Overview

Dataset statistics

Number of variables12
Number of observations148
Missing cells23
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.4 KiB
Average record size in memory99.9 B

Variable types

Categorical5
Text4
Numeric3

Dataset

Description경기도_의정부시_환경오염물질 배출시설 현황 데이터로 시군명, 사업장명, 업종명, 대표자명, 관할기관명, 폐수관리등급, 폐수종별구분명, 소재지우편번호, 소재지지번주소, 소재지도로명주소, 위도, 경도
Author경기도 의정부시
URLhttps://www.data.go.kr/data/15039899/fileData.do

Alerts

시군명 has constant value ""Constant
관할기관명 has constant value ""Constant
소재지우편번호 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 소재지우편번호High correlation
업종명 is highly overall correlated with 폐수종별구분명High correlation
폐수종별구분명 is highly overall correlated with 업종명High correlation
업종명 is highly imbalanced (51.8%)Imbalance
폐수종별구분명 is highly imbalanced (92.6%)Imbalance
소재지우편번호 has 11 (7.4%) missing valuesMissing
소재지도로명주소 has 12 (8.1%) missing valuesMissing
위도 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2023-12-12 20:19:46.071203
Analysis finished2023-12-12 20:19:47.927485
Duration1.86 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
의정부시
148 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
의정부시 148
100.0%

Length

2023-12-13T05:19:47.999349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:19:48.113221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
의정부시 148
100.0%

사업장명
Text

Distinct145
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-13T05:19:48.300934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length8.4324324
Min length3

Characters and Unicode

Total characters1248
Distinct characters243
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique142 ?
Unique (%)95.9%

Sample

1st row오누이손세차장
2nd row흥산에너지
3rd row제이모터스
4th row한신세차장
5th row훼미리제2주유소
ValueCountFrequency (%)
주식회사 3
 
1.6%
손세차장 2
 
1.1%
세차장 2
 
1.1%
의료법인 2
 
1.1%
의정부 2
 
1.1%
주)대련건설 2
 
1.1%
주)호반티비엠 2
 
1.1%
주)대원여객 2
 
1.1%
도로교통공단 1
 
0.5%
바다주유소 1
 
0.5%
Other values (170) 170
89.9%
2023-12-13T05:19:48.677113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 67
 
5.4%
차 44
 
3.5%
41
 
3.3%
소 40
 
3.2%
( 35
 
2.8%
) 35
 
2.8%
유 35
 
2.8%
부 31
 
2.5%
세 31
 
2.5%
의 29
 
2.3%
Other values (233) 860
68.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1104
88.5%
Space Separator 41
 
3.3%
Open Punctuation 35
 
2.8%
Close Punctuation 35
 
2.8%
Uppercase Letter 20
 
1.6%
Decimal Number 12
 
1.0%
Other Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 67
 
6.1%
차 44
 
4.0%
소 40
 
3.6%
유 35
 
3.2%
부 31
 
2.8%
세 31
 
2.8%
의 29
 
2.6%
스 28
 
2.5%
장 28
 
2.5%
정 26
 
2.4%
Other values (214) 745
67.5%
Uppercase Letter
ValueCountFrequency (%)
K 3
15.0%
S 3
15.0%
G 3
15.0%
P 2
10.0%
L 2
10.0%
I 2
10.0%
C 2
10.0%
V 1
 
5.0%
E 1
 
5.0%
H 1
 
5.0%
Decimal Number
ValueCountFrequency (%)
2 5
41.7%
4 3
25.0%
1 2
 
16.7%
0 1
 
8.3%
7 1
 
8.3%
Space Separator
ValueCountFrequency (%)
41
100.0%
Open Punctuation
ValueCountFrequency (%)
( 35
100.0%
Close Punctuation
ValueCountFrequency (%)
) 35
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1105
88.5%
Common 123
 
9.9%
Latin 20
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 67
 
6.1%
차 44
 
4.0%
소 40
 
3.6%
유 35
 
3.2%
부 31
 
2.8%
세 31
 
2.8%
의 29
 
2.6%
스 28
 
2.5%
장 28
 
2.5%
정 26
 
2.4%
Other values (215) 746
67.5%
Latin
ValueCountFrequency (%)
K 3
15.0%
S 3
15.0%
G 3
15.0%
P 2
10.0%
L 2
10.0%
I 2
10.0%
C 2
10.0%
V 1
 
5.0%
E 1
 
5.0%
H 1
 
5.0%
Common
ValueCountFrequency (%)
41
33.3%
( 35
28.5%
) 35
28.5%
2 5
 
4.1%
4 3
 
2.4%
1 2
 
1.6%
0 1
 
0.8%
7 1
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1104
88.5%
ASCII 143
 
11.5%
None 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 67
 
6.1%
차 44
 
4.0%
소 40
 
3.6%
유 35
 
3.2%
부 31
 
2.8%
세 31
 
2.8%
의 29
 
2.6%
스 28
 
2.5%
장 28
 
2.5%
정 26
 
2.4%
Other values (214) 745
67.5%
ASCII
ValueCountFrequency (%)
41
28.7%
( 35
24.5%
) 35
24.5%
2 5
 
3.5%
4 3
 
2.1%
K 3
 
2.1%
S 3
 
2.1%
G 3
 
2.1%
1 2
 
1.4%
P 2
 
1.4%
Other values (8) 11
 
7.7%
None
ValueCountFrequency (%)
㈜ 1
100.0%

업종명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct13
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
기타
105 
자동차 세차업
12 
 
7
<NA>
 
5
교량 터널 및 철도 건설업
 
4
Other values (8)
15 

Length

Max length15
Median length2
Mean length3.4797297
Min length1

Unique

Unique3 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
기타 105
70.9%
자동차 세차업 12
 
8.1%
7
 
4.7%
<NA> 5
 
3.4%
교량 터널 및 철도 건설업 4
 
2.7%
자동차 수리 및 세차업 3
 
2.0%
차량용 주유소 운영업 3
 
2.0%
비금속광물 2
 
1.4%
종합 병원 2
 
1.4%
자동차 종합 수리업 2
 
1.4%
Other values (3) 3
 
2.0%

Length

2023-12-13T05:19:48.820602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타 105
54.1%
자동차 17
 
8.8%
세차업 15
 
7.7%
및 7
 
3.6%
na 5
 
2.6%
종합 4
 
2.1%
운영업 4
 
2.1%
주유소 4
 
2.1%
차량용 4
 
2.1%
건설업 4
 
2.1%
Other values (11) 25
 
12.9%

대표자명
Text

Distinct121
Distinct (%)81.8%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-13T05:19:49.095042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length3
Mean length3.7432432
Min length2

Characters and Unicode

Total characters554
Distinct characters143
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique114 ?
Unique (%)77.0%

Sample

1st row김국화
2nd row신현근
3rd row유진원
4th row한필우
5th row김경미 김범수
ValueCountFrequency (%)
대표이사 23
 
14.6%
병원장 3
 
1.9%
최인선 2
 
1.3%
송진수 2
 
1.3%
박상덕 2
 
1.3%
신호철 2
 
1.3%
허상준 2
 
1.3%
김경미 2
 
1.3%
임기범 1
 
0.6%
양준모 1
 
0.6%
Other values (118) 118
74.7%
2023-12-13T05:19:49.543839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
이 44
 
7.9%
사 29
 
5.2%
대 26
 
4.7%
표 24
 
4.3%
김 22
 
4.0%
정 15
 
2.7%
장 14
 
2.5%
영 13
 
2.3%
13
 
2.3%
박 10
 
1.8%
Other values (133) 344
62.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 528
95.3%
Space Separator 13
 
2.3%
Decimal Number 7
 
1.3%
Open Punctuation 3
 
0.5%
Close Punctuation 3
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 44
 
8.3%
사 29
 
5.5%
대 26
 
4.9%
표 24
 
4.5%
김 22
 
4.2%
정 15
 
2.8%
장 14
 
2.7%
영 13
 
2.5%
박 10
 
1.9%
병 9
 
1.7%
Other values (127) 322
61.0%
Decimal Number
ValueCountFrequency (%)
1 5
71.4%
7 1
 
14.3%
0 1
 
14.3%
Space Separator
ValueCountFrequency (%)
13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 528
95.3%
Common 26
 
4.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 44
 
8.3%
사 29
 
5.5%
대 26
 
4.9%
표 24
 
4.5%
김 22
 
4.2%
정 15
 
2.8%
장 14
 
2.7%
영 13
 
2.5%
박 10
 
1.9%
병 9
 
1.7%
Other values (127) 322
61.0%
Common
ValueCountFrequency (%)
13
50.0%
1 5
 
19.2%
( 3
 
11.5%
) 3
 
11.5%
7 1
 
3.8%
0 1
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 528
95.3%
ASCII 26
 
4.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
이 44
 
8.3%
사 29
 
5.5%
대 26
 
4.9%
표 24
 
4.5%
김 22
 
4.2%
정 15
 
2.8%
장 14
 
2.7%
영 13
 
2.5%
박 10
 
1.9%
병 9
 
1.7%
Other values (127) 322
61.0%
ASCII
ValueCountFrequency (%)
13
50.0%
1 5
 
19.2%
( 3
 
11.5%
) 3
 
11.5%
7 1
 
3.8%
0 1
 
3.8%

관할기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
의정부시
148 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
의정부시 148
100.0%

Length

2023-12-13T05:19:49.683262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:19:49.777196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
의정부시 148
100.0%

폐수관리등급
Categorical

Distinct2
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
우수
99 
일반
49 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row우수
2nd row일반
3rd row일반
4th row일반
5th row일반

Common Values

ValueCountFrequency (%)
우수 99
66.9%
일반 49
33.1%

Length

2023-12-13T05:19:49.873259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:19:49.965391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
우수 99
66.9%
일반 49
33.1%

폐수종별구분명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
5종
146 
4종
 
1
3종
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique2 ?
Unique (%)1.4%

Sample

1st row5종
2nd row5종
3rd row5종
4th row5종
5th row5종

Common Values

ValueCountFrequency (%)
5종 146
98.6%
4종 1
 
0.7%
3종 1
 
0.7%

Length

2023-12-13T05:19:50.073422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:19:50.168170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5종 146
98.6%
4종 1
 
0.7%
3종 1
 
0.7%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct83
Distinct (%)60.6%
Missing11
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean11714.175
Minimum11601
Maximum11815
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T05:19:50.285588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11601
5-th percentile11606
Q111671
median11722
Q311766
95-th percentile11803.8
Maximum11815
Range214
Interquartile range (IQR)95

Descriptive statistics

Standard deviation63.38954
Coefficient of variation (CV)0.0054113533
Kurtosis-1.0814598
Mean11714.175
Median Absolute Deviation (MAD)48
Skewness-0.23487158
Sum1604842
Variance4018.2338
MonotonicityNot monotonic
2023-12-13T05:19:50.467803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11753 5
 
3.4%
11601 5
 
3.4%
11619 4
 
2.7%
11625 3
 
2.0%
11766 3
 
2.0%
11738 3
 
2.0%
11801 3
 
2.0%
11671 3
 
2.0%
11726 3
 
2.0%
11755 3
 
2.0%
Other values (73) 102
68.9%
(Missing) 11
 
7.4%
ValueCountFrequency (%)
11601 5
3.4%
11604 1
 
0.7%
11606 2
 
1.4%
11612 2
 
1.4%
11617 2
 
1.4%
11619 4
2.7%
11623 1
 
0.7%
11625 3
2.0%
11626 1
 
0.7%
11628 2
 
1.4%
ValueCountFrequency (%)
11815 2
1.4%
11814 2
1.4%
11812 1
 
0.7%
11810 1
 
0.7%
11807 1
 
0.7%
11803 2
1.4%
11801 3
2.0%
11800 1
 
0.7%
11799 1
 
0.7%
11796 1
 
0.7%

소재지지번주소
Text

Distinct147
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2023-12-13T05:19:50.839564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length32
Mean length18.72973
Min length1

Characters and Unicode

Total characters2772
Distinct characters67
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique146 ?
Unique (%)98.6%

Sample

1st row경기도 의정부시 낙양동 756
2nd row경기도 의정부시 신곡동 312-38
3rd row경기도 의정부시 가능동 675-3
4th row경기도 의정부시 의정부동 481-9
5th row경기도 의정부시 장암동 90-11
ValueCountFrequency (%)
경기도 146
24.1%
의정부시 146
24.1%
의정부동 25
 
4.1%
가능동 22
 
3.6%
금오동 18
 
3.0%
호원동 17
 
2.8%
신곡동 15
 
2.5%
용현동 11
 
1.8%
장암동 10
 
1.6%
민락동 8
 
1.3%
Other values (169) 189
31.1%
2023-12-13T05:19:51.361683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
514
18.5%
의 172
 
6.2%
정 172
 
6.2%
부 172
 
6.2%
시 147
 
5.3%
기 146
 
5.3%
도 146
 
5.3%
경 146
 
5.3%
동 146
 
5.3%
- 124
 
4.5%
Other values (57) 887
32.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1533
55.3%
Decimal Number 599
 
21.6%
Space Separator 514
 
18.5%
Dash Punctuation 124
 
4.5%
Other Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 172
11.2%
정 172
11.2%
부 172
11.2%
시 147
9.6%
기 146
9.5%
도 146
9.5%
경 146
9.5%
동 146
9.5%
가 22
 
1.4%
능 22
 
1.4%
Other values (44) 242
15.8%
Decimal Number
ValueCountFrequency (%)
1 87
14.5%
4 77
12.9%
3 75
12.5%
2 72
12.0%
5 70
11.7%
6 49
8.2%
0 46
7.7%
8 44
7.3%
9 41
6.8%
7 38
6.3%
Space Separator
ValueCountFrequency (%)
514
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 124
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1533
55.3%
Common 1239
44.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 172
11.2%
정 172
11.2%
부 172
11.2%
시 147
9.6%
기 146
9.5%
도 146
9.5%
경 146
9.5%
동 146
9.5%
가 22
 
1.4%
능 22
 
1.4%
Other values (44) 242
15.8%
Common
ValueCountFrequency (%)
514
41.5%
- 124
 
10.0%
1 87
 
7.0%
4 77
 
6.2%
3 75
 
6.1%
2 72
 
5.8%
5 70
 
5.6%
6 49
 
4.0%
0 46
 
3.7%
8 44
 
3.6%
Other values (3) 81
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1533
55.3%
ASCII 1239
44.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
514
41.5%
- 124
 
10.0%
1 87
 
7.0%
4 77
 
6.2%
3 75
 
6.1%
2 72
 
5.8%
5 70
 
5.6%
6 49
 
4.0%
0 46
 
3.7%
8 44
 
3.6%
Other values (3) 81
 
6.5%
Hangul
ValueCountFrequency (%)
의 172
11.2%
정 172
11.2%
부 172
11.2%
시 147
9.6%
기 146
9.5%
도 146
9.5%
경 146
9.5%
동 146
9.5%
가 22
 
1.4%
능 22
 
1.4%
Other values (44) 242
15.8%

소재지도로명주소
Text

MISSING 

Distinct128
Distinct (%)94.1%
Missing12
Missing (%)8.1%
Memory size1.3 KiB
2023-12-13T05:19:51.661921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length31
Mean length17.558824
Min length1

Characters and Unicode

Total characters2388
Distinct characters101
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

Unique127 ?
Unique (%)93.4%

Sample

1st row경기도 의정부시 송양로 23
2nd row경기도 의정부시 금신로 91
3rd row경기도 의정부시 의정로 179
4th row경기도 의정부시 둔야로33번길 30
5th row경기도 의정부시 동일로 207
ValueCountFrequency (%)
경기도 127
23.2%
의정부시 127
23.2%
평화로 16
 
2.9%
동일로 11
 
2.0%
호국로 10
 
1.8%
의정로 6
 
1.1%
금신로 5
 
0.9%
금오동 5
 
0.9%
장암동 4
 
0.7%
의정부동 4
 
0.7%
Other values (176) 232
42.4%
2023-12-13T05:19:52.128753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
434
18.2%
의 143
 
6.0%
정 138
 
5.8%
시 136
 
5.7%
부 134
 
5.6%
도 130
 
5.4%
경 130
 
5.4%
기 128
 
5.4%
로 127
 
5.3%
1 85
 
3.6%
Other values (91) 803
33.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1467
61.4%
Space Separator 434
 
18.2%
Decimal Number 404
 
16.9%
Close Punctuation 36
 
1.5%
Open Punctuation 36
 
1.5%
Dash Punctuation 9
 
0.4%
Other Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
의 143
9.7%
정 138
9.4%
시 136
9.3%
부 134
9.1%
도 130
 
8.9%
경 130
 
8.9%
기 128
 
8.7%
로 127
 
8.7%
동 49
 
3.3%
번 24
 
1.6%
Other values (74) 328
22.4%
Decimal Number
ValueCountFrequency (%)
1 85
21.0%
2 58
14.4%
5 41
10.1%
4 40
9.9%
3 39
9.7%
7 34
 
8.4%
9 32
 
7.9%
0 32
 
7.9%
6 31
 
7.7%
8 12
 
3.0%
Close Punctuation
ValueCountFrequency (%)
) 34
94.4%
] 2
 
5.6%
Open Punctuation
ValueCountFrequency (%)
( 34
94.4%
[ 2
 
5.6%
Space Separator
ValueCountFrequency (%)
434
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Other Punctuation
ValueCountFrequency (%)
* 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1467
61.4%
Common 921
38.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
의 143
9.7%
정 138
9.4%
시 136
9.3%
부 134
9.1%
도 130
 
8.9%
경 130
 
8.9%
기 128
 
8.7%
로 127
 
8.7%
동 49
 
3.3%
번 24
 
1.6%
Other values (74) 328
22.4%
Common
ValueCountFrequency (%)
434
47.1%
1 85
 
9.2%
2 58
 
6.3%
5 41
 
4.5%
4 40
 
4.3%
3 39
 
4.2%
) 34
 
3.7%
7 34
 
3.7%
( 34
 
3.7%
9 32
 
3.5%
Other values (7) 90
 
9.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1467
61.4%
ASCII 921
38.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
434
47.1%
1 85
 
9.2%
2 58
 
6.3%
5 41
 
4.5%
4 40
 
4.3%
3 39
 
4.2%
) 34
 
3.7%
7 34
 
3.7%
( 34
 
3.7%
9 32
 
3.5%
Other values (7) 90
 
9.8%
Hangul
ValueCountFrequency (%)
의 143
9.7%
정 138
9.4%
시 136
9.3%
부 134
9.1%
도 130
 
8.9%
경 130
 
8.9%
기 128
 
8.7%
로 127
 
8.7%
동 49
 
3.3%
번 24
 
1.6%
Other values (74) 328
22.4%

위도
Real number (ℝ)

UNIQUE 

Distinct148
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.73826
Minimum37.69152
Maximum37.770203
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T05:19:52.310260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.69152
5-th percentile37.702498
Q137.730736
median37.741888
Q337.751368
95-th percentile37.759269
Maximum37.770203
Range0.0786835
Interquartile range (IQR)0.020632323

Descriptive statistics

Standard deviation0.017447368
Coefficient of variation (CV)0.00046232572
Kurtosis0.23486103
Mean37.73826
Median Absolute Deviation (MAD)0.01018957
Skewness-0.87957977
Sum5585.2625
Variance0.00030441067
MonotonicityNot monotonic
2023-12-13T05:19:52.534229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.7523258 1
 
0.7%
37.7402901 1
 
0.7%
37.7360146 1
 
0.7%
37.7339626 1
 
0.7%
37.7356295 1
 
0.7%
37.7533196 1
 
0.7%
37.7455129 1
 
0.7%
37.7202099 1
 
0.7%
37.7392086 1
 
0.7%
37.7414325 1
 
0.7%
Other values (138) 138
93.2%
ValueCountFrequency (%)
37.6915198 1
0.7%
37.6921325 1
0.7%
37.6931006 1
0.7%
37.6951734 1
0.7%
37.6956562 1
0.7%
37.69961315 1
0.7%
37.70035688 1
0.7%
37.701737 1
0.7%
37.7039114 1
0.7%
37.7048848 1
0.7%
ValueCountFrequency (%)
37.7702033 1
0.7%
37.7686494 1
0.7%
37.76417195 1
0.7%
37.7632589 1
0.7%
37.76180601 1
0.7%
37.7601816 1
0.7%
37.760036 1
0.7%
37.75939836 1
0.7%
37.7590286 1
0.7%
37.75865061 1
0.7%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct148
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.05884
Minimum127.0101
Maximum127.11853
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2023-12-13T05:19:52.741169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum127.0101
5-th percentile127.023
Q1127.04275
median127.05317
Q3127.07675
95-th percentile127.10565
Maximum127.11853
Range0.108434
Interquartile range (IQR)0.033992425

Descriptive statistics

Standard deviation0.024101473
Coefficient of variation (CV)0.00018968749
Kurtosis-0.28184376
Mean127.05884
Median Absolute Deviation (MAD)0.01461275
Skewness0.58735782
Sum18804.709
Variance0.00058088099
MonotonicityNot monotonic
2023-12-13T05:19:53.243802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.1086821 1
 
0.7%
127.0209662 1
 
0.7%
127.0415443 1
 
0.7%
127.0585937 1
 
0.7%
127.0751012 1
 
0.7%
127.067459 1
 
0.7%
127.034265 1
 
0.7%
127.0470484 1
 
0.7%
127.0853153 1
 
0.7%
127.0873486 1
 
0.7%
Other values (138) 138
93.2%
ValueCountFrequency (%)
127.0100971 1
0.7%
127.018258 1
0.7%
127.0196306 1
0.7%
127.0201866 1
0.7%
127.0205594 1
0.7%
127.0209368 1
0.7%
127.0209662 1
0.7%
127.0224814 1
0.7%
127.0239619 1
0.7%
127.0256301 1
0.7%
ValueCountFrequency (%)
127.1185311 1
0.7%
127.11828 1
0.7%
127.1156764 1
0.7%
127.1126165 1
0.7%
127.111954 1
0.7%
127.1086821 1
0.7%
127.106313 1
0.7%
127.1060307 1
0.7%
127.1049428 1
0.7%
127.1038473 1
0.7%

Interactions

2023-12-13T05:19:47.252496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:19:46.684945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:19:46.967208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:19:47.332478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:19:46.766482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:19:47.072235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:19:47.435508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:19:46.864678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:19:47.165873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:19:53.374673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종명폐수관리등급폐수종별구분명소재지우편번호위도경도
업종명1.0000.5470.8320.1420.5340.527
폐수관리등급0.5471.0000.0720.0000.1810.000
폐수종별구분명0.8320.0721.0000.0000.0000.000
소재지우편번호0.1420.0000.0001.0000.8050.877
위도0.5340.1810.0000.8051.0000.497
경도0.5270.0000.0000.8770.4971.000
2023-12-13T05:19:53.508538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
폐수관리등급폐수종별구분명업종명
폐수관리등급1.0000.1190.411
폐수종별구분명0.1191.0000.650
업종명0.4110.6501.000
2023-12-13T05:19:53.602030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지우편번호위도경도업종명폐수관리등급폐수종별구분명
소재지우편번호1.000-0.0190.9270.1040.0000.000
위도-0.0191.0000.0150.2540.1330.000
경도0.9270.0151.0000.2500.0000.000
업종명0.1040.2540.2501.0000.4110.650
폐수관리등급0.0000.1330.0000.4111.0000.119
폐수종별구분명0.0000.0000.0000.6500.1191.000

Missing values

2023-12-13T05:19:47.564987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:19:47.717033image/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.
2023-12-13T05:19:47.871245image/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

시군명사업장명업종명대표자명관할기관명폐수관리등급폐수종별구분명소재지우편번호소재지지번주소소재지도로명주소위도경도
0의정부시오누이손세차장기타김국화의정부시우수5종11812경기도 의정부시 낙양동 756경기도 의정부시 송양로 2337.752326127.108682
1의정부시흥산에너지기타신현근의정부시일반5종11738경기도 의정부시 신곡동 312-38경기도 의정부시 금신로 9137.729131127.063286
2의정부시제이모터스기타유진원의정부시일반5종11675경기도 의정부시 가능동 675-3경기도 의정부시 의정로 17937.748846127.03413
3의정부시한신세차장기타한필우의정부시일반5종11673경기도 의정부시 의정부동 481-9경기도 의정부시 둔야로33번길 3037.738989127.037983
4의정부시훼미리제2주유소기타김경미 김범수의정부시일반5종11723경기도 의정부시 장암동 90-11경기도 의정부시 동일로 20737.707376127.057542
5의정부시한주모터스기타한현오의정부시우수5종11698경기도 의정부시 의정부동 97-7<NA>37.734849127.049734
6의정부시신진세차장기타박영길의정부시우수5종11693경기도 의정부시 의정부동 19-2<NA>37.743678127.052735
7의정부시(주)대원여객기타허상준의정부시일반5종11685경기도 의정부시 가능동 76-4경기도 의정부시 평화로 69237.754015127.043732
8의정부시한전세차장기타김일웅의정부시일반5종11689경기도 의정부시 의정부동 215-60경기도 의정부시 가능로 16437.746823127.049458
9의정부시팔광세차장기타하은주의정부시우수5종11672경기도 의정부시 가능동 742-3경기도 의정부시 의정로 11037.742594127.034933
시군명사업장명업종명대표자명관할기관명폐수관리등급폐수종별구분명소재지우편번호소재지지번주소소재지도로명주소위도경도
138의정부시포커스온디테일링자동차 세차업고아라의정부시일반5종11628경기도 의정부시 의정부동 604경기도 의정부시 범골로 70 101호 (의정부동)37.731139127.038629
139의정부시서울교통공사 도봉차량사업소도시철도 운송업사장의정부시우수5종11723경기도 의정부시 장암동 168경기도 의정부시 서계로 42 (장암동 서울특별시도시철도공사도봉차량기지)37.705176127.055614
140의정부시장암전기차충전소자동차 세차업신호철의정부시일반5종11722경기도 의정부시 장암동 산 15-11경기도 의정부시 동일로 377 (장암동)37.722394127.060417
141의정부시호원동전기차충전소자동차 세차업이지홍의정부시일반5종<NA>경기도 의정부시 호원동 146-137.700357127.046824
142의정부시신한대학교총장의정부시일반5종11644경기도 의정부시 호원동 산 101-1 신한대학교경기도 의정부시 호암로 95 신한대학교 (호원동)37.711108127.044878
143의정부시주식회사 에스앤케이컴퍼니<NA>신경철의정부시일반5종11801경기도 의정부시 고산동 989-4경기도 의정부시 문충로 106 (고산동)37.73009127.106313
144의정부시(주)대련건설<NA>대표이사의정부시일반5종<NA>경기도 의정부시 장암동 9137.711076127.058122
145의정부시(주)대련건설<NA>대표이사의정부시일반3종<NA>경기도 의정부시 장암동 산 111-937.708911127.059322
146의정부시워시존EV스테이션<NA>조병헌의정부시일반5종<NA>경기도 의정부시 고산동 315-137.738429127.111954
147의정부시엠카워시<NA>맹수영의정부시일반5종11738경기도 의정부시 신곡동 353-103경기도 의정부시 동일로 490 (신곡동)37.731956127.059086