Overview

Dataset statistics

Number of variables6
Number of observations4173
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory208.0 KiB
Average record size in memory51.0 B

Variable types

Numeric3
Text2
DateTime1

Dataset

Description광주광역시 서구의 2020년 1월 부터 2022년 5월까지 폐업업소에 대한 업종명, 폐업일자, 도로명주소 등에 대한 현황입니다.
Author광주광역시 서구
URLhttps://www.data.go.kr/data/15104674/fileData.do

Alerts

연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 03:13:14.694609
Analysis finished2023-12-12 03:13:16.717622
Duration2.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct4173
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2087
Minimum1
Maximum4173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.8 KiB
2023-12-12T12:13:16.830867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile209.6
Q11044
median2087
Q33130
95-th percentile3964.4
Maximum4173
Range4172
Interquartile range (IQR)2086

Descriptive statistics

Standard deviation1204.7857
Coefficient of variation (CV)0.5772811
Kurtosis-1.2
Mean2087
Median Absolute Deviation (MAD)1043
Skewness0
Sum8709051
Variance1451508.5
MonotonicityStrictly increasing
2023-12-12T12:13:16.975224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
2773 1
 
< 0.1%
2775 1
 
< 0.1%
2776 1
 
< 0.1%
2777 1
 
< 0.1%
2778 1
 
< 0.1%
2779 1
 
< 0.1%
2780 1
 
< 0.1%
2781 1
 
< 0.1%
2782 1
 
< 0.1%
Other values (4163) 4163
99.8%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
4173 1
< 0.1%
4172 1
< 0.1%
4171 1
< 0.1%
4170 1
< 0.1%
4169 1
< 0.1%
4168 1
< 0.1%
4167 1
< 0.1%
4166 1
< 0.1%
4165 1
< 0.1%
4164 1
< 0.1%

업종명
Text

Distinct86
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size32.7 KiB
2023-12-12T12:13:17.226495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length5
Mean length6.4838246
Min length2

Characters and Unicode

Total characters27057
Distinct characters149
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

Unique17 ?
Unique (%)0.4%

Sample

1st row통신판매업
2nd row통신판매업
3rd row건강기능식품일반판매업
4th row담배소매업
5th row미용업
ValueCountFrequency (%)
즉석판매제조가공업 759
17.9%
일반음식점 728
17.2%
통신판매업 601
14.2%
휴게음식점 325
 
7.7%
건강기능식품일반판매업 280
 
6.6%
미용업 243
 
5.7%
담배소매업 173
 
4.1%
의료기기판매(임대)업 76
 
1.8%
식품자동판매기업 72
 
1.7%
안전상비의약품 56
 
1.3%
Other values (77) 916
21.7%
2023-12-12T12:13:17.670185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
업 2993
 
11.1%
매 2192
 
8.1%
판 2020
 
7.5%
식 1567
 
5.8%
점 1142
 
4.2%
음 1055
 
3.9%
반 1023
 
3.8%
일 1015
 
3.8%
제 898
 
3.3%
공 872
 
3.2%
Other values (139) 12280
45.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 26827
99.1%
Open Punctuation 86
 
0.3%
Close Punctuation 86
 
0.3%
Space Separator 56
 
0.2%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 2993
 
11.2%
매 2192
 
8.2%
판 2020
 
7.5%
식 1567
 
5.8%
점 1142
 
4.3%
음 1055
 
3.9%
반 1023
 
3.8%
일 1015
 
3.8%
제 898
 
3.3%
공 872
 
3.3%
Other values (134) 12050
44.9%
Other Punctuation
ValueCountFrequency (%)
. 1
50.0%
/ 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 86
100.0%
Close Punctuation
ValueCountFrequency (%)
) 86
100.0%
Space Separator
ValueCountFrequency (%)
56
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 26827
99.1%
Common 230
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 2993
 
11.2%
매 2192
 
8.2%
판 2020
 
7.5%
식 1567
 
5.8%
점 1142
 
4.3%
음 1055
 
3.9%
반 1023
 
3.8%
일 1015
 
3.8%
제 898
 
3.3%
공 872
 
3.3%
Other values (134) 12050
44.9%
Common
ValueCountFrequency (%)
( 86
37.4%
) 86
37.4%
56
24.3%
. 1
 
0.4%
/ 1
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 26827
99.1%
ASCII 230
 
0.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
업 2993
 
11.2%
매 2192
 
8.2%
판 2020
 
7.5%
식 1567
 
5.8%
점 1142
 
4.3%
음 1055
 
3.9%
반 1023
 
3.8%
일 1015
 
3.8%
제 898
 
3.3%
공 872
 
3.3%
Other values (134) 12050
44.9%
ASCII
ValueCountFrequency (%)
( 86
37.4%
) 86
37.4%
56
24.3%
. 1
 
0.4%
/ 1
 
0.4%

폐업일자
Date

Distinct743
Distinct (%)17.8%
Missing0
Missing (%)0.0%
Memory size32.7 KiB
Minimum2020-01-01 00:00:00
Maximum2022-05-31 00:00:00
2023-12-12T12:13:17.854623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:18.079716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

도로명주소
Text

Distinct3162
Distinct (%)75.8%
Missing0
Missing (%)0.0%
Memory size32.7 KiB
2023-12-12T12:13:18.364875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length52
Mean length32.452193
Min length15

Characters and Unicode

Total characters135423
Distinct characters382
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2860 ?
Unique (%)68.5%

Sample

1st row광주광역시 서구 상무오월로19번길 6, 1층 (쌍촌동)
2nd row광주광역시 서구 월드컵4강로181번길 5 (쌍촌동)
3rd row광주광역시 서구 치평동 1176-3 상무타워 5층 566호
4th row광주광역시 서구 하남대로550번길 19-40, 1층 (동천동)
5th row광주광역시 서구 운천로 153, 1층 (쌍촌동)
ValueCountFrequency (%)
광주광역시 4175
 
15.3%
서구 4173
 
15.3%
1층 1579
 
5.8%
치평동 672
 
2.5%
화정동 663
 
2.4%
쌍촌동 602
 
2.2%
풍암동 452
 
1.7%
광천동 426
 
1.6%
지하1층 395
 
1.4%
무진대로 378
 
1.4%
Other values (2495) 13737
50.4%
2023-12-12T12:13:18.867179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
23285
 
17.2%
광 9196
 
6.8%
1 6839
 
5.1%
동 4988
 
3.7%
주 4636
 
3.4%
시 4450
 
3.3%
) 4237
 
3.1%
( 4236
 
3.1%
서 4234
 
3.1%
구 4206
 
3.1%
Other values (372) 65116
48.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 76312
56.4%
Space Separator 23285
 
17.2%
Decimal Number 22116
 
16.3%
Close Punctuation 4237
 
3.1%
Open Punctuation 4236
 
3.1%
Other Punctuation 4195
 
3.1%
Dash Punctuation 861
 
0.6%
Uppercase Letter 124
 
0.1%
Math Symbol 29
 
< 0.1%
Lowercase Letter 27
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 9196
 
12.1%
동 4988
 
6.5%
주 4636
 
6.1%
시 4450
 
5.8%
서 4234
 
5.5%
구 4206
 
5.5%
역 4178
 
5.5%
로 3826
 
5.0%
층 2958
 
3.9%
화 1770
 
2.3%
Other values (321) 31870
41.8%
Uppercase Letter
ValueCountFrequency (%)
B 39
31.5%
S 26
21.0%
C 13
 
10.5%
A 13
 
10.5%
E 5
 
4.0%
D 4
 
3.2%
K 4
 
3.2%
G 3
 
2.4%
M 2
 
1.6%
H 2
 
1.6%
Other values (8) 13
 
10.5%
Decimal Number
ValueCountFrequency (%)
1 6839
30.9%
2 3045
13.8%
0 2346
 
10.6%
3 2035
 
9.2%
4 1657
 
7.5%
6 1419
 
6.4%
9 1375
 
6.2%
5 1322
 
6.0%
7 1071
 
4.8%
8 1007
 
4.6%
Lowercase Letter
ValueCountFrequency (%)
e 10
37.0%
a 3
 
11.1%
t 3
 
11.1%
l 2
 
7.4%
r 2
 
7.4%
n 2
 
7.4%
y 2
 
7.4%
b 1
 
3.7%
d 1
 
3.7%
h 1
 
3.7%
Other Punctuation
ValueCountFrequency (%)
, 4144
98.8%
* 26
 
0.6%
. 13
 
0.3%
@ 6
 
0.1%
· 3
 
0.1%
& 3
 
0.1%
Math Symbol
ValueCountFrequency (%)
~ 28
96.6%
= 1
 
3.4%
Space Separator
ValueCountFrequency (%)
23285
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4237
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4236
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 861
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 76312
56.4%
Common 58960
43.5%
Latin 151
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 9196
 
12.1%
동 4988
 
6.5%
주 4636
 
6.1%
시 4450
 
5.8%
서 4234
 
5.5%
구 4206
 
5.5%
역 4178
 
5.5%
로 3826
 
5.0%
층 2958
 
3.9%
화 1770
 
2.3%
Other values (321) 31870
41.8%
Latin
ValueCountFrequency (%)
B 39
25.8%
S 26
17.2%
C 13
 
8.6%
A 13
 
8.6%
e 10
 
6.6%
E 5
 
3.3%
D 4
 
2.6%
K 4
 
2.6%
G 3
 
2.0%
a 3
 
2.0%
Other values (18) 31
20.5%
Common
ValueCountFrequency (%)
23285
39.5%
1 6839
 
11.6%
) 4237
 
7.2%
( 4236
 
7.2%
, 4144
 
7.0%
2 3045
 
5.2%
0 2346
 
4.0%
3 2035
 
3.5%
4 1657
 
2.8%
6 1419
 
2.4%
Other values (13) 5717
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 76312
56.4%
ASCII 59108
43.6%
None 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
23285
39.4%
1 6839
 
11.6%
) 4237
 
7.2%
( 4236
 
7.2%
, 4144
 
7.0%
2 3045
 
5.2%
0 2346
 
4.0%
3 2035
 
3.4%
4 1657
 
2.8%
6 1419
 
2.4%
Other values (40) 5865
 
9.9%
Hangul
ValueCountFrequency (%)
광 9196
 
12.1%
동 4988
 
6.5%
주 4636
 
6.1%
시 4450
 
5.8%
서 4234
 
5.5%
구 4206
 
5.5%
역 4178
 
5.5%
로 3826
 
5.0%
층 2958
 
3.9%
화 1770
 
2.3%
Other values (321) 31870
41.8%
None
ValueCountFrequency (%)
· 3
100.0%

위도
Real number (ℝ)

Distinct2045
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.147589
Minimum35.09441
Maximum35.174976
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.8 KiB
2023-12-12T12:13:19.032532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.09441
5-th percentile35.121365
Q135.140778
median35.151134
Q335.15832
95-th percentile35.164584
Maximum35.174976
Range0.08056603
Interquartile range (IQR)0.01754151

Descriptive statistics

Standard deviation0.013614972
Coefficient of variation (CV)0.00038736573
Kurtosis-0.055499531
Mean35.147589
Median Absolute Deviation (MAD)0.00768333
Skewness-0.79241799
Sum146670.89
Variance0.00018536746
MonotonicityNot monotonic
2023-12-12T12:13:19.233995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.15967899 322
 
7.7%
35.15881732 219
 
5.2%
35.13364695 120
 
2.9%
35.15236613 46
 
1.1%
35.11727429 29
 
0.7%
35.16057298 28
 
0.7%
35.14749209 22
 
0.5%
35.15429793 21
 
0.5%
35.15212448 20
 
0.5%
35.11622254 20
 
0.5%
Other values (2035) 3326
79.7%
ValueCountFrequency (%)
35.09440975 2
< 0.1%
35.09471422 1
 
< 0.1%
35.09528086 4
0.1%
35.10662657 1
 
< 0.1%
35.11074303 2
< 0.1%
35.11092531 1
 
< 0.1%
35.11134476 1
 
< 0.1%
35.11191788 1
 
< 0.1%
35.11262275 3
0.1%
35.11268551 1
 
< 0.1%
ValueCountFrequency (%)
35.17497578 1
 
< 0.1%
35.17364151 1
 
< 0.1%
35.17302105 1
 
< 0.1%
35.17266686 1
 
< 0.1%
35.17232368 1
 
< 0.1%
35.17211522 1
 
< 0.1%
35.17169953 1
 
< 0.1%
35.1716936 1
 
< 0.1%
35.17168716 4
0.1%
35.17168695 1
 
< 0.1%

경도
Real number (ℝ)

Distinct2041
Distinct (%)48.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.86737
Minimum126.81334
Maximum126.90858
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size36.8 KiB
2023-12-12T12:13:19.439214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.81334
5-th percentile126.84685
Q1126.85549
median126.86623
Q3126.88205
95-th percentile126.88674
Maximum126.90858
Range0.095233
Interquartile range (IQR)0.0265637

Descriptive statistics

Standard deviation0.01472224
Coefficient of variation (CV)0.00011604434
Kurtosis-0.53532504
Mean126.86737
Median Absolute Deviation (MAD)0.0131873
Skewness0.00089756779
Sum529417.55
Variance0.00021674436
MonotonicityNot monotonic
2023-12-12T12:13:19.644664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.8826316 322
 
7.7%
126.8820506 219
 
5.2%
126.8748343 120
 
2.9%
126.8524184 46
 
1.1%
126.8590206 29
 
0.7%
126.8798753 28
 
0.7%
126.8479215 22
 
0.5%
126.8489122 21
 
0.5%
126.8478453 20
 
0.5%
126.8661323 20
 
0.5%
Other values (2031) 3326
79.7%
ValueCountFrequency (%)
126.8133424 2
< 0.1%
126.813933 4
0.1%
126.814983 1
 
< 0.1%
126.8210521 1
 
< 0.1%
126.8254026 2
< 0.1%
126.8265132 1
 
< 0.1%
126.8297784 1
 
< 0.1%
126.8307941 1
 
< 0.1%
126.8309437 1
 
< 0.1%
126.8314834 1
 
< 0.1%
ValueCountFrequency (%)
126.9085754 1
< 0.1%
126.9081619 1
< 0.1%
126.9078532 1
< 0.1%
126.907088 1
< 0.1%
126.9069991 1
< 0.1%
126.9068921 1
< 0.1%
126.9067052 1
< 0.1%
126.9063962 1
< 0.1%
126.9063885 1
< 0.1%
126.9061525 1
< 0.1%

Interactions

2023-12-12T12:13:16.170132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:15.404696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:15.766664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:16.292294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:15.530288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:15.884945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:16.413760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:15.662986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:13:16.041222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:13:19.752933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종명위도경도
연번1.0000.3400.1260.145
업종명0.3401.0000.5110.552
위도0.1260.5111.0000.841
경도0.1450.5520.8411.000
2023-12-12T12:13:19.864153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도
연번1.000-0.0250.019
위도-0.0251.0000.210
경도0.0190.2101.000

Missing values

2023-12-12T12:13:16.547877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:13:16.660892image/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

연번업종명폐업일자도로명주소위도경도
01통신판매업2020-01-01광주광역시 서구 상무오월로19번길 6, 1층 (쌍촌동)35.156023126.861663
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연번업종명폐업일자도로명주소위도경도
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41644165의료유사업2022-05-27광주광역시 서구 무진대로 932, 신세계백화점 8층 (광천동)35.159679126.882632
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