Overview

Dataset statistics

Number of variables12
Number of observations1010
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory104.7 KiB
Average record size in memory106.1 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(ALLEY_TRDA) is highly overall correlated with Y중심좌표(YDNTS_VALU) and 4 other fieldsHigh correlation
X중심좌표(XCNTS_VALU) is highly overall correlated with X최소좌표(XCNTS_MIN) and 1 other fieldsHigh correlation
Y중심좌표(YDNTS_VALU) is highly overall correlated with 상권코드(ALLEY_TRDA) and 4 other fieldsHigh correlation
X최소좌표(XCNTS_MIN) is highly overall correlated with X중심좌표(XCNTS_VALU) and 1 other fieldsHigh correlation
Y최소좌표(YDNTS_MIN) is highly overall correlated with 상권코드(ALLEY_TRDA) and 4 other fieldsHigh correlation
X최대좌표(XCNTS_MAX) is highly overall correlated with X중심좌표(XCNTS_VALU) and 1 other fieldsHigh correlation
Y최대좌표(YDNTS_MAX) is highly overall correlated with 상권코드(ALLEY_TRDA) and 4 other fieldsHigh correlation
시군구코드(SIGNGU_CD) is highly overall correlated with 상권코드(ALLEY_TRDA) and 4 other fieldsHigh correlation
행정동코드(ADSTRD_CD) is highly overall correlated with 상권코드(ALLEY_TRDA) and 4 other fieldsHigh correlation
상권코드(ALLEY_TRDA) has unique valuesUnique
도로명(ALLEY_TR_1) has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:58:51.168092
Analysis finished2023-12-10 14:59:16.817606
Duration25.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

상권코드(ALLEY_TRDA)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct1010
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1000505.5
Minimum1000001
Maximum1001010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2023-12-10T23:59:16.963526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000001
5-th percentile1000051.4
Q11000253.2
median1000505.5
Q31000757.8
95-th percentile1000959.6
Maximum1001010
Range1009
Interquartile range (IQR)504.5

Descriptive statistics

Standard deviation291.70619
Coefficient of variation (CV)0.0002915588
Kurtosis-1.2
Mean1000505.5
Median Absolute Deviation (MAD)252.5
Skewness0
Sum1.0105106 × 109
Variance85092.5
MonotonicityStrictly increasing
2023-12-10T23:59:17.240453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000001 1
 
0.1%
1000680 1
 
0.1%
1000667 1
 
0.1%
1000668 1
 
0.1%
1000669 1
 
0.1%
1000670 1
 
0.1%
1000671 1
 
0.1%
1000672 1
 
0.1%
1000673 1
 
0.1%
1000674 1
 
0.1%
Other values (1000) 1000
99.0%
ValueCountFrequency (%)
1000001 1
0.1%
1000002 1
0.1%
1000003 1
0.1%
1000004 1
0.1%
1000005 1
0.1%
1000006 1
0.1%
1000007 1
0.1%
1000008 1
0.1%
1000009 1
0.1%
1000010 1
0.1%
ValueCountFrequency (%)
1001010 1
0.1%
1001009 1
0.1%
1001008 1
0.1%
1001007 1
0.1%
1001006 1
0.1%
1001005 1
0.1%
1001004 1
0.1%
1001003 1
0.1%
1001002 1
0.1%
1001001 1
0.1%
Distinct1010
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size8.0 KiB
2023-12-10T23:59:17.749873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length6.2188119
Min length3

Characters and Unicode

Total characters6281
Distinct characters245
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1010 ?
Unique (%)100.0%

Sample

1st row계동길
2nd row난계로27길
3rd row돈화문로11가길
4th row명륜길
5th row백석동길
ValueCountFrequency (%)
계동길 1
 
0.1%
시흥대로36길 1
 
0.1%
독산로78다길 1
 
0.1%
대림로29길 1
 
0.1%
독산로85길 1
 
0.1%
문성로5길 1
 
0.1%
범안로11길 1
 
0.1%
범안로17길 1
 
0.1%
벚꽃로56길 1
 
0.1%
시흥대로120길 1
 
0.1%
Other values (1000) 1000
99.0%
2023-12-10T23:59:18.600449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1016
 
16.2%
933
 
14.9%
1 364
 
5.8%
2 272
 
4.3%
3 218
 
3.5%
4 176
 
2.8%
5 160
 
2.5%
7 133
 
2.1%
6 133
 
2.1%
130
 
2.1%
Other values (235) 2746
43.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4511
71.8%
Decimal Number 1770
 
28.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1016
22.5%
933
20.7%
130
 
2.9%
89
 
2.0%
78
 
1.7%
56
 
1.2%
53
 
1.2%
53
 
1.2%
51
 
1.1%
49
 
1.1%
Other values (225) 2003
44.4%
Decimal Number
ValueCountFrequency (%)
1 364
20.6%
2 272
15.4%
3 218
12.3%
4 176
9.9%
5 160
9.0%
7 133
 
7.5%
6 133
 
7.5%
8 118
 
6.7%
0 100
 
5.6%
9 96
 
5.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4511
71.8%
Common 1770
 
28.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1016
22.5%
933
20.7%
130
 
2.9%
89
 
2.0%
78
 
1.7%
56
 
1.2%
53
 
1.2%
53
 
1.2%
51
 
1.1%
49
 
1.1%
Other values (225) 2003
44.4%
Common
ValueCountFrequency (%)
1 364
20.6%
2 272
15.4%
3 218
12.3%
4 176
9.9%
5 160
9.0%
7 133
 
7.5%
6 133
 
7.5%
8 118
 
6.7%
0 100
 
5.6%
9 96
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4511
71.8%
ASCII 1770
 
28.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1016
22.5%
933
20.7%
130
 
2.9%
89
 
2.0%
78
 
1.7%
56
 
1.2%
53
 
1.2%
53
 
1.2%
51
 
1.1%
49
 
1.1%
Other values (225) 2003
44.4%
ASCII
ValueCountFrequency (%)
1 364
20.6%
2 272
15.4%
3 218
12.3%
4 176
9.9%
5 160
9.0%
7 133
 
7.5%
6 133
 
7.5%
8 118
 
6.7%
0 100
 
5.6%
9 96
 
5.4%

X중심좌표(XCNTS_VALU)
Real number (ℝ)

HIGH CORRELATION 

Distinct986
Distinct (%)97.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198465.71
Minimum183066
Maximum215174
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2023-12-10T23:59:18.885047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum183066
5-th percentile186198.8
Q1192349.5
median199053.5
Q3204233.75
95-th percentile210890.05
Maximum215174
Range32108
Interquartile range (IQR)11884.25

Descriptive statistics

Standard deviation7459.5432
Coefficient of variation (CV)0.037586055
Kurtosis-0.97394773
Mean198465.71
Median Absolute Deviation (MAD)6139
Skewness-0.057092252
Sum2.0045037 × 108
Variance55644784
MonotonicityNot monotonic
2023-12-10T23:59:19.203111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
207767 2
 
0.2%
205444 2
 
0.2%
202061 2
 
0.2%
201997 2
 
0.2%
202604 2
 
0.2%
202091 2
 
0.2%
203498 2
 
0.2%
203102 2
 
0.2%
205707 2
 
0.2%
201136 2
 
0.2%
Other values (976) 990
98.0%
ValueCountFrequency (%)
183066 1
0.1%
183098 1
0.1%
183157 1
0.1%
183401 1
0.1%
183464 1
0.1%
183536 1
0.1%
183601 1
0.1%
183651 1
0.1%
183681 1
0.1%
183911 1
0.1%
ValueCountFrequency (%)
215174 1
0.1%
215098 1
0.1%
214367 1
0.1%
213662 2
0.2%
213425 1
0.1%
213394 1
0.1%
213132 1
0.1%
213036 1
0.1%
212852 1
0.1%
212791 1
0.1%

Y중심좌표(YDNTS_VALU)
Real number (ℝ)

HIGH CORRELATION 

Distinct988
Distinct (%)97.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean449500.1
Minimum437964
Maximum465496
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2023-12-10T23:59:19.492507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum437964
5-th percentile441731.7
Q1444918.5
median449266
Q3453266.75
95-th percentile459350.8
Maximum465496
Range27532
Interquartile range (IQR)8348.25

Descriptive statistics

Standard deviation5452.9934
Coefficient of variation (CV)0.01213124
Kurtosis-0.39998839
Mean449500.1
Median Absolute Deviation (MAD)4148.5
Skewness0.39868314
Sum4.539951 × 108
Variance29735137
MonotonicityNot monotonic
2023-12-10T23:59:19.770769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
452855 2
 
0.2%
453872 2
 
0.2%
448858 2
 
0.2%
445695 2
 
0.2%
445351 2
 
0.2%
447913 2
 
0.2%
448189 2
 
0.2%
448560 2
 
0.2%
452970 2
 
0.2%
448011 2
 
0.2%
Other values (978) 990
98.0%
ValueCountFrequency (%)
437964 1
0.1%
438262 1
0.1%
438755 1
0.1%
439128 1
0.1%
439316 1
0.1%
439529 1
0.1%
439656 1
0.1%
439818 1
0.1%
440125 1
0.1%
440191 1
0.1%
ValueCountFrequency (%)
465496 1
0.1%
465255 1
0.1%
464982 1
0.1%
464203 1
0.1%
463980 1
0.1%
463916 1
0.1%
463717 1
0.1%
463694 1
0.1%
463290 1
0.1%
463265 1
0.1%

X최소좌표(XCNTS_MIN)
Real number (ℝ)

HIGH CORRELATION 

Distinct980
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198269.89
Minimum182790
Maximum214945
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2023-12-10T23:59:20.033370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum182790
5-th percentile186022.6
Q1192155
median198864.5
Q3204024.5
95-th percentile210683.15
Maximum214945
Range32155
Interquartile range (IQR)11869.5

Descriptive statistics

Standard deviation7463.3324
Coefficient of variation (CV)0.037642288
Kurtosis-0.97429472
Mean198269.89
Median Absolute Deviation (MAD)6129
Skewness-0.05961582
Sum2.0025259 × 108
Variance55701330
MonotonicityNot monotonic
2023-12-10T23:59:20.356831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
206040 3
 
0.3%
199123 3
 
0.3%
207908 2
 
0.2%
192680 2
 
0.2%
190098 2
 
0.2%
196112 2
 
0.2%
195779 2
 
0.2%
192596 2
 
0.2%
200992 2
 
0.2%
191352 2
 
0.2%
Other values (970) 988
97.8%
ValueCountFrequency (%)
182790 1
0.1%
182842 1
0.1%
182989 1
0.1%
183236 1
0.1%
183297 1
0.1%
183338 1
0.1%
183403 1
0.1%
183433 1
0.1%
183462 1
0.1%
183767 1
0.1%
ValueCountFrequency (%)
214945 1
0.1%
214922 1
0.1%
214204 1
0.1%
213483 1
0.1%
213441 1
0.1%
213277 1
0.1%
213174 1
0.1%
212960 1
0.1%
212882 1
0.1%
212654 1
0.1%

Y최소좌표(YDNTS_MIN)
Real number (ℝ)

HIGH CORRELATION 

Distinct989
Distinct (%)97.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean449301.95
Minimum437685
Maximum465380
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2023-12-10T23:59:20.628651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum437685
5-th percentile441544.45
Q1444703.5
median449073
Q3453083.5
95-th percentile459133.75
Maximum465380
Range27695
Interquartile range (IQR)8380

Descriptive statistics

Standard deviation5452.9663
Coefficient of variation (CV)0.012136529
Kurtosis-0.3967439
Mean449301.95
Median Absolute Deviation (MAD)4144
Skewness0.39872387
Sum4.5379497 × 108
Variance29734841
MonotonicityNot monotonic
2023-12-10T23:59:20.914201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
441601 2
 
0.2%
450179 2
 
0.2%
446768 2
 
0.2%
450880 2
 
0.2%
455865 2
 
0.2%
440780 2
 
0.2%
449645 2
 
0.2%
452783 2
 
0.2%
453730 2
 
0.2%
442215 2
 
0.2%
Other values (979) 990
98.0%
ValueCountFrequency (%)
437685 1
0.1%
438109 1
0.1%
438553 1
0.1%
438961 1
0.1%
439152 1
0.1%
439353 1
0.1%
439432 1
0.1%
439555 1
0.1%
439937 1
0.1%
439999 1
0.1%
ValueCountFrequency (%)
465380 1
0.1%
465136 1
0.1%
464797 1
0.1%
463914 1
0.1%
463827 1
0.1%
463732 1
0.1%
463581 1
0.1%
463511 1
0.1%
463044 1
0.1%
463034 1
0.1%

X최대좌표(XCNTS_MAX)
Real number (ℝ)

HIGH CORRELATION 

Distinct987
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198661.45
Minimum183250
Maximum215343
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2023-12-10T23:59:21.191615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum183250
5-th percentile186400.1
Q1192548
median199219.5
Q3204448.25
95-th percentile211082.3
Maximum215343
Range32093
Interquartile range (IQR)11900.25

Descriptive statistics

Standard deviation7456.9287
Coefficient of variation (CV)0.037535861
Kurtosis-0.97256828
Mean198661.45
Median Absolute Deviation (MAD)6134.5
Skewness-0.055100108
Sum2.0064807 × 108
Variance55605785
MonotonicityNot monotonic
2023-12-10T23:59:21.491525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
195089 2
 
0.2%
201903 2
 
0.2%
193224 2
 
0.2%
203072 2
 
0.2%
192837 2
 
0.2%
199162 2
 
0.2%
199188 2
 
0.2%
193507 2
 
0.2%
193022 2
 
0.2%
205993 2
 
0.2%
Other values (977) 990
98.0%
ValueCountFrequency (%)
183250 1
0.1%
183346 1
0.1%
183379 1
0.1%
183591 1
0.1%
183670 1
0.1%
183706 1
0.1%
183771 1
0.1%
183801 1
0.1%
183898 1
0.1%
184118 1
0.1%
ValueCountFrequency (%)
215343 1
0.1%
215270 1
0.1%
214597 1
0.1%
213863 1
0.1%
213798 1
0.1%
213626 1
0.1%
213546 1
0.1%
213324 1
0.1%
213214 1
0.1%
213050 1
0.1%

Y최대좌표(YDNTS_MAX)
Real number (ℝ)

HIGH CORRELATION 

Distinct990
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean449697.52
Minimum438121
Maximum465691
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2023-12-10T23:59:21.755889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum438121
5-th percentile441908.25
Q1445122.25
median449469
Q3453480.5
95-th percentile459511.5
Maximum465691
Range27570
Interquartile range (IQR)8358.25

Descriptive statistics

Standard deviation5457.8404
Coefficient of variation (CV)0.012136692
Kurtosis-0.40099925
Mean449697.52
Median Absolute Deviation (MAD)4153
Skewness0.39911782
Sum4.5419449 × 108
Variance29788022
MonotonicityNot monotonic
2023-12-10T23:59:22.046434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
444745 2
 
0.2%
444297 2
 
0.2%
445817 2
 
0.2%
450916 2
 
0.2%
450493 2
 
0.2%
450828 2
 
0.2%
450100 2
 
0.2%
441970 2
 
0.2%
452077 2
 
0.2%
448903 2
 
0.2%
Other values (980) 990
98.0%
ValueCountFrequency (%)
438121 1
0.1%
438453 1
0.1%
438979 1
0.1%
439258 1
0.1%
439471 1
0.1%
439675 1
0.1%
439857 1
0.1%
440059 1
0.1%
440313 1
0.1%
440396 1
0.1%
ValueCountFrequency (%)
465691 1
0.1%
465399 1
0.1%
465187 1
0.1%
464403 1
0.1%
464253 1
0.1%
464100 1
0.1%
463980 1
0.1%
463853 1
0.1%
463514 1
0.1%
463509 1
0.1%

등록일자(DW_REGIST)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.0 KiB
2018/10/15
1010 

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 1010
100.0%

Length

2023-12-10T23:59:22.381918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:59:22.636904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018/10/15 1010
100.0%

시군구코드(SIGNGU_CD)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11436.005
Minimum11110
Maximum11740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2023-12-10T23:59:22.869330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11170
Q111260
median11440
Q311590
95-th percentile11710
Maximum11740
Range630
Interquartile range (IQR)330

Descriptive statistics

Standard deviation181.7294
Coefficient of variation (CV)0.015890986
Kurtosis-1.2536342
Mean11436.005
Median Absolute Deviation (MAD)150
Skewness-0.055127803
Sum11550365
Variance33025.575
MonotonicityNot monotonic
2023-12-10T23:59:23.126215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11500 61
 
6.0%
11620 61
 
6.0%
11230 57
 
5.6%
11290 55
 
5.4%
11590 55
 
5.4%
11680 50
 
5.0%
11440 49
 
4.9%
11650 46
 
4.6%
11380 46
 
4.6%
11530 45
 
4.5%
Other values (15) 485
48.0%
ValueCountFrequency (%)
11110 24
2.4%
11140 20
 
2.0%
11170 37
3.7%
11200 38
3.8%
11215 42
4.2%
11230 57
5.6%
11260 40
4.0%
11290 55
5.4%
11305 37
3.7%
11320 26
2.6%
ValueCountFrequency (%)
11740 40
4.0%
11710 31
3.1%
11680 50
5.0%
11650 46
4.6%
11620 61
6.0%
11590 55
5.4%
11560 42
4.2%
11545 30
3.0%
11530 45
4.5%
11500 61
6.0%

행정동코드(ADSTRD_CD)
Real number (ℝ)

HIGH CORRELATION 

Distinct323
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11436628
Minimum11110515
Maximum11740700
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2023-12-10T23:59:23.398213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110515
5-th percentile11170524
Q111260655
median11440740
Q311590630
95-th percentile11710620
Maximum11740700
Range630185
Interquartile range (IQR)329975

Descriptive statistics

Standard deviation181718.37
Coefficient of variation (CV)0.015889156
Kurtosis-1.2535428
Mean11436628
Median Absolute Deviation (MAD)150135
Skewness-0.055098878
Sum1.1550995 × 1010
Variance3.3021565 × 1010
MonotonicityNot monotonic
2023-12-10T23:59:23.729962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11230650 14
 
1.4%
11500540 12
 
1.2%
11230536 11
 
1.1%
11530530 11
 
1.1%
11740685 10
 
1.0%
11590660 10
 
1.0%
11560720 9
 
0.9%
11680531 9
 
0.9%
11500530 9
 
0.9%
11260575 9
 
0.9%
Other values (313) 906
89.7%
ValueCountFrequency (%)
11110515 5
0.5%
11110530 1
 
0.1%
11110540 2
 
0.2%
11110560 2
 
0.2%
11110600 1
 
0.1%
11110615 4
0.4%
11110640 2
 
0.2%
11110650 2
 
0.2%
11110670 2
 
0.2%
11110680 2
 
0.2%
ValueCountFrequency (%)
11740700 2
 
0.2%
11740685 10
1.0%
11740650 3
 
0.3%
11740640 3
 
0.3%
11740610 7
0.7%
11740600 3
 
0.3%
11740590 1
 
0.1%
11740580 4
 
0.4%
11740570 1
 
0.1%
11740560 2
 
0.2%

면적(RELM_AR)
Real number (ℝ)

Distinct1003
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74459.307
Minimum10579
Maximum387983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.0 KiB
2023-12-10T23:59:24.060906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10579
5-th percentile35806.2
Q154150.75
median72148.5
Q389399.25
95-th percentile120422.6
Maximum387983
Range377404
Interquartile range (IQR)35248.5

Descriptive statistics

Standard deviation30223.59
Coefficient of variation (CV)0.40590748
Kurtosis20.808233
Mean74459.307
Median Absolute Deviation (MAD)17520.5
Skewness2.6073583
Sum75203900
Variance9.1346538 × 108
MonotonicityNot monotonic
2023-12-10T23:59:24.363925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
78521 2
 
0.2%
71833 2
 
0.2%
74321 2
 
0.2%
81818 2
 
0.2%
52874 2
 
0.2%
38293 2
 
0.2%
64596 2
 
0.2%
84626 1
 
0.1%
54634 1
 
0.1%
119591 1
 
0.1%
Other values (993) 993
98.3%
ValueCountFrequency (%)
10579 1
0.1%
11143 1
0.1%
12186 1
0.1%
13152 1
0.1%
15375 1
0.1%
17377 1
0.1%
21326 1
0.1%
21739 1
0.1%
23273 1
0.1%
23434 1
0.1%
ValueCountFrequency (%)
387983 1
0.1%
367064 1
0.1%
247749 1
0.1%
193251 1
0.1%
191055 1
0.1%
184188 1
0.1%
177030 1
0.1%
176959 1
0.1%
160793 1
0.1%
159803 1
0.1%

Interactions

2023-12-10T23:59:13.557080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:52.547722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:54.647066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:56.678224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:59.050669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:01.126677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:03.296114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:05.975767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:08.916840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:11.109541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:13.843213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:52.740240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:54.841099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:56.863081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:59.241874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:01.306472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:03.495005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:06.552577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:09.135404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:11.326524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:14.112236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:52.948638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:55.023271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:57.060837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:59.461012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:01.483746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:03.707254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:07.038463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:09.371739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:11.551272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:14.311867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:53.130281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:55.237007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:57.507974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:59.677662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:02.106543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:03.900526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:07.380235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:09.564225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:11.851908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:14.508352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:53.386729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:55.435785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:57.853211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:59.896929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:02.289895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:04.145969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:07.621093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:09.809695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:12.114920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:14.708852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:53.616253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:55.639930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:58.048393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:00.079354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:02.452095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:04.355304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:07.846632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:10.026031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:12.400341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:14.937258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:53.817111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:55.860365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:58.240607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:00.283677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:02.612425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:04.583630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:08.104940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:10.235737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:12.670336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:15.143790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:54.038024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:56.070620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:58.432992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:00.494371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:02.739618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:04.794914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:08.279754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:10.433846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:12.874707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:15.372823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:54.234664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:56.272711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:58.626761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:00.711486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:02.931260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:05.024007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:08.473348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:10.644534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:13.081069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:15.583885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:54.437495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:56.473516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:58:58.856522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:00.922163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:03.107706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:05.264097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:08.698658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:10.870440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:59:13.316328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:59:24.592971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상권코드(ALLEY_TRDA)X중심좌표(XCNTS_VALU)Y중심좌표(YDNTS_VALU)X최소좌표(XCNTS_MIN)Y최소좌표(YDNTS_MIN)X최대좌표(XCNTS_MAX)Y최대좌표(YDNTS_MAX)시군구코드(SIGNGU_CD)행정동코드(ADSTRD_CD)면적(RELM_AR)
상권코드(ALLEY_TRDA)1.0000.9250.8920.9250.8920.9240.8920.9850.9850.165
X중심좌표(XCNTS_VALU)0.9251.0000.6241.0000.6201.0000.6240.8940.8960.153
Y중심좌표(YDNTS_VALU)0.8920.6241.0000.6221.0000.6201.0000.9120.9080.087
X최소좌표(XCNTS_MIN)0.9251.0000.6221.0000.6181.0000.6220.8940.8960.152
Y최소좌표(YDNTS_MIN)0.8920.6201.0000.6181.0000.6161.0000.9100.9070.083
X최대좌표(XCNTS_MAX)0.9241.0000.6201.0000.6161.0000.6200.8930.8950.149
Y최대좌표(YDNTS_MAX)0.8920.6241.0000.6221.0000.6201.0000.9120.9080.061
시군구코드(SIGNGU_CD)0.9850.8940.9120.8940.9100.8930.9121.0001.0000.128
행정동코드(ADSTRD_CD)0.9850.8960.9080.8960.9070.8950.9081.0001.0000.161
면적(RELM_AR)0.1650.1530.0870.1520.0830.1490.0610.1280.1611.000
2023-12-10T23:59:25.344449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상권코드(ALLEY_TRDA)X중심좌표(XCNTS_VALU)Y중심좌표(YDNTS_VALU)X최소좌표(XCNTS_MIN)Y최소좌표(YDNTS_MIN)X최대좌표(XCNTS_MAX)Y최대좌표(YDNTS_MAX)시군구코드(SIGNGU_CD)행정동코드(ADSTRD_CD)면적(RELM_AR)
상권코드(ALLEY_TRDA)1.000-0.137-0.662-0.137-0.662-0.137-0.6620.9950.9950.070
X중심좌표(XCNTS_VALU)-0.1371.0000.2991.0000.2991.0000.299-0.140-0.139-0.001
Y중심좌표(YDNTS_VALU)-0.6620.2991.0000.2991.0000.2991.000-0.663-0.664-0.030
X최소좌표(XCNTS_MIN)-0.1371.0000.2991.0000.2991.0000.299-0.140-0.139-0.005
Y최소좌표(YDNTS_MIN)-0.6620.2991.0000.2991.0000.2991.000-0.663-0.664-0.035
X최대좌표(XCNTS_MAX)-0.1371.0000.2991.0000.2991.0000.299-0.140-0.1390.004
Y최대좌표(YDNTS_MAX)-0.6620.2991.0000.2991.0000.2991.000-0.664-0.664-0.024
시군구코드(SIGNGU_CD)0.995-0.140-0.663-0.140-0.663-0.140-0.6641.0000.9990.067
행정동코드(ADSTRD_CD)0.995-0.139-0.664-0.139-0.664-0.139-0.6640.9991.0000.071
면적(RELM_AR)0.070-0.001-0.030-0.005-0.0350.004-0.0240.0670.0711.000

Missing values

2023-12-10T23:59:16.309251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:59:16.675759image/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(ALLEY_TRDA)도로명(ALLEY_TR_1)X중심좌표(XCNTS_VALU)Y중심좌표(YDNTS_VALU)X최소좌표(XCNTS_MIN)Y최소좌표(YDNTS_MIN)X최대좌표(XCNTS_MAX)Y최대좌표(YDNTS_MAX)등록일자(DW_REGIST)시군구코드(SIGNGU_CD)행정동코드(ADSTRD_CD)면적(RELM_AR)
01000001계동길1987994536101986774533081989944540682018/10/151111011110600125351
11000002난계로27길2019964526302018654524862020634527742018/10/15112301123053631696
21000003돈화문로11가길1989774529021987784526661991774530932018/10/151111011110615113806
31000004명륜길1995584543511994184541961997144545492018/10/15111101111065044023
41000005백석동길1971534552831967914547581977194557262018/10/151111011110560367064
51000006북촌로11길1986024535291984854533101986834537672018/10/15111101111054046825
61000007북촌로5길1983834534221982354531881985694536982018/10/15111101111051563715
71000008북촌로5나길1985004540261983804536931987074542892018/10/15111101111054085534
81000009삼청로5길1982844539681981334535461984224543872018/10/151111011110515160793
91000010성균관로5길1997224538481995334536841998854540082018/10/15111101111061545115
상권코드(ALLEY_TRDA)도로명(ALLEY_TR_1)X중심좌표(XCNTS_VALU)Y중심좌표(YDNTS_VALU)X최소좌표(XCNTS_MIN)Y최소좌표(YDNTS_MIN)X최대좌표(XCNTS_MAX)Y최대좌표(YDNTS_MAX)등록일자(DW_REGIST)시군구코드(SIGNGU_CD)행정동코드(ADSTRD_CD)면적(RELM_AR)
10001001001천중로39길2123684493522121674491292125544495722018/10/151174011740685114515
10011001002천중로51길2127914494722126144492522130014496772018/10/151174011740685102045
10021001003천호대로162길2113324484452111164482092115064486482018/10/15117401174065070780
10031001004천호대로170길2116644481732114924478582118694484532018/10/151174011740640101168
10041001005천호대로187길2125914484072123694482592128054485652018/10/15117401174068574110
10051001006천호대로197길2130364487412128824484962132144490262018/10/15117401174068585540
10061001007천호대로219길2151744497832149454495912153434499692018/10/15117401174052081094
10071001008천호옛14길2110194485222107954483772112604486862018/10/15117401174065069580
10081001009천호옛길2109254482742107184480712111034484892018/10/15117101171051098533
10091001010풍성로37가길2113444481502111004479672115384483772018/10/15117401174065059036