Overview

Dataset statistics

Number of variables6
Number of observations135
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 KiB
Average record size in memory52.0 B

Variable types

Numeric3
Text2
Categorical1

Dataset

Description광주광역시 광산구 내 음식물 쓰레기통 판매업소 현황(업소명, 도로명주소, 위도, 경도 등)에 대한 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15055955/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
위도 is highly overall correlated with 경도High correlation
경도 is highly overall correlated with 위도High correlation
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 21:02:50.362520
Analysis finished2023-12-12 21:02:51.904915
Duration1.54 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct135
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean68
Minimum1
Maximum135
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-13T06:02:51.974155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7.7
Q134.5
median68
Q3101.5
95-th percentile128.3
Maximum135
Range134
Interquartile range (IQR)67

Descriptive statistics

Standard deviation39.115214
Coefficient of variation (CV)0.57522374
Kurtosis-1.2
Mean68
Median Absolute Deviation (MAD)34
Skewness0
Sum9180
Variance1530
MonotonicityStrictly increasing
2023-12-13T06:02:52.108047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.7%
94 1
 
0.7%
88 1
 
0.7%
89 1
 
0.7%
90 1
 
0.7%
91 1
 
0.7%
92 1
 
0.7%
93 1
 
0.7%
95 1
 
0.7%
2 1
 
0.7%
Other values (125) 125
92.6%
ValueCountFrequency (%)
1 1
0.7%
2 1
0.7%
3 1
0.7%
4 1
0.7%
5 1
0.7%
6 1
0.7%
7 1
0.7%
8 1
0.7%
9 1
0.7%
10 1
0.7%
ValueCountFrequency (%)
135 1
0.7%
134 1
0.7%
133 1
0.7%
132 1
0.7%
131 1
0.7%
130 1
0.7%
129 1
0.7%
128 1
0.7%
127 1
0.7%
126 1
0.7%

사업장명
Text

Distinct118
Distinct (%)87.4%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-13T06:02:52.365170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length7.1037037
Min length3

Characters and Unicode

Total characters959
Distinct characters180
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

Unique111 ?
Unique (%)82.2%

Sample

1st row영암마트 첨단호반점
2nd row마운틴마트 첨단점
3rd row드림산업안전(도림철물)
4th row플러스마트 월계점
5th row마켓 올레
ValueCountFrequency (%)
하남그릇백화점 6
 
3.0%
마운틴마트 6
 
3.0%
송정할인마트 5
 
2.5%
마트 5
 
2.5%
영암마트 5
 
2.5%
월광식자재 4
 
2.0%
첨단점 4
 
2.0%
수완점 4
 
2.0%
코스코마트 4
 
2.0%
sm마트 4
 
2.0%
Other values (130) 150
76.1%
2023-12-13T06:02:52.887910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
마 100
 
10.4%
트 87
 
9.1%
62
 
6.5%
점 53
 
5.5%
월 18
 
1.9%
) 17
 
1.8%
( 15
 
1.6%
남 14
 
1.5%
영 13
 
1.4%
운 13
 
1.4%
Other values (170) 567
59.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 826
86.1%
Space Separator 62
 
6.5%
Uppercase Letter 34
 
3.5%
Close Punctuation 17
 
1.8%
Open Punctuation 15
 
1.6%
Decimal Number 4
 
0.4%
Other Symbol 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
마 100
 
12.1%
트 87
 
10.5%
점 53
 
6.4%
월 18
 
2.2%
남 14
 
1.7%
영 13
 
1.6%
운 13
 
1.6%
스 12
 
1.5%
유 12
 
1.5%
그 11
 
1.3%
Other values (155) 493
59.7%
Uppercase Letter
ValueCountFrequency (%)
M 12
35.3%
S 6
17.6%
F 5
14.7%
C 4
 
11.8%
D 3
 
8.8%
Y 1
 
2.9%
L 1
 
2.9%
U 1
 
2.9%
G 1
 
2.9%
Decimal Number
ValueCountFrequency (%)
6 2
50.0%
1 2
50.0%
Space Separator
ValueCountFrequency (%)
62
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 827
86.2%
Common 98
 
10.2%
Latin 34
 
3.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
마 100
 
12.1%
트 87
 
10.5%
점 53
 
6.4%
월 18
 
2.2%
남 14
 
1.7%
영 13
 
1.6%
운 13
 
1.6%
스 12
 
1.5%
유 12
 
1.5%
그 11
 
1.3%
Other values (156) 494
59.7%
Latin
ValueCountFrequency (%)
M 12
35.3%
S 6
17.6%
F 5
14.7%
C 4
 
11.8%
D 3
 
8.8%
Y 1
 
2.9%
L 1
 
2.9%
U 1
 
2.9%
G 1
 
2.9%
Common
ValueCountFrequency (%)
62
63.3%
) 17
 
17.3%
( 15
 
15.3%
6 2
 
2.0%
1 2
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 826
86.1%
ASCII 132
 
13.8%
None 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
마 100
 
12.1%
트 87
 
10.5%
점 53
 
6.4%
월 18
 
2.2%
남 14
 
1.7%
영 13
 
1.6%
운 13
 
1.6%
스 12
 
1.5%
유 12
 
1.5%
그 11
 
1.3%
Other values (155) 493
59.7%
ASCII
ValueCountFrequency (%)
62
47.0%
) 17
 
12.9%
( 15
 
11.4%
M 12
 
9.1%
S 6
 
4.5%
F 5
 
3.8%
C 4
 
3.0%
D 3
 
2.3%
6 2
 
1.5%
1 2
 
1.5%
Other values (4) 4
 
3.0%
None
ValueCountFrequency (%)
㈜ 1
100.0%

주소
Text

Distinct130
Distinct (%)96.3%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-13T06:02:53.196244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length43
Median length35
Mean length22.162963
Min length16

Characters and Unicode

Total characters2992
Distinct characters131
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

Unique125 ?
Unique (%)92.6%

Sample

1st row광주광역시 광산구 월계로 59
2nd row광주광역시 광산구 첨단내촌로 71
3rd row광주광역시 광산구 첨단내촌로 38
4th row광주광역시 광산구 산월로 15
5th row광주광역시 광산구 첨단중앙로68번길 131
ValueCountFrequency (%)
광주광역시 135
22.6%
광산구 135
22.6%
월곡동 10
 
1.7%
월계동 6
 
1.0%
앞 6
 
1.0%
송정동 5
 
0.8%
월곡시장 5
 
0.8%
옆 5
 
0.8%
월계로 5
 
0.8%
월곡산정로 5
 
0.8%
Other values (220) 280
46.9%
2023-12-13T06:02:53.608620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
479
16.0%
광 409
 
13.7%
산 155
 
5.2%
시 140
 
4.7%
주 137
 
4.6%
구 136
 
4.5%
역 135
 
4.5%
1 113
 
3.8%
로 104
 
3.5%
2 62
 
2.1%
Other values (121) 1122
37.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1906
63.7%
Decimal Number 540
 
18.0%
Space Separator 479
 
16.0%
Dash Punctuation 43
 
1.4%
Other Punctuation 8
 
0.3%
Close Punctuation 8
 
0.3%
Open Punctuation 8
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 409
21.5%
산 155
 
8.1%
시 140
 
7.3%
주 137
 
7.2%
구 136
 
7.1%
역 135
 
7.1%
로 104
 
5.5%
길 60
 
3.1%
번 59
 
3.1%
월 52
 
2.7%
Other values (106) 519
27.2%
Decimal Number
ValueCountFrequency (%)
1 113
20.9%
2 62
11.5%
3 56
10.4%
6 54
10.0%
0 51
9.4%
5 51
9.4%
7 46
8.5%
4 38
 
7.0%
9 35
 
6.5%
8 34
 
6.3%
Space Separator
ValueCountFrequency (%)
479
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43
100.0%
Other Punctuation
ValueCountFrequency (%)
, 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1906
63.7%
Common 1086
36.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 409
21.5%
산 155
 
8.1%
시 140
 
7.3%
주 137
 
7.2%
구 136
 
7.1%
역 135
 
7.1%
로 104
 
5.5%
길 60
 
3.1%
번 59
 
3.1%
월 52
 
2.7%
Other values (106) 519
27.2%
Common
ValueCountFrequency (%)
479
44.1%
1 113
 
10.4%
2 62
 
5.7%
3 56
 
5.2%
6 54
 
5.0%
0 51
 
4.7%
5 51
 
4.7%
7 46
 
4.2%
- 43
 
4.0%
4 38
 
3.5%
Other values (5) 93
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1906
63.7%
ASCII 1086
36.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
479
44.1%
1 113
 
10.4%
2 62
 
5.7%
3 56
 
5.2%
6 54
 
5.0%
0 51
 
4.7%
5 51
 
4.7%
7 46
 
4.2%
- 43
 
4.0%
4 38
 
3.5%
Other values (5) 93
 
8.6%
Hangul
ValueCountFrequency (%)
광 409
21.5%
산 155
 
8.1%
시 140
 
7.3%
주 137
 
7.2%
구 136
 
7.1%
역 135
 
7.1%
로 104
 
5.5%
길 60
 
3.1%
번 59
 
3.1%
월 52
 
2.7%
Other values (106) 519
27.2%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct120
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.179123
Minimum35.120174
Maximum35.221218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-13T06:02:53.734786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.120174
5-th percentile35.137417
Q135.168309
median35.177063
Q335.19614
95-th percentile35.217018
Maximum35.221218
Range0.10104409
Interquartile range (IQR)0.027830555

Descriptive statistics

Standard deviation0.024754373
Coefficient of variation (CV)0.00070366657
Kurtosis-0.52870673
Mean35.179123
Median Absolute Deviation (MAD)0.01433823
Skewness-0.19099229
Sum4749.1816
Variance0.00061277898
MonotonicityNot monotonic
2023-12-13T06:02:53.922136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.1707212276469 3
 
2.2%
35.2160120464205 3
 
2.2%
35.1412167524733 3
 
2.2%
35.1683801557757 3
 
2.2%
35.1707223607584 3
 
2.2%
35.2160114771912 2
 
1.5%
35.1706232829871 2
 
1.5%
35.1897396920124 2
 
1.5%
35.1683930509058 2
 
1.5%
35.1412493376617 2
 
1.5%
Other values (110) 110
81.5%
ValueCountFrequency (%)
35.120174257857 1
0.7%
35.123464280431 1
0.7%
35.128272699137 1
0.7%
35.1292491379415 1
0.7%
35.1300415160634 1
0.7%
35.1353837936452 1
0.7%
35.1366936947281 1
0.7%
35.1377270535213 1
0.7%
35.1385662907242 1
0.7%
35.1402985822967 1
0.7%
ValueCountFrequency (%)
35.2212183445072 1
 
0.7%
35.2210193130946 1
 
0.7%
35.2207929456469 1
 
0.7%
35.2202599870767 1
 
0.7%
35.2200419928961 1
 
0.7%
35.2200213271829 1
 
0.7%
35.2193668163661 1
 
0.7%
35.2160120464205 3
2.2%
35.2160114771912 2
1.5%
35.2158782423141 1
 
0.7%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct120
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.81708
Minimum126.76858
Maximum126.84971
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-13T06:02:54.056134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.76858
5-th percentile126.79167
Q1126.80745
median126.81257
Q3126.83492
95-th percentile126.84492
Maximum126.84971
Range0.081126534
Interquartile range (IQR)0.027477922

Descriptive statistics

Standard deviation0.018002426
Coefficient of variation (CV)0.00014195585
Kurtosis-0.41003029
Mean126.81708
Median Absolute Deviation (MAD)0.012483043
Skewness-0.091756592
Sum17120.305
Variance0.00032408735
MonotonicityNot monotonic
2023-12-13T06:02:54.179436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.809952043127 3
 
2.2%
126.844918903461 3
 
2.2%
126.798709814873 3
 
2.2%
126.812569208552 3
 
2.2%
126.809984862168 3
 
2.2%
126.844896718378 2
 
1.5%
126.809571144853 2
 
1.5%
126.839037159003 2
 
1.5%
126.812572911088 2
 
1.5%
126.798683289269 2
 
1.5%
Other values (110) 110
81.5%
ValueCountFrequency (%)
126.768579960154 1
0.7%
126.77187509841 1
0.7%
126.776886337631 1
0.7%
126.777950708415 1
0.7%
126.777993987083 1
0.7%
126.790844617876 1
0.7%
126.791531395136 1
0.7%
126.791734400286 1
0.7%
126.792532269993 1
0.7%
126.793372410721 1
0.7%
ValueCountFrequency (%)
126.849706494095 1
 
0.7%
126.848772772434 1
 
0.7%
126.848255823375 1
 
0.7%
126.84782509167 1
 
0.7%
126.84672715505 1
 
0.7%
126.844918903461 3
2.2%
126.844896718378 2
1.5%
126.844044726574 1
 
0.7%
126.843490357522 1
 
0.7%
126.842743986997 1
 
0.7%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-05-08
135 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-05-08
2nd row2023-05-08
3rd row2023-05-08
4th row2023-05-08
5th row2023-05-08

Common Values

ValueCountFrequency (%)
2023-05-08 135
100.0%

Length

2023-12-13T06:02:54.299266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T06:02:54.397706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-05-08 135
100.0%

Interactions

2023-12-13T06:02:51.466758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:02:50.596497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:02:51.170087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:02:51.560547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:02:50.999393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:02:51.263293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:02:51.649885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:02:51.077945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T06:02:51.360686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T06:02:54.463934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도
연번1.0000.8210.793
위도0.8211.0000.840
경도0.7930.8401.000
2023-12-13T06:02:54.553969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도
연번1.000-0.256-0.259
위도-0.2561.0000.840
경도-0.2590.8401.000

Missing values

2023-12-13T06:02:51.768731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T06:02:51.860299image/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

연번사업장명주소위도경도데이터기준일자
01영암마트 첨단호반점광주광역시 광산구 월계로 5935.21276126.8323662023-05-08
12마운틴마트 첨단점광주광역시 광산구 첨단내촌로 7135.212896126.8362162023-05-08
23드림산업안전(도림철물)광주광역시 광산구 첨단내촌로 3835.2102126.8374092023-05-08
34플러스마트 월계점광주광역시 광산구 산월로 1535.208998126.8389952023-05-08
45마켓 올레광주광역시 광산구 첨단중앙로68번길 13135.211607126.8497062023-05-08
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125126월곡그릇백화점광주광역시 광산구 월곡동 696-10 월곡시장 앞35.170722126.8099852023-05-08
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133134DC홈마트광주광역시 광산구 수완동 1377 대왕종합식품 옆35.19162126.8304472023-05-08
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