Overview

Dataset statistics

Number of variables8
Number of observations126
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.4 KiB
Average record size in memory68.0 B

Variable types

Numeric3
Categorical1
Text2
DateTime2

Dataset

Description광주광역시 광산구 내 폐기물 수집운반업 현황(업소명, 도로명주소, 등록일자, 위도, 경도 등)에 대한 정보를 제공합니다.
Author광주광역시 광산구
URLhttps://www.data.go.kr/data/15055954/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique

Reproduction

Analysis started2024-05-04 07:15:37.986643
Analysis finished2024-05-04 07:15:42.451853
Duration4.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct126
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63.5
Minimum1
Maximum126
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-04T07:15:42.686519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.25
Q132.25
median63.5
Q394.75
95-th percentile119.75
Maximum126
Range125
Interquartile range (IQR)62.5

Descriptive statistics

Standard deviation36.517119
Coefficient of variation (CV)0.57507274
Kurtosis-1.2
Mean63.5
Median Absolute Deviation (MAD)31.5
Skewness0
Sum8001
Variance1333.5
MonotonicityStrictly increasing
2024-05-04T07:15:43.153019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.8%
81 1
 
0.8%
94 1
 
0.8%
93 1
 
0.8%
92 1
 
0.8%
91 1
 
0.8%
90 1
 
0.8%
89 1
 
0.8%
88 1
 
0.8%
87 1
 
0.8%
Other values (116) 116
92.1%
ValueCountFrequency (%)
1 1
0.8%
2 1
0.8%
3 1
0.8%
4 1
0.8%
5 1
0.8%
6 1
0.8%
7 1
0.8%
8 1
0.8%
9 1
0.8%
10 1
0.8%
ValueCountFrequency (%)
126 1
0.8%
125 1
0.8%
124 1
0.8%
123 1
0.8%
122 1
0.8%
121 1
0.8%
120 1
0.8%
119 1
0.8%
118 1
0.8%
117 1
0.8%

구분
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
건설폐기물 수집운반업
48 
사업배출시설계 폐기물수집운반업
44 
사업장비배출시설계 폐기물 수집운반업
33 
생활폐기물 수집운반업
 
1

Length

Max length19
Median length16
Mean length14.84127
Min length11

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row건설폐기물 수집운반업
2nd row건설폐기물 수집운반업
3rd row건설폐기물 수집운반업
4th row건설폐기물 수집운반업
5th row건설폐기물 수집운반업

Common Values

ValueCountFrequency (%)
건설폐기물 수집운반업 48
38.1%
사업배출시설계 폐기물수집운반업 44
34.9%
사업장비배출시설계 폐기물 수집운반업 33
26.2%
생활폐기물 수집운반업 1
 
0.8%

Length

2024-05-04T07:15:43.600276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T07:15:43.985458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
수집운반업 82
28.8%
건설폐기물 48
16.8%
사업배출시설계 44
15.4%
폐기물수집운반업 44
15.4%
사업장비배출시설계 33
11.6%
폐기물 33
11.6%
생활폐기물 1
 
0.4%

사업장명
Text

Distinct94
Distinct (%)74.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-05-04T07:15:44.662908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length11
Mean length5.8888889
Min length3

Characters and Unicode

Total characters742
Distinct characters153
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)56.3%

Sample

1st row㈜빛고을환경
2nd row㈜송대에코
3rd row대림환경
4th row㈜초당산업
5th row㈜극동환경
ValueCountFrequency (%)
㈜빛고을환경 3
 
2.3%
㈜극동환경 3
 
2.3%
㈜무진환경기술 3
 
2.3%
㈜청하환경산업 3
 
2.3%
국토환경㈜ 3
 
2.3%
디딤환경산업 3
 
2.3%
㈜중경 3
 
2.3%
자연환경(유 3
 
2.3%
대림환경 3
 
2.3%
송광미화 2
 
1.6%
Other values (87) 100
77.5%
2024-05-04T07:15:45.782390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
경 78
 
10.5%
환 75
 
10.1%
㈜ 55
 
7.4%
산 30
 
4.0%
유 29
 
3.9%
업 26
 
3.5%
( 24
 
3.2%
) 24
 
3.2%
지 16
 
2.2%
자 15
 
2.0%
Other values (143) 370
49.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 630
84.9%
Other Symbol 55
 
7.4%
Open Punctuation 24
 
3.2%
Close Punctuation 24
 
3.2%
Uppercase Letter 6
 
0.8%
Space Separator 3
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 78
 
12.4%
환 75
 
11.9%
산 30
 
4.8%
유 29
 
4.6%
업 26
 
4.1%
지 16
 
2.5%
자 15
 
2.4%
대 15
 
2.4%
원 14
 
2.2%
광 12
 
1.9%
Other values (136) 320
50.8%
Uppercase Letter
ValueCountFrequency (%)
K 3
50.0%
C 2
33.3%
O 1
 
16.7%
Other Symbol
ValueCountFrequency (%)
㈜ 55
100.0%
Open Punctuation
ValueCountFrequency (%)
( 24
100.0%
Close Punctuation
ValueCountFrequency (%)
) 24
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 685
92.3%
Common 51
 
6.9%
Latin 6
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 78
 
11.4%
환 75
 
10.9%
㈜ 55
 
8.0%
산 30
 
4.4%
유 29
 
4.2%
업 26
 
3.8%
지 16
 
2.3%
자 15
 
2.2%
대 15
 
2.2%
원 14
 
2.0%
Other values (137) 332
48.5%
Common
ValueCountFrequency (%)
( 24
47.1%
) 24
47.1%
3
 
5.9%
Latin
ValueCountFrequency (%)
K 3
50.0%
C 2
33.3%
O 1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 630
84.9%
ASCII 57
 
7.7%
None 55
 
7.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
경 78
 
12.4%
환 75
 
11.9%
산 30
 
4.8%
유 29
 
4.6%
업 26
 
4.1%
지 16
 
2.5%
자 15
 
2.4%
대 15
 
2.4%
원 14
 
2.2%
광 12
 
1.9%
Other values (136) 320
50.8%
None
ValueCountFrequency (%)
㈜ 55
100.0%
ASCII
ValueCountFrequency (%)
( 24
42.1%
) 24
42.1%
3
 
5.3%
K 3
 
5.3%
C 2
 
3.5%
O 1
 
1.8%

주소
Text

Distinct94
Distinct (%)74.6%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2024-05-04T07:15:47.032579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length37
Mean length25.412698
Min length16

Characters and Unicode

Total characters3202
Distinct characters112
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

Unique69 ?
Unique (%)54.8%

Sample

1st row광주광역시 광산구 도천남길 96(도천동)
2nd row광주광역시 광산구 동곡로194번길 137(유계동)
3rd row광주광역시 광산구 풍영정길 9(신창동)
4th row광주광역시 광산구 금동학동길 306-16(덕림동)
5th row광주광역시 광산구 사암로 782 2층(도천동)
ValueCountFrequency (%)
광산구 126
23.3%
광주광역시 110
20.4%
사암로 16
 
3.0%
북문대로 9
 
1.7%
도천남길 7
 
1.3%
2층(도천동 7
 
1.3%
상무대로 6
 
1.1%
임방울대로 6
 
1.1%
2층 6
 
1.1%
평동매화길 5
 
0.9%
Other values (173) 242
44.8%
2024-05-04T07:15:48.506649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
414
 
12.9%
광 346
 
10.8%
산 169
 
5.3%
동 143
 
4.5%
구 128
 
4.0%
( 122
 
3.8%
) 122
 
3.8%
역 111
 
3.5%
시 111
 
3.5%
주 111
 
3.5%
Other values (102) 1425
44.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1914
59.8%
Decimal Number 549
 
17.1%
Space Separator 414
 
12.9%
Open Punctuation 122
 
3.8%
Close Punctuation 122
 
3.8%
Dash Punctuation 43
 
1.3%
Other Punctuation 38
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 346
18.1%
산 169
 
8.8%
동 143
 
7.5%
구 128
 
6.7%
역 111
 
5.8%
시 111
 
5.8%
주 111
 
5.8%
로 99
 
5.2%
길 58
 
3.0%
천 40
 
2.1%
Other values (87) 598
31.2%
Decimal Number
ValueCountFrequency (%)
1 98
17.9%
2 86
15.7%
3 63
11.5%
6 61
11.1%
7 54
9.8%
0 47
8.6%
8 42
7.7%
5 39
 
7.1%
9 32
 
5.8%
4 27
 
4.9%
Space Separator
ValueCountFrequency (%)
414
100.0%
Open Punctuation
ValueCountFrequency (%)
( 122
100.0%
Close Punctuation
ValueCountFrequency (%)
) 122
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43
100.0%
Other Punctuation
ValueCountFrequency (%)
, 38
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1914
59.8%
Common 1288
40.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 346
18.1%
산 169
 
8.8%
동 143
 
7.5%
구 128
 
6.7%
역 111
 
5.8%
시 111
 
5.8%
주 111
 
5.8%
로 99
 
5.2%
길 58
 
3.0%
천 40
 
2.1%
Other values (87) 598
31.2%
Common
ValueCountFrequency (%)
414
32.1%
( 122
 
9.5%
) 122
 
9.5%
1 98
 
7.6%
2 86
 
6.7%
3 63
 
4.9%
6 61
 
4.7%
7 54
 
4.2%
0 47
 
3.6%
- 43
 
3.3%
Other values (5) 178
13.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1914
59.8%
ASCII 1288
40.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
414
32.1%
( 122
 
9.5%
) 122
 
9.5%
1 98
 
7.6%
2 86
 
6.7%
3 63
 
4.9%
6 61
 
4.7%
7 54
 
4.2%
0 47
 
3.6%
- 43
 
3.3%
Other values (5) 178
13.8%
Hangul
ValueCountFrequency (%)
광 346
18.1%
산 169
 
8.8%
동 143
 
7.5%
구 128
 
6.7%
역 111
 
5.8%
시 111
 
5.8%
주 111
 
5.8%
로 99
 
5.2%
길 58
 
3.0%
천 40
 
2.1%
Other values (87) 598
31.2%

등록일자
Date

Distinct118
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Minimum2000-02-08 00:00:00
Maximum2023-12-11 00:00:00
2024-05-04T07:15:49.340433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:15:49.956125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

위도
Real number (ℝ)

Distinct84
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.179784
Minimum35.092711
Maximum35.222281
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-04T07:15:50.399453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.092711
5-th percentile35.139317
Q135.153152
median35.18375
Q335.210316
95-th percentile35.219272
Maximum35.222281
Range0.12957043
Interquartile range (IQR)0.057163987

Descriptive statistics

Standard deviation0.030594602
Coefficient of variation (CV)0.0008696643
Kurtosis-0.76383374
Mean35.179784
Median Absolute Deviation (MAD)0.028322122
Skewness-0.39711982
Sum4432.6527
Variance0.00093602966
MonotonicityNot monotonic
2024-05-04T07:15:51.069843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.2120721559137 6
 
4.8%
35.1784083770328 4
 
3.2%
35.1532486368014 4
 
3.2%
35.2093379498093 3
 
2.4%
35.14533737578 3
 
2.4%
35.1422387859888 3
 
2.4%
35.1460468835553 3
 
2.4%
35.2138783747237 3
 
2.4%
35.1403371598414 3
 
2.4%
35.1837500335298 3
 
2.4%
Other values (74) 91
72.2%
ValueCountFrequency (%)
35.0927105101599 1
 
0.8%
35.0991176423084 1
 
0.8%
35.1165616212214 1
 
0.8%
35.1337908033495 1
 
0.8%
35.1367965299335 2
1.6%
35.1392978320379 1
 
0.8%
35.1393735010619 1
 
0.8%
35.1398865096532 1
 
0.8%
35.1403371598414 3
2.4%
35.1406823418965 2
1.6%
ValueCountFrequency (%)
35.2222809428725 1
0.8%
35.2220676896465 1
0.8%
35.2215326011543 2
1.6%
35.2198745774554 1
0.8%
35.2197270343835 1
0.8%
35.2193858696243 1
0.8%
35.2189306475232 2
1.6%
35.2185503013828 1
0.8%
35.2171101043423 1
0.8%
35.2170087819318 2
1.6%

경도
Real number (ℝ)

Distinct84
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.80869
Minimum126.66156
Maximum126.85553
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2024-05-04T07:15:51.535194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.66156
5-th percentile126.75839
Q1126.79889
median126.81829
Q3126.82644
95-th percentile126.84932
Maximum126.85553
Range0.19397124
Interquartile range (IQR)0.027554852

Descriptive statistics

Standard deviation0.035359102
Coefficient of variation (CV)0.00027883816
Kurtosis6.3263617
Mean126.80869
Median Absolute Deviation (MAD)0.013141664
Skewness-2.1219113
Sum15977.895
Variance0.0012502661
MonotonicityNot monotonic
2024-05-04T07:15:52.055809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.818465625691 6
 
4.8%
126.837803924255 4
 
3.2%
126.810788464634 4
 
3.2%
126.82060964514 3
 
2.4%
126.825546487074 3
 
2.4%
126.825934860729 3
 
2.4%
126.790110569999 3
 
2.4%
126.826144513895 3
 
2.4%
126.7583870761 3
 
2.4%
126.849282080424 3
 
2.4%
Other values (74) 91
72.2%
ValueCountFrequency (%)
126.661555972439 2
1.6%
126.671211883841 1
 
0.8%
126.673288197951 1
 
0.8%
126.750358763644 1
 
0.8%
126.756174489593 1
 
0.8%
126.7583870761 3
2.4%
126.759364198515 2
1.6%
126.763633270469 2
1.6%
126.769871880602 1
 
0.8%
126.771602749877 1
 
0.8%
ValueCountFrequency (%)
126.855527209892 2
1.6%
126.853369524203 1
 
0.8%
126.85239146838 2
1.6%
126.84962437653 1
 
0.8%
126.849335656292 1
 
0.8%
126.849282080424 3
2.4%
126.848782735411 1
 
0.8%
126.847031920772 1
 
0.8%
126.846889273488 1
 
0.8%
126.843210508318 1
 
0.8%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
Minimum2024-04-30 00:00:00
Maximum2024-04-30 00:00:00
2024-05-04T07:15:52.528508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:15:52.835724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-05-04T07:15:40.978190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:15:39.409587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:15:40.160899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:15:41.258458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:15:39.628112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:15:40.418502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:15:41.544817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:15:39.888191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:15:40.694603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T07:15:53.062812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분사업장명주소위도경도
연번1.0000.8850.0000.5420.2430.000
구분0.8851.0000.0000.7130.3110.000
사업장명0.0000.0001.0001.0001.0001.000
주소0.5420.7131.0001.0001.0001.000
위도0.2430.3111.0001.0001.0000.673
경도0.0000.0001.0001.0000.6731.000
2024-05-04T07:15:53.375926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도구분
연번1.000-0.017-0.0540.736
위도-0.0171.0000.4870.198
경도-0.0540.4871.0000.000
구분0.7360.1980.0001.000

Missing values

2024-05-04T07:15:41.919172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T07:15:42.305437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

연번구분사업장명주소등록일자위도경도데이터기준일자
01건설폐기물 수집운반업㈜빛고을환경광주광역시 광산구 도천남길 96(도천동)2001-09-2035.209338126.820612024-04-30
12건설폐기물 수집운반업㈜송대에코광주광역시 광산구 동곡로194번길 137(유계동)2002-09-1735.092711126.7888742024-04-30
23건설폐기물 수집운반업대림환경광주광역시 광산구 풍영정길 9(신창동)2003-08-1835.178408126.8378042024-04-30
34건설폐기물 수집운반업㈜초당산업광주광역시 광산구 금동학동길 306-16(덕림동)2005-03-3135.190142126.6732882024-04-30
45건설폐기물 수집운반업㈜극동환경광주광역시 광산구 사암로 782 2층(도천동)2008-09-1635.212072126.8184662024-04-30
56건설폐기물 수집운반업㈜무진환경기술광주광역시 광산구 사암로 782 2층(도천동)2009-03-1835.212072126.8184662024-04-30
67건설폐기물 수집운반업㈜청하환경산업광주광역시 광산구 평동산단외로 177(지죽동)2009-09-0135.140337126.7583872024-04-30
78건설폐기물 수집운반업국토환경㈜광주광역시 광산구 북문대로 363-86(신창동)2011-10-2835.18375126.8492822024-04-30
89건설폐기물 수집운반업가가환경광주광역시 광산구 고봉로 76(하남동)2011-11-2235.18245126.7988862024-04-30
910건설폐기물 수집운반업동부환경광주광역시 광산구 왕버들로289번길 7(신창동)2014-01-2735.19586126.8403422024-04-30
연번구분사업장명주소등록일자위도경도데이터기준일자
116117사업장비배출시설계 폐기물 수집운반업미래환경주식회사광주광역시 광산구 송도로320번길 3, 1층(신촌동)2021-06-1835.141221126.8024262024-04-30
117118사업장비배출시설계 폐기물 수집운반업㈜유현환경광주광역시 광산구 우산로96번길 67, 2층(우산동)2019-03-1235.153249126.8107882024-04-30
118119사업장비배출시설계 폐기물 수집운반업㈜자유환경광주광역시 광산구 상무대로 551(우산동)2020-06-2935.142239126.8259352024-04-30
119120사업장비배출시설계 폐기물 수집운반업㈜연합기업광주광역시 광산구 평동산단외로 303(송촌동)2022-07-1235.139887126.7716032024-04-30
120121사업장비배출시설계 폐기물 수집운반업(유)지원환경건설광주광역시 광산구 상무대로 529-26(우산동)2021-04-0835.144925126.8243712024-04-30
121122사업장비배출시설계 폐기물 수집운반업㈜조선우드광산구 하남산단5번로 99-11(장덕동)2023-01-2735.198058126.8058672024-04-30
122123사업장비배출시설계 폐기물 수집운반업㈜중경광산구 임방울대로 611-18(도천동)2008-03-0435.213878126.8261452024-04-30
123124사업장비배출시설계 폐기물 수집운반업디딤환경산업광산구 어등대로647번길 3(소촌동)2023-05-2235.146047126.7901112024-04-30
124125사업장비배출시설계 폐기물 수집운반업㈜동운광산구 사암로 660, 202호(장덕동)2023-03-0635.202717126.8114922024-04-30
125126생활폐기물 수집운반업광산구시설관리공단광주광역시 광산구 무진대로211번길 28(우산동)2015-07-0135.163503126.8044632024-04-30