Overview

Dataset statistics

Number of variables12
Number of observations1113
Missing cells947
Missing cells (%)7.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory111.0 KiB
Average record size in memory102.1 B

Variable types

Numeric5
Text4
Categorical3

Dataset

Description허가신고번호,상호(사업장명칭),소재지_지번_법정동,소재지_지번_산,소재지_지번_번지,소재지_지번_호,소재지_지번_통주소,대표업종명,휴폐업구분,구명,X좌표,Y좌표
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-21104/S/1/datasetView.do

Alerts

허가신고번호 is highly overall correlated with 소재지_지번_법정동 and 2 other fieldsHigh correlation
소재지_지번_법정동 is highly overall correlated with 허가신고번호 and 2 other fieldsHigh correlation
X좌표 is highly overall correlated with 구명High correlation
Y좌표 is highly overall correlated with 허가신고번호 and 2 other fieldsHigh correlation
구명 is highly overall correlated with 허가신고번호 and 3 other fieldsHigh correlation
소재지_지번_산 is highly imbalanced (94.3%)Imbalance
소재지_지번_호 has 325 (29.2%) missing valuesMissing
대표업종명 has 620 (55.7%) missing valuesMissing
허가신고번호 has unique valuesUnique

Reproduction

Analysis started2023-12-11 08:54:06.334592
Analysis finished2023-12-11 08:54:10.742568
Duration4.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

허가신고번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1113
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.140899 × 1017
Minimum3.0000005 × 1017
Maximum3.2400005 × 1017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2023-12-11T17:54:10.822958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0000005 × 1017
5-th percentile3.0100005 × 1017
Q13.0800005 × 1017
median3.1500005 × 1017
Q33.2100005 × 1017
95-th percentile3.2400005 × 1017
Maximum3.2400005 × 1017
Range2.4 × 1016
Interquartile range (IQR)1.3 × 1016

Descriptive statistics

Standard deviation7.3640566 × 1015
Coefficient of variation (CV)0.023445697
Kurtosis-1.1211816
Mean3.140899 × 1017
Median Absolute Deviation (MAD)6 × 1015
Skewness-0.38865326
Sum-9.0608264 × 1017
Variance5.422933 × 1031
MonotonicityStrictly increasing
2023-12-11T17:54:10.999803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300000049199600001 1
 
0.1%
318000049199700008 1
 
0.1%
318000049200300001 1
 
0.1%
318000049200000001 1
 
0.1%
318000049199900001 1
 
0.1%
318000049199800002 1
 
0.1%
318000049199800001 1
 
0.1%
318000049199700009 1
 
0.1%
318000049199700007 1
 
0.1%
318000049199600092 1
 
0.1%
Other values (1103) 1103
99.1%
ValueCountFrequency (%)
300000049199600001 1
0.1%
300000049199600002 1
0.1%
300000049199600003 1
0.1%
300000049199600004 1
0.1%
300000049199600006 1
0.1%
300000049199600007 1
0.1%
300000049199600008 1
0.1%
300000049199600009 1
0.1%
300000049199600010 1
0.1%
300000049199600011 1
0.1%
ValueCountFrequency (%)
324000049201800001 1
0.1%
324000049200100077 1
0.1%
324000049200000076 1
0.1%
324000049199900075 1
0.1%
324000049199800074 1
0.1%
324000049199600076 1
0.1%
324000049199600074 1
0.1%
324000049199600073 1
0.1%
324000049199600072 1
0.1%
324000049199600071 1
0.1%

상호(사업장명칭)
Text

Distinct1060
Distinct (%)95.4%
Missing2
Missing (%)0.2%
Memory size8.8 KiB
2023-12-11T17:54:11.296869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length20
Mean length9.2574257
Min length3

Characters and Unicode

Total characters10285
Distinct characters443
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1017 ?
Unique (%)91.5%

Sample

1st row종로주유소
2nd row자하문주유소
3rd rowSK글로벌 (주)동대문주유소
4th rowSK네트웍스(주)재동주유소
5th row경일석유(주)경일주유소
ValueCountFrequency (%)
주유소 12
 
0.9%
직영 12
 
0.9%
주식회사 11
 
0.8%
현대오일뱅크(주 10
 
0.7%
현대오일뱅크(주)직영 9
 
0.7%
sk네트웍스(주 8
 
0.6%
구도일주유소 8
 
0.6%
주)삼표에너지 6
 
0.4%
주)중앙에너비스 5
 
0.4%
현대주유소 5
 
0.4%
Other values (1162) 1271
93.7%
2023-12-11T17:54:11.744531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 1187
 
11.5%
유 783
 
7.6%
소 708
 
6.9%
( 513
 
5.0%
) 513
 
5.0%
248
 
2.4%
대 192
 
1.9%
지 172
 
1.7%
동 156
 
1.5%
스 147
 
1.4%
Other values (433) 5666
55.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8619
83.8%
Open Punctuation 513
 
5.0%
Close Punctuation 513
 
5.0%
Uppercase Letter 298
 
2.9%
Space Separator 248
 
2.4%
Decimal Number 77
 
0.7%
Lowercase Letter 6
 
0.1%
Dash Punctuation 5
 
< 0.1%
Other Punctuation 4
 
< 0.1%
Other Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 1187
 
13.8%
유 783
 
9.1%
소 708
 
8.2%
대 192
 
2.2%
지 172
 
2.0%
동 156
 
1.8%
스 147
 
1.7%
에 136
 
1.6%
트 124
 
1.4%
일 119
 
1.4%
Other values (392) 4895
56.8%
Uppercase Letter
ValueCountFrequency (%)
K 100
33.6%
S 100
33.6%
G 12
 
4.0%
T 11
 
3.7%
L 11
 
3.7%
O 8
 
2.7%
H 7
 
2.3%
A 7
 
2.3%
I 6
 
2.0%
E 6
 
2.0%
Other values (12) 30
 
10.1%
Decimal Number
ValueCountFrequency (%)
2 24
31.2%
1 18
23.4%
3 14
18.2%
6 5
 
6.5%
5 4
 
5.2%
9 3
 
3.9%
7 3
 
3.9%
8 3
 
3.9%
0 2
 
2.6%
4 1
 
1.3%
Other Punctuation
ValueCountFrequency (%)
. 3
75.0%
？ 1
 
25.0%
Lowercase Letter
ValueCountFrequency (%)
k 3
50.0%
s 3
50.0%
Open Punctuation
ValueCountFrequency (%)
( 513
100.0%
Close Punctuation
ValueCountFrequency (%)
) 513
100.0%
Space Separator
ValueCountFrequency (%)
248
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8621
83.8%
Common 1360
 
13.2%
Latin 304
 
3.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 1187
 
13.8%
유 783
 
9.1%
소 708
 
8.2%
대 192
 
2.2%
지 172
 
2.0%
동 156
 
1.8%
스 147
 
1.7%
에 136
 
1.6%
트 124
 
1.4%
일 119
 
1.4%
Other values (393) 4897
56.8%
Latin
ValueCountFrequency (%)
K 100
32.9%
S 100
32.9%
G 12
 
3.9%
T 11
 
3.6%
L 11
 
3.6%
O 8
 
2.6%
H 7
 
2.3%
A 7
 
2.3%
I 6
 
2.0%
E 6
 
2.0%
Other values (14) 36
 
11.8%
Common
ValueCountFrequency (%)
( 513
37.7%
) 513
37.7%
248
18.2%
2 24
 
1.8%
1 18
 
1.3%
3 14
 
1.0%
- 5
 
0.4%
6 5
 
0.4%
5 4
 
0.3%
. 3
 
0.2%
Other values (6) 13
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8619
83.8%
ASCII 1663
 
16.2%
None 3
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 1187
 
13.8%
유 783
 
9.1%
소 708
 
8.2%
대 192
 
2.2%
지 172
 
2.0%
동 156
 
1.8%
스 147
 
1.7%
에 136
 
1.6%
트 124
 
1.4%
일 119
 
1.4%
Other values (392) 4895
56.8%
ASCII
ValueCountFrequency (%)
( 513
30.8%
) 513
30.8%
248
14.9%
K 100
 
6.0%
S 100
 
6.0%
2 24
 
1.4%
1 18
 
1.1%
3 14
 
0.8%
G 12
 
0.7%
T 11
 
0.7%
Other values (29) 110
 
6.6%
None
ValueCountFrequency (%)
㈜ 2
66.7%
？ 1
33.3%

소재지_지번_법정동
Real number (ℝ)

HIGH CORRELATION 

Distinct262
Distinct (%)23.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1471364 × 109
Minimum1.1110105 × 109
Maximum1.174011 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2023-12-11T17:54:11.927866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110105 × 109
5-th percentile1.1140162 × 109
Q11.1305101 × 109
median1.150011 × 109
Q31.1650102 × 109
95-th percentile1.1740101 × 109
Maximum1.174011 × 109
Range63000500
Interquartile range (IQR)34500100

Descriptive statistics

Standard deviation19125943
Coefficient of variation (CV)0.016672771
Kurtosis-1.177147
Mean1.1471364 × 109
Median Absolute Deviation (MAD)17999500
Skewness-0.31290134
Sum1.2767629 × 1012
Variance3.6580171 × 1014
MonotonicityNot monotonic
2023-12-11T17:54:12.089008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1156011000 24
 
2.2%
1156013200 23
 
2.1%
1154510200 22
 
2.0%
1154510100 20
 
1.8%
1132010500 20
 
1.8%
1168010100 20
 
1.8%
1154510300 20
 
1.8%
1162010200 19
 
1.7%
1147010100 18
 
1.6%
1132010700 18
 
1.6%
Other values (252) 909
81.7%
ValueCountFrequency (%)
1111010500 1
 
0.1%
1111011300 1
 
0.1%
1111011500 1
 
0.1%
1111011600 1
 
0.1%
1111011700 2
0.2%
1111011900 3
0.3%
1111012300 1
 
0.1%
1111012400 1
 
0.1%
1111012500 2
0.2%
1111012700 1
 
0.1%
ValueCountFrequency (%)
1174011000 3
 
0.3%
1174010900 9
0.8%
1174010800 9
0.8%
1174010700 3
 
0.3%
1174010600 8
0.7%
1174010500 14
1.3%
1174010300 4
 
0.4%
1174010200 2
 
0.2%
1174010100 7
0.6%
1171011400 4
 
0.4%

소재지_지번_산
Categorical

IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
1
1102 
2
 
6
<NA>
 
5

Length

Max length4
Median length1
Mean length1.0134771
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 1102
99.0%
2 6
 
0.5%
<NA> 5
 
0.4%

Length

2023-12-11T17:54:12.255478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T17:54:12.372491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 1102
99.0%
2 6
 
0.5%
na 5
 
0.4%

소재지_지번_번지
Text

Distinct634
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
2023-12-11T17:54:12.739979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length2.9200359
Min length1

Characters and Unicode

Total characters3250
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique385 ?
Unique (%)34.6%

Sample

1st row155
2nd row155
3rd row310
4th row96
5th row1367
ValueCountFrequency (%)
1 14
 
1.3%
19 11
 
1.0%
40 9
 
0.8%
14 7
 
0.6%
2 7
 
0.6%
103 7
 
0.6%
13 6
 
0.5%
9 6
 
0.5%
81 6
 
0.5%
30 6
 
0.5%
Other values (624) 1034
92.9%
2023-12-11T17:54:13.304646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 570
17.5%
2 383
11.8%
3 332
10.2%
6 302
9.3%
4 298
9.2%
5 289
8.9%
0 284
8.7%
9 248
7.6%
7 247
7.6%
8 211
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 3164
97.4%
Dash Punctuation 86
 
2.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 570
18.0%
2 383
12.1%
3 332
10.5%
6 302
9.5%
4 298
9.4%
5 289
9.1%
0 284
9.0%
9 248
7.8%
7 247
7.8%
8 211
 
6.7%
Dash Punctuation
ValueCountFrequency (%)
- 86
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3250
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 570
17.5%
2 383
11.8%
3 332
10.2%
6 302
9.3%
4 298
9.2%
5 289
8.9%
0 284
8.7%
9 248
7.6%
7 247
7.6%
8 211
 
6.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3250
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 570
17.5%
2 383
11.8%
3 332
10.2%
6 302
9.3%
4 298
9.2%
5 289
8.9%
0 284
8.7%
9 248
7.6%
7 247
7.6%
8 211
 
6.5%

소재지_지번_호
Real number (ℝ)

MISSING 

Distinct115
Distinct (%)14.6%
Missing325
Missing (%)29.2%
Infinite0
Infinite (%)0.0%
Mean26.706853
Minimum0
Maximum969
Zeros2
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2023-12-11T17:54:13.456590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median7
Q318
95-th percentile100.65
Maximum969
Range969
Interquartile range (IQR)16

Descriptive statistics

Standard deviation73.199153
Coefficient of variation (CV)2.7408378
Kurtosis60.330509
Mean26.706853
Median Absolute Deviation (MAD)6
Skewness6.8696994
Sum21045
Variance5358.116
MonotonicityNot monotonic
2023-12-11T17:54:13.603346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 138
12.4%
2 67
 
6.0%
3 51
 
4.6%
4 44
 
4.0%
6 44
 
4.0%
5 37
 
3.3%
7 32
 
2.9%
8 28
 
2.5%
9 25
 
2.2%
11 18
 
1.6%
Other values (105) 304
27.3%
(Missing) 325
29.2%
ValueCountFrequency (%)
0 2
 
0.2%
1 138
12.4%
2 67
6.0%
3 51
 
4.6%
4 44
 
4.0%
5 37
 
3.3%
6 44
 
4.0%
7 32
 
2.9%
8 28
 
2.5%
9 25
 
2.2%
ValueCountFrequency (%)
969 1
0.1%
669 1
0.1%
604 1
0.1%
545 1
0.1%
543 1
0.1%
500 1
0.1%
478 1
0.1%
466 1
0.1%
446 1
0.1%
370 1
0.1%

소재지_지번_통주소
Text

Distinct1061
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
2023-12-11T17:54:13.962312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length44
Mean length21.398023
Min length13

Characters and Unicode

Total characters23816
Distinct characters246
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1013 ?
Unique (%)91.0%

Sample

1st row서울특별시 종로구 관철동 155번지
2nd row서울특별시 종로구 부암동 155-3번지
3rd row서울특별시 종로구 숭인동 310번지
4th row서울특별시 종로구 경운동 96-18번지
5th row서울특별시 종로구 숭인동 1367번지
ValueCountFrequency (%)
서울특별시 1113
 
24.5%
송파구 95
 
2.1%
영등포구 94
 
2.1%
강남구 90
 
2.0%
금천구 62
 
1.4%
도봉구 61
 
1.3%
강동구 59
 
1.3%
서초구 56
 
1.2%
강서구 51
 
1.1%
양천구 48
 
1.1%
Other values (1331) 2813
61.9%
2023-12-11T17:54:14.530262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3434
 
14.4%
서 1284
 
5.4%
동 1267
 
5.3%
구 1168
 
4.9%
지 1141
 
4.8%
시 1134
 
4.8%
번 1122
 
4.7%
울 1117
 
4.7%
특 1113
 
4.7%
별 1113
 
4.7%
Other values (236) 9923
41.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15008
63.0%
Decimal Number 4471
 
18.8%
Space Separator 3434
 
14.4%
Dash Punctuation 876
 
3.7%
Other Punctuation 15
 
0.1%
Uppercase Letter 6
 
< 0.1%
Close Punctuation 3
 
< 0.1%
Open Punctuation 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 1284
 
8.6%
동 1267
 
8.4%
구 1168
 
7.8%
지 1141
 
7.6%
시 1134
 
7.6%
번 1122
 
7.5%
울 1117
 
7.4%
특 1113
 
7.4%
별 1113
 
7.4%
강 227
 
1.5%
Other values (218) 4322
28.8%
Decimal Number
ValueCountFrequency (%)
1 946
21.2%
2 563
12.6%
3 495
11.1%
4 413
9.2%
5 409
9.1%
6 406
9.1%
7 333
 
7.4%
0 323
 
7.2%
9 306
 
6.8%
8 277
 
6.2%
Other Punctuation
ValueCountFrequency (%)
, 14
93.3%
: 1
 
6.7%
Uppercase Letter
ValueCountFrequency (%)
S 3
50.0%
K 3
50.0%
Space Separator
ValueCountFrequency (%)
3434
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 876
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15008
63.0%
Common 8802
37.0%
Latin 6
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 1284
 
8.6%
동 1267
 
8.4%
구 1168
 
7.8%
지 1141
 
7.6%
시 1134
 
7.6%
번 1122
 
7.5%
울 1117
 
7.4%
특 1113
 
7.4%
별 1113
 
7.4%
강 227
 
1.5%
Other values (218) 4322
28.8%
Common
ValueCountFrequency (%)
3434
39.0%
1 946
 
10.7%
- 876
 
10.0%
2 563
 
6.4%
3 495
 
5.6%
4 413
 
4.7%
5 409
 
4.6%
6 406
 
4.6%
7 333
 
3.8%
0 323
 
3.7%
Other values (6) 604
 
6.9%
Latin
ValueCountFrequency (%)
S 3
50.0%
K 3
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15008
63.0%
ASCII 8808
37.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3434
39.0%
1 946
 
10.7%
- 876
 
9.9%
2 563
 
6.4%
3 495
 
5.6%
4 413
 
4.7%
5 409
 
4.6%
6 406
 
4.6%
7 333
 
3.8%
0 323
 
3.7%
Other values (8) 610
 
6.9%
Hangul
ValueCountFrequency (%)
서 1284
 
8.6%
동 1267
 
8.4%
구 1168
 
7.8%
지 1141
 
7.6%
시 1134
 
7.6%
번 1122
 
7.5%
울 1117
 
7.4%
특 1113
 
7.4%
별 1113
 
7.4%
강 227
 
1.5%
Other values (218) 4322
28.8%

대표업종명
Text

MISSING 

Distinct52
Distinct (%)10.5%
Missing620
Missing (%)55.7%
Memory size8.8 KiB
2023-12-11T17:54:14.819059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length7
Mean length7.3610548
Min length2

Characters and Unicode

Total characters3629
Distinct characters119
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)6.3%

Sample

1st row주유소 운영업
2nd row주유소 운영업
3rd row기타 서비스업
4th row통신업
5th row사적지 관리 운영업
ValueCountFrequency (%)
운영업 335
32.8%
주유소 333
32.6%
액체연료 51
 
5.0%
소매업 51
 
5.0%
운송업 16
 
1.6%
시내버스 14
 
1.4%
서비스업 12
 
1.2%
자동차 9
 
0.9%
세차업 9
 
0.9%
제조업 9
 
0.9%
Other values (75) 181
17.7%
2023-12-11T17:54:15.304063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
527
14.5%
업 477
13.1%
소 389
10.7%
운 356
9.8%
유 343
9.5%
주 341
9.4%
영 335
9.2%
연 54
 
1.5%
체 52
 
1.4%
료 52
 
1.4%
Other values (109) 703
19.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3100
85.4%
Space Separator 527
 
14.5%
Other Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 477
15.4%
소 389
12.5%
운 356
11.5%
유 343
11.1%
주 341
11.0%
영 335
10.8%
연 54
 
1.7%
체 52
 
1.7%
료 52
 
1.7%
매 52
 
1.7%
Other values (107) 649
20.9%
Space Separator
ValueCountFrequency (%)
527
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3100
85.4%
Common 529
 
14.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 477
15.4%
소 389
12.5%
운 356
11.5%
유 343
11.1%
주 341
11.0%
영 335
10.8%
연 54
 
1.7%
체 52
 
1.7%
료 52
 
1.7%
매 52
 
1.7%
Other values (107) 649
20.9%
Common
ValueCountFrequency (%)
527
99.6%
, 2
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3100
85.4%
ASCII 529
 
14.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
527
99.6%
, 2
 
0.4%
Hangul
ValueCountFrequency (%)
업 477
15.4%
소 389
12.5%
운 356
11.5%
유 343
11.1%
주 341
11.0%
영 335
10.8%
연 54
 
1.7%
체 52
 
1.7%
료 52
 
1.7%
매 52
 
1.7%
Other values (107) 649
20.9%

휴폐업구분
Categorical

Distinct4
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
<NA>
705 
폐쇄
199 
폐업
191 
제외사
 
18

Length

Max length4
Median length4
Mean length3.2830189
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 705
63.3%
폐쇄 199
 
17.9%
폐업 191
 
17.2%
제외사 18
 
1.6%

Length

2023-12-11T17:54:15.561693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T17:54:15.723733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 705
63.3%
폐쇄 199
 
17.9%
폐업 191
 
17.2%
제외사 18
 
1.6%

구명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
송파구
95 
영등포구
94 
강남구
90 
금천구
 
62
도봉구
 
61
Other values (20)
711 

Length

Max length4
Median length3
Mean length3.1257862
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
송파구 95
 
8.5%
영등포구 94
 
8.4%
강남구 90
 
8.1%
금천구 62
 
5.6%
도봉구 61
 
5.5%
강동구 59
 
5.3%
서초구 56
 
5.0%
강서구 51
 
4.6%
양천구 48
 
4.3%
성동구 46
 
4.1%
Other values (15) 451
40.5%

Length

2023-12-11T17:54:15.851423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
송파구 95
 
8.5%
영등포구 94
 
8.4%
강남구 90
 
8.1%
금천구 62
 
5.6%
도봉구 61
 
5.5%
강동구 59
 
5.3%
서초구 56
 
5.0%
강서구 51
 
4.6%
양천구 48
 
4.3%
성동구 46
 
4.1%
Other values (15) 451
40.5%

X좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct1056
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199388.75
Minimum179534.9
Maximum215352.34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2023-12-11T17:54:16.013043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum179534.9
5-th percentile186448.57
Q1192335.62
median200757.76
Q3205240.63
95-th percentile212073.57
Maximum215352.34
Range35817.441
Interquartile range (IQR)12905.006

Descriptive statistics

Standard deviation8001.7987
Coefficient of variation (CV)0.040131646
Kurtosis-1.0477915
Mean199388.75
Median Absolute Deviation (MAD)6716.1303
Skewness-0.073623974
Sum2.2191968 × 108
Variance64028783
MonotonicityNot monotonic
2023-12-11T17:54:16.185660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
203845.788347 3
 
0.3%
201204.268084 3
 
0.3%
203482.220234 3
 
0.3%
190218.216115 3
 
0.3%
202611.193024 2
 
0.2%
192808.08111 2
 
0.2%
215041.519101 2
 
0.2%
202834.214589 2
 
0.2%
204367.358089 2
 
0.2%
204027.272598 2
 
0.2%
Other values (1046) 1089
97.8%
ValueCountFrequency (%)
179534.897829 1
0.1%
182044.15525 1
0.1%
182718.754895 1
0.1%
182867.745886 1
0.1%
183045.409563 1
0.1%
183214.035503 1
0.1%
183223.958796 1
0.1%
183426.61655 1
0.1%
183517.047131 1
0.1%
183609.396253 1
0.1%
ValueCountFrequency (%)
215352.338461 1
0.1%
215109.689403 2
0.2%
215041.519101 2
0.2%
215028.215791 1
0.1%
214935.837571 1
0.1%
214828.045012 1
0.1%
214305.582617 1
0.1%
213843.079476 1
0.1%
213778.915135 1
0.1%
213737.659284 1
0.1%

Y좌표
Real number (ℝ)

HIGH CORRELATION 

Distinct1056
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean448998.79
Minimum437537.86
Maximum465418.82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2023-12-11T17:54:16.401067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum437537.86
5-th percentile441148.42
Q1444642.75
median448187.83
Q3452472.63
95-th percentile460739.42
Maximum465418.82
Range27880.957
Interquartile range (IQR)7829.8751

Descriptive statistics

Standard deviation5736.8262
Coefficient of variation (CV)0.01277693
Kurtosis-0.039493092
Mean448998.79
Median Absolute Deviation (MAD)3850.2083
Skewness0.63429929
Sum4.9973566 × 108
Variance32911175
MonotonicityNot monotonic
2023-12-11T17:54:16.562411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
463090.93599 3
 
0.3%
461833.127902 3
 
0.3%
460797.889492 3
 
0.3%
440895.106889 3
 
0.3%
460282.198124 2
 
0.2%
446118.624143 2
 
0.2%
449539.270535 2
 
0.2%
460604.081838 2
 
0.2%
464902.372271 2
 
0.2%
464970.527942 2
 
0.2%
Other values (1046) 1089
97.8%
ValueCountFrequency (%)
437537.857849 1
0.1%
438219.241585 1
0.1%
438345.822462 1
0.1%
438437.441228 1
0.1%
438763.273994 1
0.1%
438924.369707 1
0.1%
438977.097674 1
0.1%
439006.374585 1
0.1%
439019.197551 1
0.1%
439024.300045 1
0.1%
ValueCountFrequency (%)
465418.815099 1
0.1%
465383.096104 1
0.1%
465222.728954 1
0.1%
464970.527942 2
0.2%
464902.372271 2
0.2%
464857.654495 1
0.1%
464600.799493 2
0.2%
464370.42972 1
0.1%
464351.174892 1
0.1%
464192.023729 1
0.1%

Interactions

2023-12-11T17:54:09.378857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:07.243988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:07.784288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:08.347950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:08.860739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:09.478900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:07.347292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:07.896178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:08.448830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:08.970633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:09.889561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:07.464703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:08.006299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:08.550773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:09.074849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:09.994608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:07.564892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:08.111089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:08.647221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:09.175639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:10.172269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:07.687095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:08.243182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:08.750031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T17:54:09.280211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T17:54:16.681536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
허가신고번호소재지_지번_법정동소재지_지번_산소재지_지번_호대표업종명휴폐업구분구명X좌표Y좌표
허가신고번호1.0000.9970.0630.1560.7140.2841.0000.9110.899
소재지_지번_법정동0.9971.0000.0520.1760.7180.2511.0000.9040.908
소재지_지번_산0.0630.0521.0000.0000.6780.0160.0530.0000.095
소재지_지번_호0.1560.1760.0001.0000.0000.0000.1870.0540.115
대표업종명0.7140.7180.6780.0001.0000.0000.7750.6270.425
휴폐업구분0.2840.2510.0160.0000.0001.0000.4690.1750.133
구명1.0001.0000.0530.1870.7750.4691.0000.9250.924
X좌표0.9110.9040.0000.0540.6270.1750.9251.0000.601
Y좌표0.8990.9080.0950.1150.4250.1330.9240.6011.000
2023-12-11T17:54:16.838717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구명휴폐업구분소재지_지번_산
구명1.0000.2670.045
휴폐업구분0.2671.0000.027
소재지_지번_산0.0450.0271.000
2023-12-11T17:54:16.972985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
허가신고번호소재지_지번_법정동소재지_지번_호X좌표Y좌표소재지_지번_산휴폐업구분구명
허가신고번호1.0000.997-0.1300.204-0.6610.0500.1720.992
소재지_지번_법정동0.9971.000-0.1300.206-0.6630.0400.1530.993
소재지_지번_호-0.130-0.1301.0000.0020.0960.0000.0000.074
X좌표0.2040.2060.0021.0000.1640.0000.0770.650
Y좌표-0.661-0.6630.0960.1641.0000.0730.0780.649
소재지_지번_산0.0500.0400.0000.0000.0731.0000.0270.045
휴폐업구분0.1720.1530.0000.0770.0780.0271.0000.267
구명0.9920.9930.0740.6500.6490.0450.2671.000

Missing values

2023-12-11T17:54:10.330276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T17:54:10.525195image/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.
2023-12-11T17:54:10.661300image/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

허가신고번호상호(사업장명칭)소재지_지번_법정동소재지_지번_산소재지_지번_번지소재지_지번_호소재지_지번_통주소대표업종명휴폐업구분구명X좌표Y좌표
0300000049199600001종로주유소11110135001155<NA>서울특별시 종로구 관철동 155번지<NA>폐업종로구198684.234632452147.684531
1300000049199600002자하문주유소111101840011553서울특별시 종로구 부암동 155-3번지<NA><NA>종로구196846.634773455195.595655
2300000049199600003SK글로벌 (주)동대문주유소11110175001310<NA>서울특별시 종로구 숭인동 310번지<NA>폐업종로구201420.058617452584.967862
3300000049199600004SK네트웍스(주)재동주유소111101340019618서울특별시 종로구 경운동 96-18번지주유소 운영업<NA>종로구198749.407544452986.644944
4300000049199600006경일석유(주)경일주유소111101750011367<NA>서울특별시 종로구 숭인동 1367번지<NA>폐업종로구202030.527878452811.410211
5300000049199600007(주)중앙에너비스 혜화지점111101690011022서울특별시 종로구 혜화동 102-2번지<NA><NA>종로구200094.348286454058.396653
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허가신고번호상호(사업장명칭)소재지_지번_법정동소재지_지번_산소재지_지번_번지소재지_지번_호소재지_지번_통주소대표업종명휴폐업구분구명X좌표Y좌표
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1105324000049199600073삼익파크맨션1174010500155<NA>서울특별시 강동구 길동 55번지<NA>폐쇄강동구213126.493469449434.561147
1106324000049199600074sk네트윅스(주)길동주유소117401050012552서울특별시 강동구 길동 255-2번지<NA><NA>강동구212937.900812448518.97204
1107324000049199600076하일동주유소11740110001616<NA>서울특별시 강동구 강일동 616번지<NA><NA>강동구215109.689403451227.439557
1108324000049199800074에스케이길동주유소11740105001255-2<NA>서울특별시 강동구 길동 255-2번지<NA><NA>강동구212937.900812448518.97204
1109324000049199900075방아다리주유소117401010012165서울특별시 강동구 명일동 216-5번지주유소 운영업<NA>강동구213737.659284449625.440343
1110324000049200000076하일동주유소11740110001616<NA>서울특별시 강동구 강일동 616번지<NA>폐업강동구215109.689403451227.439557
1111324000049200100077(주)서울승합11740110001565<NA>서울특별시 강동구 강일동 565번지<NA><NA>강동구215352.338461451574.052148
1112324000049201800001(주)동서울주유소117401030014438서울특별시 강동구 상일동 443-8번지 동서울주유소<NA><NA>강동구215041.519101449539.270535