequency (%)
B 1
50.0%
H 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 675
90.5%
ASCII 70
 
9.4%
None 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 48
 
7.1%
사 43
 
6.4%
지 39
 
5.8%
설 28
 
4.1%
계 26
 
3.9%
측 25
 
3.7%
량 24
 
3.6%
엔 24
 
3.6%
니 21
 
3.1%
이 20
 
3.0%
Other values (118) 377
55.9%
ASCII
ValueCountFrequency (%)
) 29
41.4%
( 29
41.4%
10
 
14.3%
B 1
 
1.4%
H 1
 
1.4%
None
ValueCountFrequency (%)
㈜ 1
100.0%

영업여부
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size796.0 B
영업
83 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
영업 83
100.0%

Length

2024-01-28T15:44:26.253356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T15:44:26.324606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업 83
100.0%

개인/법인
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size796.0 B
법인
47 
개인
36 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row개인
2nd row법인
3rd row법인
4th row개인
5th row개인

Common Values

ValueCountFrequency (%)
법인 47
56.6%
개인 36
43.4%

Length

2024-01-28T15:44:26.395078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T15:44:26.466679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
법인 47
56.6%
개인 36
43.4%

본점/지점
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size796.0 B
본점
83 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
본점 83
100.0%

Length

2024-01-28T15:44:26.544801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T15:44:26.641447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
본점 83
100.0%

대표자 수
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size796.0 B
1
77 
2
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 77
92.8%
2 6
 
7.2%

Length

2024-01-28T15:44:26.727279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T15:44:26.798385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 77
92.8%
2 6
 
7.2%

임원 수
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)7.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0361446
Minimum0
Maximum13
Zeros51
Zeros (%)61.4%
Negative0
Negative (%)0.0%
Memory size879.0 B
2024-01-28T15:44:26.863253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile3.9
Maximum13
Range13
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8442117
Coefficient of variation (CV)1.7798787
Kurtosis20.706033
Mean1.0361446
Median Absolute Deviation (MAD)0
Skewness3.6632854
Sum86
Variance3.4011167
MonotonicityNot monotonic
2024-01-28T15:44:26.944544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 51
61.4%
2 12
 
14.5%
3 9
 
10.8%
1 6
 
7.2%
4 4
 
4.8%
13 1
 
1.2%
ValueCountFrequency (%)
0 51
61.4%
1 6
 
7.2%
2 12
 
14.5%
3 9
 
10.8%
4 4
 
4.8%
13 1
 
1.2%
ValueCountFrequency (%)
13 1
 
1.2%
4 4
 
4.8%
3 9
 
10.8%
2 12
 
14.5%
1 6
 
7.2%
0 51
61.4%

기술자 수
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3493976
Minimum1
Maximum17
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size879.0 B
2024-01-28T15:44:27.026752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q36
95-th percentile12.9
Maximum17
Range16
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.607018
Coefficient of variation (CV)0.82931439
Kurtosis3.6414448
Mean4.3493976
Median Absolute Deviation (MAD)0
Skewness1.9249664
Sum361
Variance13.010579
MonotonicityNot monotonic
2024-01-28T15:44:27.106257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 46
55.4%
6 22
26.5%
5 2
 
2.4%
4 2
 
2.4%
12 2
 
2.4%
17 2
 
2.4%
1 1
 
1.2%
8 1
 
1.2%
13 1
 
1.2%
7 1
 
1.2%
Other values (3) 3
 
3.6%
ValueCountFrequency (%)
1 1
 
1.2%
2 46
55.4%
3 1
 
1.2%
4 2
 
2.4%
5 2
 
2.4%
6 22
26.5%
7 1
 
1.2%
8 1
 
1.2%
12 2
 
2.4%
13 1
 
1.2%
ValueCountFrequency (%)
17 2
 
2.4%
15 1
 
1.2%
14 1
 
1.2%
13 1
 
1.2%
12 2
 
2.4%
8 1
 
1.2%
7 1
 
1.2%
6 22
26.5%
5 2
 
2.4%
4 2
 
2.4%

장비 수
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)14.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2289157
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size879.0 B
2024-01-28T15:44:27.182860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median2
Q33
95-th percentile11.9
Maximum19
Range18
Interquartile range (IQR)1

Descriptive statistics

Standard deviation3.250986
Coefficient of variation (CV)1.0068352
Kurtosis9.6267518
Mean3.2289157
Median Absolute Deviation (MAD)0
Skewness3.1309656
Sum268
Variance10.56891
MonotonicityNot monotonic
2024-01-28T15:44:27.538183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 60
72.3%
3 10
 
12.0%
4 3
 
3.6%
12 2
 
2.4%
1 1
 
1.2%
5 1
 
1.2%
8 1
 
1.2%
10 1
 
1.2%
11 1
 
1.2%
13 1
 
1.2%
Other values (2) 2
 
2.4%
ValueCountFrequency (%)
1 1
 
1.2%
2 60
72.3%
3 10
 
12.0%
4 3
 
3.6%
5 1
 
1.2%
8 1
 
1.2%
10 1
 
1.2%
11 1
 
1.2%
12 2
 
2.4%
13 1
 
1.2%
ValueCountFrequency (%)
19 1
 
1.2%
15 1
 
1.2%
13 1
 
1.2%
12 2
 
2.4%
11 1
 
1.2%
10 1
 
1.2%
8 1
 
1.2%
5 1
 
1.2%
4 3
 
3.6%
3 10
12.0%

Interactions

2024-01-28T15:44:23.795104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:22.891233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:23.208926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:23.492720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:23.877146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:22.966433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:23.284397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:23.564092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:23.955775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:23.048884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:23.354628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:23.651649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:24.041975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:23.132362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:23.420444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T15:44:23.723693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T15:44:27.624494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종업체일련번호업등록번호측량업체명개인/법인대표자 수임원 수기술자 수장비 수
업종1.0000.3631.0000.0000.4310.0000.2290.9980.739
업체일련번호0.3631.0001.0001.0000.0000.0000.3770.2680.208
업등록번호1.0001.0001.0001.0001.0001.0001.0001.0001.000
측량업체명0.0001.0001.0001.0001.0001.0001.0000.0000.000
개인/법인0.4310.0001.0001.0001.0000.2560.4170.6460.255
대표자 수0.0000.0001.0001.0000.2561.0000.3550.0000.000
임원 수0.2290.3771.0001.0000.4170.3551.0000.3020.000
기술자 수0.9980.2681.0000.0000.6460.0000.3021.0000.897
장비 수0.7390.2081.0000.0000.2550.0000.0000.8971.000
2024-01-28T15:44:27.743090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종대표자 수개인/법인
업종1.0000.0000.668
대표자 수0.0001.0000.165
개인/법인0.6680.1651.000
2024-01-28T15:44:27.847106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업체일련번호임원 수기술자 수장비 수업종개인/법인대표자 수
업체일련번호1.000-0.074-0.315-0.2640.1570.0000.000
임원 수-0.0741.0000.3610.1760.1730.4980.424
기술자 수-0.3150.3611.0000.5830.9130.6230.000
장비 수-0.2640.1760.5831.0000.6060.1430.000
업종0.1570.1730.9130.6061.0000.6680.000
개인/법인0.0000.4980.6230.1430.6681.0000.165
대표자 수0.0000.4240.0000.0000.0000.1651.000

Missing values

2024-01-28T15:44:24.176581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T15:44:24.369831image/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

등록기관시도_시군구업종업체일련번호업등록번호측량업체명영업여부개인/법인본점/지점대표자 수임원 수기술자 수장비 수
0지자체인천광역시지적측량712802-000394송하지적측량공사영업개인본점1052
1지자체인천광역시일반측량32804-005011주식회사신진지적지아이에스영업법인본점1022
2지자체인천광역시공공측량32503-001340주식회사삼조이앤씨영업법인본점1441
3지자체인천광역시일반측량705704-004978강화공간측량사무소영업개인본점1023
4지자체인천광역시일반측량702704-004957그린토목설계영업개인본점1022
5지자체인천광역시일반측량698304-004924주식회사 우리엔지니어링영업법인본점1022
6지자체인천광역시일반측량565104-004915(주)은혜엔지니어링영업법인본점1322
7지자체인천광역시공공측량676203-001250주식회사 소유엔지니어링영업법인본점1062
8지자체인천광역시일반측량689304-004870제이측량영업개인본점1022
9지자체인천광역시일반측량682004-004819신일측량토목설계영업개인본점1023
등록기관시도_시군구업종업체일련번호업등록번호측량업체명영업여부개인/법인본점/지점대표자 수임원 수기술자 수장비 수
73지자체인천광역시일반측량36104-000086(주)삼성측량설계공사영업법인본점1032
74지자체인천광역시일반측량39704-000830광명측량설계공사영업개인본점1022
75지자체인천광역시공공측량36503-000031(주)장원영업법인본점1062
76지자체인천광역시공공측량37203-000032(주)삼지이앤씨건축사사무소영업법인본점1063
77지자체인천광역시공공측량33503-000025(주)명인기술단영업법인본점1062
78지자체인천광역시일반측량36304-000834토지엔지니어링영업개인본점1022
79지자체인천광역시지적측량33002-000032지오서베이주식회사영업법인본점12612
80지자체인천광역시공공측량40803-000425(주)서해기술단영업법인본점11362
81지자체인천광역시일반측량32904-000900주식회사경원지적엔지니어링영업법인본점1222
82지자체인천광역시일반측량39604-000842명지공간개발(주)영업법인본점1022