Overview

Dataset statistics

Number of variables13
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.3 KiB
Average record size in memory111.4 B

Variable types

DateTime2
Categorical6
Text2
Numeric3

Dataset

Description샘플 데이터
Author한국평가데이터㈜
URLhttps://bigdata-region.kr/#/dataset/6601c180-e37d-4e8a-a6f6-11f88407bdd7

Alerts

STDR_YM has constant value ""Constant
CTPRVN_NM has constant value ""Constant
SIGNGU_NM has constant value ""Constant
ADSTRD_NM has constant value ""Constant
REGIST_DE has constant value ""Constant
OPERTOR_NM has constant value ""Constant
INDUTY_LCLAS_NM is highly overall correlated with INDUTY_LCLAS_CODEHigh correlation
INDUTY_LCLAS_CODE is highly overall correlated with INDUTY_LCLAS_NMHigh correlation
AVRG_PREDICT_EMPLY_CO is highly overall correlated with AVRG_PREDICT_LWLT and 1 other fieldsHigh correlation
AVRG_PREDICT_LWLT is highly overall correlated with AVRG_PREDICT_EMPLY_CO and 1 other fieldsHigh correlation
AVRG_PREDICT_UPLMT is highly overall correlated with AVRG_PREDICT_EMPLY_CO and 1 other fieldsHigh correlation
INDUTY_MLCLAS_CODE has unique valuesUnique
INDUTY_MLSFC_NM has unique valuesUnique
AVRG_PREDICT_EMPLY_CO has 16 (53.3%) zerosZeros
AVRG_PREDICT_LWLT has 16 (53.3%) zerosZeros
AVRG_PREDICT_UPLMT has 16 (53.3%) zerosZeros

Reproduction

Analysis started2023-12-10 14:16:18.681781
Analysis finished2023-12-10 14:16:21.392462
Duration2.71 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

STDR_YM
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2020-08-01 00:00:00
Maximum2020-08-01 00:00:00
2023-12-10T23:16:21.471804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:21.620809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

CTPRVN_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기
30 

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 30
100.0%

Length

2023-12-10T23:16:21.814783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:16:22.336636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기 30
100.0%

SIGNGU_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
고양시덕양구
30 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
고양시덕양구 30
100.0%

Length

2023-12-10T23:16:22.494005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:16:22.624466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고양시덕양구 30
100.0%

ADSTRD_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
관산동
30 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
관산동 30
100.0%

Length

2023-12-10T23:16:22.784551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:16:22.926172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
관산동 30
100.0%

INDUTY_LCLAS_CODE
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
C
16 
E
4 
H
4 
G
3 
F
2 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
C 16
53.3%
E 4
 
13.3%
H 4
 
13.3%
G 3
 
10.0%
F 2
 
6.7%
D 1
 
3.3%

Length

2023-12-10T23:16:23.072970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:16:23.278446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
c 16
53.3%
e 4
 
13.3%
h 4
 
13.3%
g 3
 
10.0%
f 2
 
6.7%
d 1
 
3.3%

INDUTY_MLCLAS_CODE
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:16:23.591087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters90
Distinct characters16
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st rowC19
2nd rowC20
3rd rowC21
4th rowC22
5th rowC23
ValueCountFrequency (%)
c19 1
 
3.3%
c20 1
 
3.3%
h51 1
 
3.3%
h50 1
 
3.3%
h49 1
 
3.3%
g47 1
 
3.3%
g46 1
 
3.3%
g45 1
 
3.3%
f42 1
 
3.3%
f41 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T23:16:24.176231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
C 16
17.8%
2 14
15.6%
3 12
13.3%
4 8
8.9%
5 6
 
6.7%
1 5
 
5.6%
9 4
 
4.4%
E 4
 
4.4%
H 4
 
4.4%
0 3
 
3.3%
Other values (6) 14
15.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60
66.7%
Uppercase Letter 30
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 14
23.3%
3 12
20.0%
4 8
13.3%
5 6
10.0%
1 5
 
8.3%
9 4
 
6.7%
0 3
 
5.0%
6 3
 
5.0%
7 3
 
5.0%
8 2
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
C 16
53.3%
E 4
 
13.3%
H 4
 
13.3%
G 3
 
10.0%
F 2
 
6.7%
D 1
 
3.3%

Most occurring scripts

ValueCountFrequency (%)
Common 60
66.7%
Latin 30
33.3%

Most frequent character per script

Common
ValueCountFrequency (%)
2 14
23.3%
3 12
20.0%
4 8
13.3%
5 6
10.0%
1 5
 
8.3%
9 4
 
6.7%
0 3
 
5.0%
6 3
 
5.0%
7 3
 
5.0%
8 2
 
3.3%
Latin
ValueCountFrequency (%)
C 16
53.3%
E 4
 
13.3%
H 4
 
13.3%
G 3
 
10.0%
F 2
 
6.7%
D 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 90
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
C 16
17.8%
2 14
15.6%
3 12
13.3%
4 8
8.9%
5 6
 
6.7%
1 5
 
5.6%
9 4
 
4.4%
E 4
 
4.4%
H 4
 
4.4%
0 3
 
3.3%
Other values (6) 14
15.6%

INDUTY_LCLAS_NM
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
제조업
16 
수도; 하수 및 폐기물 처리; 원료 재생업
4 
운수 및 창고업
4 
도매 및 소매업
3 
건설업
2 

Length

Max length23
Median length3
Mean length7.4333333
Min length3

Unique

Unique1 ?
Unique (%)3.3%

Sample

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

Common Values

ValueCountFrequency (%)
제조업 16
53.3%
수도; 하수 및 폐기물 처리; 원료 재생업 4
 
13.3%
운수 및 창고업 4
 
13.3%
도매 및 소매업 3
 
10.0%
건설업 2
 
6.7%
전기; 가스; 증기 및 공기조절 공급업 1
 
3.3%

Length

2023-12-10T23:16:24.450633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:16:24.667109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제조업 16
21.9%
및 12
16.4%
원료 4
 
5.5%
수도 4
 
5.5%
운수 4
 
5.5%
재생업 4
 
5.5%
창고업 4
 
5.5%
처리 4
 
5.5%
폐기물 4
 
5.5%
하수 4
 
5.5%
Other values (8) 13
17.8%

INDUTY_MLSFC_NM
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:16:25.132874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length19
Mean length13.5
Min length3

Characters and Unicode

Total characters405
Distinct characters106
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row코크스; 연탄 및 석유정제품 제조업
2nd row화학물질 및 화학제품 제조업; 의약품 제외
3rd row의료용 물질 및 의약품 제조업
4th row고무 및 플라스틱제품 제조업
5th row비금속 광물제품 제조업
ValueCountFrequency (%)
및 18
 
15.1%
제조업 15
 
12.6%
자동차 3
 
2.5%
제외 3
 
2.5%
운송업 3
 
2.5%
기계 3
 
2.5%
기타 3
 
2.5%
의약품 2
 
1.7%
가구 2
 
1.7%
장비 2
 
1.7%
Other values (65) 65
54.6%
2023-12-10T23:16:25.691505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
89
22.0%
업 31
 
7.7%
제 24
 
5.9%
및 18
 
4.4%
조 16
 
4.0%
; 14
 
3.5%
기 13
 
3.2%
품 11
 
2.7%
비 7
 
1.7%
운 7
 
1.7%
Other values (96) 175
43.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 301
74.3%
Space Separator 89
 
22.0%
Other Punctuation 14
 
3.5%
Decimal Number 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 31
 
10.3%
제 24
 
8.0%
및 18
 
6.0%
조 16
 
5.3%
기 13
 
4.3%
품 11
 
3.7%
비 7
 
2.3%
운 7
 
2.3%
수 6
 
2.0%
송 6
 
2.0%
Other values (93) 162
53.8%
Space Separator
ValueCountFrequency (%)
89
100.0%
Other Punctuation
ValueCountFrequency (%)
; 14
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 301
74.3%
Common 104
 
25.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 31
 
10.3%
제 24
 
8.0%
및 18
 
6.0%
조 16
 
5.3%
기 13
 
4.3%
품 11
 
3.7%
비 7
 
2.3%
운 7
 
2.3%
수 6
 
2.0%
송 6
 
2.0%
Other values (93) 162
53.8%
Common
ValueCountFrequency (%)
89
85.6%
; 14
 
13.5%
1 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 301
74.3%
ASCII 104
 
25.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
89
85.6%
; 14
 
13.5%
1 1
 
1.0%
Hangul
ValueCountFrequency (%)
업 31
 
10.3%
제 24
 
8.0%
및 18
 
6.0%
조 16
 
5.3%
기 13
 
4.3%
품 11
 
3.7%
비 7
 
2.3%
운 7
 
2.3%
수 6
 
2.0%
송 6
 
2.0%
Other values (93) 162
53.8%

AVRG_PREDICT_EMPLY_CO
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.97228
Minimum0
Maximum47.7917
Zeros16
Zeros (%)53.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:16:25.884843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37.3696
95-th percentile27.93473
Maximum47.7917
Range47.7917
Interquartile range (IQR)7.3696

Descriptive statistics

Standard deviation10.975066
Coefficient of variation (CV)1.8376677
Kurtosis8.714265
Mean5.97228
Median Absolute Deviation (MAD)0
Skewness2.8862298
Sum179.1684
Variance120.45207
MonotonicityNot monotonic
2023-12-10T23:16:26.051477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.0 16
53.3%
9.2842 1
 
3.3%
4.9911 1
 
3.3%
4.0879 1
 
3.3%
10.7012 1
 
3.3%
5.9999 1
 
3.3%
4.491 1
 
3.3%
7.6473 1
 
3.3%
15.8135 1
 
3.3%
47.7917 1
 
3.3%
Other values (5) 5
 
16.7%
ValueCountFrequency (%)
0.0 16
53.3%
4.0879 1
 
3.3%
4.491 1
 
3.3%
4.9911 1
 
3.3%
5.9999 1
 
3.3%
6.4517 1
 
3.3%
6.5365 1
 
3.3%
7.6473 1
 
3.3%
7.9101 1
 
3.3%
9.2842 1
 
3.3%
ValueCountFrequency (%)
47.7917 1
3.3%
37.8521 1
3.3%
15.8135 1
3.3%
10.7012 1
3.3%
9.6102 1
3.3%
9.2842 1
3.3%
7.9101 1
3.3%
7.6473 1
3.3%
6.5365 1
3.3%
6.4517 1
3.3%

AVRG_PREDICT_LWLT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.49981
Minimum0
Maximum40.7503
Zeros16
Zeros (%)53.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:16:26.235584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35.8282
95-th percentile20.933365
Maximum40.7503
Range40.7503
Interquartile range (IQR)5.8282

Descriptive statistics

Standard deviation8.8528171
Coefficient of variation (CV)1.9673758
Kurtosis10.632506
Mean4.49981
Median Absolute Deviation (MAD)0
Skewness3.146358
Sum134.9943
Variance78.37237
MonotonicityNot monotonic
2023-12-10T23:16:26.493168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.0 16
53.3%
7.0701 1
 
3.3%
2.4778 1
 
3.3%
2.1695 1
 
3.3%
6.3809 1
 
3.3%
4.8871 1
 
3.3%
2.9447 1
 
3.3%
6.1419 1
 
3.3%
12.791 1
 
3.3%
40.7503 1
 
3.3%
Other values (5) 5
 
16.7%
ValueCountFrequency (%)
0.0 16
53.3%
2.1695 1
 
3.3%
2.4778 1
 
3.3%
2.9447 1
 
3.3%
4.3343 1
 
3.3%
4.5501 1
 
3.3%
4.8871 1
 
3.3%
6.1419 1
 
3.3%
6.3715 1
 
3.3%
6.3809 1
 
3.3%
ValueCountFrequency (%)
40.7503 1
3.3%
27.5953 1
3.3%
12.791 1
3.3%
7.0701 1
3.3%
6.5298 1
3.3%
6.3809 1
3.3%
6.3715 1
3.3%
6.1419 1
3.3%
4.8871 1
3.3%
4.5501 1
3.3%

AVRG_PREDICT_UPLMT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4447433
Minimum0
Maximum54.8331
Zeros16
Zeros (%)53.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:16:26.707253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q39.0492
95-th percentile34.93604
Maximum54.8331
Range54.8331
Interquartile range (IQR)9.0492

Descriptive statistics

Standard deviation13.172334
Coefficient of variation (CV)1.769347
Kurtosis7.7201232
Mean7.4447433
Median Absolute Deviation (MAD)0
Skewness2.7351693
Sum223.3423
Variance173.51038
MonotonicityNot monotonic
2023-12-10T23:16:26.900945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0.0 16
53.3%
11.4984 1
 
3.3%
7.5043 1
 
3.3%
6.0063 1
 
3.3%
15.0215 1
 
3.3%
7.1128 1
 
3.3%
6.0372 1
 
3.3%
9.1527 1
 
3.3%
18.836 1
 
3.3%
54.8331 1
 
3.3%
Other values (5) 5
 
16.7%
ValueCountFrequency (%)
0.0 16
53.3%
6.0063 1
 
3.3%
6.0372 1
 
3.3%
7.1128 1
 
3.3%
7.5043 1
 
3.3%
8.3533 1
 
3.3%
8.7387 1
 
3.3%
9.1527 1
 
3.3%
9.2903 1
 
3.3%
11.4984 1
 
3.3%
ValueCountFrequency (%)
54.8331 1
3.3%
48.1088 1
3.3%
18.836 1
3.3%
15.0215 1
3.3%
12.8489 1
3.3%
11.4984 1
3.3%
9.2903 1
3.3%
9.1527 1
3.3%
8.7387 1
3.3%
8.3533 1
3.3%

REGIST_DE
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2020-12-10 00:00:00
Maximum2020-12-10 00:00:00
2023-12-10T23:16:27.109711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:27.290672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

OPERTOR_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
KEDSYS
30 

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 30
100.0%

Length

2023-12-10T23:16:27.651512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:16:27.969398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kedsys 30
100.0%

Interactions

2023-12-10T23:16:20.249516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:19.265198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:19.708098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:20.444389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:19.427639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:19.862883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:20.670453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:19.582621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:16:20.062422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:16:28.097596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
INDUTY_LCLAS_CODEINDUTY_MLCLAS_CODEINDUTY_LCLAS_NMINDUTY_MLSFC_NMAVRG_PREDICT_EMPLY_COAVRG_PREDICT_LWLTAVRG_PREDICT_UPLMT
INDUTY_LCLAS_CODE1.0001.0001.0001.0000.1280.0000.000
INDUTY_MLCLAS_CODE1.0001.0001.0001.0001.0001.0001.000
INDUTY_LCLAS_NM1.0001.0001.0001.0000.1280.0000.000
INDUTY_MLSFC_NM1.0001.0001.0001.0001.0001.0001.000
AVRG_PREDICT_EMPLY_CO0.1281.0000.1281.0001.0000.9720.996
AVRG_PREDICT_LWLT0.0001.0000.0001.0000.9721.0000.957
AVRG_PREDICT_UPLMT0.0001.0000.0001.0000.9960.9571.000
2023-12-10T23:16:28.321975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
INDUTY_LCLAS_NMINDUTY_LCLAS_CODE
INDUTY_LCLAS_NM1.0001.000
INDUTY_LCLAS_CODE1.0001.000
2023-12-10T23:16:28.641225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
AVRG_PREDICT_EMPLY_COAVRG_PREDICT_LWLTAVRG_PREDICT_UPLMTINDUTY_LCLAS_CODEINDUTY_LCLAS_NM
AVRG_PREDICT_EMPLY_CO1.0000.9930.9990.0000.000
AVRG_PREDICT_LWLT0.9931.0000.9910.0000.000
AVRG_PREDICT_UPLMT0.9990.9911.0000.0000.000
INDUTY_LCLAS_CODE0.0000.0000.0001.0001.000
INDUTY_LCLAS_NM0.0000.0000.0001.0001.000

Missing values

2023-12-10T23:16:20.930868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:16:21.262318image/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_NMADSTRD_NMINDUTY_LCLAS_CODEINDUTY_MLCLAS_CODEINDUTY_LCLAS_NMINDUTY_MLSFC_NMAVRG_PREDICT_EMPLY_COAVRG_PREDICT_LWLTAVRG_PREDICT_UPLMTREGIST_DEOPERTOR_NM
02020-08경기고양시덕양구관산동CC19제조업코크스; 연탄 및 석유정제품 제조업0.00.00.02020-12-10KEDSYS
12020-08경기고양시덕양구관산동CC20제조업화학물질 및 화학제품 제조업; 의약품 제외0.00.00.02020-12-10KEDSYS
22020-08경기고양시덕양구관산동CC21제조업의료용 물질 및 의약품 제조업0.00.00.02020-12-10KEDSYS
32020-08경기고양시덕양구관산동CC22제조업고무 및 플라스틱제품 제조업9.28427.070111.49842020-12-10KEDSYS
42020-08경기고양시덕양구관산동CC23제조업비금속 광물제품 제조업0.00.00.02020-12-10KEDSYS
52020-08경기고양시덕양구관산동CC24제조업1차 금속 제조업0.00.00.02020-12-10KEDSYS
62020-08경기고양시덕양구관산동CC25제조업금속가공제품 제조업; 기계 및 가구 제외4.99112.47787.50432020-12-10KEDSYS
72020-08경기고양시덕양구관산동CC26제조업전자부품; 컴퓨터; 영상; 음향 및 통신장비 제조업4.08792.16956.00632020-12-10KEDSYS
82020-08경기고양시덕양구관산동CC27제조업의료; 정밀; 광학기기 및 시계 제조업10.70126.380915.02152020-12-10KEDSYS
92020-08경기고양시덕양구관산동CC28제조업전기장비 제조업5.99994.88717.11282020-12-10KEDSYS
STDR_YMCTPRVN_NMSIGNGU_NMADSTRD_NMINDUTY_LCLAS_CODEINDUTY_MLCLAS_CODEINDUTY_LCLAS_NMINDUTY_MLSFC_NMAVRG_PREDICT_EMPLY_COAVRG_PREDICT_LWLTAVRG_PREDICT_UPLMTREGIST_DEOPERTOR_NM
202020-08경기고양시덕양구관산동EE39수도; 하수 및 폐기물 처리; 원료 재생업환경 정화 및 복원업0.00.00.02020-12-10KEDSYS
212020-08경기고양시덕양구관산동FF41건설업종합 건설업0.00.00.02020-12-10KEDSYS
222020-08경기고양시덕양구관산동FF42건설업전문직별 공사업9.61026.371512.84892020-12-10KEDSYS
232020-08경기고양시덕양구관산동GG45도매 및 소매업자동차 및 부품 판매업0.00.00.02020-12-10KEDSYS
242020-08경기고양시덕양구관산동GG46도매 및 소매업도매 및 상품 중개업6.53654.33438.73872020-12-10KEDSYS
252020-08경기고양시덕양구관산동GG47도매 및 소매업소매업; 자동차 제외6.45174.55018.35332020-12-10KEDSYS
262020-08경기고양시덕양구관산동HH49운수 및 창고업육상운송 및 파이프라인 운송업37.852127.595348.10882020-12-10KEDSYS
272020-08경기고양시덕양구관산동HH50운수 및 창고업수상 운송업0.00.00.02020-12-10KEDSYS
282020-08경기고양시덕양구관산동HH51운수 및 창고업항공 운송업0.00.00.02020-12-10KEDSYS
292020-08경기고양시덕양구관산동HH52운수 및 창고업창고 및 운송관련 서비스업7.91016.52989.29032020-12-10KEDSYS