Overview

Dataset statistics

Number of variables11
Number of observations3291
Missing cells554
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory299.0 KiB
Average record size in memory93.0 B

Variable types

Numeric5
Text4
Categorical2

Dataset

Description광주광역시 광산구 내 제조업체(회사명, 개별/산업단지 여부, 공장대표주소, 전화번호, 생산품, 용지면적 등) 등록 현황을 제공합니다.
URLhttps://www.data.go.kr/data/15034990/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
종업원수 is highly overall correlated with 제조시설면적High correlation
용지면적 is highly overall correlated with 제조시설면적 and 1 other fieldsHigh correlation
제조시설면적 is highly overall correlated with 종업원수 and 1 other fieldsHigh correlation
부대시설면적 is highly overall correlated with 용지면적High correlation
전화번호 has 545 (16.6%) missing valuesMissing
종업원수 is highly skewed (γ1 = 28.30816727)Skewed
용지면적 is highly skewed (γ1 = 24.94322811)Skewed
부대시설면적 is highly skewed (γ1 = 25.45992405)Skewed
연번 has unique valuesUnique
종업원수 has 166 (5.0%) zerosZeros
용지면적 has 350 (10.6%) zerosZeros
제조시설면적 has 119 (3.6%) zerosZeros
부대시설면적 has 1039 (31.6%) zerosZeros

Reproduction

Analysis started2023-12-12 08:50:00.492912
Analysis finished2023-12-12 08:50:06.021248
Duration5.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct3291
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1646
Minimum1
Maximum3291
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size29.1 KiB
2023-12-12T17:50:06.105671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile165.5
Q1823.5
median1646
Q32468.5
95-th percentile3126.5
Maximum3291
Range3290
Interquartile range (IQR)1645

Descriptive statistics

Standard deviation950.17419
Coefficient of variation (CV)0.57726257
Kurtosis-1.2
Mean1646
Median Absolute Deviation (MAD)823
Skewness0
Sum5416986
Variance902831
MonotonicityStrictly increasing
2023-12-12T17:50:06.256379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
2200 1
 
< 0.1%
2190 1
 
< 0.1%
2191 1
 
< 0.1%
2192 1
 
< 0.1%
2193 1
 
< 0.1%
2194 1
 
< 0.1%
2195 1
 
< 0.1%
2196 1
 
< 0.1%
2197 1
 
< 0.1%
Other values (3281) 3281
99.7%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
3291 1
< 0.1%
3290 1
< 0.1%
3289 1
< 0.1%
3288 1
< 0.1%
3287 1
< 0.1%
3286 1
< 0.1%
3285 1
< 0.1%
3284 1
< 0.1%
3283 1
< 0.1%
3282 1
< 0.1%

회사명
Text

Distinct3052
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Memory size25.8 KiB
2023-12-12T17:50:06.550669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length6.9674871
Min length1

Characters and Unicode

Total characters22930
Distinct characters563
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2850 ?
Unique (%)86.6%

Sample

1st row재경TEC
2nd row(명)호남고압가스
3rd row(사)라온복지기전사업소
4th row(사)중증장애인복지협회 도둠
5th row(사)한국금형산업진흥회
ValueCountFrequency (%)
주식회사 126
 
3.6%
유한회사 14
 
0.4%
농업회사법인 13
 
0.4%
제2공장 10
 
0.3%
그린테크(주 5
 
0.1%
광주지점 5
 
0.1%
주)금명하이텍 4
 
0.1%
주)호원 4
 
0.1%
주)신일세라믹스 4
 
0.1%
주)지용금속 4
 
0.1%
Other values (3090) 3345
94.7%
2023-12-12T17:50:06.973223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 2126
 
9.3%
( 1992
 
8.7%
) 1992
 
8.7%
이 710
 
3.1%
스 566
 
2.5%
업 511
 
2.2%
에 389
 
1.7%
산 382
 
1.7%
지 337
 
1.5%
크 328
 
1.4%
Other values (553) 13597
59.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18284
79.7%
Open Punctuation 1992
 
8.7%
Close Punctuation 1992
 
8.7%
Space Separator 244
 
1.1%
Uppercase Letter 192
 
0.8%
Other Symbol 124
 
0.5%
Decimal Number 52
 
0.2%
Lowercase Letter 22
 
0.1%
Other Punctuation 20
 
0.1%
Dash Punctuation 6
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 2126
 
11.6%
이 710
 
3.9%
스 566
 
3.1%
업 511
 
2.8%
에 389
 
2.1%
산 382
 
2.1%
지 337
 
1.8%
크 328
 
1.8%
테 305
 
1.7%
기 293
 
1.6%
Other values (502) 12337
67.5%
Uppercase Letter
ValueCountFrequency (%)
E 30
15.6%
G 26
13.5%
N 26
13.5%
T 15
 
7.8%
S 10
 
5.2%
H 9
 
4.7%
O 9
 
4.7%
I 7
 
3.6%
C 7
 
3.6%
K 7
 
3.6%
Other values (13) 46
24.0%
Lowercase Letter
ValueCountFrequency (%)
i 4
18.2%
c 3
13.6%
e 2
9.1%
n 2
9.1%
h 2
9.1%
s 2
9.1%
f 1
 
4.5%
o 1
 
4.5%
g 1
 
4.5%
l 1
 
4.5%
Other values (3) 3
13.6%
Decimal Number
ValueCountFrequency (%)
2 32
61.5%
1 13
25.0%
3 3
 
5.8%
0 2
 
3.8%
5 1
 
1.9%
6 1
 
1.9%
Other Punctuation
ValueCountFrequency (%)
. 16
80.0%
& 4
 
20.0%
Open Punctuation
ValueCountFrequency (%)
( 1992
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1992
100.0%
Space Separator
ValueCountFrequency (%)
244
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 124
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 18408
80.3%
Common 4308
 
18.8%
Latin 214
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 2126
 
11.5%
이 710
 
3.9%
스 566
 
3.1%
업 511
 
2.8%
에 389
 
2.1%
산 382
 
2.1%
지 337
 
1.8%
크 328
 
1.8%
테 305
 
1.7%
기 293
 
1.6%
Other values (503) 12461
67.7%
Latin
ValueCountFrequency (%)
E 30
14.0%
G 26
 
12.1%
N 26
 
12.1%
T 15
 
7.0%
S 10
 
4.7%
H 9
 
4.2%
O 9
 
4.2%
I 7
 
3.3%
C 7
 
3.3%
K 7
 
3.3%
Other values (26) 68
31.8%
Common
ValueCountFrequency (%)
( 1992
46.2%
) 1992
46.2%
244
 
5.7%
2 32
 
0.7%
. 16
 
0.4%
1 13
 
0.3%
- 6
 
0.1%
& 4
 
0.1%
3 3
 
0.1%
0 2
 
< 0.1%
Other values (4) 4
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 18284
79.7%
ASCII 4522
 
19.7%
None 124
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 2126
 
11.6%
이 710
 
3.9%
스 566
 
3.1%
업 511
 
2.8%
에 389
 
2.1%
산 382
 
2.1%
지 337
 
1.8%
크 328
 
1.8%
테 305
 
1.7%
기 293
 
1.6%
Other values (502) 12337
67.5%
ASCII
ValueCountFrequency (%)
( 1992
44.1%
) 1992
44.1%
244
 
5.4%
2 32
 
0.7%
E 30
 
0.7%
G 26
 
0.6%
N 26
 
0.6%
. 16
 
0.4%
T 15
 
0.3%
1 13
 
0.3%
Other values (40) 136
 
3.0%
None
ValueCountFrequency (%)
㈜ 124
100.0%

단지명
Categorical

Distinct9
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size25.8 KiB
광주하남일반산업단지
1141 
개별입지
818 
광주평동일반산업단지
703 
광주연구개발특구
304 
광주평동3차일반산업단지
116 
Other values (4)
209 

Length

Max length18
Median length10
Mean length8.3460954
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row광주하남일반산업단지
2nd row광주하남일반산업단지
3rd row광주하남일반산업단지
4th row광주소촌일반산업단지
5th row광주평동일반산업단지

Common Values

ValueCountFrequency (%)
광주하남일반산업단지 1141
34.7%
개별입지 818
24.9%
광주평동일반산업단지 703
21.4%
광주연구개발특구 304
 
9.2%
광주평동3차일반산업단지 116
 
3.5%
광주소촌농공단지 72
 
2.2%
광주소촌일반산업단지 68
 
2.1%
빛그린국가산업단지 63
 
1.9%
광주월전중소협력단지형외국인투자지역 6
 
0.2%

Length

2023-12-12T17:50:07.119694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:50:07.266392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광주하남일반산업단지 1141
34.7%
개별입지 818
24.9%
광주평동일반산업단지 703
21.4%
광주연구개발특구 304
 
9.2%
광주평동3차일반산업단지 116
 
3.5%
광주소촌농공단지 72
 
2.2%
광주소촌일반산업단지 68
 
2.1%
빛그린국가산업단지 63
 
1.9%
광주월전중소협력단지형외국인투자지역 6
 
0.2%

주소
Text

Distinct2749
Distinct (%)83.5%
Missing0
Missing (%)0.0%
Memory size25.8 KiB
2023-12-12T17:50:07.581383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length57
Median length54
Mean length21.228806
Min length13

Characters and Unicode

Total characters69864
Distinct characters222
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2327 ?
Unique (%)70.7%

Sample

1st row광주광역시 광산구 오선동 273-27
2nd row광주광역시 광산구 장덕동 973-4번지
3rd row광주광역시 광산구 장덕동 975-7
4th row광주광역시 광산구 소촌동 652-4
5th row광주광역시 광산구 월전동 944번지
ValueCountFrequency (%)
광주광역시 3291
24.0%
광산구 3290
24.0%
오선동 444
 
3.2%
안청동 285
 
2.1%
장덕동 264
 
1.9%
월전동 259
 
1.9%
용동 209
 
1.5%
옥동 209
 
1.5%
도천동 194
 
1.4%
소촌동 176
 
1.3%
Other values (2761) 5081
37.1%
2023-12-12T17:50:07.995457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10533
15.1%
광 9898
14.2%
산 3605
 
5.2%
동 3416
 
4.9%
주 3353
 
4.8%
시 3299
 
4.7%
구 3297
 
4.7%
역 3296
 
4.7%
지 2642
 
3.8%
- 2543
 
3.6%
Other values (212) 23982
34.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 42089
60.2%
Decimal Number 14477
 
20.7%
Space Separator 10533
 
15.1%
Dash Punctuation 2543
 
3.6%
Open Punctuation 71
 
0.1%
Close Punctuation 71
 
0.1%
Uppercase Letter 56
 
0.1%
Other Punctuation 17
 
< 0.1%
Lowercase Letter 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 9898
23.5%
산 3605
 
8.6%
동 3416
 
8.1%
주 3353
 
8.0%
시 3299
 
7.8%
구 3297
 
7.8%
역 3296
 
7.8%
지 2642
 
6.3%
번 2527
 
6.0%
선 455
 
1.1%
Other values (182) 6301
15.0%
Decimal Number
ValueCountFrequency (%)
1 2510
17.3%
2 1846
12.8%
7 1570
10.8%
3 1349
9.3%
0 1340
9.3%
6 1339
9.2%
5 1246
8.6%
9 1241
8.6%
8 1037
7.2%
4 999
 
6.9%
Uppercase Letter
ValueCountFrequency (%)
A 37
66.1%
B 10
 
17.9%
C 3
 
5.4%
D 2
 
3.6%
F 1
 
1.8%
M 1
 
1.8%
G 1
 
1.8%
Y 1
 
1.8%
Lowercase Letter
ValueCountFrequency (%)
a 3
42.9%
y 1
 
14.3%
t 1
 
14.3%
i 1
 
14.3%
b 1
 
14.3%
Other Punctuation
ValueCountFrequency (%)
, 11
64.7%
/ 4
 
23.5%
. 2
 
11.8%
Space Separator
ValueCountFrequency (%)
10533
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2543
100.0%
Open Punctuation
ValueCountFrequency (%)
( 71
100.0%
Close Punctuation
ValueCountFrequency (%)
) 71
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 42089
60.2%
Common 27712
39.7%
Latin 63
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 9898
23.5%
산 3605
 
8.6%
동 3416
 
8.1%
주 3353
 
8.0%
시 3299
 
7.8%
구 3297
 
7.8%
역 3296
 
7.8%
지 2642
 
6.3%
번 2527
 
6.0%
선 455
 
1.1%
Other values (182) 6301
15.0%
Common
ValueCountFrequency (%)
10533
38.0%
- 2543
 
9.2%
1 2510
 
9.1%
2 1846
 
6.7%
7 1570
 
5.7%
3 1349
 
4.9%
0 1340
 
4.8%
6 1339
 
4.8%
5 1246
 
4.5%
9 1241
 
4.5%
Other values (7) 2195
 
7.9%
Latin
ValueCountFrequency (%)
A 37
58.7%
B 10
 
15.9%
C 3
 
4.8%
a 3
 
4.8%
D 2
 
3.2%
y 1
 
1.6%
t 1
 
1.6%
F 1
 
1.6%
M 1
 
1.6%
G 1
 
1.6%
Other values (3) 3
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 42089
60.2%
ASCII 27775
39.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10533
37.9%
- 2543
 
9.2%
1 2510
 
9.0%
2 1846
 
6.6%
7 1570
 
5.7%
3 1349
 
4.9%
0 1340
 
4.8%
6 1339
 
4.8%
5 1246
 
4.5%
9 1241
 
4.5%
Other values (20) 2258
 
8.1%
Hangul
ValueCountFrequency (%)
광 9898
23.5%
산 3605
 
8.6%
동 3416
 
8.1%
주 3353
 
8.0%
시 3299
 
7.8%
구 3297
 
7.8%
역 3296
 
7.8%
지 2642
 
6.3%
번 2527
 
6.0%
선 455
 
1.1%
Other values (182) 6301
15.0%

전화번호
Text

MISSING 

Distinct2362
Distinct (%)86.0%
Missing545
Missing (%)16.6%
Memory size25.8 KiB
2023-12-12T17:50:08.253783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.997815
Min length3

Characters and Unicode

Total characters32946
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

Unique2051 ?
Unique (%)74.7%

Sample

1st row062-714-2156
2nd row062-951-4148
3rd row062-944-6337
4th row062-363-4455
5th row062-945-2216
ValueCountFrequency (%)
062-946-6900 8
 
0.3%
062-955-9195 5
 
0.2%
062 5
 
0.2%
062-376-6007 5
 
0.2%
062-955-0651 4
 
0.1%
062-956-1234 4
 
0.1%
062-953-0910 4
 
0.1%
062-941-7913 3
 
0.1%
062-942-1814 3
 
0.1%
062-956-2090 3
 
0.1%
Other values (2352) 2702
98.4%
2023-12-12T17:50:08.690214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 5469
16.6%
0 4801
14.6%
2 4176
12.7%
6 4145
12.6%
9 3118
9.5%
5 2817
8.6%
4 2147
 
6.5%
1 2020
 
6.1%
3 1729
 
5.2%
7 1459
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 27477
83.4%
Dash Punctuation 5469
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 4801
17.5%
2 4176
15.2%
6 4145
15.1%
9 3118
11.3%
5 2817
10.3%
4 2147
7.8%
1 2020
7.4%
3 1729
 
6.3%
7 1459
 
5.3%
8 1065
 
3.9%
Dash Punctuation
ValueCountFrequency (%)
- 5469
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 32946
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 5469
16.6%
0 4801
14.6%
2 4176
12.7%
6 4145
12.6%
9 3118
9.5%
5 2817
8.6%
4 2147
 
6.5%
1 2020
 
6.1%
3 1729
 
5.2%
7 1459
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 32946
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 5469
16.6%
0 4801
14.6%
2 4176
12.7%
6 4145
12.6%
9 3118
9.5%
5 2817
8.6%
4 2147
 
6.5%
1 2020
 
6.1%
3 1729
 
5.2%
7 1459
 
4.4%

종업원수
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct148
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.109693
Minimum0
Maximum3286
Zeros166
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size29.1 KiB
2023-12-12T17:50:08.879324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median6
Q313
95-th percentile56
Maximum3286
Range3286
Interquartile range (IQR)10

Descriptive statistics

Standard deviation88.52736
Coefficient of variation (CV)4.8883965
Kurtosis973.48431
Mean18.109693
Median Absolute Deviation (MAD)4
Skewness28.308167
Sum59599
Variance7837.0934
MonotonicityNot monotonic
2023-12-12T17:50:09.028765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5 382
 
11.6%
3 337
 
10.2%
4 259
 
7.9%
2 215
 
6.5%
1 171
 
5.2%
0 166
 
5.0%
6 160
 
4.9%
7 155
 
4.7%
8 151
 
4.6%
10 151
 
4.6%
Other values (138) 1144
34.8%
ValueCountFrequency (%)
0 166
5.0%
1 171
5.2%
2 215
6.5%
3 337
10.2%
4 259
7.9%
5 382
11.6%
6 160
4.9%
7 155
4.7%
8 151
 
4.6%
9 112
 
3.4%
ValueCountFrequency (%)
3286 1
< 0.1%
3022 1
< 0.1%
792 1
< 0.1%
720 1
< 0.1%
717 1
< 0.1%
678 1
< 0.1%
600 1
< 0.1%
520 1
< 0.1%
509 1
< 0.1%
500 1
< 0.1%

생산품
Text

Distinct2478
Distinct (%)75.5%
Missing9
Missing (%)0.3%
Memory size25.8 KiB
2023-12-12T17:50:09.408440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length76
Median length53
Mean length8.3717246
Min length1

Characters and Unicode

Total characters27476
Distinct characters647
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2211 ?
Unique (%)67.4%

Sample

1st row절삭가공
2nd row산소및 가스류
3rd row제진기,벨트컨베이어,평면스크린,수문,가동보,권양기,급수개폐기외
4th row사무용가구
5th row시험생산시설
ValueCountFrequency (%)
등 209
 
3.5%
및 176
 
3.0%
부품 131
 
2.2%
금형 124
 
2.1%
자동차 101
 
1.7%
자동차부품 86
 
1.4%
전자부품 69
 
1.2%
플라스틱 51
 
0.9%
배전반 46
 
0.8%
철구조물 37
 
0.6%
Other values (2837) 4905
82.6%
2023-12-12T17:50:10.426547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2658
 
9.7%
, 1158
 
4.2%
기 1002
 
3.6%
품 850
 
3.1%
자 665
 
2.4%
부 639
 
2.3%
동 543
 
2.0%
전 491
 
1.8%
장 476
 
1.7%
금 460
 
1.7%
Other values (637) 18534
67.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22387
81.5%
Space Separator 2658
 
9.7%
Other Punctuation 1235
 
4.5%
Uppercase Letter 655
 
2.4%
Lowercase Letter 251
 
0.9%
Open Punctuation 127
 
0.5%
Close Punctuation 125
 
0.5%
Decimal Number 27
 
0.1%
Dash Punctuation 11
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 1002
 
4.5%
품 850
 
3.8%
자 665
 
3.0%
부 639
 
2.9%
동 543
 
2.4%
전 491
 
2.2%
장 476
 
2.1%
금 460
 
2.1%
차 449
 
2.0%
스 430
 
1.9%
Other values (575) 16382
73.2%
Uppercase Letter
ValueCountFrequency (%)
E 79
12.1%
C 77
11.8%
L 62
 
9.5%
D 57
 
8.7%
A 43
 
6.6%
T 43
 
6.6%
P 42
 
6.4%
S 38
 
5.8%
O 29
 
4.4%
V 26
 
4.0%
Other values (13) 159
24.3%
Lowercase Letter
ValueCountFrequency (%)
e 38
15.1%
r 26
10.4%
s 20
 
8.0%
t 20
 
8.0%
l 19
 
7.6%
o 19
 
7.6%
a 18
 
7.2%
d 12
 
4.8%
i 11
 
4.4%
p 11
 
4.4%
Other values (13) 57
22.7%
Other Punctuation
ValueCountFrequency (%)
, 1158
93.8%
. 47
 
3.8%
/ 21
 
1.7%
' 5
 
0.4%
& 2
 
0.2%
· 2
 
0.2%
Decimal Number
ValueCountFrequency (%)
2 8
29.6%
3 6
22.2%
4 5
18.5%
1 4
14.8%
7 2
 
7.4%
5 2
 
7.4%
Space Separator
ValueCountFrequency (%)
2658
100.0%
Open Punctuation
ValueCountFrequency (%)
( 127
100.0%
Close Punctuation
ValueCountFrequency (%)
) 125
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22386
81.5%
Common 4183
 
15.2%
Latin 906
 
3.3%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 1002
 
4.5%
품 850
 
3.8%
자 665
 
3.0%
부 639
 
2.9%
동 543
 
2.4%
전 491
 
2.2%
장 476
 
2.1%
금 460
 
2.1%
차 449
 
2.0%
스 430
 
1.9%
Other values (574) 16381
73.2%
Latin
ValueCountFrequency (%)
E 79
 
8.7%
C 77
 
8.5%
L 62
 
6.8%
D 57
 
6.3%
A 43
 
4.7%
T 43
 
4.7%
P 42
 
4.6%
S 38
 
4.2%
e 38
 
4.2%
O 29
 
3.2%
Other values (36) 398
43.9%
Common
ValueCountFrequency (%)
2658
63.5%
, 1158
27.7%
( 127
 
3.0%
) 125
 
3.0%
. 47
 
1.1%
/ 21
 
0.5%
- 11
 
0.3%
2 8
 
0.2%
3 6
 
0.1%
' 5
 
0.1%
Other values (6) 17
 
0.4%
Han
ValueCountFrequency (%)
外 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22385
81.5%
ASCII 5087
 
18.5%
None 2
 
< 0.1%
Compat Jamo 1
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2658
52.3%
, 1158
22.8%
( 127
 
2.5%
) 125
 
2.5%
E 79
 
1.6%
C 77
 
1.5%
L 62
 
1.2%
D 57
 
1.1%
. 47
 
0.9%
A 43
 
0.8%
Other values (51) 654
 
12.9%
Hangul
ValueCountFrequency (%)
기 1002
 
4.5%
품 850
 
3.8%
자 665
 
3.0%
부 639
 
2.9%
동 543
 
2.4%
전 491
 
2.2%
장 476
 
2.1%
금 460
 
2.1%
차 449
 
2.0%
스 430
 
1.9%
Other values (573) 16380
73.2%
None
ValueCountFrequency (%)
· 2
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅎ 1
100.0%
CJK
ValueCountFrequency (%)
外 1
100.0%

용지면적
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct2158
Distinct (%)65.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4265.9376
Minimum0
Maximum604338.9
Zeros350
Zeros (%)10.6%
Negative0
Negative (%)0.0%
Memory size29.1 KiB
2023-12-12T17:50:10.644033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1499
median1754
Q33600.3
95-th percentile13550
Maximum604338.9
Range604338.9
Interquartile range (IQR)3101.3

Descriptive statistics

Standard deviation16626.938
Coefficient of variation (CV)3.8976047
Kurtosis768.4116
Mean4265.9376
Median Absolute Deviation (MAD)1491
Skewness24.943228
Sum14039201
Variance2.7645508 × 108
MonotonicityNot monotonic
2023-12-12T17:50:10.848375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 350
 
10.6%
13550.0 43
 
1.3%
165.0 38
 
1.2%
1653.0 30
 
0.9%
330.0 29
 
0.9%
99.0 21
 
0.6%
1652.9 18
 
0.5%
3305.8 15
 
0.5%
1653.1 14
 
0.4%
3306.0 13
 
0.4%
Other values (2148) 2720
82.6%
ValueCountFrequency (%)
0.0 350
10.6%
14.4 1
 
< 0.1%
15.0 1
 
< 0.1%
21.78 1
 
< 0.1%
23.28 1
 
< 0.1%
25.155 1
 
< 0.1%
25.74 1
 
< 0.1%
28.08 1
 
< 0.1%
30.0 1
 
< 0.1%
30.42 1
 
< 0.1%
ValueCountFrequency (%)
604338.9 1
< 0.1%
438811.1 1
< 0.1%
409371.0 1
< 0.1%
164835.0 1
< 0.1%
128435.8 1
< 0.1%
119088.4 1
< 0.1%
99174.6 1
< 0.1%
93600.4 1
< 0.1%
74811.0 1
< 0.1%
72274.4 1
< 0.1%

제조시설면적
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2426
Distinct (%)73.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1827.1638
Minimum-150
Maximum180291
Zeros119
Zeros (%)3.6%
Negative1
Negative (%)< 0.1%
Memory size29.1 KiB
2023-12-12T17:50:11.062904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-150
5-th percentile39.225
Q1230.19
median604.5
Q31575.725
95-th percentile5816.285
Maximum180291
Range180441
Interquartile range (IQR)1345.535

Descriptive statistics

Standard deviation6540.4272
Coefficient of variation (CV)3.5795516
Kurtosis368.37716
Mean1827.1638
Median Absolute Deviation (MAD)466.5
Skewness16.881838
Sum6013196.1
Variance42777188
MonotonicityNot monotonic
2023-12-12T17:50:11.296397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 119
 
3.6%
330.0 53
 
1.6%
165.0 49
 
1.5%
300.0 29
 
0.9%
99.0 26
 
0.8%
150.0 18
 
0.5%
495.0 16
 
0.5%
396.0 16
 
0.5%
198.0 14
 
0.4%
100.0 12
 
0.4%
Other values (2416) 2939
89.3%
ValueCountFrequency (%)
-150.0 1
 
< 0.1%
0.0 119
3.6%
12.0 1
 
< 0.1%
15.0 1
 
< 0.1%
20.0 4
 
0.1%
21.78 3
 
0.1%
22.68 1
 
< 0.1%
25.15 3
 
0.1%
25.155 1
 
< 0.1%
25.74 1
 
< 0.1%
ValueCountFrequency (%)
180291.0 1
< 0.1%
155312.49 1
< 0.1%
140419.0 1
< 0.1%
109345.73 1
< 0.1%
100404.0 1
< 0.1%
69876.14 1
< 0.1%
65704.83 1
< 0.1%
52730.48 1
< 0.1%
49054.39 1
< 0.1%
48449.81 1
< 0.1%

부대시설면적
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct1873
Distinct (%)56.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean614.90318
Minimum0
Maximum134234.26
Zeros1039
Zeros (%)31.6%
Negative0
Negative (%)0.0%
Memory size29.1 KiB
2023-12-12T17:50:11.521124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median106.82
Q3361.925
95-th percentile1937.61
Maximum134234.26
Range134234.26
Interquartile range (IQR)361.925

Descriptive statistics

Standard deviation3941.281
Coefficient of variation (CV)6.4095961
Kurtosis788.64252
Mean614.90318
Median Absolute Deviation (MAD)106.82
Skewness25.459924
Sum2023646.4
Variance15533696
MonotonicityNot monotonic
2023-12-12T17:50:11.771959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1039
31.6%
66.0 15
 
0.5%
30.0 11
 
0.3%
200.0 10
 
0.3%
132.0 8
 
0.2%
20.0 8
 
0.2%
50.0 8
 
0.2%
60.0 8
 
0.2%
300.0 7
 
0.2%
120.0 7
 
0.2%
Other values (1863) 2170
65.9%
ValueCountFrequency (%)
0.0 1039
31.6%
1.0 1
 
< 0.1%
1.2 1
 
< 0.1%
1.44 2
 
0.1%
2.0 4
 
0.1%
2.4 1
 
< 0.1%
2.5 2
 
0.1%
2.52 1
 
< 0.1%
2.88 2
 
0.1%
3.0 2
 
0.1%
ValueCountFrequency (%)
134234.26 1
< 0.1%
130093.0 1
< 0.1%
63611.86 1
< 0.1%
50265.9 1
< 0.1%
38647.87 1
< 0.1%
36295.71 1
< 0.1%
30290.34 1
< 0.1%
27242.02 1
< 0.1%
19800.0 1
< 0.1%
19237.0 1
< 0.1%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size25.8 KiB
2022-12-31
3291 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-12-31
2nd row2022-12-31
3rd row2022-12-31
4th row2022-12-31
5th row2022-12-31

Common Values

ValueCountFrequency (%)
2022-12-31 3291
100.0%

Length

2023-12-12T17:50:11.959962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:50:12.101756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-12-31 3291
100.0%

Interactions

2023-12-12T17:50:04.828659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:02.272261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:02.830531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:03.526529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:04.197895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:04.965089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:02.378364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:02.955953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:03.688383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:04.335938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:05.110804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:02.497257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:03.088526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:03.825160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:04.438570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:05.234903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:02.596926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:03.238638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:03.955089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:04.551716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:05.400660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:02.719369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:03.388070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:04.082539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:50:04.683201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:50:12.186308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번단지명종업원수용지면적제조시설면적부대시설면적
연번1.0000.1860.0000.0120.0280.000
단지명0.1861.0000.0100.0750.1010.087
종업원수0.0000.0101.0000.8300.8280.820
용지면적0.0120.0750.8301.0000.9730.865
제조시설면적0.0280.1010.8280.9731.0000.753
부대시설면적0.0000.0870.8200.8650.7531.000
2023-12-12T17:50:12.350077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번종업원수용지면적제조시설면적부대시설면적단지명
연번1.000-0.194-0.084-0.139-0.1000.085
종업원수-0.1941.0000.4300.5180.4730.007
용지면적-0.0840.4301.0000.6450.5940.037
제조시설면적-0.1390.5180.6451.0000.4990.032
부대시설면적-0.1000.4730.5940.4991.0000.043
단지명0.0850.0070.0370.0320.0431.000

Missing values

2023-12-12T17:50:05.592720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:50:05.795751image/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.
2023-12-12T17:50:05.944975image/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

연번회사명단지명주소전화번호종업원수생산품용지면적제조시설면적부대시설면적데이터기준일자
01재경TEC광주하남일반산업단지광주광역시 광산구 오선동 273-27062-714-21566절삭가공1803.0912.0205.02022-12-31
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