Overview

Dataset statistics

Number of variables17
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.8 KiB
Average record size in memory141.3 B

Variable types

DateTime2
Categorical14
Numeric1

Dataset

Description샘플 데이터
Author한국기업데이터㈜
URLhttps://bigdata-region.kr/#/dataset/37748a02-4e21-4df9-b58f-63f6f95449b8

Alerts

STDR_YM has constant value ""Constant
CTPRVN_NM has constant value ""Constant
SIGNGU_NM has constant value ""Constant
LEGALDONG has constant value ""Constant
REGIST_DE has constant value ""Constant
OPERTOR_NM has constant value ""Constant
PRCSS_ENTRPRS_SE_CODE is highly overall correlated with INDUTY_MLSFC_CODE and 4 other fieldsHigh correlation
PDSMLPZ_SCTN_CODE is highly overall correlated with INDUTY_LCLAS_CODE and 7 other fieldsHigh correlation
INDUTY_MLSFC_CODE is highly overall correlated with INDUTY_LCLAS_CODE and 6 other fieldsHigh correlation
PDSMLPZ_SCTN is highly overall correlated with INDUTY_LCLAS_CODE and 7 other fieldsHigh correlation
INDUTY_LCLAS_NM is highly overall correlated with INDUTY_LCLAS_CODE and 4 other fieldsHigh correlation
INDUTY_MLSFC_NM is highly overall correlated with INDUTY_LCLAS_CODE and 6 other fieldsHigh correlation
PRCSS_ENTRPRS_SE is highly overall correlated with INDUTY_MLSFC_CODE and 4 other fieldsHigh correlation
INDUTY_LCLAS_CODE is highly overall correlated with INDUTY_LCLAS_NM and 4 other fieldsHigh correlation
PNILP is highly overall correlated with LNDCGRHigh correlation
TOT_ENTRPRS_CO is highly overall correlated with PDSMLPZ_SCTN_CODE and 1 other fieldsHigh correlation
LNDCGR is highly overall correlated with PNILPHigh correlation
INDUTY_LCLAS_CODE is highly imbalanced (53.1%)Imbalance
INDUTY_LCLAS_NM is highly imbalanced (53.1%)Imbalance

Reproduction

Analysis started2023-12-10 13:43:10.114996
Analysis finished2023-12-10 13:43:12.805195
Duration2.69 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

STDR_YM
Date

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2020-08-01 00:00:00
Maximum2020-08-01 00:00:00
2023-12-10T22:43:12.879297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:43:13.061359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

CTPRVN_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기
100 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기 100
100.0%

Length

2023-12-10T22:43:13.227119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:43:13.375042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기 100
100.0%

SIGNGU_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
광명시
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
광명시 100
100.0%

Length

2023-12-10T22:43:13.527683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:43:13.790683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광명시 100
100.0%

LEGALDONG
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
가학동
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
가학동 100
100.0%

Length

2023-12-10T22:43:13.973058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:43:14.116812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가학동 100
100.0%

INDUTY_LCLAS_CODE
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
A
90 
C
10 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
A 90
90.0%
C 10
 
10.0%

Length

2023-12-10T22:43:14.274378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:43:14.425373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 90
90.0%
c 10
 
10.0%

INDUTY_LCLAS_NM
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
농업; 임업 및 어업
90 
제조업
10 

Length

Max length11
Median length11
Mean length10.2
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row농업; 임업 및 어업
2nd row농업; 임업 및 어업
3rd row농업; 임업 및 어업
4th row농업; 임업 및 어업
5th row농업; 임업 및 어업

Common Values

ValueCountFrequency (%)
농업; 임업 및 어업 90
90.0%
제조업 10
 
10.0%

Length

2023-12-10T22:43:14.627526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:43:14.787185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농업 90
24.3%
임업 90
24.3%
및 90
24.3%
어업 90
24.3%
제조업 10
 
2.7%

INDUTY_MLSFC_CODE
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
농업
54 
임업
36 
1차 금속 제조업
10 

Length

Max length9
Median length2
Mean length2.7
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
농업 54
54.0%
임업 36
36.0%
1차 금속 제조업 10
 
10.0%

Length

2023-12-10T22:43:14.953020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:43:15.198898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농업 54
45.0%
임업 36
30.0%
1차 10
 
8.3%
금속 10
 
8.3%
제조업 10
 
8.3%

INDUTY_MLSFC_NM
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
A01
54 
A02
36 
C24
10 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowA01
2nd rowA01
3rd rowA01
4th rowA01
5th rowA01

Common Values

ValueCountFrequency (%)
A01 54
54.0%
A02 36
36.0%
C24 10
 
10.0%

Length

2023-12-10T22:43:15.498179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:43:15.713297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a01 54
54.0%
a02 36
36.0%
c24 10
 
10.0%

PRCSS_ENTRPRS_SE_CODE
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
99
72 
4
28 

Length

Max length2
Median length2
Mean length1.72
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
99 72
72.0%
4 28
 
28.0%

Length

2023-12-10T22:43:15.999171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:43:16.221844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
99 72
72.0%
4 28
 
28.0%

PRCSS_ENTRPRS_SE
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
미분류
72 
소기업
28 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
미분류 72
72.0%
소기업 28
 
28.0%

Length

2023-12-10T22:43:16.395027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:43:16.544311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미분류 72
72.0%
소기업 28
 
28.0%

PDSMLPZ_SCTN_CODE
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
36 
2
18 
50
18 
98
18 
5
10 

Length

Max length2
Median length1
Mean length1.36
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 36
36.0%
2 18
18.0%
50 18
18.0%
98 18
18.0%
5 10
 
10.0%

Length

2023-12-10T22:43:16.792976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:43:17.015873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 36
36.0%
2 18
18.0%
50 18
18.0%
98 18
18.0%
5 10
 
10.0%

PDSMLPZ_SCTN
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1년이상 2년미만
36 
2년이상 5년미만
18 
50년이상 60년미만
18 
100년이상
18 
5년이상 10년미만
10 

Length

Max length11
Median length9
Mean length8.92
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1년이상 2년미만
2nd row1년이상 2년미만
3rd row1년이상 2년미만
4th row1년이상 2년미만
5th row1년이상 2년미만

Common Values

ValueCountFrequency (%)
1년이상 2년미만 36
36.0%
2년이상 5년미만 18
18.0%
50년이상 60년미만 18
18.0%
100년이상 18
18.0%
5년이상 10년미만 10
 
10.0%

Length

2023-12-10T22:43:17.209920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:43:17.387861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1년이상 36
19.8%
2년미만 36
19.8%
2년이상 18
9.9%
5년미만 18
9.9%
50년이상 18
9.9%
60년미만 18
9.9%
100년이상 18
9.9%
5년이상 10
 
5.5%
10년미만 10
 
5.5%

TOT_ENTRPRS_CO
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
82 
2
18 

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 82
82.0%
2 18
 
18.0%

Length

2023-12-10T22:43:17.566535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:43:17.719983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 82
82.0%
2 18
 
18.0%

LNDCGR
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
과수원
 
6
도로
 
6
목장용지
 
6
답
 
6
대
 
6
Other values (13)
70 

Length

Max length5
Median length4
Mean length2.58
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row과수원
2nd row구거
3rd row기타
4th row답
5th row대

Common Values

ValueCountFrequency (%)
과수원 6
 
6.0%
도로 6
 
6.0%
목장용지 6
 
6.0%
답 6
 
6.0%
대 6
 
6.0%
기타 6
 
6.0%
임야 6
 
6.0%
묘지 6
 
6.0%
수도용지 6
 
6.0%
구거 6
 
6.0%
Other values (8) 40
40.0%

Length

2023-12-10T22:43:17.913920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
과수원 6
 
6.0%
임야 6
 
6.0%
도로 6
 
6.0%
수도용지 6
 
6.0%
묘지 6
 
6.0%
구거 6
 
6.0%
기타 6
 
6.0%
대 6
 
6.0%
답 6
 
6.0%
목장용지 6
 
6.0%
Other values (8) 40
40.0%

PNILP
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean441300.29
Minimum82583.67
Maximum1295882.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:43:18.093992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum82583.67
5-th percentile82583.67
Q192964.71
median236800
Q3644461.54
95-th percentile1195592.8
Maximum1295882.4
Range1213298.7
Interquartile range (IQR)551496.83

Descriptive statistics

Standard deviation424873.92
Coefficient of variation (CV)0.96277734
Kurtosis-0.50882957
Mean441300.29
Median Absolute Deviation (MAD)151000
Skewness1.0456274
Sum44130029
Variance1.8051785 × 1011
MonotonicityNot monotonic
2023-12-10T22:43:18.281827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
236800.0 6
 
6.0%
644461.54 6
 
6.0%
82893.33 6
 
6.0%
82583.67 6
 
6.0%
163111.11 6
 
6.0%
87621.43 6
 
6.0%
168534.62 6
 
6.0%
1190314.43 6
 
6.0%
244851.34 6
 
6.0%
207875.0 6
 
6.0%
Other values (8) 40
40.0%
ValueCountFrequency (%)
82583.67 6
6.0%
82893.33 6
6.0%
85800.0 5
5.0%
87621.43 6
6.0%
92964.71 5
5.0%
163111.11 6
6.0%
168534.62 6
6.0%
207875.0 6
6.0%
236800.0 6
6.0%
244851.34 6
6.0%
ValueCountFrequency (%)
1295882.35 5
5.0%
1190314.43 6
6.0%
1187380.0 5
5.0%
1138000.0 5
5.0%
644461.54 6
6.0%
559462.5 5
5.0%
412500.0 5
5.0%
323160.52 5
5.0%
244851.34 6
6.0%
236800.0 6
6.0%

REGIST_DE
Date

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2020-12-10 00:00:00
Maximum2020-12-10 00:00:00
2023-12-10T22:43:18.476736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:43:18.638145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

OPERTOR_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
KEDSYS
100 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
KEDSYS 100
100.0%

Length

2023-12-10T22:43:18.865946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:43:19.064936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kedsys 100
100.0%

Interactions

2023-12-10T22:43:12.046157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:43:19.206777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
INDUTY_LCLAS_CODEINDUTY_LCLAS_NMINDUTY_MLSFC_CODEINDUTY_MLSFC_NMPRCSS_ENTRPRS_SE_CODEPRCSS_ENTRPRS_SEPDSMLPZ_SCTN_CODEPDSMLPZ_SCTNTOT_ENTRPRS_COLNDCGRPNILP
INDUTY_LCLAS_CODE1.0000.9961.0001.0000.6960.6961.0001.0000.0820.0000.000
INDUTY_LCLAS_NM0.9961.0001.0001.0000.6960.6961.0001.0000.0820.0000.000
INDUTY_MLSFC_CODE1.0001.0001.0001.0000.3970.3971.0001.0000.2540.0000.000
INDUTY_MLSFC_NM1.0001.0001.0001.0000.3970.3971.0001.0000.2540.0000.000
PRCSS_ENTRPRS_SE_CODE0.6960.6960.3970.3971.0000.9990.6070.6070.3750.0000.000
PRCSS_ENTRPRS_SE0.6960.6960.3970.3970.9991.0000.6070.6070.3750.0000.000
PDSMLPZ_SCTN_CODE1.0001.0001.0001.0000.6070.6071.0001.0000.4980.0000.000
PDSMLPZ_SCTN1.0001.0001.0001.0000.6070.6071.0001.0000.4980.0000.000
TOT_ENTRPRS_CO0.0820.0820.2540.2540.3750.3750.4980.4981.0000.0000.000
LNDCGR0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.000
PNILP0.0000.0000.0000.0000.0000.0000.0000.0000.0001.0001.000
2023-12-10T22:43:19.454254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PRCSS_ENTRPRS_SE_CODEPDSMLPZ_SCTN_CODEINDUTY_MLSFC_CODEPDSMLPZ_SCTNLNDCGRTOT_ENTRPRS_COINDUTY_LCLAS_NMINDUTY_MLSFC_NMPRCSS_ENTRPRS_SEINDUTY_LCLAS_CODE
PRCSS_ENTRPRS_SE_CODE1.0000.7200.6230.7200.0000.2440.4900.6230.9750.490
PDSMLPZ_SCTN_CODE0.7201.0000.9901.0000.0000.5940.9850.9900.7200.985
INDUTY_MLSFC_CODE0.6230.9901.0000.9900.0000.4100.9951.0000.6230.995
PDSMLPZ_SCTN0.7201.0000.9901.0000.0000.5940.9850.9900.7200.985
LNDCGR0.0000.0000.0000.0001.0000.0000.0000.0000.0000.000
TOT_ENTRPRS_CO0.2440.5940.4100.5940.0001.0000.0510.4100.2440.051
INDUTY_LCLAS_NM0.4900.9850.9950.9850.0000.0511.0000.9950.4900.944
INDUTY_MLSFC_NM0.6230.9901.0000.9900.0000.4100.9951.0000.6230.995
PRCSS_ENTRPRS_SE0.9750.7200.6230.7200.0000.2440.4900.6231.0000.490
INDUTY_LCLAS_CODE0.4900.9850.9950.9850.0000.0510.9440.9950.4901.000
2023-12-10T22:43:19.696188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
PNILPINDUTY_LCLAS_CODEINDUTY_LCLAS_NMINDUTY_MLSFC_CODEINDUTY_MLSFC_NMPRCSS_ENTRPRS_SE_CODEPRCSS_ENTRPRS_SEPDSMLPZ_SCTN_CODEPDSMLPZ_SCTNTOT_ENTRPRS_COLNDCGR
PNILP1.0000.0000.0000.0000.0000.0000.0000.0000.0000.0000.939
INDUTY_LCLAS_CODE0.0001.0000.9440.9950.9950.4900.4900.9850.9850.0510.000
INDUTY_LCLAS_NM0.0000.9441.0000.9950.9950.4900.4900.9850.9850.0510.000
INDUTY_MLSFC_CODE0.0000.9950.9951.0001.0000.6230.6230.9900.9900.4100.000
INDUTY_MLSFC_NM0.0000.9950.9951.0001.0000.6230.6230.9900.9900.4100.000
PRCSS_ENTRPRS_SE_CODE0.0000.4900.4900.6230.6231.0000.9750.7200.7200.2440.000
PRCSS_ENTRPRS_SE0.0000.4900.4900.6230.6230.9751.0000.7200.7200.2440.000
PDSMLPZ_SCTN_CODE0.0000.9850.9850.9900.9900.7200.7201.0001.0000.5940.000
PDSMLPZ_SCTN0.0000.9850.9850.9900.9900.7200.7201.0001.0000.5940.000
TOT_ENTRPRS_CO0.0000.0510.0510.4100.4100.2440.2440.5940.5941.0000.000
LNDCGR0.9390.0000.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-10T22:43:12.282150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:43:12.653137image/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

STDR_YMCTPRVN_NMSIGNGU_NMLEGALDONGINDUTY_LCLAS_CODEINDUTY_LCLAS_NMINDUTY_MLSFC_CODEINDUTY_MLSFC_NMPRCSS_ENTRPRS_SE_CODEPRCSS_ENTRPRS_SEPDSMLPZ_SCTN_CODEPDSMLPZ_SCTNTOT_ENTRPRS_COLNDCGRPNILPREGIST_DEOPERTOR_NM
02020-08경기광명시가학동A농업; 임업 및 어업농업A014소기업11년이상 2년미만1과수원236800.02020-12-10KEDSYS
12020-08경기광명시가학동A농업; 임업 및 어업농업A014소기업11년이상 2년미만1구거82893.332020-12-10KEDSYS
22020-08경기광명시가학동A농업; 임업 및 어업농업A014소기업11년이상 2년미만1기타207875.02020-12-10KEDSYS
32020-08경기광명시가학동A농업; 임업 및 어업농업A014소기업11년이상 2년미만1답244851.342020-12-10KEDSYS
42020-08경기광명시가학동A농업; 임업 및 어업농업A014소기업11년이상 2년미만1대1190314.432020-12-10KEDSYS
52020-08경기광명시가학동A농업; 임업 및 어업농업A014소기업11년이상 2년미만1도로168534.622020-12-10KEDSYS
62020-08경기광명시가학동A농업; 임업 및 어업농업A014소기업11년이상 2년미만1목장용지644461.542020-12-10KEDSYS
72020-08경기광명시가학동A농업; 임업 및 어업농업A014소기업11년이상 2년미만1묘지163111.112020-12-10KEDSYS
82020-08경기광명시가학동A농업; 임업 및 어업농업A014소기업11년이상 2년미만1수도용지82583.672020-12-10KEDSYS
92020-08경기광명시가학동A농업; 임업 및 어업농업A014소기업11년이상 2년미만1임야87621.432020-12-10KEDSYS
STDR_YMCTPRVN_NMSIGNGU_NMLEGALDONGINDUTY_LCLAS_CODEINDUTY_LCLAS_NMINDUTY_MLSFC_CODEINDUTY_MLSFC_NMPRCSS_ENTRPRS_SE_CODEPRCSS_ENTRPRS_SEPDSMLPZ_SCTN_CODEPDSMLPZ_SCTNTOT_ENTRPRS_COLNDCGRPNILPREGIST_DEOPERTOR_NM
902020-08경기광명시가학동C제조업1차 금속 제조업C244소기업55년이상 10년미만1과수원236800.02020-12-10KEDSYS
912020-08경기광명시가학동C제조업1차 금속 제조업C244소기업55년이상 10년미만1구거82893.332020-12-10KEDSYS
922020-08경기광명시가학동C제조업1차 금속 제조업C244소기업55년이상 10년미만1기타207875.02020-12-10KEDSYS
932020-08경기광명시가학동C제조업1차 금속 제조업C244소기업55년이상 10년미만1답244851.342020-12-10KEDSYS
942020-08경기광명시가학동C제조업1차 금속 제조업C244소기업55년이상 10년미만1대1190314.432020-12-10KEDSYS
952020-08경기광명시가학동C제조업1차 금속 제조업C244소기업55년이상 10년미만1도로168534.622020-12-10KEDSYS
962020-08경기광명시가학동C제조업1차 금속 제조업C244소기업55년이상 10년미만1목장용지644461.542020-12-10KEDSYS
972020-08경기광명시가학동C제조업1차 금속 제조업C244소기업55년이상 10년미만1묘지163111.112020-12-10KEDSYS
982020-08경기광명시가학동C제조업1차 금속 제조업C244소기업55년이상 10년미만1수도용지82583.672020-12-10KEDSYS
992020-08경기광명시가학동C제조업1차 금속 제조업C244소기업55년이상 10년미만1임야87621.432020-12-10KEDSYS