Overview

Dataset statistics

Number of variables14
Number of observations53
Missing cells4
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.2 KiB
Average record size in memory120.5 B

Variable types

Numeric6
Text4
Categorical4

Dataset

Description대구광역시 달성군 공장등록 현황 데이터로 회사명, 도로명주소, 전화번호, 공장등록일, 공장용지면적, 제조시설명적, 부대시설면접 종업원수 업종명 용도지역, 지목, 공장상태, 데이터기준일자 항목에 대한 데이터를 제공합니다.
URLhttps://www.data.go.kr/data/15113443/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
공장용지면적 is highly overall correlated with 제조시설면적High correlation
제조시설면적 is highly overall correlated with 공장용지면적 and 1 other fieldsHigh correlation
공장상태 is highly overall correlated with 제조시설면적High correlation
전화번호 has 4 (7.5%) missing valuesMissing
순번 has unique valuesUnique
회사명 has unique valuesUnique
부대시설면적 has 9 (17.0%) zerosZeros
종업원수 has 3 (5.7%) zerosZeros

Reproduction

Analysis started2023-12-12 01:48:51.709782
Analysis finished2023-12-12 01:48:56.414252
Duration4.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct53
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27
Minimum1
Maximum53
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-12T10:48:56.476335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.6
Q114
median27
Q340
95-th percentile50.4
Maximum53
Range52
Interquartile range (IQR)26

Descriptive statistics

Standard deviation15.443445
Coefficient of variation (CV)0.57197945
Kurtosis-1.2
Mean27
Median Absolute Deviation (MAD)13
Skewness0
Sum1431
Variance238.5
MonotonicityStrictly increasing
2023-12-12T10:48:56.605796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.9%
41 1
 
1.9%
30 1
 
1.9%
31 1
 
1.9%
32 1
 
1.9%
33 1
 
1.9%
34 1
 
1.9%
35 1
 
1.9%
36 1
 
1.9%
37 1
 
1.9%
Other values (43) 43
81.1%
ValueCountFrequency (%)
1 1
1.9%
2 1
1.9%
3 1
1.9%
4 1
1.9%
5 1
1.9%
6 1
1.9%
7 1
1.9%
8 1
1.9%
9 1
1.9%
10 1
1.9%
ValueCountFrequency (%)
53 1
1.9%
52 1
1.9%
51 1
1.9%
50 1
1.9%
49 1
1.9%
48 1
1.9%
47 1
1.9%
46 1
1.9%
45 1
1.9%
44 1
1.9%

회사명
Text

UNIQUE 

Distinct53
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size556.0 B
2023-12-12T10:48:56.787233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length11
Mean length7
Min length3

Characters and Unicode

Total characters371
Distinct characters130
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)100.0%

Sample

1st row(주)가온하이텍
2nd row(주)광전이앤에스
3rd row(주)대영냉장
4th row(주)대유정보통신
5th row(주)동성하이텍
ValueCountFrequency (%)
주식회사 2
 
3.6%
주)가온하이텍 1
 
1.8%
안성전기 1
 
1.8%
달성섬유질배합사료 1
 
1.8%
영농조합법인 1
 
1.8%
대경기업 1
 
1.8%
대영창호건설(주 1
 
1.8%
모다아그로 1
 
1.8%
백호이엔지(주 1
 
1.8%
부광맥스테크 1
 
1.8%
Other values (45) 45
80.4%
2023-12-12T10:48:57.090286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 32
 
8.6%
( 31
 
8.4%
) 31
 
8.4%
이 13
 
3.5%
크 10
 
2.7%
테 9
 
2.4%
업 9
 
2.4%
지 8
 
2.2%
기 8
 
2.2%
스 8
 
2.2%
Other values (120) 212
57.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 299
80.6%
Open Punctuation 31
 
8.4%
Close Punctuation 31
 
8.4%
Uppercase Letter 5
 
1.3%
Space Separator 3
 
0.8%
Other Punctuation 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 32
 
10.7%
이 13
 
4.3%
크 10
 
3.3%
테 9
 
3.0%
업 9
 
3.0%
지 8
 
2.7%
기 8
 
2.7%
스 8
 
2.7%
삼 6
 
2.0%
에 6
 
2.0%
Other values (113) 190
63.5%
Uppercase Letter
ValueCountFrequency (%)
P 2
40.0%
C 2
40.0%
N 1
20.0%
Open Punctuation
ValueCountFrequency (%)
( 31
100.0%
Close Punctuation
ValueCountFrequency (%)
) 31
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Other Punctuation
ValueCountFrequency (%)
& 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 299
80.6%
Common 67
 
18.1%
Latin 5
 
1.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 32
 
10.7%
이 13
 
4.3%
크 10
 
3.3%
테 9
 
3.0%
업 9
 
3.0%
지 8
 
2.7%
기 8
 
2.7%
스 8
 
2.7%
삼 6
 
2.0%
에 6
 
2.0%
Other values (113) 190
63.5%
Common
ValueCountFrequency (%)
( 31
46.3%
) 31
46.3%
3
 
4.5%
& 2
 
3.0%
Latin
ValueCountFrequency (%)
P 2
40.0%
C 2
40.0%
N 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 299
80.6%
ASCII 72
 
19.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 32
 
10.7%
이 13
 
4.3%
크 10
 
3.3%
테 9
 
3.0%
업 9
 
3.0%
지 8
 
2.7%
기 8
 
2.7%
스 8
 
2.7%
삼 6
 
2.0%
에 6
 
2.0%
Other values (113) 190
63.5%
ASCII
ValueCountFrequency (%)
( 31
43.1%
) 31
43.1%
3
 
4.2%
& 2
 
2.8%
P 2
 
2.8%
C 2
 
2.8%
N 1
 
1.4%

도로명주소
Text

Distinct50
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Memory size556.0 B
2023-12-12T10:48:57.354290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length41
Mean length26
Min length20

Characters and Unicode

Total characters1378
Distinct characters76
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique47 ?
Unique (%)88.7%

Sample

1st row대구광역시 달성군 논공읍 비슬로262길 113
2nd row대구광역시 달성군 옥포읍 비슬로447길 46-3, (2층)
3rd row대구광역시 달성군 논공읍 비슬로264길 46
4th row대구광역시 달성군 논공읍 비슬로366길 6, 1층
5th row대구광역시 달성군 화원읍 비슬로543길 81-1
ValueCountFrequency (%)
대구광역시 56
19.4%
달성군 56
19.4%
논공읍 22
 
7.6%
다사읍 11
 
3.8%
옥포읍 8
 
2.8%
세천북로8길 6
 
2.1%
비슬로262길 6
 
2.1%
화원읍 6
 
2.1%
비슬로264길 5
 
1.7%
상리 4
 
1.4%
Other values (93) 108
37.5%
2023-12-12T10:48:57.855422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
235
 
17.1%
구 60
 
4.4%
군 56
 
4.1%
성 56
 
4.1%
달 56
 
4.1%
대 56
 
4.1%
시 56
 
4.1%
역 56
 
4.1%
광 56
 
4.1%
읍 50
 
3.6%
Other values (66) 641
46.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 845
61.3%
Decimal Number 250
 
18.1%
Space Separator 235
 
17.1%
Dash Punctuation 31
 
2.2%
Close Punctuation 6
 
0.4%
Open Punctuation 6
 
0.4%
Other Punctuation 5
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 60
 
7.1%
군 56
 
6.6%
성 56
 
6.6%
달 56
 
6.6%
대 56
 
6.6%
시 56
 
6.6%
역 56
 
6.6%
광 56
 
6.6%
읍 50
 
5.9%
길 45
 
5.3%
Other values (51) 298
35.3%
Decimal Number
ValueCountFrequency (%)
2 48
19.2%
1 44
17.6%
4 28
11.2%
3 28
11.2%
6 27
10.8%
5 17
 
6.8%
7 16
 
6.4%
9 15
 
6.0%
8 14
 
5.6%
0 13
 
5.2%
Space Separator
ValueCountFrequency (%)
235
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 31
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 845
61.3%
Common 533
38.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 60
 
7.1%
군 56
 
6.6%
성 56
 
6.6%
달 56
 
6.6%
대 56
 
6.6%
시 56
 
6.6%
역 56
 
6.6%
광 56
 
6.6%
읍 50
 
5.9%
길 45
 
5.3%
Other values (51) 298
35.3%
Common
ValueCountFrequency (%)
235
44.1%
2 48
 
9.0%
1 44
 
8.3%
- 31
 
5.8%
4 28
 
5.3%
3 28
 
5.3%
6 27
 
5.1%
5 17
 
3.2%
7 16
 
3.0%
9 15
 
2.8%
Other values (5) 44
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 845
61.3%
ASCII 533
38.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
235
44.1%
2 48
 
9.0%
1 44
 
8.3%
- 31
 
5.8%
4 28
 
5.3%
3 28
 
5.3%
6 27
 
5.1%
5 17
 
3.2%
7 16
 
3.0%
9 15
 
2.8%
Other values (5) 44
 
8.3%
Hangul
ValueCountFrequency (%)
구 60
 
7.1%
군 56
 
6.6%
성 56
 
6.6%
달 56
 
6.6%
대 56
 
6.6%
시 56
 
6.6%
역 56
 
6.6%
광 56
 
6.6%
읍 50
 
5.9%
길 45
 
5.3%
Other values (51) 298
35.3%

전화번호
Text

MISSING 

Distinct48
Distinct (%)98.0%
Missing4
Missing (%)7.5%
Memory size556.0 B
2023-12-12T10:48:58.099541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.081633
Min length12

Characters and Unicode

Total characters592
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

Unique47 ?
Unique (%)95.9%

Sample

1st row070-5030-3964
2nd row053-561-9504
3rd row053-631-0805
4th row053-616-9789
5th row053-635-5533
ValueCountFrequency (%)
053-710-7990 2
 
4.1%
042-826-8783 1
 
2.0%
053-585-2255 1
 
2.0%
070-5030-3964 1
 
2.0%
053-262-8822 1
 
2.0%
053-634-9833 1
 
2.0%
053-616-1478 1
 
2.0%
053-611-0081 1
 
2.0%
053-636-8750 1
 
2.0%
053-611-4401 1
 
2.0%
Other values (38) 38
77.6%
2023-12-12T10:48:58.470016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 98
16.6%
0 91
15.4%
5 89
15.0%
3 83
14.0%
1 46
7.8%
6 42
7.1%
8 32
 
5.4%
2 31
 
5.2%
7 30
 
5.1%
4 26
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 494
83.4%
Dash Punctuation 98
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 91
18.4%
5 89
18.0%
3 83
16.8%
1 46
9.3%
6 42
8.5%
8 32
 
6.5%
2 31
 
6.3%
7 30
 
6.1%
4 26
 
5.3%
9 24
 
4.9%
Dash Punctuation
ValueCountFrequency (%)
- 98
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 592
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 98
16.6%
0 91
15.4%
5 89
15.0%
3 83
14.0%
1 46
7.8%
6 42
7.1%
8 32
 
5.4%
2 31
 
5.2%
7 30
 
5.1%
4 26
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 592
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 98
16.6%
0 91
15.4%
5 89
15.0%
3 83
14.0%
1 46
7.8%
6 42
7.1%
8 32
 
5.4%
2 31
 
5.2%
7 30
 
5.1%
4 26
 
4.4%

공장등록일
Real number (ℝ)

Distinct41
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20223701
Minimum20220419
Maximum20230418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-12T10:48:58.611915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20220419
5-th percentile20220473
Q120220811
median20221028
Q320230109
95-th percentile20230328
Maximum20230418
Range9999
Interquartile range (IQR)9298

Descriptive statistics

Standard deviation4347.5423
Coefficient of variation (CV)0.00021497263
Kurtosis-1.2594643
Mean20223701
Median Absolute Deviation (MAD)313
Skewness0.88151609
Sum1.0718562 × 109
Variance18901124
MonotonicityNot monotonic
2023-12-12T10:48:58.768828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
20220715 3
 
5.7%
20220811 3
 
5.7%
20221209 2
 
3.8%
20230418 2
 
3.8%
20221102 2
 
3.8%
20220502 2
 
3.8%
20221223 2
 
3.8%
20221025 2
 
3.8%
20230328 2
 
3.8%
20220830 2
 
3.8%
Other values (31) 31
58.5%
ValueCountFrequency (%)
20220419 1
 
1.9%
20220426 1
 
1.9%
20220429 1
 
1.9%
20220502 2
3.8%
20220527 1
 
1.9%
20220608 1
 
1.9%
20220627 1
 
1.9%
20220715 3
5.7%
20220726 1
 
1.9%
20220801 1
 
1.9%
ValueCountFrequency (%)
20230418 2
3.8%
20230328 2
3.8%
20230323 1
1.9%
20230315 1
1.9%
20230314 1
1.9%
20230303 1
1.9%
20230227 1
1.9%
20230217 1
1.9%
20230127 1
1.9%
20230118 1
1.9%

공장용지면적
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1108.7358
Minimum223.1
Maximum4682
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-12T10:48:58.904758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum223.1
5-th percentile341.4
Q1587
median789
Q31469
95-th percentile2527.2
Maximum4682
Range4458.9
Interquartile range (IQR)882

Descriptive statistics

Standard deviation861.11164
Coefficient of variation (CV)0.77666077
Kurtosis6.0340105
Mean1108.7358
Median Absolute Deviation (MAD)359
Skewness2.1854451
Sum58763
Variance741513.25
MonotonicityNot monotonic
2023-12-12T10:48:59.029428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
988.0 2
 
3.8%
438.0 2
 
3.8%
1469.0 2
 
3.8%
1122.0 1
 
1.9%
608.9 1
 
1.9%
687.0 1
 
1.9%
953.0 1
 
1.9%
4682.0 1
 
1.9%
595.0 1
 
1.9%
976.0 1
 
1.9%
Other values (40) 40
75.5%
ValueCountFrequency (%)
223.1 1
1.9%
330.6 1
1.9%
331.8 1
1.9%
347.8 1
1.9%
348.2 1
1.9%
365.0 1
1.9%
394.0 1
1.9%
402.0 1
1.9%
430.0 1
1.9%
438.0 2
3.8%
ValueCountFrequency (%)
4682.0 1
1.9%
3732.0 1
1.9%
3132.0 1
1.9%
2124.0 1
1.9%
2119.0 1
1.9%
1907.0 1
1.9%
1822.0 1
1.9%
1803.0 1
1.9%
1751.0 1
1.9%
1648.0 1
1.9%

제조시설면적
Real number (ℝ)

HIGH CORRELATION 

Distinct50
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean370.05453
Minimum33
Maximum1191
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-12T10:48:59.158682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33
5-th percentile58.64
Q1198.84
median330.4
Q3470
95-th percentile838.082
Maximum1191
Range1158
Interquartile range (IQR)271.16

Descriptive statistics

Standard deviation249.56941
Coefficient of variation (CV)0.67441253
Kurtosis2.6616542
Mean370.05453
Median Absolute Deviation (MAD)134.6
Skewness1.4040324
Sum19612.89
Variance62284.891
MonotonicityNot monotonic
2023-12-12T10:48:59.291842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
465.0 2
 
3.8%
490.0 2
 
3.8%
197.0 2
 
3.8%
495.0 1
 
1.9%
290.0 1
 
1.9%
456.0 1
 
1.9%
330.75 1
 
1.9%
773.25 1
 
1.9%
348.34 1
 
1.9%
342.19 1
 
1.9%
Other values (40) 40
75.5%
ValueCountFrequency (%)
33.0 1
1.9%
37.2 1
1.9%
50.0 1
1.9%
64.4 1
1.9%
90.38 1
1.9%
96.75 1
1.9%
99.0 1
1.9%
100.0 1
1.9%
135.8 1
1.9%
174.9 1
1.9%
ValueCountFrequency (%)
1191.0 1
1.9%
1146.69 1
1.9%
935.33 1
1.9%
773.25 1
1.9%
695.02 1
1.9%
681.0 1
1.9%
602.93 1
1.9%
570.0 1
1.9%
498.0 1
1.9%
497.5 1
1.9%

부대시설면적
Real number (ℝ)

ZEROS 

Distinct44
Distinct (%)83.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170.92981
Minimum0
Maximum2602.7
Zeros9
Zeros (%)17.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-12T10:48:59.422691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118
median51.36
Q3135
95-th percentile733.08
Maximum2602.7
Range2602.7
Interquartile range (IQR)117

Descriptive statistics

Standard deviation397.43663
Coefficient of variation (CV)2.3251452
Kurtosis27.818191
Mean170.92981
Median Absolute Deviation (MAD)51.36
Skewness4.9015971
Sum9059.28
Variance157955.87
MonotonicityNot monotonic
2023-12-12T10:48:59.586410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0.0 9
 
17.0%
33.0 2
 
3.8%
28.38 1
 
1.9%
599.8 1
 
1.9%
37.8 1
 
1.9%
166.96 1
 
1.9%
135.0 1
 
1.9%
184.8 1
 
1.9%
61.0 1
 
1.9%
129.1 1
 
1.9%
Other values (34) 34
64.2%
ValueCountFrequency (%)
0.0 9
17.0%
2.4 1
 
1.9%
8.6 1
 
1.9%
9.0 1
 
1.9%
16.53 1
 
1.9%
18.0 1
 
1.9%
21.15 1
 
1.9%
22.0 1
 
1.9%
28.2 1
 
1.9%
28.38 1
 
1.9%
ValueCountFrequency (%)
2602.7 1
1.9%
997.0 1
1.9%
933.0 1
1.9%
599.8 1
1.9%
498.0 1
1.9%
266.5 1
1.9%
265.2 1
1.9%
253.39 1
1.9%
245.44 1
1.9%
184.8 1
1.9%

종업원수
Real number (ℝ)

ZEROS 

Distinct20
Distinct (%)37.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4528302
Minimum0
Maximum41
Zeros3
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size609.0 B
2023-12-12T10:48:59.766426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.6
Q13
median4
Q38
95-th percentile18.4
Maximum41
Range41
Interquartile range (IQR)5

Descriptive statistics

Standard deviation6.9767913
Coefficient of variation (CV)1.0811987
Kurtosis10.788622
Mean6.4528302
Median Absolute Deviation (MAD)2
Skewness2.7792106
Sum342
Variance48.675617
MonotonicityNot monotonic
2023-12-12T10:48:59.891807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
3 10
18.9%
2 6
11.3%
4 6
11.3%
5 5
9.4%
1 4
 
7.5%
0 3
 
5.7%
8 3
 
5.7%
6 2
 
3.8%
7 2
 
3.8%
10 2
 
3.8%
Other values (10) 10
18.9%
ValueCountFrequency (%)
0 3
 
5.7%
1 4
 
7.5%
2 6
11.3%
3 10
18.9%
4 6
11.3%
5 5
9.4%
6 2
 
3.8%
7 2
 
3.8%
8 3
 
5.7%
9 1
 
1.9%
ValueCountFrequency (%)
41 1
1.9%
20 1
1.9%
19 1
1.9%
18 1
1.9%
16 1
1.9%
15 1
1.9%
14 1
1.9%
13 1
1.9%
12 1
1.9%
10 2
3.8%

업종명
Text

Distinct47
Distinct (%)88.7%
Missing0
Missing (%)0.0%
Memory size556.0 B
2023-12-12T10:49:00.158679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length24
Mean length19
Min length6

Characters and Unicode

Total characters1007
Distinct characters134
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

Unique42 ?
Unique (%)79.2%

Sample

1st row자동차 차체용 신품 부품 제조업 외 1종
2nd row배전반 및 전기 자동제어반 제조업
3rd row육류 포장육 및 냉동육 가공업 (가금류 제외) 외 3종
4th row유선 통신장비 제조업 외 6종
5th row플라스틱 창호 제조업 외 1종
ValueCountFrequency (%)
제조업 45
 
14.4%
및 33
 
10.5%
외 29
 
9.3%
기타 13
 
4.2%
1종 9
 
2.9%
금속 8
 
2.6%
관련제품 6
 
1.9%
3종 6
 
1.9%
2종 6
 
1.9%
창 5
 
1.6%
Other values (102) 153
48.9%
2023-12-12T10:49:00.533759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
260
25.8%
제 69
 
6.9%
업 56
 
5.6%
조 49
 
4.9%
및 33
 
3.3%
외 32
 
3.2%
기 31
 
3.1%
종 25
 
2.5%
, 18
 
1.8%
품 18
 
1.8%
Other values (124) 416
41.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 697
69.2%
Space Separator 260
 
25.8%
Decimal Number 26
 
2.6%
Other Punctuation 18
 
1.8%
Close Punctuation 3
 
0.3%
Open Punctuation 3
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 69
 
9.9%
업 56
 
8.0%
조 49
 
7.0%
및 33
 
4.7%
외 32
 
4.6%
기 31
 
4.4%
종 25
 
3.6%
품 18
 
2.6%
금 13
 
1.9%
타 13
 
1.9%
Other values (113) 358
51.4%
Decimal Number
ValueCountFrequency (%)
1 10
38.5%
2 6
23.1%
3 6
23.1%
4 1
 
3.8%
5 1
 
3.8%
6 1
 
3.8%
8 1
 
3.8%
Space Separator
ValueCountFrequency (%)
260
100.0%
Other Punctuation
ValueCountFrequency (%)
, 18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 697
69.2%
Common 310
30.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 69
 
9.9%
업 56
 
8.0%
조 49
 
7.0%
및 33
 
4.7%
외 32
 
4.6%
기 31
 
4.4%
종 25
 
3.6%
품 18
 
2.6%
금 13
 
1.9%
타 13
 
1.9%
Other values (113) 358
51.4%
Common
ValueCountFrequency (%)
260
83.9%
, 18
 
5.8%
1 10
 
3.2%
2 6
 
1.9%
3 6
 
1.9%
) 3
 
1.0%
( 3
 
1.0%
4 1
 
0.3%
5 1
 
0.3%
6 1
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 697
69.2%
ASCII 310
30.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
260
83.9%
, 18
 
5.8%
1 10
 
3.2%
2 6
 
1.9%
3 6
 
1.9%
) 3
 
1.0%
( 3
 
1.0%
4 1
 
0.3%
5 1
 
0.3%
6 1
 
0.3%
Hangul
ValueCountFrequency (%)
제 69
 
9.9%
업 56
 
8.0%
조 49
 
7.0%
및 33
 
4.7%
외 32
 
4.6%
기 31
 
4.4%
종 25
 
3.6%
품 18
 
2.6%
금 13
 
1.9%
타 13
 
1.9%
Other values (113) 358
51.4%

용도지역
Categorical

Distinct8
Distinct (%)15.1%
Missing0
Missing (%)0.0%
Memory size556.0 B
도시지역/주거지역/준주거지역
17 
도시지역/주거지역/제2종일반주거지역
12 
도시지역/공업지역/일반공업지역
11 
도시지역/녹지지역/자연녹지지역
6 
도시지역/주거지역/제1종일반주거지역
3 
Other values (3)
4 

Length

Max length19
Median length16
Mean length16.54717
Min length15

Unique

Unique2 ?
Unique (%)3.8%

Sample

1st row도시지역/공업지역/준공업지역
2nd row도시지역/주거지역/준주거지역
3rd row도시지역/공업지역/일반공업지역
4th row도시지역/주거지역/제2종전용주거지역
5th row도시지역/주거지역/제2종일반주거지역

Common Values

ValueCountFrequency (%)
도시지역/주거지역/준주거지역 17
32.1%
도시지역/주거지역/제2종일반주거지역 12
22.6%
도시지역/공업지역/일반공업지역 11
20.8%
도시지역/녹지지역/자연녹지지역 6
 
11.3%
도시지역/주거지역/제1종일반주거지역 3
 
5.7%
도시지역/공업지역/준공업지역 2
 
3.8%
도시지역/주거지역/제2종전용주거지역 1
 
1.9%
도시지역/녹지지역/생산녹지지역 1
 
1.9%

Length

2023-12-12T10:49:00.664522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:49:00.777503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도시지역/주거지역/준주거지역 17
32.1%
도시지역/주거지역/제2종일반주거지역 12
22.6%
도시지역/공업지역/일반공업지역 11
20.8%
도시지역/녹지지역/자연녹지지역 6
 
11.3%
도시지역/주거지역/제1종일반주거지역 3
 
5.7%
도시지역/공업지역/준공업지역 2
 
3.8%
도시지역/주거지역/제2종전용주거지역 1
 
1.9%
도시지역/녹지지역/생산녹지지역 1
 
1.9%

지목
Categorical

Distinct5
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size556.0 B
공장용지
30 
대
15 
전
 
3
답
 
3
창고용지
 
2

Length

Max length4
Median length4
Mean length2.8113208
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대
2nd row대
3rd row전
4th row대
5th row공장용지

Common Values

ValueCountFrequency (%)
공장용지 30
56.6%
대 15
28.3%
전 3
 
5.7%
답 3
 
5.7%
창고용지 2
 
3.8%

Length

2023-12-12T10:49:00.898134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:49:01.010721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공장용지 30
56.6%
대 15
28.3%
전 3
 
5.7%
답 3
 
5.7%
창고용지 2
 
3.8%

공장상태
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size556.0 B
신규등록
24 
등록변경
23 
완료신고
5 
<NA>
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique1 ?
Unique (%)1.9%

Sample

1st row등록변경
2nd row신규등록
3rd row등록변경
4th row신규등록
5th row신규등록

Common Values

ValueCountFrequency (%)
신규등록 24
45.3%
등록변경 23
43.4%
완료신고 5
 
9.4%
<NA> 1
 
1.9%

Length

2023-12-12T10:49:01.133835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:49:01.233897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신규등록 24
45.3%
등록변경 23
43.4%
완료신고 5
 
9.4%
na 1
 
1.9%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size556.0 B
2023-04-20
53 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-04-20
2nd row2023-04-20
3rd row2023-04-20
4th row2023-04-20
5th row2023-04-20

Common Values

ValueCountFrequency (%)
2023-04-20 53
100.0%

Length

2023-12-12T10:49:01.345954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:49:01.441139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-04-20 53
100.0%

Interactions

2023-12-12T10:48:55.403898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:52.549961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:53.121743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:53.699184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:54.250559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:54.857269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:55.488686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:52.650810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:53.208231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:53.780874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:54.340970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:54.980100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:55.585830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:52.759335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:53.297657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:53.869614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:54.442998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:55.097884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:55.658046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:52.860205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:53.391684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:53.957819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:54.530598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:55.179769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:55.730267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:52.951876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:53.507720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:54.071370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:54.616846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:55.256986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:55.801389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:53.036631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:53.611502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:54.162261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:54.736980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:48:55.328543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:49:01.533058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번회사명도로명주소전화번호공장등록일공장용지면적제조시설면적부대시설면적종업원수업종명용도지역지목공장상태
순번1.0001.0000.9391.0000.0000.0000.0000.0000.3850.4700.0000.0000.000
회사명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
도로명주소0.9391.0001.0001.0001.0000.0000.8890.0000.9410.9861.0000.9950.960
전화번호1.0001.0001.0001.0001.0000.0000.8840.0000.7650.9861.0001.0001.000
공장등록일0.0001.0001.0001.0001.0000.0000.1600.2230.0000.3540.0000.0000.000
공장용지면적0.0001.0000.0000.0000.0001.0000.8510.8380.4750.9750.8340.6590.409
제조시설면적0.0001.0000.8890.8840.1600.8511.0000.6250.5980.9340.6160.6030.874
부대시설면적0.0001.0000.0000.0000.2230.8380.6251.0000.5930.9170.0000.6200.414
종업원수0.3851.0000.9410.7650.0000.4750.5980.5931.0000.9710.2580.0650.753
업종명0.4701.0000.9860.9860.3540.9750.9340.9170.9711.0000.9140.8030.720
용도지역0.0001.0001.0001.0000.0000.8340.6160.0000.2580.9141.0000.4660.333
지목0.0001.0000.9951.0000.0000.6590.6030.6200.0650.8030.4661.0000.000
공장상태0.0001.0000.9601.0000.0000.4090.8740.4140.7530.7200.3330.0001.000
2023-12-12T10:49:01.706174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
용도지역공장상태지목
용도지역1.0000.2060.295
공장상태0.2061.0000.000
지목0.2950.0001.000
2023-12-12T10:49:01.827851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번공장등록일공장용지면적제조시설면적부대시설면적종업원수용도지역지목공장상태
순번1.000-0.181-0.0240.077-0.160-0.3160.0000.0000.000
공장등록일-0.1811.000-0.015-0.1420.0880.2140.0000.0000.000
공장용지면적-0.024-0.0151.0000.7350.3480.3750.4240.4190.276
제조시설면적0.077-0.1420.7351.0000.3270.4690.3520.3830.558
부대시설면적-0.1600.0880.3480.3271.0000.4410.0000.2710.335
종업원수-0.3160.2140.3750.4690.4411.0000.1330.0000.416
용도지역0.0000.0000.4240.3520.0000.1331.0000.2950.206
지목0.0000.0000.4190.3830.2710.0000.2951.0000.000
공장상태0.0000.0000.2760.5580.3350.4160.2060.0001.000

Missing values

2023-12-12T10:48:55.910975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:48:56.079528image/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

순번회사명도로명주소전화번호공장등록일공장용지면적제조시설면적부대시설면적종업원수업종명용도지역지목공장상태데이터기준일자
01(주)가온하이텍대구광역시 달성군 논공읍 비슬로262길 113070-5030-3964202207261122.0495.028.3816자동차 차체용 신품 부품 제조업 외 1종도시지역/공업지역/준공업지역대등록변경2023-04-20
12(주)광전이앤에스대구광역시 달성군 옥포읍 비슬로447길 46-3, (2층)053-561-950420220419347.864.4135.14배전반 및 전기 자동제어반 제조업도시지역/주거지역/준주거지역대신규등록2023-04-20
23(주)대영냉장대구광역시 달성군 논공읍 비슬로264길 46053-631-0805202301171392.0272.051.365육류 포장육 및 냉동육 가공업 (가금류 제외) 외 3종도시지역/공업지역/일반공업지역전등록변경2023-04-20
34(주)대유정보통신대구광역시 달성군 논공읍 비슬로366길 6, 1층053-616-978920221102618.937.29.00유선 통신장비 제조업 외 6종도시지역/주거지역/제2종전용주거지역대신규등록2023-04-20
45(주)동성하이텍대구광역시 달성군 화원읍 비슬로543길 81-1053-635-553320220627694.0305.35114.069플라스틱 창호 제조업 외 1종도시지역/주거지역/제2종일반주거지역공장용지신규등록2023-04-20
56(주)마스터아이디대구광역시 달성군 옥포읍 신당길 86-14053-634-1152202207151803.0935.33253.3919육류 포장육 및 냉동육 가공업 (가금류 제외)도시지역/주거지역/준주거지역공장용지완료신고2023-04-20
67(주)메이딘대구광역시 달성군 논공읍 금강로4길 33070-4790-099420221102348.2135.869.52물질 검사, 측정 및 분석기구 제조업 외 2종도시지역/주거지역/제2종일반주거지역대신규등록2023-04-20
78(주)명성테크대구광역시 달성군 논공읍 비슬로262길 59-20053-721-8906202303032124.01191.0933.020자동차용 신품 제동장치 제조업도시지역/공업지역/일반공업지역공장용지완료신고2023-04-20
89(주)빅컴퍼니대구광역시 달성군 옥포읍 비슬로447길 7053-526-048820221028665.1351.7436.344금속 캔 및 기타 포장용기 제조업 외 8종도시지역/주거지역/준주거지역대신규등록2023-04-20
910(주)삼오산업대구광역시 달성군 현풍읍 현풍서로 78053-615-353520230328777.096.75101.254그 외 기타 분류 안된 섬유제품 제조업도시지역/녹지지역/자연녹지지역공장용지신규등록2023-04-20
순번회사명도로명주소전화번호공장등록일공장용지면적제조시설면적부대시설면적종업원수업종명용도지역지목공장상태데이터기준일자
4344에프엘C&P대구광역시 달성군 논공읍 노이리 1123-1 (대구광역시 달성군 논공읍 노이길 183-1)053-611-4055202204291648.0329.00.02용기 세척, 포장 및 충전기 제조업 외 1종도시지역/녹지지역/자연녹지지역공장용지등록변경2023-04-20
4445엔씨피(NCP)&테크대구광역시 달성군 다사읍 서재로14길 35, 1층053-326-894520221209394.0174.922.02금속 문, 창, 셔터 및 관련제품 제조업 외 3종도시지역/주거지역/준주거지역대신규등록2023-04-20
4546엠디테크대구광역시 달성군 다사읍 세천북로8길 9-15053-584-029120221103438.0227.4112.783무기 및 총포탄 제조업 외 4종도시지역/주거지역/제2종일반주거지역공장용지등록변경2023-04-20
4647제이엠테크대구광역시 달성군 논공읍 논공로 200-9053-615-430020220715703.0330.40.02공기 조화장치 제조업도시지역/공업지역/준공업지역공장용지신규등록2023-04-20
4748제일기공대구광역시 달성군 옥포읍 비슬로432길 22-2053-633-005420220830639.0218.4518.07커튼 및 유사제품 제조업 외 3종도시지역/주거지역/준주거지역대등록변경2023-04-20
4849주식회사 삼광기업대구광역시 달성군 화원읍 사문진로1길 12053-765-842120221025402.090.3870.00교통 신호장치 제조업도시지역/주거지역/준주거지역대신규등록2023-04-20
4950주식회사 웰코글로벌대구광역시 달성군 논공읍 금포새터길 9053-615-095920221025988.0339.090.03기타 직물제품 제조업도시지역/주거지역/제2종일반주거지역공장용지신규등록2023-04-20
5051티앤티스포츠대구광역시 달성군 논공읍 금강로2길 19-8053-428-171720221027223.1197.00.01기타 운동 및 경기용구 제조업 외 1종도시지역/주거지역/제2종일반주거지역답등록변경2023-04-20
5152한국장애인문화협회장애인기업기전사업본부대구광역시 달성군 옥포면 간경길 30-5, (간경리 875번지)053-341-908320220929988.0490.0498.013배전반 및 전기 자동제어반 제조업 외 1종도시지역/주거지역/준주거지역공장용지등록변경2023-04-20
5253현지제면대구광역시 달성군 논공읍 상리 811-2<NA>20230109992.0602.9382.524그 외 기타 분류 안된 섬유제품 제조업도시지역/공업지역/일반공업지역공장용지완료신고2023-04-20