Overview

Dataset statistics

Number of variables9
Number of observations2010
Missing cells259
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory149.3 KiB
Average record size in memory76.1 B

Variable types

Numeric3
Text4
Categorical1
DateTime1

Dataset

Description경상남도 김해시 환경오염물질 배출시설 현황에 대한 데이터로 업체명,전화번호,도로명주소 등의 항목을 제공합니다.
Author경상남도 김해시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15033435

Alerts

종수 is highly imbalanced (50.3%)Imbalance
전화번호 has 238 (11.8%) missing valuesMissing
기준 has unique valuesUnique

Reproduction

Analysis started2023-12-11 00:50:19.150102
Analysis finished2023-12-11 00:50:20.937464
Duration1.79 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준
Real number (ℝ)

UNIQUE 

Distinct2010
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1005.5
Minimum1
Maximum2010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2023-12-11T09:50:21.006148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile101.45
Q1503.25
median1005.5
Q31507.75
95-th percentile1909.55
Maximum2010
Range2009
Interquartile range (IQR)1004.5

Descriptive statistics

Standard deviation580.38134
Coefficient of variation (CV)0.5772067
Kurtosis-1.2
Mean1005.5
Median Absolute Deviation (MAD)502.5
Skewness0
Sum2021055
Variance336842.5
MonotonicityStrictly increasing
2023-12-11T09:50:21.171988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
1337 1
 
< 0.1%
1350 1
 
< 0.1%
1349 1
 
< 0.1%
1348 1
 
< 0.1%
1347 1
 
< 0.1%
1346 1
 
< 0.1%
1345 1
 
< 0.1%
1344 1
 
< 0.1%
1343 1
 
< 0.1%
Other values (2000) 2000
99.5%
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 (%)
2010 1
< 0.1%
2009 1
< 0.1%
2008 1
< 0.1%
2007 1
< 0.1%
2006 1
< 0.1%
2005 1
< 0.1%
2004 1
< 0.1%
2003 1
< 0.1%
2002 1
< 0.1%
2001 1
< 0.1%

업체명
Text

Distinct1924
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
2023-12-11T09:50:21.432574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length20
Mean length5.8810945
Min length2

Characters and Unicode

Total characters11821
Distinct characters488
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1850 ?
Unique (%)92.0%

Sample

1st row한국내화㈜ 김해공장
2nd row㈜중일옥사이드
3rd row㈜씨앤엠
4th row아세아식품
5th row진례산업㈜
ValueCountFrequency (%)
주식회사 28
 
1.3%
2공장 12
 
0.6%
김해공장 8
 
0.4%
제2공장 8
 
0.4%
김해지점 8
 
0.4%
삼부정밀화학㈜ 6
 
0.3%
안동지점 4
 
0.2%
동남산업 4
 
0.2%
보성산업 4
 
0.2%
경성산업㈜ 3
 
0.1%
Other values (1945) 2068
96.1%
2023-12-11T09:50:22.105937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
㈜ 1213
 
10.3%
업 401
 
3.4%
산 370
 
3.1%
이 322
 
2.7%
스 247
 
2.1%
성 234
 
2.0%
에 197
 
1.7%
동 196
 
1.7%
공 191
 
1.6%
대 183
 
1.5%
Other values (478) 8267
69.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9953
84.2%
Other Symbol 1213
 
10.3%
Uppercase Letter 295
 
2.5%
Space Separator 147
 
1.2%
Decimal Number 68
 
0.6%
Close Punctuation 46
 
0.4%
Open Punctuation 46
 
0.4%
Other Punctuation 46
 
0.4%
Dash Punctuation 5
 
< 0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 401
 
4.0%
산 370
 
3.7%
이 322
 
3.2%
스 247
 
2.5%
성 234
 
2.4%
에 197
 
2.0%
동 196
 
2.0%
공 191
 
1.9%
대 183
 
1.8%
지 158
 
1.6%
Other values (436) 7454
74.9%
Uppercase Letter
ValueCountFrequency (%)
C 36
12.2%
S 30
 
10.2%
E 27
 
9.2%
P 22
 
7.5%
M 18
 
6.1%
N 18
 
6.1%
R 18
 
6.1%
T 16
 
5.4%
O 13
 
4.4%
H 12
 
4.1%
Other values (12) 85
28.8%
Decimal Number
ValueCountFrequency (%)
2 42
61.8%
1 14
 
20.6%
3 7
 
10.3%
5 1
 
1.5%
4 1
 
1.5%
6 1
 
1.5%
0 1
 
1.5%
8 1
 
1.5%
Other Punctuation
ValueCountFrequency (%)
. 31
67.4%
& 10
 
21.7%
, 2
 
4.3%
: 2
 
4.3%
/ 1
 
2.2%
Lowercase Letter
ValueCountFrequency (%)
n 1
50.0%
o 1
50.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1213
100.0%
Space Separator
ValueCountFrequency (%)
147
100.0%
Close Punctuation
ValueCountFrequency (%)
) 46
100.0%
Open Punctuation
ValueCountFrequency (%)
( 46
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11166
94.5%
Common 358
 
3.0%
Latin 297
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
㈜ 1213
 
10.9%
업 401
 
3.6%
산 370
 
3.3%
이 322
 
2.9%
스 247
 
2.2%
성 234
 
2.1%
에 197
 
1.8%
동 196
 
1.8%
공 191
 
1.7%
대 183
 
1.6%
Other values (437) 7612
68.2%
Latin
ValueCountFrequency (%)
C 36
12.1%
S 30
 
10.1%
E 27
 
9.1%
P 22
 
7.4%
M 18
 
6.1%
N 18
 
6.1%
R 18
 
6.1%
T 16
 
5.4%
O 13
 
4.4%
H 12
 
4.0%
Other values (14) 87
29.3%
Common
ValueCountFrequency (%)
147
41.1%
) 46
 
12.8%
( 46
 
12.8%
2 42
 
11.7%
. 31
 
8.7%
1 14
 
3.9%
& 10
 
2.8%
3 7
 
2.0%
- 5
 
1.4%
, 2
 
0.6%
Other values (7) 8
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9953
84.2%
None 1213
 
10.3%
ASCII 655
 
5.5%

Most frequent character per block

None
ValueCountFrequency (%)
㈜ 1213
100.0%
Hangul
ValueCountFrequency (%)
업 401
 
4.0%
산 370
 
3.7%
이 322
 
3.2%
스 247
 
2.5%
성 234
 
2.4%
에 197
 
2.0%
동 196
 
2.0%
공 191
 
1.9%
대 183
 
1.8%
지 158
 
1.6%
Other values (436) 7454
74.9%
ASCII
ValueCountFrequency (%)
147
22.4%
) 46
 
7.0%
( 46
 
7.0%
2 42
 
6.4%
C 36
 
5.5%
. 31
 
4.7%
S 30
 
4.6%
E 27
 
4.1%
P 22
 
3.4%
M 18
 
2.7%
Other values (31) 210
32.1%

전화번호
Text

MISSING 

Distinct1636
Distinct (%)92.3%
Missing238
Missing (%)11.8%
Memory size15.8 KiB
2023-12-11T09:50:22.359590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.002822
Min length9

Characters and Unicode

Total characters21269
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1515 ?
Unique (%)85.5%

Sample

1st row055-345-9911
2nd row055-330-5021
3rd row055-323-9910
4th row055-345-2600
5th row055-345-8844
ValueCountFrequency (%)
055-338-2650 4
 
0.2%
055-343-1491 4
 
0.2%
055-338-2277 3
 
0.2%
055-323-8181 3
 
0.2%
055-329-0490 3
 
0.2%
055-327-3675 3
 
0.2%
055-322-8321 3
 
0.2%
055-314-2206 3
 
0.2%
055-322-5141 3
 
0.2%
055-322-1462 3
 
0.2%
Other values (1626) 1740
98.2%
2023-12-11T09:50:22.732069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 4455
20.9%
- 3539
16.6%
3 3039
14.3%
0 2846
13.4%
2 1499
 
7.0%
4 1396
 
6.6%
1 1172
 
5.5%
6 950
 
4.5%
7 866
 
4.1%
8 796
 
3.7%
Other values (3) 711
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 17728
83.4%
Dash Punctuation 3539
 
16.6%
Math Symbol 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 4455
25.1%
3 3039
17.1%
0 2846
16.1%
2 1499
 
8.5%
4 1396
 
7.9%
1 1172
 
6.6%
6 950
 
5.4%
7 866
 
4.9%
8 796
 
4.5%
9 709
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 3539
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 21269
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 4455
20.9%
- 3539
16.6%
3 3039
14.3%
0 2846
13.4%
2 1499
 
7.0%
4 1396
 
6.6%
1 1172
 
5.5%
6 950
 
4.5%
7 866
 
4.1%
8 796
 
3.7%
Other values (3) 711
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 21269
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 4455
20.9%
- 3539
16.6%
3 3039
14.3%
0 2846
13.4%
2 1499
 
7.0%
4 1396
 
6.6%
1 1172
 
5.5%
6 950
 
4.5%
7 866
 
4.1%
8 796
 
3.7%
Other values (3) 711
 
3.3%

도로명주소
Text

Distinct1929
Distinct (%)96.3%
Missing7
Missing (%)0.3%
Memory size15.8 KiB
2023-12-11T09:50:22.965356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length40
Mean length24.522217
Min length14

Characters and Unicode

Total characters49118
Distinct characters135
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1861 ?
Unique (%)92.9%

Sample

1st row경상남도 김해시 진영읍 본산로 69
2nd row경상남도 김해시 진영읍 김해대로 94-11
3rd row경상남도 김해시 김해대로2635번길 29
4th row경상남도 김해시 생림면 장재로520번안길 8
5th row경상남도 김해시 진례면 서부로476번길 34
ValueCountFrequency (%)
경상남도 2003
20.5%
김해시 2003
20.5%
한림면 487
 
5.0%
주촌면 311
 
3.2%
진례면 239
 
2.4%
상동면 223
 
2.3%
진영읍 218
 
2.2%
생림면 202
 
2.1%
서부로1499번길 89
 
0.9%
상동로 69
 
0.7%
Other values (1602) 3913
40.1%
2023-12-11T09:50:23.342051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8704
17.7%
김 2348
 
4.8%
해 2348
 
4.8%
상 2340
 
4.8%
1 2127
 
4.3%
경 2005
 
4.1%
시 2004
 
4.1%
남 2003
 
4.1%
도 2003
 
4.1%
로 1972
 
4.0%
Other values (125) 21264
43.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 28392
57.8%
Decimal Number 10905
 
22.2%
Space Separator 8704
 
17.7%
Dash Punctuation 1047
 
2.1%
Close Punctuation 24
 
< 0.1%
Open Punctuation 24
 
< 0.1%
Other Punctuation 14
 
< 0.1%
Uppercase Letter 7
 
< 0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 2348
 
8.3%
해 2348
 
8.3%
상 2340
 
8.2%
경 2005
 
7.1%
시 2004
 
7.1%
남 2003
 
7.1%
도 2003
 
7.1%
로 1972
 
6.9%
면 1467
 
5.2%
길 1253
 
4.4%
Other values (104) 8649
30.5%
Decimal Number
ValueCountFrequency (%)
1 2127
19.5%
2 1447
13.3%
3 1188
10.9%
4 1084
9.9%
5 1058
9.7%
9 973
8.9%
6 942
8.6%
7 829
 
7.6%
0 652
 
6.0%
8 605
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
A 3
42.9%
B 2
28.6%
C 1
 
14.3%
L 1
 
14.3%
Other Punctuation
ValueCountFrequency (%)
, 12
85.7%
: 2
 
14.3%
Space Separator
ValueCountFrequency (%)
8704
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1047
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 28393
57.8%
Common 20718
42.2%
Latin 7
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 2348
 
8.3%
해 2348
 
8.3%
상 2340
 
8.2%
경 2005
 
7.1%
시 2004
 
7.1%
남 2003
 
7.1%
도 2003
 
7.1%
로 1972
 
6.9%
면 1467
 
5.2%
길 1253
 
4.4%
Other values (105) 8650
30.5%
Common
ValueCountFrequency (%)
8704
42.0%
1 2127
 
10.3%
2 1447
 
7.0%
3 1188
 
5.7%
4 1084
 
5.2%
5 1058
 
5.1%
- 1047
 
5.1%
9 973
 
4.7%
6 942
 
4.5%
7 829
 
4.0%
Other values (6) 1319
 
6.4%
Latin
ValueCountFrequency (%)
A 3
42.9%
B 2
28.6%
C 1
 
14.3%
L 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 28392
57.8%
ASCII 20725
42.2%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8704
42.0%
1 2127
 
10.3%
2 1447
 
7.0%
3 1188
 
5.7%
4 1084
 
5.2%
5 1058
 
5.1%
- 1047
 
5.1%
9 973
 
4.7%
6 942
 
4.5%
7 829
 
4.0%
Other values (10) 1326
 
6.4%
Hangul
ValueCountFrequency (%)
김 2348
 
8.3%
해 2348
 
8.3%
상 2340
 
8.2%
경 2005
 
7.1%
시 2004
 
7.1%
남 2003
 
7.1%
도 2003
 
7.1%
로 1972
 
6.9%
면 1467
 
5.2%
길 1253
 
4.4%
Other values (104) 8649
30.5%
None
ValueCountFrequency (%)
㈜ 1
100.0%

업종
Text

Distinct823
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
2023-12-11T09:50:23.571008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length41
Mean length12.141294
Min length2

Characters and Unicode

Total characters24404
Distinct characters317
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

Unique568 ?
Unique (%)28.3%

Sample

1st row구조용정형내화제품제조업
2nd row기초무기화합물제조
3rd row전동기및발전기제조업
4th row식품제조
5th row스폰지제조업
ValueCountFrequency (%)
및 70
 
2.9%
도장및기타피막처리업 67
 
2.8%
선박구성부분품제조업 59
 
2.5%
그외기타자동차부품제조업 43
 
1.8%
자동차종합수리업 42
 
1.8%
금속열처리업 38
 
1.6%
외 36
 
1.5%
플라스틱제품제조 34
 
1.4%
제조업 31
 
1.3%
비금속원료재생업 30
 
1.2%
Other values (906) 1950
81.2%
2023-12-11T09:50:23.917983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
제 1890
 
7.7%
업 1777
 
7.3%
조 1510
 
6.2%
기 898
 
3.7%
품 812
 
3.3%
2 652
 
2.7%
및 577
 
2.4%
타 560
 
2.3%
1 454
 
1.9%
금 452
 
1.9%
Other values (307) 14822
60.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 20517
84.1%
Decimal Number 2321
 
9.5%
Open Punctuation 445
 
1.8%
Space Separator 443
 
1.8%
Close Punctuation 442
 
1.8%
Other Punctuation 236
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 1890
 
9.2%
업 1777
 
8.7%
조 1510
 
7.4%
기 898
 
4.4%
품 812
 
4.0%
및 577
 
2.8%
타 560
 
2.7%
금 452
 
2.2%
리 433
 
2.1%
속 414
 
2.0%
Other values (292) 11194
54.6%
Decimal Number
ValueCountFrequency (%)
2 652
28.1%
1 454
19.6%
3 382
16.5%
9 298
12.8%
0 205
 
8.8%
5 117
 
5.0%
8 92
 
4.0%
4 82
 
3.5%
6 24
 
1.0%
7 15
 
0.6%
Other Punctuation
ValueCountFrequency (%)
, 234
99.2%
. 2
 
0.8%
Open Punctuation
ValueCountFrequency (%)
( 445
100.0%
Space Separator
ValueCountFrequency (%)
443
100.0%
Close Punctuation
ValueCountFrequency (%)
) 442
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 20517
84.1%
Common 3887
 
15.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 1890
 
9.2%
업 1777
 
8.7%
조 1510
 
7.4%
기 898
 
4.4%
품 812
 
4.0%
및 577
 
2.8%
타 560
 
2.7%
금 452
 
2.2%
리 433
 
2.1%
속 414
 
2.0%
Other values (292) 11194
54.6%
Common
ValueCountFrequency (%)
2 652
16.8%
1 454
11.7%
( 445
11.4%
443
11.4%
) 442
11.4%
3 382
9.8%
9 298
7.7%
, 234
 
6.0%
0 205
 
5.3%
5 117
 
3.0%
Other values (5) 215
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 20517
84.1%
ASCII 3887
 
15.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
제 1890
 
9.2%
업 1777
 
8.7%
조 1510
 
7.4%
기 898
 
4.4%
품 812
 
4.0%
및 577
 
2.8%
타 560
 
2.7%
금 452
 
2.2%
리 433
 
2.1%
속 414
 
2.0%
Other values (292) 11194
54.6%
ASCII
ValueCountFrequency (%)
2 652
16.8%
1 454
11.7%
( 445
11.4%
443
11.4%
) 442
11.4%
3 382
9.8%
9 298
7.7%
, 234
 
6.0%
0 205
 
5.3%
5 117
 
3.0%
Other values (5) 215
 
5.5%

종수
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
5
1161 
4
743 
3
 
59
2
 
35
1
 
11

Length

Max length4
Median length1
Mean length1.0014925
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row2
2nd row2
3rd row4
4th row5
5th row5

Common Values

ValueCountFrequency (%)
5 1161
57.8%
4 743
37.0%
3 59
 
2.9%
2 35
 
1.7%
1 11
 
0.5%
<NA> 1
 
< 0.1%

Length

2023-12-11T09:50:24.045496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:50:24.159380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5 1161
57.8%
4 743
37.0%
3 59
 
2.9%
2 35
 
1.7%
1 11
 
0.5%
na 1
 
< 0.1%

신고일자
Date

Distinct1689
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Memory size15.8 KiB
Minimum1977-05-11 00:00:00
Maximum2020-10-19 00:00:00
2023-12-11T09:50:24.263784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:24.390580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

위도
Real number (ℝ)

Distinct1881
Distinct (%)93.9%
Missing7
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean35.287854
Minimum34.748768
Maximum38.069251
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2023-12-11T09:50:24.531677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.748768
5-th percentile35.218691
Q135.2379
median35.284973
Q335.307607
95-th percentile35.332155
Maximum38.069251
Range3.3204832
Interquartile range (IQR)0.06970697

Descriptive statistics

Standard deviation0.16168679
Coefficient of variation (CV)0.0045819389
Kurtosis192.96163
Mean35.287854
Median Absolute Deviation (MAD)0.03056764
Skewness13.313443
Sum70681.571
Variance0.026142618
MonotonicityNot monotonic
2023-12-11T09:50:24.689044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.26375636 6
 
0.3%
35.31384507 4
 
0.2%
35.31263595 4
 
0.2%
35.23261895 3
 
0.1%
35.22620618 3
 
0.1%
35.29971431 3
 
0.1%
35.32054528 3
 
0.1%
35.23479332 3
 
0.1%
35.23290716 3
 
0.1%
35.33791045 3
 
0.1%
Other values (1871) 1968
97.9%
(Missing) 7
 
0.3%
ValueCountFrequency (%)
34.74876786 1
< 0.1%
35.16657907 1
< 0.1%
35.17260495 1
< 0.1%
35.17924539 1
< 0.1%
35.18592349 1
< 0.1%
35.18681553 1
< 0.1%
35.18738413 1
< 0.1%
35.18768125 1
< 0.1%
35.18868263 1
< 0.1%
35.193392 1
< 0.1%
ValueCountFrequency (%)
38.06925107 1
< 0.1%
37.82886266 1
< 0.1%
37.82785176 1
< 0.1%
37.48220794 1
< 0.1%
37.48212605 1
< 0.1%
37.48196669 1
< 0.1%
37.48130549 1
< 0.1%
37.47989911 1
< 0.1%
36.89569015 1
< 0.1%
36.38750654 1
< 0.1%

경도
Real number (ℝ)

Distinct1879
Distinct (%)93.8%
Missing7
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean128.81664
Minimum126.62354
Maximum129.09286
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.8 KiB
2023-12-11T09:50:24.814623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.62354
5-th percentile128.74616
Q1128.77807
median128.81505
Q3128.86968
95-th percentile128.91925
Maximum129.09286
Range2.4693179
Interquartile range (IQR)0.09161555

Descriptive statistics

Standard deviation0.14269676
Coefficient of variation (CV)0.001107751
Kurtosis152.87537
Mean128.81664
Median Absolute Deviation (MAD)0.0395418
Skewness-11.179547
Sum258019.73
Variance0.020362364
MonotonicityNot monotonic
2023-12-11T09:50:24.950366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.7343016 6
 
0.3%
128.75369 4
 
0.2%
128.7504389 4
 
0.2%
128.7711373 3
 
0.1%
128.7624753 3
 
0.1%
128.8146419 3
 
0.1%
128.8153097 3
 
0.1%
128.7784645 3
 
0.1%
128.8761462 3
 
0.1%
128.8459322 3
 
0.1%
Other values (1869) 1968
97.9%
(Missing) 7
 
0.3%
ValueCountFrequency (%)
126.6235446 1
< 0.1%
126.8087448 1
< 0.1%
126.8137544 1
< 0.1%
126.816133 1
< 0.1%
126.8168911 1
< 0.1%
126.8177493 1
< 0.1%
126.8180843 1
< 0.1%
127.3461644 1
< 0.1%
127.3476998 1
< 0.1%
128.1722125 1
< 0.1%
ValueCountFrequency (%)
129.0928625 1
< 0.1%
128.9732766 1
< 0.1%
128.9681696 1
< 0.1%
128.9670595 1
< 0.1%
128.9669398 1
< 0.1%
128.9669 2
0.1%
128.9666391 1
< 0.1%
128.9663704 1
< 0.1%
128.9662231 1
< 0.1%
128.9661695 1
< 0.1%

Interactions

2023-12-11T09:50:20.347632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:19.818472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:20.089425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:20.429377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:19.912084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:20.180849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:20.510871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:19.998621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:50:20.266815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:50:25.028519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준종수위도경도
기준1.0000.2640.0480.105
종수0.2641.0000.2640.000
위도0.0480.2641.0000.899
경도0.1050.0000.8991.000
2023-12-11T09:50:25.116200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준위도경도종수
기준1.0000.007-0.1060.113
위도0.0071.000-0.0610.165
경도-0.106-0.0611.0000.000
종수0.1130.1650.0001.000

Missing values

2023-12-11T09:50:20.613517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:50:20.754002image/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.
2023-12-11T09:50:20.876485image/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

기준업체명전화번호도로명주소업종종수신고일자위도경도
01한국내화㈜ 김해공장<NA>경상남도 김해시 진영읍 본산로 69구조용정형내화제품제조업21977-05-1135.311303128.739257
12㈜중일옥사이드055-345-9911경상남도 김해시 진영읍 김해대로 94-11기초무기화합물제조21978-06-0135.296271128.707756
23㈜씨앤엠055-330-5021경상남도 김해시 김해대로2635번길 29전동기및발전기제조업41979-08-1035.231492128.913593
34아세아식품055-323-9910경상남도 김해시 생림면 장재로520번안길 8식품제조51980-07-1035.323123128.847839
45진례산업㈜055-345-2600경상남도 김해시 진례면 서부로476번길 34스폰지제조업51982-09-0135.261738128.746888
56김해축협배합사료공장055-345-8844경상남도 김해시 한림면 고모로 775사료제조21983-03-1235.289943128.778154
67㈜제이케이유화055-329-4445경상남도 김해시 김해대로2579번길 36윤활유및그리스제조업41984-04-1035.231622128.910726
78한성기업㈜김해공장055-333-4676경상남도 김해시 삼안로 51음식료품제조시설41984-06-2235.234525128.916658
89한통아스콘㈜055-346-1100경상남도 김해시 한림면 안곡로 265아스콘제조업21999-06-2135.284914128.850662
910르노삼성자동차 김해정비사업소㈜055-333-2626경상남도 김해시 김해대로2635번길 6자동차종합수리업41986-01-2535.229589128.915296
기준업체명전화번호도로명주소업종종수신고일자위도경도
20002001별이되다1566-9399경상남도 김해시 생림면 나전로137번길 31동물장묘업52020-09-2935.298045128.873332
20012002대명산업1522-2253경상남도 김해시 상동면 묵방로109번길 3동물장묘업52020-09-2935.299037128.911601
20022003㈜수림디앤씨051-302-6273경상남도 김해시 한림면 김해대로1099번길 120-1표면가공목재및특정목적용제재목제조업52020-10-0535.30348128.799668
20032004㈜서보055-342-4083경상남도 김해시 진영읍 서부로179번길 77자동차용동력저날장치제조업 외 152020-10-0535.287729128.772679
20042005세계산업<NA>경상남도 김해시 한림면 명동로 62-12지정외폐기물처리업52020-01-0735.29955128.816802
20052006㈜청암산업<NA>경상남도 김해시 한림면 장방로327번길 44-11비금속류원료재생업52020-10-0635.33159128.780816
20062007㈜대경055-345-4711경상남도 김해시 한림면 김해대로 1102-172증류기, 열교환기 및 가스발생기 제조업52020-10-1335.288427128.802092
20072008청명페트㈜<NA>경상남도 김해시 주촌면 서부로1499번길 49-53 외1지정 외 폐기물처리업52020-10-1935.230951128.81459
20082009거성수지<NA>경상남도 김해시 진례면 고모로364번길 74-24지정외폐기물처리업52020-10-1635.256503128.77788
20092010㈜동림스틸<NA>경상남도 김해시 주촌면 서부로1701번안길 58-246지정외폐기물처리업52020-10-1935.245398128.846863