Overview

Dataset statistics

Number of variables12
Number of observations253
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.3 KiB
Average record size in memory106.5 B

Variable types

Numeric10
Text1
Categorical1

Dataset

Description샘플 데이터
Author신한은행
URLhttps://bigdata.seoul.go.kr/data/selectSampleData.do?sample_data_seq=320

Alerts

등록일자(DW_REGIST) has constant value ""Constant
상권코드(DEVLOP_TRDA) is highly overall correlated with Y최소좌표(YDNTS_MIN) and 4 other fieldsHigh correlation
X최소좌표(XCNTS_MIN) is highly overall correlated with X최소좌표(XCNTS_MAX) and 1 other fieldsHigh correlation
Y최소좌표(YDNTS_MIN) is highly overall correlated with 상권코드(DEVLOP_TRDA) and 4 other fieldsHigh correlation
X최소좌표(XCNTS_MAX) is highly overall correlated with X최소좌표(XCNTS_MIN) and 1 other fieldsHigh correlation
Y최대좌표(YDNTS_MAX) is highly overall correlated with 상권코드(DEVLOP_TRDA) and 4 other fieldsHigh correlation
X중심좌표(XCNTS_CENT) is highly overall correlated with X최소좌표(XCNTS_MIN) and 1 other fieldsHigh correlation
Y중심좌표(YDNTS_CENT) is highly overall correlated with 상권코드(DEVLOP_TRDA) and 4 other fieldsHigh correlation
행정동코드(ADSTRD_CD) is highly overall correlated with 상권코드(DEVLOP_TRDA) and 4 other fieldsHigh correlation
시군구코드(SIGNGU_CD) is highly overall correlated with 상권코드(DEVLOP_TRDA) and 4 other fieldsHigh correlation
상권코드(DEVLOP_TRDA) has unique valuesUnique
상권명(DEVLOP_T_1) has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:59:47.355945
Analysis finished2023-12-10 15:00:14.508406
Duration27.15 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

상권코드(DEVLOP_TRDA)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct253
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1001137
Minimum1001011
Maximum1001263
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2023-12-11T00:00:14.643400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001011
5-th percentile1001023.6
Q11001074
median1001137
Q31001200
95-th percentile1001250.4
Maximum1001263
Range252
Interquartile range (IQR)126

Descriptive statistics

Standard deviation73.179004
Coefficient of variation (CV)7.3095894 × 10-5
Kurtosis-1.2
Mean1001137
Median Absolute Deviation (MAD)63
Skewness0
Sum2.5328766 × 108
Variance5355.1667
MonotonicityStrictly increasing
2023-12-11T00:00:14.919486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001011 1
 
0.4%
1001185 1
 
0.4%
1001172 1
 
0.4%
1001173 1
 
0.4%
1001174 1
 
0.4%
1001175 1
 
0.4%
1001176 1
 
0.4%
1001177 1
 
0.4%
1001178 1
 
0.4%
1001179 1
 
0.4%
Other values (243) 243
96.0%
ValueCountFrequency (%)
1001011 1
0.4%
1001012 1
0.4%
1001013 1
0.4%
1001014 1
0.4%
1001015 1
0.4%
1001016 1
0.4%
1001017 1
0.4%
1001018 1
0.4%
1001019 1
0.4%
1001020 1
0.4%
ValueCountFrequency (%)
1001263 1
0.4%
1001262 1
0.4%
1001261 1
0.4%
1001260 1
0.4%
1001259 1
0.4%
1001258 1
0.4%
1001257 1
0.4%
1001256 1
0.4%
1001255 1
0.4%
1001254 1
0.4%

상권명(DEVLOP_T_1)
Text

UNIQUE 

Distinct253
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2023-12-11T00:00:15.425550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length9.1383399
Min length3

Characters and Unicode

Total characters2312
Distinct characters179
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique253 ?
Unique (%)100.0%

Sample

1st row서울 금천구 시흥1동_4
2nd row양재 화물트럭터미널앞_1
3rd row양재 화물트럭터미널앞_2
4th row양재동 꽃시장
5th row서울 금천구 독산1동_1
ValueCountFrequency (%)
서울 111
 
21.6%
강남구 23
 
4.5%
종로구 15
 
2.9%
관악구 10
 
1.9%
중구 9
 
1.8%
강동구 7
 
1.4%
마포구 7
 
1.4%
서초구 6
 
1.2%
은평구 6
 
1.2%
광진구 5
 
1.0%
Other values (273) 314
61.2%
2023-12-11T00:00:16.236675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
260
 
11.2%
역 195
 
8.4%
_ 164
 
7.1%
구 153
 
6.6%
서 131
 
5.7%
울 114
 
4.9%
1 73
 
3.2%
2 66
 
2.9%
동 50
 
2.2%
강 45
 
1.9%
Other values (169) 1061
45.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1711
74.0%
Space Separator 260
 
11.2%
Decimal Number 177
 
7.7%
Connector Punctuation 164
 
7.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
역 195
 
11.4%
구 153
 
8.9%
서 131
 
7.7%
울 114
 
6.7%
동 50
 
2.9%
강 45
 
2.6%
대 44
 
2.6%
로 36
 
2.1%
리 34
 
2.0%
남 34
 
2.0%
Other values (162) 875
51.1%
Decimal Number
ValueCountFrequency (%)
1 73
41.2%
2 66
37.3%
3 26
 
14.7%
4 7
 
4.0%
5 5
 
2.8%
Space Separator
ValueCountFrequency (%)
260
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 164
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1711
74.0%
Common 601
 
26.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
역 195
 
11.4%
구 153
 
8.9%
서 131
 
7.7%
울 114
 
6.7%
동 50
 
2.9%
강 45
 
2.6%
대 44
 
2.6%
로 36
 
2.1%
리 34
 
2.0%
남 34
 
2.0%
Other values (162) 875
51.1%
Common
ValueCountFrequency (%)
260
43.3%
_ 164
27.3%
1 73
 
12.1%
2 66
 
11.0%
3 26
 
4.3%
4 7
 
1.2%
5 5
 
0.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1711
74.0%
ASCII 601
 
26.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
260
43.3%
_ 164
27.3%
1 73
 
12.1%
2 66
 
11.0%
3 26
 
4.3%
4 7
 
1.2%
5 5
 
0.8%
Hangul
ValueCountFrequency (%)
역 195
 
11.4%
구 153
 
8.9%
서 131
 
7.7%
울 114
 
6.7%
동 50
 
2.9%
강 45
 
2.6%
대 44
 
2.6%
로 36
 
2.1%
리 34
 
2.0%
남 34
 
2.0%
Other values (162) 875
51.1%

X최소좌표(XCNTS_MIN)
Real number (ℝ)

HIGH CORRELATION 

Distinct247
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199819.47
Minimum182959
Maximum213357
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2023-12-11T00:00:16.614691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum182959
5-th percentile189701
Q1195690
median200906
Q3203657
95-th percentile209424.2
Maximum213357
Range30398
Interquartile range (IQR)7967

Descriptive statistics

Standard deviation5853.59
Coefficient of variation (CV)0.029294393
Kurtosis-0.22661591
Mean199819.47
Median Absolute Deviation (MAD)3493
Skewness-0.32872824
Sum50554326
Variance34264516
MonotonicityNot monotonic
2023-12-11T00:00:16.967213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202953 2
 
0.8%
202482 2
 
0.8%
192659 2
 
0.8%
202962 2
 
0.8%
202052 2
 
0.8%
200170 2
 
0.8%
186478 1
 
0.4%
192334 1
 
0.4%
206653 1
 
0.4%
194048 1
 
0.4%
Other values (237) 237
93.7%
ValueCountFrequency (%)
182959 1
0.4%
185660 1
0.4%
185705 1
0.4%
186478 1
0.4%
186981 1
0.4%
187104 1
0.4%
187950 1
0.4%
187969 1
0.4%
188862 1
0.4%
189076 1
0.4%
ValueCountFrequency (%)
213357 1
0.4%
212550 1
0.4%
211942 1
0.4%
211778 1
0.4%
210996 1
0.4%
210894 1
0.4%
210784 1
0.4%
210756 1
0.4%
210386 1
0.4%
210337 1
0.4%

Y최소좌표(YDNTS_MIN)
Real number (ℝ)

HIGH CORRELATION 

Distinct249
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean447979.87
Minimum439081
Maximum461634
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2023-12-11T00:00:17.378817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum439081
5-th percentile441845.8
Q1444309
median447079
Q3451306
95-th percentile456655.2
Maximum461634
Range22553
Interquartile range (IQR)6997

Descriptive statistics

Standard deviation4638.3661
Coefficient of variation (CV)0.010353961
Kurtosis0.16516424
Mean447979.87
Median Absolute Deviation (MAD)3452
Skewness0.67731439
Sum1.1333891 × 108
Variance21514440
MonotonicityNot monotonic
2023-12-11T00:00:17.684455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
452294 3
 
1.2%
452929 2
 
0.8%
442376 2
 
0.8%
439081 1
 
0.4%
449809 1
 
0.4%
449387 1
 
0.4%
449714 1
 
0.4%
449907 1
 
0.4%
449882 1
 
0.4%
450252 1
 
0.4%
Other values (239) 239
94.5%
ValueCountFrequency (%)
439081 1
0.4%
440142 1
0.4%
440237 1
0.4%
440602 1
0.4%
440746 1
0.4%
440939 1
0.4%
441038 1
0.4%
441600 1
0.4%
441641 1
0.4%
441646 1
0.4%
ValueCountFrequency (%)
461634 1
0.4%
461558 1
0.4%
461481 1
0.4%
461298 1
0.4%
460001 1
0.4%
459950 1
0.4%
459609 1
0.4%
459556 1
0.4%
457807 1
0.4%
457519 1
0.4%

X최소좌표(XCNTS_MAX)
Real number (ℝ)

HIGH CORRELATION 

Distinct251
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200283.54
Minimum183392
Maximum213835
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2023-12-11T00:00:18.397109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum183392
5-th percentile190396.4
Q1196204
median201383
Q3204186
95-th percentile209656.4
Maximum213835
Range30443
Interquartile range (IQR)7982

Descriptive statistics

Standard deviation5849.2463
Coefficient of variation (CV)0.029204828
Kurtosis-0.21747424
Mean200283.54
Median Absolute Deviation (MAD)3413
Skewness-0.33075049
Sum50671736
Variance34213683
MonotonicityNot monotonic
2023-12-11T00:00:18.700056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
203128 2
 
0.8%
197151 2
 
0.8%
194772 1
 
0.4%
206546 1
 
0.4%
212947 1
 
0.4%
188283 1
 
0.4%
213835 1
 
0.4%
201008 1
 
0.4%
200941 1
 
0.4%
193065 1
 
0.4%
Other values (241) 241
95.3%
ValueCountFrequency (%)
183392 1
0.4%
186045 1
0.4%
186449 1
0.4%
187100 1
0.4%
187155 1
0.4%
187360 1
0.4%
188024 1
0.4%
188283 1
0.4%
189078 1
0.4%
189677 1
0.4%
ValueCountFrequency (%)
213835 1
0.4%
212947 1
0.4%
212441 1
0.4%
211996 1
0.4%
211535 1
0.4%
211296 1
0.4%
211236 1
0.4%
211201 1
0.4%
211027 1
0.4%
210726 1
0.4%

Y최대좌표(YDNTS_MAX)
Real number (ℝ)

HIGH CORRELATION 

Distinct247
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean448425.51
Minimum439492
Maximum462075
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2023-12-11T00:00:19.008478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum439492
5-th percentile442521.2
Q1444637
median447592
Q3451792
95-th percentile457136.2
Maximum462075
Range22583
Interquartile range (IQR)7155

Descriptive statistics

Standard deviation4603.7277
Coefficient of variation (CV)0.010266427
Kurtosis0.15457707
Mean448425.51
Median Absolute Deviation (MAD)3541
Skewness0.68060728
Sum1.1345166 × 108
Variance21194309
MonotonicityNot monotonic
2023-12-11T00:00:19.319664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
452295 2
 
0.8%
447748 2
 
0.8%
451625 2
 
0.8%
453319 2
 
0.8%
452958 2
 
0.8%
442991 2
 
0.8%
450647 1
 
0.4%
450784 1
 
0.4%
450717 1
 
0.4%
450724 1
 
0.4%
Other values (237) 237
93.7%
ValueCountFrequency (%)
439492 1
0.4%
440424 1
0.4%
440957 1
0.4%
441148 1
0.4%
441399 1
0.4%
441469 1
0.4%
441915 1
0.4%
441954 1
0.4%
442141 1
0.4%
442253 1
0.4%
ValueCountFrequency (%)
462075 1
0.4%
461784 1
0.4%
461728 1
0.4%
461644 1
0.4%
460256 1
0.4%
460246 1
0.4%
460171 1
0.4%
460001 1
0.4%
458019 1
0.4%
457947 1
0.4%

X중심좌표(XCNTS_CENT)
Real number (ℝ)

HIGH CORRELATION 

Distinct252
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean200049.11
Minimum183218
Maximum213654
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2023-12-11T00:00:19.603924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum183218
5-th percentile190057.6
Q1196006
median201137
Q3203846
95-th percentile209548
Maximum213654
Range30436
Interquartile range (IQR)7840

Descriptive statistics

Standard deviation5847.3651
Coefficient of variation (CV)0.029229648
Kurtosis-0.22243422
Mean200049.11
Median Absolute Deviation (MAD)3520
Skewness-0.32961771
Sum50612424
Variance34191678
MonotonicityNot monotonic
2023-12-11T00:00:19.887751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
204125 2
 
0.8%
191286 1
 
0.4%
195103 1
 
0.4%
212716 1
 
0.4%
188105 1
 
0.4%
213654 1
 
0.4%
200955 1
 
0.4%
200870 1
 
0.4%
192849 1
 
0.4%
186804 1
 
0.4%
Other values (242) 242
95.7%
ValueCountFrequency (%)
183218 1
0.4%
185865 1
0.4%
186098 1
0.4%
186804 1
0.4%
187064 1
0.4%
187247 1
0.4%
187986 1
0.4%
188105 1
0.4%
188955 1
0.4%
189388 1
0.4%
ValueCountFrequency (%)
213654 1
0.4%
212716 1
0.4%
212214 1
0.4%
211888 1
0.4%
211211 1
0.4%
211139 1
0.4%
210941 1
0.4%
210869 1
0.4%
210795 1
0.4%
210540 1
0.4%

Y중심좌표(YDNTS_CENT)
Real number (ℝ)

HIGH CORRELATION 

Distinct251
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean448199.8
Minimum439286
Maximum461822
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2023-12-11T00:00:20.204576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum439286
5-th percentile442103
Q1444479
median447314
Q3451482
95-th percentile456899.8
Maximum461822
Range22536
Interquartile range (IQR)7003

Descriptive statistics

Standard deviation4618.5777
Coefficient of variation (CV)0.010304729
Kurtosis0.16183272
Mean448199.8
Median Absolute Deviation (MAD)3521
Skewness0.67892786
Sum1.1339455 × 108
Variance21331260
MonotonicityNot monotonic
2023-12-11T00:00:20.511362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
447314 2
 
0.8%
443852 2
 
0.8%
439286 1
 
0.4%
450960 1
 
0.4%
450061 1
 
0.4%
450446 1
 
0.4%
450518 1
 
0.4%
450532 1
 
0.4%
450553 1
 
0.4%
450362 1
 
0.4%
Other values (241) 241
95.3%
ValueCountFrequency (%)
439286 1
0.4%
440282 1
0.4%
440563 1
0.4%
440820 1
0.4%
441181 1
0.4%
441227 1
0.4%
441401 1
0.4%
441788 1
0.4%
441907 1
0.4%
441953 1
0.4%
ValueCountFrequency (%)
461822 1
0.4%
461669 1
0.4%
461611 1
0.4%
461465 1
0.4%
460138 1
0.4%
460099 1
0.4%
459896 1
0.4%
459771 1
0.4%
457903 1
0.4%
457727 1
0.4%

등록일자(DW_REGIST)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
2018/10/15
253 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018/10/15
2nd row2018/10/15
3rd row2018/10/15
4th row2018/10/15
5th row2018/10/15

Common Values

ValueCountFrequency (%)
2018/10/15 253
100.0%

Length

2023-12-11T00:00:20.794642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:00:20.998790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018/10/15 253
100.0%

행정동코드(ADSTRD_CD)
Real number (ℝ)

HIGH CORRELATION 

Distinct116
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11463490
Minimum11110515
Maximum11740690
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2023-12-11T00:00:21.237800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110515
5-th percentile11110615
Q111215847
median11560535
Q311680545
95-th percentile11710635
Maximum11740690
Range630175
Interquartile range (IQR)464698

Descriptive statistics

Standard deviation224604.55
Coefficient of variation (CV)0.019593034
Kurtosis-1.4316256
Mean11463490
Median Absolute Deviation (MAD)120105
Skewness-0.45228082
Sum2.900263 × 109
Variance5.0447205 × 1010
MonotonicityNot monotonic
2023-12-11T00:00:21.574970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11680640 18
 
7.1%
11110615 15
 
5.9%
11680610 10
 
4.0%
11650530 8
 
3.2%
11680545 8
 
3.2%
11680565 6
 
2.4%
11170625 6
 
2.4%
11650652 5
 
2.0%
11560540 5
 
2.0%
11650540 4
 
1.6%
Other values (106) 168
66.4%
ValueCountFrequency (%)
11110515 1
 
0.4%
11110530 3
 
1.2%
11110540 1
 
0.4%
11110600 1
 
0.4%
11110615 15
5.9%
11110630 3
 
1.2%
11110640 1
 
0.4%
11110650 1
 
0.4%
11110670 1
 
0.4%
11110710 1
 
0.4%
ValueCountFrequency (%)
11740690 1
 
0.4%
11740685 1
 
0.4%
11740610 3
1.2%
11740590 1
 
0.4%
11740550 1
 
0.4%
11710710 1
 
0.4%
11710680 2
0.8%
11710670 1
 
0.4%
11710646 1
 
0.4%
11710642 1
 
0.4%

시군구코드(SIGNGU_CD)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11462.885
Minimum11110
Maximum11740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2023-12-11T00:00:21.887200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11110
Q111215
median11560
Q311680
95-th percentile11710
Maximum11740
Range630
Interquartile range (IQR)465

Descriptive statistics

Standard deviation224.61293
Coefficient of variation (CV)0.019594798
Kurtosis-1.4317535
Mean11462.885
Median Absolute Deviation (MAD)120
Skewness-0.45230068
Sum2900110
Variance50450.967
MonotonicityNot monotonic
2023-12-11T00:00:22.493848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
11680 52
20.6%
11110 28
11.1%
11650 27
10.7%
11140 16
 
6.3%
11560 15
 
5.9%
11710 11
 
4.3%
11170 11
 
4.3%
11440 10
 
4.0%
11620 9
 
3.6%
11215 7
 
2.8%
Other values (14) 67
26.5%
ValueCountFrequency (%)
11110 28
11.1%
11140 16
6.3%
11170 11
 
4.3%
11200 5
 
2.0%
11215 7
 
2.8%
11230 6
 
2.4%
11290 4
 
1.6%
11305 5
 
2.0%
11320 1
 
0.4%
11350 4
 
1.6%
ValueCountFrequency (%)
11740 7
 
2.8%
11710 11
 
4.3%
11680 52
20.6%
11650 27
10.7%
11620 9
 
3.6%
11590 6
 
2.4%
11560 15
 
5.9%
11545 6
 
2.4%
11530 6
 
2.4%
11500 5
 
2.0%

면적(RELM_AR)
Real number (ℝ)

Distinct161
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116.45059
Minimum5
Maximum1143
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2023-12-11T00:00:23.363018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile19.6
Q146
median81
Q3135
95-th percentile334.4
Maximum1143
Range1138
Interquartile range (IQR)89

Descriptive statistics

Standard deviation130.12172
Coefficient of variation (CV)1.1173985
Kurtosis23.947766
Mean116.45059
Median Absolute Deviation (MAD)41
Skewness4.080656
Sum29462
Variance16931.661
MonotonicityNot monotonic
2023-12-11T00:00:24.721010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
26 5
 
2.0%
43 5
 
2.0%
81 5
 
2.0%
70 4
 
1.6%
45 4
 
1.6%
46 4
 
1.6%
39 4
 
1.6%
76 4
 
1.6%
50 4
 
1.6%
111 3
 
1.2%
Other values (151) 211
83.4%
ValueCountFrequency (%)
5 2
0.8%
9 1
0.4%
11 1
0.4%
12 2
0.8%
13 1
0.4%
14 1
0.4%
16 2
0.8%
17 1
0.4%
18 1
0.4%
19 1
0.4%
ValueCountFrequency (%)
1143 1
0.4%
978 1
0.4%
697 1
0.4%
584 1
0.4%
477 1
0.4%
443 1
0.4%
425 1
0.4%
397 1
0.4%
396 1
0.4%
389 1
0.4%

Interactions

2023-12-11T00:00:11.813996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:48.319051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:50.725482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:54.625463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:56.716970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:58.657266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:01.528887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:04.376153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:07.045639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:09.285623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:12.053947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:48.534710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:50.962899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:54.840422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:56.918991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:58.859336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:01.812801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:04.764315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:07.254823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:09.509056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:12.287155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:48.724718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:51.235537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:55.040377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:57.104785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:59.043177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:02.101325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:05.000741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:07.433639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:09.776986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:12.519793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:48.903485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:51.647254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:55.239623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:57.298324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:59.238596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:02.297835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:05.255454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:07.644177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:09.999827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:12.754282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:49.094579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:52.426485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:55.442443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:57.525932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:59.460034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:02.489337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:05.529924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:07.833713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:10.241031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:12.939861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:49.275857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:52.973004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:55.646120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:57.705311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:59.660609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:03.210395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:05.820860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:08.046871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:10.485504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:13.131443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:49.476032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:53.483352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:55.862515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:57.891885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:59.956061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:03.421425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:06.098874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:08.274786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:10.684911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:13.317968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:49.631200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:53.895604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:56.114539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:58.038239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:00.303912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:03.640313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:06.348089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:08.491974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:10.979826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:13.505378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:50.069984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:54.128065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:56.309201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:58.250422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:00.694675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:03.912020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:06.565818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:08.789561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:11.300436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:13.695139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:50.476261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:54.393805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:56.530269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:58.447899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:01.103020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:04.161079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:06.801165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:09.030185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:11.600092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T00:00:25.084894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상권코드(DEVLOP_TRDA)X최소좌표(XCNTS_MIN)Y최소좌표(YDNTS_MIN)X최소좌표(XCNTS_MAX)Y최대좌표(YDNTS_MAX)X중심좌표(XCNTS_CENT)Y중심좌표(YDNTS_CENT)행정동코드(ADSTRD_CD)시군구코드(SIGNGU_CD)면적(RELM_AR)
상권코드(DEVLOP_TRDA)1.0000.6370.9560.6360.9580.6420.9600.8870.8910.149
X최소좌표(XCNTS_MIN)0.6371.0000.6440.9990.6121.0000.6110.8920.8840.000
Y최소좌표(YDNTS_MIN)0.9560.6441.0000.6500.9990.6511.0000.9180.9170.000
X최소좌표(XCNTS_MAX)0.6360.9990.6501.0000.6230.9990.6190.8900.8820.000
Y최대좌표(YDNTS_MAX)0.9580.6120.9990.6231.0000.6211.0000.9170.9170.000
X중심좌표(XCNTS_CENT)0.6421.0000.6510.9990.6211.0000.6190.8920.8850.000
Y중심좌표(YDNTS_CENT)0.9600.6111.0000.6191.0000.6191.0000.9180.9160.000
행정동코드(ADSTRD_CD)0.8870.8920.9180.8900.9170.8920.9181.0001.0000.191
시군구코드(SIGNGU_CD)0.8910.8840.9170.8820.9170.8850.9161.0001.0000.233
면적(RELM_AR)0.1490.0000.0000.0000.0000.0000.0000.1910.2331.000
2023-12-11T00:00:25.431621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상권코드(DEVLOP_TRDA)X최소좌표(XCNTS_MIN)Y최소좌표(YDNTS_MIN)X최소좌표(XCNTS_MAX)Y최대좌표(YDNTS_MAX)X중심좌표(XCNTS_CENT)Y중심좌표(YDNTS_CENT)행정동코드(ADSTRD_CD)시군구코드(SIGNGU_CD)면적(RELM_AR)
상권코드(DEVLOP_TRDA)1.000-0.0780.998-0.0821.000-0.0790.999-0.702-0.694-0.117
X최소좌표(XCNTS_MIN)-0.0781.000-0.0640.999-0.0691.000-0.0660.4520.447-0.034
Y최소좌표(YDNTS_MIN)0.998-0.0641.000-0.0690.998-0.0651.000-0.698-0.690-0.153
X최소좌표(XCNTS_MAX)-0.0820.999-0.0691.000-0.0721.000-0.0710.4560.451-0.002
Y최대좌표(YDNTS_MAX)1.000-0.0690.998-0.0721.000-0.0700.999-0.698-0.689-0.115
X중심좌표(XCNTS_CENT)-0.0791.000-0.0651.000-0.0701.000-0.0680.4540.449-0.019
Y중심좌표(YDNTS_CENT)0.999-0.0661.000-0.0710.999-0.0681.000-0.698-0.690-0.136
행정동코드(ADSTRD_CD)-0.7020.452-0.6980.456-0.6980.454-0.6981.0000.9940.092
시군구코드(SIGNGU_CD)-0.6940.447-0.6900.451-0.6890.449-0.6900.9941.0000.084
면적(RELM_AR)-0.117-0.034-0.153-0.002-0.115-0.019-0.1360.0920.0841.000

Missing values

2023-12-11T00:00:13.942486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T00:00:14.365761image/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

상권코드(DEVLOP_TRDA)상권명(DEVLOP_T_1)X최소좌표(XCNTS_MIN)Y최소좌표(YDNTS_MIN)X최소좌표(XCNTS_MAX)Y최대좌표(YDNTS_MAX)X중심좌표(XCNTS_CENT)Y중심좌표(YDNTS_CENT)등록일자(DW_REGIST)행정동코드(ADSTRD_CD)시군구코드(SIGNGU_CD)면적(RELM_AR)
01001011서울 금천구 시흥1동_41910684390811915074394921912864392862018/10/151154567011545106
11001012양재 화물트럭터미널앞_12030314401422032984404242031614402822018/10/15116506521165037
21001013양재 화물트럭터미널앞_22030784402372035354409572032694405632018/10/151165065211650161
31001014양재동 꽃시장2033754406022038074411482035474408202018/10/151165065211650122
41001015서울 금천구 독산1동_11907024409391910624414691908424411812018/10/151154561011545122
51001016서울 관악구 대학동_11944444410381949494413991946714412272018/10/15116207351162097
61001017관악구 사당역_31981954416001984154419151983174417882018/10/15116206301162037
71001018포이사거리_22036604416582041964422532039074419072018/10/151165065211650132
81001019포이사거리_12039404417262042284421412040784419532018/10/15116506521165026
91001020장지역2103374416462112364423122107954419862018/10/151171064611710278
상권코드(DEVLOP_TRDA)상권명(DEVLOP_T_1)X최소좌표(XCNTS_MIN)Y최소좌표(YDNTS_MIN)X최소좌표(XCNTS_MAX)Y최대좌표(YDNTS_MAX)X중심좌표(XCNTS_CENT)Y중심좌표(YDNTS_CENT)등록일자(DW_REGIST)행정동코드(ADSTRD_CD)시군구코드(SIGNGU_CD)면적(RELM_AR)
2431001254서울 은평구 연신내역_21926594575191930824579471928664577272018/10/15113805301138085
2441001255서울 은평구 연신내역_31929254578071930694580191929794579032018/10/1511380530113809
2451001256서울 강북구 수유역_12020894595562024904600012022944597712018/10/15113056301130555
2461001257서울 강북구 수유역_22020054596092024154601712021804598962018/10/151130563011305109
2471001258등나무근린공원 주변2059404599502061224602462060324600992018/10/15113506251135024
2481001259서울 강북구 수유역_32022224600012025944602562024014601382018/10/15113056301130539
2491001260창동역2040554612982046284616442043244614652018/10/151132051411320104
2501001261서울 노원구 노원역_12053484614812057114617282055324616112018/10/15113506951135046
2511001262서울 노원구 노원역_22050424615582053494617842051984616692018/10/15113506951135043
2521001263서울 노원구 노원역_32053034616342057474620752055164618222018/10/15113506951135098