Overview

Dataset statistics

Number of variables20
Number of observations109
Missing cells676
Missing cells (%)31.0%
Duplicate rows1
Duplicate rows (%)0.9%
Total size in memory18.3 KiB
Average record size in memory172.2 B

Variable types

Categorical4
Text4
DateTime2
Unsupported3
Numeric7

Alerts

Dataset has 1 (0.9%) duplicate rowsDuplicates
영업상태명 is highly overall correlated with 전문인력총수 and 1 other fieldsHigh correlation
영업상태구분코드 is highly overall correlated with 전문인력총수 and 1 other fieldsHigh correlation
도로명우편번호 is highly overall correlated with 소재지우편번호 and 1 other fieldsHigh correlation
소재지우편번호 is highly overall correlated with 도로명우편번호 and 3 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
WGS84경도 is highly overall correlated with X좌표값 and 1 other fieldsHigh correlation
X좌표값 is highly overall correlated with WGS84경도 and 1 other fieldsHigh correlation
Y좌표값 is highly overall correlated with 소재지우편번호 and 2 other fieldsHigh correlation
전문인력총수 is highly overall correlated with 영업상태구분코드 and 1 other fieldsHigh correlation
시군명 is highly overall correlated with 도로명우편번호 and 5 other fieldsHigh correlation
인허가일자 has 4 (3.7%) missing valuesMissing
인허가취소일자 has 85 (78.0%) missing valuesMissing
소재지시설전화번호 has 109 (100.0%) missing valuesMissing
소재지면적정보 has 109 (100.0%) missing valuesMissing
도로명우편번호 has 73 (67.0%) missing valuesMissing
소재지도로명주소 has 7 (6.4%) missing valuesMissing
소재지지번주소 has 2 (1.8%) missing valuesMissing
소재지우편번호 has 5 (4.6%) missing valuesMissing
업태구분명정보 has 109 (100.0%) missing valuesMissing
X좌표값 has 9 (8.3%) missing valuesMissing
Y좌표값 has 9 (8.3%) missing valuesMissing
전문인력총수 has 66 (60.6%) missing valuesMissing
시설장비 has 87 (79.8%) missing valuesMissing
소재지시설전화번호 is an unsupported type, check if it needs cleaning or further analysisUnsupported
소재지면적정보 is an unsupported type, check if it needs cleaning or further analysisUnsupported
업태구분명정보 is an unsupported type, check if it needs cleaning or further analysisUnsupported
전문인력총수 has 18 (16.5%) zerosZeros

Reproduction

Analysis started2023-12-10 21:48:50.818018
Analysis finished2023-12-10 21:48:56.461399
Duration5.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Memory size1004.0 B
성남시
34 
<NA>
21 
안양시
17 
군포시
4 
시흥시
4 
Other values (11)
29 

Length

Max length4
Median length3
Mean length3.2110092
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row고양시
2nd row고양시
3rd row고양시
4th row과천시
5th row과천시

Common Values

ValueCountFrequency (%)
성남시 34
31.2%
<NA> 21
19.3%
안양시 17
15.6%
군포시 4
 
3.7%
시흥시 4
 
3.7%
여주시 4
 
3.7%
포천시 4
 
3.7%
고양시 3
 
2.8%
양주시 3
 
2.8%
하남시 3
 
2.8%
Other values (6) 12
 
11.0%

Length

2023-12-11T06:48:56.522174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
성남시 34
31.2%
na 21
19.3%
안양시 17
15.6%
군포시 4
 
3.7%
시흥시 4
 
3.7%
여주시 4
 
3.7%
포천시 4
 
3.7%
고양시 3
 
2.8%
양주시 3
 
2.8%
하남시 3
 
2.8%
Other values (6) 12
 
11.0%

사업장명
Text

Distinct57
Distinct (%)52.3%
Missing0
Missing (%)0.0%
Memory size1004.0 B
2023-12-11T06:48:56.778625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length8.6330275
Min length4

Characters and Unicode

Total characters941
Distinct characters112
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)16.5%

Sample

1st row(주)케이디이엔지
2nd row(주)태영건설
3rd row(주)태영건설
4th row코오롱글로벌(주)
5th row코오롱글로벌(주)
ValueCountFrequency (%)
주식회사 9
 
7.6%
주)한국종합기술 5
 
4.2%
아름다운환경건설(주 4
 
3.4%
주)효림 4
 
3.4%
주)이데아이엔에스 4
 
3.4%
주)한맥이앤씨 4
 
3.4%
현대건설(주 3
 
2.5%
주)에코필 3
 
2.5%
주)태영건설 2
 
1.7%
성지엔지니어링 2
 
1.7%
Other values (48) 78
66.1%
2023-12-11T06:48:57.392514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 98
 
10.4%
( 87
 
9.2%
) 87
 
9.2%
이 44
 
4.7%
지 32
 
3.4%
엔 29
 
3.1%
에 27
 
2.9%
설 21
 
2.2%
건 19
 
2.0%
링 19
 
2.0%
Other values (102) 478
50.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 751
79.8%
Open Punctuation 87
 
9.2%
Close Punctuation 87
 
9.2%
Space Separator 9
 
1.0%
Other Symbol 5
 
0.5%
Dash Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 98
 
13.0%
이 44
 
5.9%
지 32
 
4.3%
엔 29
 
3.9%
에 27
 
3.6%
설 21
 
2.8%
건 19
 
2.5%
링 19
 
2.5%
어 19
 
2.5%
니 19
 
2.5%
Other values (97) 424
56.5%
Open Punctuation
ValueCountFrequency (%)
( 87
100.0%
Close Punctuation
ValueCountFrequency (%)
) 87
100.0%
Space Separator
ValueCountFrequency (%)
9
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 756
80.3%
Common 185
 
19.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 98
 
13.0%
이 44
 
5.8%
지 32
 
4.2%
엔 29
 
3.8%
에 27
 
3.6%
설 21
 
2.8%
건 19
 
2.5%
링 19
 
2.5%
어 19
 
2.5%
니 19
 
2.5%
Other values (98) 429
56.7%
Common
ValueCountFrequency (%)
( 87
47.0%
) 87
47.0%
9
 
4.9%
- 2
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 751
79.8%
ASCII 185
 
19.7%
None 5
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 98
 
13.0%
이 44
 
5.9%
지 32
 
4.3%
엔 29
 
3.9%
에 27
 
3.6%
설 21
 
2.8%
건 19
 
2.5%
링 19
 
2.5%
어 19
 
2.5%
니 19
 
2.5%
Other values (97) 424
56.5%
ASCII
ValueCountFrequency (%)
( 87
47.0%
) 87
47.0%
9
 
4.9%
- 2
 
1.1%
None
ValueCountFrequency (%)
㈜ 5
100.0%

인허가일자
Date

MISSING 

Distinct51
Distinct (%)48.6%
Missing4
Missing (%)3.7%
Memory size1004.0 B
Minimum2003-11-07 00:00:00
Maximum2023-01-03 00:00:00
2023-12-11T06:48:57.537616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:57.660209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Date

MISSING 

Distinct16
Distinct (%)66.7%
Missing85
Missing (%)78.0%
Memory size1004.0 B
Minimum2008-03-25 00:00:00
Maximum2022-11-07 00:00:00
2023-12-11T06:48:57.771479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:57.883295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)

영업상태구분코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1004.0 B
1
85 
2
24 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 85
78.0%
2 24
 
22.0%

Length

2023-12-11T06:48:57.983243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:48:58.070762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 85
78.0%
2 24
 
22.0%

영업상태명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1004.0 B
영업
85 
취소정지업체
24 

Length

Max length6
Median length2
Mean length2.8807339
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row영업
2nd row취소정지업체
3rd row취소정지업체
4th row취소정지업체
5th row취소정지업체

Common Values

ValueCountFrequency (%)
영업 85
78.0%
취소정지업체 24
 
22.0%

Length

2023-12-11T06:48:58.169654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:48:58.263151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
영업 85
78.0%
취소정지업체 24
 
22.0%

소재지시설전화번호
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing109
Missing (%)100.0%
Memory size1.1 KiB

소재지면적정보
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing109
Missing (%)100.0%
Memory size1.1 KiB

도로명우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)61.1%
Missing73
Missing (%)67.0%
Infinite0
Infinite (%)0.0%
Mean95944.056
Minimum10237
Maximum463400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T06:48:58.350601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10237
5-th percentile11183
Q113422.25
median13639
Q315281
95-th percentile451025.75
Maximum463400
Range453163
Interquartile range (IQR)1858.75

Descriptive statistics

Standard deviation164685.99
Coefficient of variation (CV)1.7164793
Kurtosis0.99355743
Mean95944.056
Median Absolute Deviation (MAD)455
Skewness1.6614125
Sum3453986
Variance2.7121474 × 1010
MonotonicityNot monotonic
2023-12-11T06:48:58.460872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
13901 4
 
3.7%
200956 2
 
1.8%
13591 2
 
1.8%
13207 2
 
1.8%
13161 2
 
1.8%
13503 2
 
1.8%
463400 2
 
1.8%
13631 2
 
1.8%
11183 2
 
1.8%
427709 2
 
1.8%
Other values (12) 14
 
12.8%
(Missing) 73
67.0%
ValueCountFrequency (%)
10237 1
0.9%
11183 2
1.8%
12767 1
0.9%
12927 1
0.9%
13161 2
1.8%
13207 2
1.8%
13494 1
0.9%
13503 2
1.8%
13517 2
1.8%
13591 2
1.8%
ValueCountFrequency (%)
463400 2
1.8%
446901 2
1.8%
427709 2
1.8%
200956 2
1.8%
15809 1
 
0.9%
15105 1
 
0.9%
14959 1
 
0.9%
13989 1
 
0.9%
13930 1
 
0.9%
13901 4
3.7%

소재지도로명주소
Text

MISSING 

Distinct61
Distinct (%)59.8%
Missing7
Missing (%)6.4%
Memory size1004.0 B
2023-12-11T06:48:58.708530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length62
Median length40
Mean length31.627451
Min length16

Characters and Unicode

Total characters3226
Distinct characters210
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

Unique24 ?
Unique (%)23.5%

Sample

1st row경기도 고양시 일산서구 덕이로 30-28, 양우씨네플렉스 2층 제3에이치-202호 (덕이동)
2nd row경기도 고양시 일산동구 정발산로 24 (장항동)
3rd row경기도 고양시 일산동구 정발산로 24 (장항동)
4th row경기도 과천시 코오롱로 11 (별양동)
5th row경기도 과천시 코오롱로 11 (별양동)
ValueCountFrequency (%)
경기도 83
 
12.6%
성남시 33
 
5.0%
분당구 21
 
3.2%
안양시 16
 
2.4%
서울특별시 14
 
2.1%
중원구 10
 
1.5%
동안구 9
 
1.4%
만안구 7
 
1.1%
75 7
 
1.1%
124 6
 
0.9%
Other values (217) 454
68.8%
2023-12-11T06:48:59.102223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
568
 
17.6%
동 115
 
3.6%
로 113
 
3.5%
시 111
 
3.4%
1 110
 
3.4%
기 95
 
2.9%
) 90
 
2.8%
( 90
 
2.8%
도 87
 
2.7%
경 85
 
2.6%
Other values (200) 1762
54.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1870
58.0%
Space Separator 568
 
17.6%
Decimal Number 495
 
15.3%
Close Punctuation 90
 
2.8%
Open Punctuation 90
 
2.8%
Other Punctuation 65
 
2.0%
Lowercase Letter 18
 
0.6%
Uppercase Letter 17
 
0.5%
Dash Punctuation 13
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 115
 
6.1%
로 113
 
6.0%
시 111
 
5.9%
기 95
 
5.1%
도 87
 
4.7%
경 85
 
4.5%
구 79
 
4.2%
양 48
 
2.6%
안 46
 
2.5%
성 39
 
2.1%
Other values (172) 1052
56.3%
Decimal Number
ValueCountFrequency (%)
1 110
22.2%
2 78
15.8%
0 62
12.5%
4 53
10.7%
5 49
9.9%
6 43
 
8.7%
3 33
 
6.7%
8 29
 
5.9%
7 23
 
4.6%
9 15
 
3.0%
Lowercase Letter
ValueCountFrequency (%)
r 4
22.2%
w 2
11.1%
i 2
11.1%
s 2
11.1%
t 2
11.1%
o 2
11.1%
e 2
11.1%
n 2
11.1%
Uppercase Letter
ValueCountFrequency (%)
S 6
35.3%
K 6
35.3%
F 2
 
11.8%
T 2
 
11.8%
D 1
 
5.9%
Space Separator
ValueCountFrequency (%)
568
100.0%
Close Punctuation
ValueCountFrequency (%)
) 90
100.0%
Open Punctuation
ValueCountFrequency (%)
( 90
100.0%
Other Punctuation
ValueCountFrequency (%)
, 65
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1870
58.0%
Common 1321
40.9%
Latin 35
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 115
 
6.1%
로 113
 
6.0%
시 111
 
5.9%
기 95
 
5.1%
도 87
 
4.7%
경 85
 
4.5%
구 79
 
4.2%
양 48
 
2.6%
안 46
 
2.5%
성 39
 
2.1%
Other values (172) 1052
56.3%
Common
ValueCountFrequency (%)
568
43.0%
1 110
 
8.3%
) 90
 
6.8%
( 90
 
6.8%
2 78
 
5.9%
, 65
 
4.9%
0 62
 
4.7%
4 53
 
4.0%
5 49
 
3.7%
6 43
 
3.3%
Other values (5) 113
 
8.6%
Latin
ValueCountFrequency (%)
S 6
17.1%
K 6
17.1%
r 4
11.4%
w 2
 
5.7%
F 2
 
5.7%
i 2
 
5.7%
s 2
 
5.7%
t 2
 
5.7%
T 2
 
5.7%
o 2
 
5.7%
Other values (3) 5
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1870
58.0%
ASCII 1356
42.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
568
41.9%
1 110
 
8.1%
) 90
 
6.6%
( 90
 
6.6%
2 78
 
5.8%
, 65
 
4.8%
0 62
 
4.6%
4 53
 
3.9%
5 49
 
3.6%
6 43
 
3.2%
Other values (18) 148
 
10.9%
Hangul
ValueCountFrequency (%)
동 115
 
6.1%
로 113
 
6.0%
시 111
 
5.9%
기 95
 
5.1%
도 87
 
4.7%
경 85
 
4.5%
구 79
 
4.2%
양 48
 
2.6%
안 46
 
2.5%
성 39
 
2.1%
Other values (172) 1052
56.3%

소재지지번주소
Text

MISSING 

Distinct74
Distinct (%)69.2%
Missing2
Missing (%)1.8%
Memory size1004.0 B
2023-12-11T06:48:59.424793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length39
Mean length26.831776
Min length6

Characters and Unicode

Total characters2871
Distinct characters190
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

Unique45 ?
Unique (%)42.1%

Sample

1st row경기도 고양시 일산서구 덕이동 249-8 양우씨네플렉스
2nd row경기도 고양시 일산서구 대화동 868번지
3rd row경기도 고양시 일산서구 대화동 868번지
4th row경기도 과천시 별양동 1-23번지
5th row경기도 과천시 별양동 1-23
ValueCountFrequency (%)
경기도 83
 
13.9%
성남시 32
 
5.4%
분당구 21
 
3.5%
안양시 16
 
2.7%
서울특별시 12
 
2.0%
중원구 11
 
1.8%
동안구 10
 
1.7%
관양동 8
 
1.3%
상대원동 7
 
1.2%
만안구 6
 
1.0%
Other values (202) 390
65.4%
2023-12-11T06:48:59.844383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
515
 
17.9%
동 115
 
4.0%
시 111
 
3.9%
1 100
 
3.5%
도 95
 
3.3%
기 89
 
3.1%
경 87
 
3.0%
구 78
 
2.7%
지 78
 
2.7%
- 77
 
2.7%
Other values (180) 1526
53.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1704
59.4%
Space Separator 515
 
17.9%
Decimal Number 513
 
17.9%
Dash Punctuation 77
 
2.7%
Lowercase Letter 24
 
0.8%
Uppercase Letter 20
 
0.7%
Open Punctuation 8
 
0.3%
Close Punctuation 8
 
0.3%
Other Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 115
 
6.7%
시 111
 
6.5%
도 95
 
5.6%
기 89
 
5.2%
경 87
 
5.1%
구 78
 
4.6%
지 78
 
4.6%
번 77
 
4.5%
남 40
 
2.3%
안 39
 
2.3%
Other values (151) 895
52.5%
Decimal Number
ValueCountFrequency (%)
1 100
19.5%
2 70
13.6%
8 52
10.1%
0 50
9.7%
4 47
9.2%
9 43
8.4%
7 41
8.0%
5 39
 
7.6%
3 36
 
7.0%
6 35
 
6.8%
Lowercase Letter
ValueCountFrequency (%)
r 4
16.7%
i 4
16.7%
e 4
16.7%
t 2
8.3%
s 2
8.3%
o 2
8.3%
w 2
8.3%
z 2
8.3%
n 2
8.3%
Uppercase Letter
ValueCountFrequency (%)
S 8
40.0%
K 6
30.0%
T 2
 
10.0%
F 2
 
10.0%
B 2
 
10.0%
Space Separator
ValueCountFrequency (%)
515
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 77
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1704
59.4%
Common 1123
39.1%
Latin 44
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 115
 
6.7%
시 111
 
6.5%
도 95
 
5.6%
기 89
 
5.2%
경 87
 
5.1%
구 78
 
4.6%
지 78
 
4.6%
번 77
 
4.5%
남 40
 
2.3%
안 39
 
2.3%
Other values (151) 895
52.5%
Common
ValueCountFrequency (%)
515
45.9%
1 100
 
8.9%
- 77
 
6.9%
2 70
 
6.2%
8 52
 
4.6%
0 50
 
4.5%
4 47
 
4.2%
9 43
 
3.8%
7 41
 
3.7%
5 39
 
3.5%
Other values (5) 89
 
7.9%
Latin
ValueCountFrequency (%)
S 8
18.2%
K 6
13.6%
r 4
9.1%
i 4
9.1%
e 4
9.1%
T 2
 
4.5%
t 2
 
4.5%
s 2
 
4.5%
o 2
 
4.5%
w 2
 
4.5%
Other values (4) 8
18.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1704
59.4%
ASCII 1167
40.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
515
44.1%
1 100
 
8.6%
- 77
 
6.6%
2 70
 
6.0%
8 52
 
4.5%
0 50
 
4.3%
4 47
 
4.0%
9 43
 
3.7%
7 41
 
3.5%
5 39
 
3.3%
Other values (19) 133
 
11.4%
Hangul
ValueCountFrequency (%)
동 115
 
6.7%
시 111
 
6.5%
도 95
 
5.6%
기 89
 
5.2%
경 87
 
5.1%
구 78
 
4.6%
지 78
 
4.6%
번 77
 
4.5%
남 40
 
2.3%
안 39
 
2.3%
Other values (151) 895
52.5%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct48
Distinct (%)46.2%
Missing5
Missing (%)4.6%
Infinite0
Infinite (%)0.0%
Mean14458.067
Minimum3058
Maximum58140
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T06:49:00.011872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3058
5-th percentile4799
Q112611.5
median13591
Q314056
95-th percentile27178
Maximum58140
Range55082
Interquartile range (IQR)1444.5

Descriptive statistics

Standard deviation8630.4958
Coefficient of variation (CV)0.59693288
Kurtosis13.485705
Mean14458.067
Median Absolute Deviation (MAD)501
Skewness3.2661124
Sum1503639
Variance74485458
MonotonicityNot monotonic
2023-12-11T06:49:00.179020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
13591 7
 
6.4%
13207 6
 
5.5%
14056 5
 
4.6%
13161 4
 
3.7%
13901 4
 
3.7%
13503 4
 
3.7%
13631 4
 
3.7%
3058 3
 
2.8%
13494 3
 
2.8%
11431 3
 
2.8%
Other values (38) 61
56.0%
(Missing) 5
 
4.6%
ValueCountFrequency (%)
3058 3
2.8%
3182 2
1.8%
4799 2
1.8%
5288 1
 
0.9%
5610 2
1.8%
5703 2
1.8%
10237 1
 
0.9%
10403 2
1.8%
11168 2
1.8%
11183 2
1.8%
ValueCountFrequency (%)
58140 2
1.8%
44962 1
0.9%
37863 2
1.8%
27178 2
1.8%
21315 1
0.9%
17423 2
1.8%
17086 2
1.8%
15828 1
0.9%
15809 1
0.9%
15588 2
1.8%

WGS84위도
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)62.0%
Missing1
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean37.356953
Minimum35.049476
Maximum37.840733
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T06:49:00.352278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.049476
5-th percentile37.087079
Q137.379504
median37.407237
Q337.500127
95-th percentile37.810118
Maximum37.840733
Range2.7912567
Interquartile range (IQR)0.1206234

Descriptive statistics

Standard deviation0.44488269
Coefficient of variation (CV)0.011908966
Kurtosis16.253932
Mean37.356953
Median Absolute Deviation (MAD)0.046400794
Skewness-3.8185263
Sum4034.5509
Variance0.19792061
MonotonicityNot monotonic
2023-12-11T06:49:00.509513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.3856187 4
 
3.7%
37.439938 4
 
3.7%
37.4118636 3
 
2.8%
37.4536381 3
 
2.8%
37.4013668 3
 
2.8%
37.0870794 2
 
1.8%
37.5468173812 2
 
1.8%
37.8405845 2
 
1.8%
37.2907869 2
 
1.8%
37.4014178 2
 
1.8%
Other values (57) 81
74.3%
ValueCountFrequency (%)
35.0494763 2
1.8%
35.4729665726 1
0.9%
35.9952127 2
1.8%
37.0870794 2
1.8%
37.1356902 2
1.8%
37.1855528 1
0.9%
37.1855528188 1
0.9%
37.227796 2
1.8%
37.2907869 2
1.8%
37.3310387027 1
0.9%
ValueCountFrequency (%)
37.8407330338 1
0.9%
37.8405845 2
1.8%
37.838167 2
1.8%
37.8101178 2
1.8%
37.7369200549 1
0.9%
37.7368476 1
0.9%
37.6905125364 1
0.9%
37.6546825 2
1.8%
37.5783414 2
1.8%
37.5780155764 1
0.9%

WGS84경도
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)62.0%
Missing1
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean127.15302
Minimum126.72074
Maximum129.37362
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T06:49:00.647873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.72074
5-th percentile126.78123
Q1126.96737
median127.10543
Q3127.14782
95-th percentile127.65926
Maximum129.37362
Range2.6528759
Interquartile range (IQR)0.1804553

Descriptive statistics

Standard deviation0.43779794
Coefficient of variation (CV)0.0034430793
Kurtosis15.762825
Mean127.15302
Median Absolute Deviation (MAD)0.090625164
Skewness3.7408057
Sum13732.526
Variance0.19166704
MonotonicityNot monotonic
2023-12-11T06:49:00.783463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.1256555 4
 
3.7%
127.1776552 4
 
3.7%
127.1431499 3
 
2.8%
127.1614011 3
 
2.8%
127.1086047 3
 
2.8%
127.5895874 2
 
1.8%
127.0632539522 2
 
1.8%
127.0145151 2
 
1.8%
126.8298526 2
 
1.8%
126.9676447 2
 
1.8%
Other values (57) 81
74.3%
ValueCountFrequency (%)
126.7207418087 1
0.9%
126.7335440083 1
0.9%
126.7574659164 1
0.9%
126.7698050701 1
0.9%
126.7726001 2
1.8%
126.7972476 2
1.8%
126.8298526 2
1.8%
126.8908893475 2
1.8%
126.8911007 2
1.8%
126.9138435 1
0.9%
ValueCountFrequency (%)
129.3736177 2
1.8%
129.2195902859 1
 
0.9%
128.2249782 2
1.8%
127.6593462293 1
 
0.9%
127.6591049 1
 
0.9%
127.5895874 2
1.8%
127.5866453 2
1.8%
127.2076953235 1
 
0.9%
127.1946579 2
1.8%
127.1776552 4
3.7%

업태구분명정보
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing109
Missing (%)100.0%
Memory size1.1 KiB

X좌표값
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct50
Distinct (%)50.0%
Missing9
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean214421.87
Minimum170022.52
Maximum413979.51
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T06:49:00.914601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum170022.52
5-th percentile179513.66
Q1197467.34
median209379.03
Q3214216.3
95-th percentile260849.28
Maximum413979.51
Range243956.99
Interquartile range (IQR)16748.951

Descriptive statistics

Standard deviation40992.792
Coefficient of variation (CV)0.19117822
Kurtosis14.361828
Mean214421.87
Median Absolute Deviation (MAD)7961.3412
Skewness3.5564001
Sum21442187
Variance1.680409 × 109
MonotonicityNot monotonic
2023-12-11T06:49:01.044492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
215657.504440664 7
 
6.4%
211062.652375892 5
 
4.6%
214216.296234676 4
 
3.7%
212608.265238111 4
 
3.7%
209271.991810963 3
 
2.8%
197069.82356942 3
 
2.8%
198817.289034611 3
 
2.8%
201214.837030951 3
 
2.8%
209549.874547889 3
 
2.8%
196988.166986686 2
 
1.8%
Other values (40) 63
57.8%
(Missing) 9
 
8.3%
ValueCountFrequency (%)
170022.51843639 2
1.8%
175244.313461602 1
0.9%
176326.365355399 1
0.9%
178538.286614895 1
0.9%
179565.0 1
0.9%
179791.0 2
1.8%
181992.191375186 2
1.8%
184846.499195767 2
1.8%
190294.347420973 1
0.9%
192303.0 1
0.9%
ValueCountFrequency (%)
413979.509031068 2
 
1.8%
401395.900909949 1
 
0.9%
308780.974753 2
 
1.8%
258326.559760199 2
 
1.8%
252356.26073287 2
 
1.8%
251966.536321 2
 
1.8%
218281.159611217 1
 
0.9%
217137.519410187 2
 
1.8%
215657.504440664 7
6.4%
215423.76462869 1
 
0.9%

Y좌표값
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct50
Distinct (%)50.0%
Missing9
Missing (%)8.3%
Infinite0
Infinite (%)0.0%
Mean428551.61
Minimum172284.96
Maximum482011.36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T06:49:01.175546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum172284.96
5-th percentile392605.63
Q1430946.63
median434230.63
Q3444994.75
95-th percentile478789.95
Maximum482011.36
Range309726.4
Interquartile range (IQR)14048.113

Descriptive statistics

Standard deviation50980.021
Coefficient of variation (CV)0.11895888
Kurtosis15.196033
Mean428551.61
Median Absolute Deviation (MAD)4844.0607
Skewness-3.7150217
Sum42855161
Variance2.5989625 × 109
MonotonicityNot monotonic
2023-12-11T06:49:01.312187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
437557.61664539 7
 
6.4%
431520.433354292 5
 
4.6%
439074.691838595 4
 
3.7%
434433.262207739 4
 
3.7%
426903.175177001 3
 
2.8%
433267.205928793 3
 
2.8%
452901.786745273 3
 
2.8%
482011.361282707 3
 
2.8%
433248.085400252 3
 
2.8%
433309.061488993 2
 
1.8%
Other values (40) 63
57.8%
(Missing) 9
 
8.3%
ValueCountFrequency (%)
172284.95866 2
1.8%
221527.673646137 1
 
0.9%
279815.884287316 2
1.8%
398541.930193233 2
1.8%
404475.485002 2
1.8%
414003.385854003 2
1.8%
421001.454559559 2
1.8%
425463.57662942 1
 
0.9%
426903.175177001 3
2.8%
428747.814108026 2
1.8%
ValueCountFrequency (%)
482011.361282707 3
2.8%
481752.979335302 2
1.8%
478634.0 2
1.8%
470500.550673177 2
1.8%
465372.877636637 1
 
0.9%
461597.0 2
1.8%
452901.786745273 3
2.8%
452039.262262313 2
1.8%
450158.241480494 2
1.8%
449964.900329853 1
 
0.9%

전문인력총수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct6
Distinct (%)14.0%
Missing66
Missing (%)60.6%
Infinite0
Infinite (%)0.0%
Mean3.7674419
Minimum0
Maximum18
Zeros18
Zeros (%)16.5%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-11T06:49:01.425893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5
Q37
95-th percentile7
Maximum18
Range18
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.7912004
Coefficient of variation (CV)1.0063063
Kurtosis2.9058849
Mean3.7674419
Median Absolute Deviation (MAD)2
Skewness1.0850092
Sum162
Variance14.3732
MonotonicityNot monotonic
2023-12-11T06:49:01.516695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 18
 
16.5%
5 12
 
11.0%
7 10
 
9.2%
6 1
 
0.9%
18 1
 
0.9%
8 1
 
0.9%
(Missing) 66
60.6%
ValueCountFrequency (%)
0 18
16.5%
5 12
11.0%
6 1
 
0.9%
7 10
9.2%
8 1
 
0.9%
18 1
 
0.9%
ValueCountFrequency (%)
18 1
 
0.9%
8 1
 
0.9%
7 10
9.2%
6 1
 
0.9%
5 12
11.0%
0 18
16.5%

시설장비
Text

MISSING 

Distinct21
Distinct (%)95.5%
Missing87
Missing (%)79.8%
Memory size1004.0 B
2023-12-11T06:49:01.707253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length110
Median length67
Mean length65.318182
Min length28

Characters and Unicode

Total characters1437
Distinct characters125
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

Unique20 ?
Unique (%)90.9%

Sample

1st row휘발성유기화합물질(VOC) 측정기 산소 측정기 이산화탄소 측정기 메탄가스 측정기 수소이온농도계, 산화환원전위 전기전도도 용존산소, 수온 지하수위 측정기
2nd row수위측정기(WL50M) 1대, 수질측정기(YK2001MULTI) 1식, 가스크로마토그래피(HP-6890serise GC/HP5973MSD, Tekmar AQUA Tek70) -한국환경수도연구원 임대
3rd row지하수 수위측정장비, PH.수온.전기전도도.DO.Eh측정장비, VOC측정용 가스크로마토그라프 장비
4th row지하수 수위측정장비. 수소이온농도, 전기전도도, 용존산소, 가스 크로마트그래프장비,
5th row지하수의 수위측정장비 1식, 수질측정장비 1식, 가스크로마토그래프 1식0
ValueCountFrequency (%)
측정기 7
 
4.4%
수위측정장비 7
 
4.4%
전기전도도 5
 
3.1%
장비 4
 
2.5%
지하수의 4
 
2.5%
지하수 4
 
2.5%
가스크로마토그래프 4
 
2.5%
측정장비 4
 
2.5%
1식 3
 
1.9%
용존산소 3
 
1.9%
Other values (90) 115
71.9%
2023-12-11T06:49:01.995925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
138
 
9.6%
, 80
 
5.6%
수 67
 
4.7%
측 53
 
3.7%
정 52
 
3.6%
기 41
 
2.9%
도 33
 
2.3%
전 33
 
2.3%
( 32
 
2.2%
) 32
 
2.2%
Other values (115) 876
61.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 786
54.7%
Uppercase Letter 142
 
9.9%
Space Separator 138
 
9.6%
Lowercase Letter 130
 
9.0%
Other Punctuation 86
 
6.0%
Decimal Number 80
 
5.6%
Open Punctuation 32
 
2.2%
Close Punctuation 32
 
2.2%
Dash Punctuation 11
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 67
 
8.5%
측 53
 
6.7%
정 52
 
6.6%
기 41
 
5.2%
도 33
 
4.2%
전 33
 
4.2%
위 31
 
3.9%
장 28
 
3.6%
비 26
 
3.3%
소 23
 
2.9%
Other values (56) 399
50.8%
Uppercase Letter
ValueCountFrequency (%)
O 18
12.7%
C 16
11.3%
M 16
11.3%
H 11
 
7.7%
E 10
 
7.0%
L 9
 
6.3%
A 9
 
6.3%
T 7
 
4.9%
V 6
 
4.2%
D 6
 
4.2%
Other values (12) 34
23.9%
Lowercase Letter
ValueCountFrequency (%)
i 19
14.6%
t 15
11.5%
o 14
10.8%
e 12
9.2%
r 11
8.5%
n 10
7.7%
l 8
 
6.2%
p 7
 
5.4%
u 6
 
4.6%
a 5
 
3.8%
Other values (10) 23
17.7%
Decimal Number
ValueCountFrequency (%)
0 26
32.5%
1 22
27.5%
2 11
13.8%
3 4
 
5.0%
5 4
 
5.0%
4 3
 
3.8%
7 3
 
3.8%
9 3
 
3.8%
8 2
 
2.5%
6 2
 
2.5%
Other Punctuation
ValueCountFrequency (%)
, 80
93.0%
. 5
 
5.8%
/ 1
 
1.2%
Space Separator
ValueCountFrequency (%)
138
100.0%
Open Punctuation
ValueCountFrequency (%)
( 32
100.0%
Close Punctuation
ValueCountFrequency (%)
) 32
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 786
54.7%
Common 379
26.4%
Latin 272
 
18.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 67
 
8.5%
측 53
 
6.7%
정 52
 
6.6%
기 41
 
5.2%
도 33
 
4.2%
전 33
 
4.2%
위 31
 
3.9%
장 28
 
3.6%
비 26
 
3.3%
소 23
 
2.9%
Other values (56) 399
50.8%
Latin
ValueCountFrequency (%)
i 19
 
7.0%
O 18
 
6.6%
C 16
 
5.9%
M 16
 
5.9%
t 15
 
5.5%
o 14
 
5.1%
e 12
 
4.4%
r 11
 
4.0%
H 11
 
4.0%
E 10
 
3.7%
Other values (32) 130
47.8%
Common
ValueCountFrequency (%)
138
36.4%
, 80
21.1%
( 32
 
8.4%
) 32
 
8.4%
0 26
 
6.9%
1 22
 
5.8%
2 11
 
2.9%
- 11
 
2.9%
. 5
 
1.3%
3 4
 
1.1%
Other values (7) 18
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 786
54.7%
ASCII 651
45.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
138
21.2%
, 80
 
12.3%
( 32
 
4.9%
) 32
 
4.9%
0 26
 
4.0%
1 22
 
3.4%
i 19
 
2.9%
O 18
 
2.8%
C 16
 
2.5%
M 16
 
2.5%
Other values (49) 252
38.7%
Hangul
ValueCountFrequency (%)
수 67
 
8.5%
측 53
 
6.7%
정 52
 
6.6%
기 41
 
5.2%
도 33
 
4.2%
전 33
 
4.2%
위 31
 
3.9%
장 28
 
3.6%
비 26
 
3.3%
소 23
 
2.9%
Other values (56) 399
50.8%

타기관이전여부
Categorical

Distinct5
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size1004.0 B
<NA>
49 
0
28 
N
28 
1
 
3
Y
 
1

Length

Max length4
Median length1
Mean length2.3486239
Min length1

Unique

Unique1 ?
Unique (%)0.9%

Sample

1st row0
2nd row<NA>
3rd rowN
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
<NA> 49
45.0%
0 28
25.7%
N 28
25.7%
1 3
 
2.8%
Y 1
 
0.9%

Length

2023-12-11T06:49:02.118394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T06:49:02.212446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 49
45.0%
0 28
25.7%
n 28
25.7%
1 3
 
2.8%
y 1
 
0.9%

Interactions

2023-12-11T06:48:55.108151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:51.816779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:52.539007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:53.036077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:53.567066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:54.093754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:54.585501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:55.187273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:51.879887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:52.608078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:53.108997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:53.635373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:54.168999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:54.659241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:55.273649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:52.173119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:52.678099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:53.187659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:53.713110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:54.247211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:54.737900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:55.364632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:52.243236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:52.748882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:53.258580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:53.793745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:54.319651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:54.827164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:55.434181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:52.308801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:52.820028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:53.328742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:53.864736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:54.389397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:54.905751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:55.526022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:52.385064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:52.883513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:53.395809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:53.938214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:54.448797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:54.965886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:55.622646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:52.459057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:52.953756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:53.479977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:54.012678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:54.513487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T06:48:55.034080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T06:49:02.292041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명사업장명인허가일자인허가취소일자영업상태구분코드영업상태명도로명우편번호소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도X좌표값Y좌표값전문인력총수시설장비타기관이전여부
시군명1.0000.9990.9971.0000.5610.5610.8121.0001.0000.9771.0001.0000.9541.0000.7951.0000.609
사업장명0.9991.0000.9991.0000.9240.9241.0001.0001.0001.0001.0001.0001.0001.0000.0001.0000.000
인허가일자0.9970.9991.0001.0000.9450.9451.0000.9980.9991.0001.0000.9970.9921.0000.0001.0000.000
인허가취소일자1.0001.0001.0001.000NaNNaN1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
영업상태구분코드0.5610.9240.945NaN1.0000.9990.0000.9530.9960.2450.0000.2350.2820.0000.5741.0000.377
영업상태명0.5610.9240.945NaN0.9991.0000.0000.9530.9960.2450.0000.2350.2820.0000.5741.0000.377
도로명우편번호0.8121.0001.0001.0000.0000.0001.0001.0001.0000.0610.6570.1330.0000.6540.4391.0000.633
소재지도로명주소1.0001.0000.9981.0000.9530.9531.0001.0000.9991.0001.0001.0001.0001.0000.0001.0000.697
소재지지번주소1.0001.0000.9991.0000.9960.9961.0000.9991.0001.0001.0001.0001.0001.0000.9831.0000.864
소재지우편번호0.9771.0001.0001.0000.2450.2450.0611.0001.0001.0000.9240.8410.8590.8620.0001.0000.000
WGS84위도1.0001.0001.0001.0000.0000.0000.6571.0001.0000.9241.0000.6860.7031.0000.4131.0000.000
WGS84경도1.0001.0000.9971.0000.2350.2350.1331.0001.0000.8410.6861.0000.9970.9030.0001.0000.329
X좌표값0.9541.0000.9921.0000.2820.2820.0001.0001.0000.8590.7030.9971.0000.9020.0001.0000.299
Y좌표값1.0001.0001.0001.0000.0000.0000.6541.0001.0000.8621.0000.9030.9021.0000.3831.0000.000
전문인력총수0.7950.0000.0001.0000.5740.5740.4390.0000.9830.0000.4130.0000.0000.3831.0001.0000.571
시설장비1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
타기관이전여부0.6090.0000.0001.0000.3770.3770.6330.6970.8640.0000.0000.3290.2990.0000.5711.0001.000
2023-12-11T06:49:02.428749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영업상태명시군명영업상태구분코드타기관이전여부
영업상태명1.0000.4730.9730.246
시군명0.4731.0000.4730.330
영업상태구분코드0.9730.4731.0000.246
타기관이전여부0.2460.3300.2461.000
2023-12-11T06:49:02.729522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로명우편번호소재지우편번호WGS84위도WGS84경도X좌표값Y좌표값전문인력총수시군명영업상태구분코드영업상태명타기관이전여부
도로명우편번호1.0000.602-0.363-0.363-0.358-0.4030.3650.6630.0000.0000.303
소재지우편번호0.6021.000-0.742-0.198-0.159-0.758-0.1890.8070.2010.2010.000
WGS84위도-0.363-0.7421.000-0.102-0.1590.9780.1630.9270.0000.0000.000
WGS84경도-0.363-0.198-0.1021.0000.990-0.124-0.2120.9270.2820.2820.270
X좌표값-0.358-0.159-0.1590.9901.000-0.112-0.2490.8350.3450.3450.246
Y좌표값-0.403-0.7580.978-0.124-0.1121.0000.1540.9260.0000.0000.000
전문인력총수0.365-0.1890.163-0.212-0.2490.1541.0000.4730.6670.6670.490
시군명0.6630.8070.9270.9270.8350.9260.4731.0000.4730.4730.330
영업상태구분코드0.0000.2010.0000.2820.3450.0000.6670.4731.0000.9730.246
영업상태명0.0000.2010.0000.2820.3450.0000.6670.4730.9731.0000.246
타기관이전여부0.3030.0000.0000.2700.2460.0000.4900.3300.2460.2461.000

Missing values

2023-12-11T06:48:55.769829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T06:48:56.056779image/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.
2023-12-11T06:48:56.306990image/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

시군명사업장명인허가일자인허가취소일자영업상태구분코드영업상태명소재지시설전화번호소재지면적정보도로명우편번호소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도업태구분명정보X좌표값Y좌표값전문인력총수시설장비타기관이전여부
0고양시(주)케이디이엔지20230103<NA>1영업<NA><NA>10237경기도 고양시 일산서구 덕이로 30-28, 양우씨네플렉스 2층 제3에이치-202호 (덕이동)경기도 고양시 일산서구 덕이동 249-8 양우씨네플렉스1023737.690513126.757466<NA>178538.286615465372.8776375휘발성유기화합물질(VOC) 측정기 산소 측정기 이산화탄소 측정기 메탄가스 측정기 수소이온농도계, 산화환원전위 전기전도도 용존산소, 수온 지하수위 측정기0
1고양시(주)태영건설20100709201302222취소정지업체<NA><NA><NA>경기도 고양시 일산동구 정발산로 24 (장항동)경기도 고양시 일산서구 대화동 868번지1040337.654682126.7726<NA>179791.0461597.0<NA><NA><NA>
2고양시(주)태영건설20100709201302222취소정지업체<NA><NA><NA>경기도 고양시 일산동구 정발산로 24 (장항동)경기도 고양시 일산서구 대화동 868번지1040337.654682126.7726<NA>179791.0461597.07수위측정기(WL50M) 1대, 수질측정기(YK2001MULTI) 1식, 가스크로마토그래피(HP-6890serise GC/HP5973MSD, Tekmar AQUA Tek70) -한국환경수도연구원 임대N
3과천시코오롱글로벌(주)20140502201105182취소정지업체<NA><NA>427709경기도 과천시 코오롱로 11 (별양동)경기도 과천시 별양동 1-23번지1383737.425353126.990692<NA>199112.070211435924.357615<NA><NA><NA>
4과천시코오롱글로벌(주)20140502201105182취소정지업체<NA><NA>427709경기도 과천시 코오롱로 11 (별양동)경기도 과천시 별양동 1-231383737.425555126.990529<NA>199112.070211435924.3576157지하수 수위측정장비, PH.수온.전기전도도.DO.Eh측정장비, VOC측정용 가스크로마토그라프 장비0
5군포시(주)케이에이치이2015-04-24<NA>1영업<NA><NA>15809경기도 군포시 공단로 294, 한림테크노빌딩 5층 (금정동)경기도 군포시 금정동 689-28 한림테크노빌딩1580937.370384126.945015<NA>195061.979768429817.4669410<NA>0
6군포시한국위험물환경기술(주)20150424<NA>1영업<NA><NA><NA>경기도 군포시 번영로557번길 46-6, 4층 (금정동)경기도 군포시 금정동 718-1번지 4층1582837.362057126.936395<NA>194299.049837428900.876219<NA><NA><NA>
7군포시신강하이텍(주)20120522201312112취소정지업체<NA><NA><NA>경기도 군포시 광정로 1123-4 (산본동, 유공프라자 508호)<NA><NA>37.379504126.942285<NA><NA><NA><NA><NA><NA>
8군포시신강하이텍(주)20120522201312112취소정지업체<NA><NA><NA>경기도 군포시 광정로 1123-4 (산본동, 유공프라자 508호)<NA><NA>37.379504126.942285<NA><NA><NA>7지하수 수위측정장비. 수소이온농도, 전기전도도, 용존산소, 가스 크로마트그래프장비,N
9성남시(주)한국종합기술20150721<NA>1영업<NA><NA><NA>경기도 성남시 중원구 산성대로476번길 6 (금광동)경기도 성남시 중원구 금광동 4845번지1316137.453638127.161401<NA>214216.296235439074.691839<NA>지하수의 수위측정장비 1식, 수질측정장비 1식, 가스크로마토그래프 1식0N
시군명사업장명인허가일자인허가취소일자영업상태구분코드영업상태명소재지시설전화번호소재지면적정보도로명우편번호소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도업태구분명정보X좌표값Y좌표값전문인력총수시설장비타기관이전여부
99<NA>㈜한서엔지니어링20031107<NA>1영업<NA><NA><NA>서울특별시 송파구 양재대로66길 39 (가락동)서울특별시 송파구 가락동 16-15번지570337.500127127.125266<NA>211012.501708444227.7669397<NA>N
100<NA>㈜한서엔지니어링20031107<NA>1영업<NA><NA><NA>서울특별시 송파구 양재대로66길 39 (가락동)서울특별시 송파구 가락동 16-15번지570337.500127127.125266<NA>211012.501708444227.766939<NA><NA><NA>
101<NA>(주)화동건설20120403<NA>1영업<NA><NA><NA>충청북도 제천시 탑안로 33 (신백동)충청북도 제천시 신백동 106번지2717837.13569128.224978<NA>308780.974753404475.485002<NA><NA><NA>
102<NA>(주)화동건설20120403<NA>1영업<NA><NA><NA>충청북도 제천시 탑안로 33 (신백동)충청북도 제천시 신백동 106번지2717837.13569128.224978<NA>308780.974753404475.485002<NA><NA>N
103<NA>(주)대우건설20081119<NA>1영업<NA><NA><NA>서울특별시 종로구 새문안로 75 (신문로1가)서울특별시 종로구 신문로1가 57번지318237.570563126.972919<NA>197542.319572452039.262262<NA><NA><NA>
104<NA>(주)포스코건설20121002<NA>1영업<NA><NA><NA>경상북도 포항시 남구 대송로 180 (괴동동)경상북도 포항시 남구 괴동동 568-1번지3786335.995213129.373618<NA>413979.509031279815.884287<NA><NA><NA>
105<NA>(주)코텍이엔지<NA><NA>1영업<NA><NA><NA><NA>전라남도 화순군 화순읍 삼천리 417번지5814035.049476126.989707<NA>198987.317845172284.95866<NA><NA>N
106<NA>에이치플러스에코(주)20180320<NA>1영업<NA><NA><NA>서울특별시 송파구 석촌호수로 222 6층 (석촌동,제이타워)서울특별시 송파구 석촌동 158-3번지 제이타워6층561037.507042127.101459<NA>208906.361987444994.7460025<NA>N
107<NA>(주)대우건설20081119<NA>1영업<NA><NA><NA>서울특별시 종로구 새문안로 75 (신문로1가)서울특별시 종로구 신문로1가 57번지318237.570563126.972919<NA>197542.319572452039.262262<NA><NA>N
108<NA>(주)코텍이엔지<NA><NA>1영업<NA><NA><NA><NA>전라남도 화순군 화순읍 삼천리 417번지5814035.049476126.989707<NA>198987.317845172284.95866<NA><NA><NA>

Duplicate rows

Most frequently occurring

시군명사업장명인허가일자인허가취소일자영업상태구분코드영업상태명도로명우편번호소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도X좌표값Y좌표값전문인력총수시설장비타기관이전여부# duplicates
0<NA>(주)동해종합기술공사20170912<NA>1영업<NA>서울특별시 성동구 광나루로6길 35서울특별시 성동구 성수동2가 280-21 우림e-Biz센터 610호479937.546817127.063254205525.616971449394.2841275VOC Monitor(MiniRAE3000) O2,CH4,CO2 Monitor(MultiRAE Lite) 복합수질측정기(YK-2001-Multi) 수위측정기(WL-50M)02