Overview

Dataset statistics

Number of variables11
Number of observations1650
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory151.6 KiB
Average record size in memory94.1 B

Variable types

Categorical3
Numeric6
Text2

Dataset

Description상권_구분_코드,상권_구분_코드_명,상권_코드,상권_코드_명,엑스좌표_값,와이좌표_값,자치구_코드,자치구_코드_명,행정동_코드,행정동_코드_명,영역_면적
Author서울신용보증재단
URLhttps://data.seoul.go.kr/dataList/OA-15560/S/1/datasetView.do

Alerts

상권_구분_코드_명 is highly overall correlated with 상권_코드 and 1 other fieldsHigh correlation
상권_구분_코드 is highly overall correlated with 상권_코드 and 1 other fieldsHigh correlation
상권_코드 is highly overall correlated with 상권_구분_코드 and 1 other fieldsHigh correlation
엑스좌표_값 is highly overall correlated with 자치구_코드_명High correlation
와이좌표_값 is highly overall correlated with 자치구_코드 and 2 other fieldsHigh correlation
자치구_코드 is highly overall correlated with 와이좌표_값 and 2 other fieldsHigh correlation
행정동_코드 is highly overall correlated with 와이좌표_값 and 2 other fieldsHigh correlation
자치구_코드_명 is highly overall correlated with 엑스좌표_값 and 3 other fieldsHigh correlation
상권_코드 has unique valuesUnique
상권_코드_명 has unique valuesUnique

Reproduction

Analysis started2024-03-13 10:36:01.562312
Analysis finished2024-03-13 10:36:07.280074
Duration5.72 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

상권_구분_코드
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.0 KiB
A
1090 
R
305 
D
249 
U
 
6

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
A 1090
66.1%
R 305
 
18.5%
D 249
 
15.1%
U 6
 
0.4%

Length

2024-03-13T19:36:07.345014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T19:36:07.445925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
a 1090
66.1%
r 305
 
18.5%
d 249
 
15.1%
u 6
 
0.4%

상권_구분_코드_명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size13.0 KiB
골목상권
1090 
전통시장
305 
발달상권
249 
관광특구
 
6

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
골목상권 1090
66.1%
전통시장 305
 
18.5%
발달상권 249
 
15.1%
관광특구 6
 
0.4%

Length

2024-03-13T19:36:07.548786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T19:36:07.648352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
골목상권 1090
66.1%
전통시장 305
 
18.5%
발달상권 249
 
15.1%
관광특구 6
 
0.4%

상권_코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1650
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3115221.9
Minimum3001491
Maximum3130327
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 KiB
2024-03-13T19:36:07.765496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3001491
5-th percentile3110077.5
Q13110407.2
median3110819.5
Q33120141.8
95-th percentile3130238.5
Maximum3130327
Range128836
Interquartile range (IQR)9734.5

Descriptive statistics

Standard deviation10321.659
Coefficient of variation (CV)0.0033132981
Kurtosis51.673035
Mean3115221.9
Median Absolute Deviation (MAD)554
Skewness-4.3569757
Sum5.1401161 × 109
Variance1.0653664 × 108
MonotonicityNot monotonic
2024-03-13T19:36:07.901252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3110008 1
 
0.1%
3120048 1
 
0.1%
3120043 1
 
0.1%
3120042 1
 
0.1%
3120156 1
 
0.1%
3120155 1
 
0.1%
3120154 1
 
0.1%
3120157 1
 
0.1%
3120104 1
 
0.1%
3120103 1
 
0.1%
Other values (1640) 1640
99.4%
ValueCountFrequency (%)
3001491 1
0.1%
3001492 1
0.1%
3001493 1
0.1%
3001494 1
0.1%
3001495 1
0.1%
3001496 1
0.1%
3110001 1
0.1%
3110002 1
0.1%
3110003 1
0.1%
3110004 1
0.1%
ValueCountFrequency (%)
3130327 1
0.1%
3130326 1
0.1%
3130325 1
0.1%
3130324 1
0.1%
3130323 1
0.1%
3130322 1
0.1%
3130321 1
0.1%
3130320 1
0.1%
3130319 1
0.1%
3130318 1
0.1%

상권_코드_명
Text

UNIQUE 

Distinct1650
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size13.0 KiB
2024-03-13T19:36:08.233182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length28
Mean length7.5309091
Min length2

Characters and Unicode

Total characters12426
Distinct characters447
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

Unique1650 ?
Unique (%)100.0%

Sample

1st row배화여자대학교(박노수미술관)
2nd row자하문터널
3rd row평창동서측
4th row정독도서관
5th row중앙고등학교
ValueCountFrequency (%)
1번 79
 
3.7%
2번 59
 
2.7%
3번 55
 
2.6%
4번 52
 
2.4%
5번 25
 
1.2%
6번 20
 
0.9%
8번 15
 
0.7%
7번 15
 
0.7%
골목형상점가 15
 
0.7%
상점가 7
 
0.3%
Other values (1517) 1804
84.1%
2024-03-13T19:36:08.708325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
역 553
 
4.5%
496
 
4.0%
번 369
 
3.0%
동 368
 
3.0%
장 361
 
2.9%
시 340
 
2.7%
교 305
 
2.5%
학 283
 
2.3%
) 236
 
1.9%
( 236
 
1.9%
Other values (437) 8879
71.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10826
87.1%
Decimal Number 540
 
4.3%
Space Separator 496
 
4.0%
Close Punctuation 236
 
1.9%
Open Punctuation 236
 
1.9%
Uppercase Letter 52
 
0.4%
Other Punctuation 30
 
0.2%
Lowercase Letter 6
 
< 0.1%
Connector Punctuation 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 553
 
5.1%
번 369
 
3.4%
동 368
 
3.4%
장 361
 
3.3%
시 340
 
3.1%
교 305
 
2.8%
학 283
 
2.6%
대 208
 
1.9%
등 196
 
1.8%
신 183
 
1.7%
Other values (399) 7660
70.8%
Uppercase Letter
ValueCountFrequency (%)
K 10
19.2%
B 7
13.5%
T 6
11.5%
C 5
9.6%
G 4
 
7.7%
D 4
 
7.7%
S 3
 
5.8%
I 3
 
5.8%
A 2
 
3.8%
M 2
 
3.8%
Other values (4) 6
11.5%
Decimal Number
ValueCountFrequency (%)
1 154
28.5%
2 101
18.7%
3 84
15.6%
4 77
14.3%
5 38
 
7.0%
6 28
 
5.2%
8 20
 
3.7%
7 19
 
3.5%
9 12
 
2.2%
0 7
 
1.3%
Other Punctuation
ValueCountFrequency (%)
, 23
76.7%
. 3
 
10.0%
& 2
 
6.7%
! 1
 
3.3%
? 1
 
3.3%
Lowercase Letter
ValueCountFrequency (%)
a 2
33.3%
m 1
16.7%
t 1
16.7%
e 1
16.7%
h 1
16.7%
Space Separator
ValueCountFrequency (%)
496
100.0%
Close Punctuation
ValueCountFrequency (%)
) 236
100.0%
Open Punctuation
ValueCountFrequency (%)
( 236
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10826
87.1%
Common 1542
 
12.4%
Latin 58
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 553
 
5.1%
번 369
 
3.4%
동 368
 
3.4%
장 361
 
3.3%
시 340
 
3.1%
교 305
 
2.8%
학 283
 
2.6%
대 208
 
1.9%
등 196
 
1.8%
신 183
 
1.7%
Other values (399) 7660
70.8%
Common
ValueCountFrequency (%)
496
32.2%
) 236
15.3%
( 236
15.3%
1 154
 
10.0%
2 101
 
6.5%
3 84
 
5.4%
4 77
 
5.0%
5 38
 
2.5%
6 28
 
1.8%
, 23
 
1.5%
Other values (9) 69
 
4.5%
Latin
ValueCountFrequency (%)
K 10
17.2%
B 7
12.1%
T 6
10.3%
C 5
8.6%
G 4
 
6.9%
D 4
 
6.9%
S 3
 
5.2%
I 3
 
5.2%
a 2
 
3.4%
A 2
 
3.4%
Other values (9) 12
20.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10826
87.1%
ASCII 1600
 
12.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
역 553
 
5.1%
번 369
 
3.4%
동 368
 
3.4%
장 361
 
3.3%
시 340
 
3.1%
교 305
 
2.8%
학 283
 
2.6%
대 208
 
1.9%
등 196
 
1.8%
신 183
 
1.7%
Other values (399) 7660
70.8%
ASCII
ValueCountFrequency (%)
496
31.0%
) 236
14.8%
( 236
14.8%
1 154
 
9.6%
2 101
 
6.3%
3 84
 
5.2%
4 77
 
4.8%
5 38
 
2.4%
6 28
 
1.8%
, 23
 
1.4%
Other values (28) 127
 
7.9%

엑스좌표_값
Real number (ℝ)

HIGH CORRELATION 

Distinct1606
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198981.85
Minimum182509
Maximum215352
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 KiB
2024-03-13T19:36:08.854732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum182509
5-th percentile186416.9
Q1192815.25
median200096
Q3204366.25
95-th percentile211038.35
Maximum215352
Range32843
Interquartile range (IQR)11551

Descriptive statistics

Standard deviation7280.7206
Coefficient of variation (CV)0.036589874
Kurtosis-0.82852127
Mean198981.85
Median Absolute Deviation (MAD)5738
Skewness-0.11028963
Sum3.2832004 × 108
Variance53008893
MonotonicityNot monotonic
2024-03-13T19:36:08.986663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
197093 2
 
0.1%
198068 2
 
0.1%
205004 2
 
0.1%
202776 2
 
0.1%
191404 2
 
0.1%
193904 2
 
0.1%
201112 2
 
0.1%
202454 2
 
0.1%
192506 2
 
0.1%
198745 2
 
0.1%
Other values (1596) 1630
98.8%
ValueCountFrequency (%)
182509 1
0.1%
183081 1
0.1%
183092 1
0.1%
183093 1
0.1%
183179 1
0.1%
183212 1
0.1%
183234 1
0.1%
183317 1
0.1%
183423 1
0.1%
183479 1
0.1%
ValueCountFrequency (%)
215352 1
0.1%
215142 1
0.1%
215015 1
0.1%
215010 1
0.1%
214375 1
0.1%
213853 1
0.1%
213696 1
0.1%
213661 1
0.1%
213598 1
0.1%
213476 1
0.1%

와이좌표_값
Real number (ℝ)

HIGH CORRELATION 

Distinct1581
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean449875.45
Minimum437249
Maximum465573
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 KiB
2024-03-13T19:36:09.125496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum437249
5-th percentile441754.75
Q1445178.5
median449849.5
Q3453563.5
95-th percentile460122.5
Maximum465573
Range28324
Interquartile range (IQR)8385

Descriptive statistics

Standard deviation5590.5033
Coefficient of variation (CV)0.01242678
Kurtosis-0.51382154
Mean449875.45
Median Absolute Deviation (MAD)4215.5
Skewness0.30373208
Sum7.422945 × 108
Variance31253727
MonotonicityNot monotonic
2024-03-13T19:36:09.393248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
451158 3
 
0.2%
452276 3
 
0.2%
443233 3
 
0.2%
452821 2
 
0.1%
445778 2
 
0.1%
444220 2
 
0.1%
451117 2
 
0.1%
442391 2
 
0.1%
449309 2
 
0.1%
445288 2
 
0.1%
Other values (1571) 1627
98.6%
ValueCountFrequency (%)
437249 1
0.1%
437869 1
0.1%
437977 1
0.1%
438077 1
0.1%
438236 1
0.1%
438567 1
0.1%
438635 1
0.1%
438797 1
0.1%
438831 1
0.1%
439057 1
0.1%
ValueCountFrequency (%)
465573 1
0.1%
464982 1
0.1%
464883 1
0.1%
464087 1
0.1%
464016 1
0.1%
464014 1
0.1%
463983 1
0.1%
463707 1
0.1%
463673 1
0.1%
463610 1
0.1%

자치구_코드
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11426.218
Minimum11110
Maximum11740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 KiB
2024-03-13T19:36:09.530409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11140
Q111260
median11440
Q311590
95-th percentile11710
Maximum11740
Range630
Interquartile range (IQR)330

Descriptive statistics

Standard deviation190.53363
Coefficient of variation (CV)0.016675126
Kurtosis-1.2691142
Mean11426.218
Median Absolute Deviation (MAD)180
Skewness-0.017269577
Sum18853260
Variance36303.063
MonotonicityNot monotonic
2024-03-13T19:36:09.652717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11680 103
 
6.2%
11560 96
 
5.8%
11230 77
 
4.7%
11440 77
 
4.7%
11290 76
 
4.6%
11110 74
 
4.5%
11650 74
 
4.5%
11620 74
 
4.5%
11710 70
 
4.2%
11500 70
 
4.2%
Other values (15) 859
52.1%
ValueCountFrequency (%)
11110 74
4.5%
11140 68
4.1%
11170 57
3.5%
11200 55
3.3%
11215 62
3.8%
11230 77
4.7%
11260 61
3.7%
11290 76
4.6%
11305 63
3.8%
11320 43
2.6%
ValueCountFrequency (%)
11740 60
3.6%
11710 70
4.2%
11680 103
6.2%
11650 74
4.5%
11620 74
4.5%
11590 57
3.5%
11560 96
5.8%
11545 45
2.7%
11530 60
3.6%
11500 70
4.2%

자치구_코드_명
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size13.0 KiB
강남구
 
103
영등포구
 
96
마포구
 
77
동대문구
 
77
성북구
 
76
Other values (20)
1221 

Length

Max length4
Median length3
Mean length3.1012121
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
강남구 103
 
6.2%
영등포구 96
 
5.8%
마포구 77
 
4.7%
동대문구 77
 
4.7%
성북구 76
 
4.6%
서초구 74
 
4.5%
관악구 74
 
4.5%
종로구 74
 
4.5%
송파구 70
 
4.2%
강서구 70
 
4.2%
Other values (15) 859
52.1%

Length

2024-03-13T19:36:09.788244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
강남구 103
 
6.2%
영등포구 96
 
5.8%
마포구 77
 
4.7%
동대문구 77
 
4.7%
성북구 76
 
4.6%
서초구 74
 
4.5%
관악구 74
 
4.5%
종로구 74
 
4.5%
송파구 70
 
4.2%
강서구 70
 
4.2%
Other values (15) 859
52.1%

행정동_코드
Real number (ℝ)

HIGH CORRELATION 

Distinct399
Distinct (%)24.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11426859
Minimum11110515
Maximum11740700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 KiB
2024-03-13T19:36:09.921511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110515
5-th percentile11140540
Q111260575
median11440590
Q311590560
95-th percentile11710620
Maximum11740700
Range630185
Interquartile range (IQR)329985

Descriptive statistics

Standard deviation190548.8
Coefficient of variation (CV)0.016675519
Kurtosis-1.2691131
Mean11426859
Median Absolute Deviation (MAD)179932.5
Skewness-0.017138843
Sum1.8854317 × 1010
Variance3.6308846 × 1010
MonotonicityNot monotonic
2024-03-13T19:36:10.378304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11230545 17
 
1.0%
11560535 16
 
1.0%
11680640 14
 
0.8%
11230536 12
 
0.7%
11680565 12
 
0.7%
11440660 12
 
0.7%
11410615 12
 
0.7%
11110670 11
 
0.7%
11110615 11
 
0.7%
11410585 10
 
0.6%
Other values (389) 1523
92.3%
ValueCountFrequency (%)
11110515 5
0.3%
11110530 6
0.4%
11110540 1
 
0.1%
11110550 5
0.3%
11110560 3
 
0.2%
11110570 1
 
0.1%
11110580 2
 
0.1%
11110600 3
 
0.2%
11110615 11
0.7%
11110630 9
0.5%
ValueCountFrequency (%)
11740700 5
0.3%
11740685 8
0.5%
11740660 5
0.3%
11740650 3
 
0.2%
11740640 5
0.3%
11740620 4
0.2%
11740610 7
0.4%
11740600 5
0.3%
11740580 3
 
0.2%
11740570 4
0.2%

행정동_코드_명
Text

Distinct398
Distinct (%)24.1%
Missing0
Missing (%)0.0%
Memory size13.0 KiB
2024-03-13T19:36:10.771668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length4
Mean length3.7818182
Min length2

Characters and Unicode

Total characters6240
Distinct characters185
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

Unique41 ?
Unique (%)2.5%

Sample

1st row청운효자동
2nd row부암동
3rd row평창동
4th row가회동
5th row가회동
ValueCountFrequency (%)
제기동 17
 
1.0%
영등포동 16
 
1.0%
역삼1동 14
 
0.8%
연희동 12
 
0.7%
청담동 12
 
0.7%
용신동 12
 
0.7%
서교동 12
 
0.7%
종로1?2?3?4가동 11
 
0.7%
신사동 11
 
0.7%
창신1동 11
 
0.7%
Other values (388) 1522
92.2%
2024-03-13T19:36:11.173382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 1655
26.5%
1 411
 
6.6%
2 327
 
5.2%
신 153
 
2.5%
3 145
 
2.3%
4 100
 
1.6%
가 90
 
1.4%
성 73
 
1.2%
산 72
 
1.2%
화 71
 
1.1%
Other values (175) 3143
50.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5111
81.9%
Decimal Number 1069
 
17.1%
Other Punctuation 60
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1655
32.4%
신 153
 
3.0%
가 90
 
1.8%
성 73
 
1.4%
산 72
 
1.4%
화 71
 
1.4%
방 70
 
1.4%
곡 70
 
1.4%
수 63
 
1.2%
양 59
 
1.2%
Other values (164) 2735
53.5%
Decimal Number
ValueCountFrequency (%)
1 411
38.4%
2 327
30.6%
3 145
 
13.6%
4 100
 
9.4%
5 37
 
3.5%
6 28
 
2.6%
7 11
 
1.0%
8 8
 
0.7%
0 1
 
0.1%
9 1
 
0.1%
Other Punctuation
ValueCountFrequency (%)
? 60
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5111
81.9%
Common 1129
 
18.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1655
32.4%
신 153
 
3.0%
가 90
 
1.8%
성 73
 
1.4%
산 72
 
1.4%
화 71
 
1.4%
방 70
 
1.4%
곡 70
 
1.4%
수 63
 
1.2%
양 59
 
1.2%
Other values (164) 2735
53.5%
Common
ValueCountFrequency (%)
1 411
36.4%
2 327
29.0%
3 145
 
12.8%
4 100
 
8.9%
? 60
 
5.3%
5 37
 
3.3%
6 28
 
2.5%
7 11
 
1.0%
8 8
 
0.7%
0 1
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5111
81.9%
ASCII 1129
 
18.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 1655
32.4%
신 153
 
3.0%
가 90
 
1.8%
성 73
 
1.4%
산 72
 
1.4%
화 71
 
1.4%
방 70
 
1.4%
곡 70
 
1.4%
수 63
 
1.2%
양 59
 
1.2%
Other values (164) 2735
53.5%
ASCII
ValueCountFrequency (%)
1 411
36.4%
2 327
29.0%
3 145
 
12.8%
4 100
 
8.9%
? 60
 
5.3%
5 37
 
3.3%
6 28
 
2.5%
7 11
 
1.0%
8 8
 
0.7%
0 1
 
0.1%

영역_면적
Real number (ℝ)

Distinct1638
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean99927.952
Minimum1854
Maximum2462734
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size14.6 KiB
2024-03-13T19:36:11.322451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1854
5-th percentile8255.45
Q135330
median71927.5
Q3128147.25
95-th percentile266194.25
Maximum2462734
Range2460880
Interquartile range (IQR)92817.25

Descriptive statistics

Standard deviation118818.94
Coefficient of variation (CV)1.1890461
Kurtosis120.5507
Mean99927.952
Median Absolute Deviation (MAD)42655
Skewness7.790056
Sum1.6488112 × 108
Variance1.4117941 × 1010
MonotonicityNot monotonic
2024-03-13T19:36:11.491388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
105010 2
 
0.1%
95041 2
 
0.1%
106562 2
 
0.1%
47879 2
 
0.1%
7426 2
 
0.1%
76869 2
 
0.1%
88816 2
 
0.1%
70693 2
 
0.1%
27617 2
 
0.1%
18531 2
 
0.1%
Other values (1628) 1630
98.8%
ValueCountFrequency (%)
1854 1
0.1%
1869 1
0.1%
2065 1
0.1%
2130 1
0.1%
2212 1
0.1%
2721 1
0.1%
2739 1
0.1%
2889 1
0.1%
2954 1
0.1%
2975 1
0.1%
ValueCountFrequency (%)
2462734 1
0.1%
1713620 1
0.1%
1031423 1
0.1%
983618 1
0.1%
700542 1
0.1%
657519 1
0.1%
653127 1
0.1%
617547 1
0.1%
615953 1
0.1%
606058 1
0.1%

Interactions

2024-03-13T19:36:05.995730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:02.499929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:03.123876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:03.994615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:04.647198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:05.380133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:06.130382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:02.620054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:03.230988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:04.087780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:04.847830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:05.491659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:06.263958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:02.732024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:03.354906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:04.175557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:04.955905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:05.595089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:06.401304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:02.824729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:03.724918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:04.282713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:05.042739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:05.687562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:06.554991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:02.920849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:03.815367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:04.406573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:05.138113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:05.779902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:06.681076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:03.032060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:03.904351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:04.519365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:05.281479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T19:36:05.894695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T19:36:11.665715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상권_구분_코드상권_구분_코드_명상권_코드엑스좌표_값와이좌표_값자치구_코드자치구_코드_명행정동_코드영역_면적
상권_구분_코드1.0001.0001.0000.0710.1320.2120.3020.2160.556
상권_구분_코드_명1.0001.0001.0000.0710.1320.2120.3020.2160.556
상권_코드1.0001.0001.0000.0110.1040.1560.2310.1740.604
엑스좌표_값0.0710.0710.0111.0000.5910.9020.9400.9060.046
와이좌표_값0.1320.1320.1040.5911.0000.9080.9260.9090.039
자치구_코드0.2120.2120.1560.9020.9081.0001.0001.0000.076
자치구_코드_명0.3020.3020.2310.9400.9261.0001.0001.0000.153
행정동_코드0.2160.2160.1740.9060.9091.0001.0001.0000.057
영역_면적0.5560.5560.6040.0460.0390.0760.1530.0571.000
2024-03-13T19:36:11.792062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상권_구분_코드_명상권_구분_코드자치구_코드_명
상권_구분_코드_명1.0001.0000.163
상권_구분_코드1.0001.0000.163
자치구_코드_명0.1630.1631.000
2024-03-13T19:36:11.875671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상권_코드엑스좌표_값와이좌표_값자치구_코드행정동_코드영역_면적상권_구분_코드상권_구분_코드_명자치구_코드_명
상권_코드1.000-0.054-0.3420.4530.452-0.1961.0001.0000.118
엑스좌표_값-0.0541.0000.240-0.066-0.0650.0260.0410.0410.694
와이좌표_값-0.3420.2401.000-0.659-0.660-0.0450.0790.0790.657
자치구_코드0.453-0.066-0.6591.0000.9990.0600.1280.1280.995
행정동_코드0.452-0.065-0.6600.9991.0000.0590.1280.1280.995
영역_면적-0.1960.026-0.0450.0600.0591.0000.4170.4170.065
상권_구분_코드1.0000.0410.0790.1280.1280.4171.0001.0000.163
상권_구분_코드_명1.0000.0410.0790.1280.1280.4171.0001.0000.163
자치구_코드_명0.1180.6940.6570.9950.9950.0650.1630.1631.000

Missing values

2024-03-13T19:36:06.845675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T19:36:07.204712image/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

상권_구분_코드상권_구분_코드_명상권_코드상권_코드_명엑스좌표_값와이좌표_값자치구_코드자치구_코드_명행정동_코드행정동_코드_명영역_면적
0A골목상권3110008배화여자대학교(박노수미술관)19709345341811110종로구11110515청운효자동149264
1A골목상권3110009자하문터널19699145505711110종로구11110550부암동178306
2A골목상권3110010평창동서측19706445664311110종로구11110560평창동369415
3A골목상권3110017정독도서관19858145378111110종로구11110600가회동83855
4A골목상권3110018중앙고등학교19888345369011110종로구11110600가회동166872
5A골목상권3110019창덕궁19922245292611110종로구11110615종로1?2?3?4가동40999
6A골목상권3110020서울국제고등학교19959845430211110종로구11110650혜화동133692
7A골목상권3110001이북5도청사19626245666011110종로구11110560평창동108529
8A골목상권3110002독립문역 1번19622045291211110종로구11110570무악동31531
9A골목상권3110003세검정초등학교19638945599811110종로구11110550부암동191570
상권_구분_코드상권_구분_코드_명상권_코드상권_코드_명엑스좌표_값와이좌표_값자치구_코드자치구_코드_명행정동_코드행정동_코드_명영역_면적
1640R전통시장3130130수유북부골목시장20142145963511305강북구11305660인수동4552
1641R전통시장3130209목사랑시장(목4동시장)18819644872311470양천구11470540목4동35003
1642R전통시장3130210목2동시장18880244964911470양천구11470520목2동42782
1643R전통시장3130211신정2동 골목시장(오목교중앙시장)18905744678911470양천구11470630신정2동21996
1644U관광특구3001491이태원 관광특구19949844831111170용산구11170650이태원1동372565
1645U관광특구3001492명동 남대문 북창동 다동 무교동 관광특구19839745161411140중구11140520소공동983618
1646U관광특구3001493동대문패션타운 관광특구20099645197611140중구11140590광희동606058
1647U관광특구3001494종로?청계 관광특구19979645227411110종로구11110615종로1?2?3?4가동653127
1648U관광특구3001495잠실 관광특구21018844633411710송파구11710562방이2동2462734
1649U관광특구3001496강남 마이스 관광특구20528244573611680강남구11680580삼성1동237892