Overview

Dataset statistics

Number of variables12
Number of observations227
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.6 KiB
Average record size in memory106.6 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
전통시장코드(TRDIT_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 전통시장코드(TRDIT_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 전통시장코드(TRDIT_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 전통시장코드(TRDIT_TRDA) and 4 other fieldsHigh correlation
시군구코드(SIGNGU_CD) is highly overall correlated with 전통시장코드(TRDIT_TRDA) and 4 other fieldsHigh correlation
행정동코드(ADSTRD_CD) is highly overall correlated with 전통시장코드(TRDIT_TRDA) and 4 other fieldsHigh correlation
전통시장코드(TRDIT_TRDA) has unique valuesUnique
전통시장명(TRDIT_TR_1) has unique valuesUnique
X최소좌표(XCNTS_MIN) has unique valuesUnique
Y최대좌표(YDNTS_MAX) has unique valuesUnique
면적(RELM_AR) has unique valuesUnique

Reproduction

Analysis started2023-12-10 15:00:33.330870
Analysis finished2023-12-10 15:01:00.968764
Duration27.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

전통시장코드(TRDIT_TRDA)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct227
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1001377
Minimum1001264
Maximum1001490
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-11T00:01:01.150188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1001264
5-th percentile1001275.3
Q11001320.5
median1001377
Q31001433.5
95-th percentile1001478.7
Maximum1001490
Range226
Interquartile range (IQR)113

Descriptive statistics

Standard deviation65.673435
Coefficient of variation (CV)6.5583127 × 10-5
Kurtosis-1.2
Mean1001377
Median Absolute Deviation (MAD)57
Skewness0
Sum2.2731258 × 108
Variance4313
MonotonicityStrictly increasing
2023-12-11T00:01:01.463907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1001264 1
 
0.4%
1001407 1
 
0.4%
1001409 1
 
0.4%
1001410 1
 
0.4%
1001411 1
 
0.4%
1001412 1
 
0.4%
1001413 1
 
0.4%
1001414 1
 
0.4%
1001415 1
 
0.4%
1001416 1
 
0.4%
Other values (217) 217
95.6%
ValueCountFrequency (%)
1001264 1
0.4%
1001265 1
0.4%
1001266 1
0.4%
1001267 1
0.4%
1001268 1
0.4%
1001269 1
0.4%
1001270 1
0.4%
1001271 1
0.4%
1001272 1
0.4%
1001273 1
0.4%
ValueCountFrequency (%)
1001490 1
0.4%
1001489 1
0.4%
1001488 1
0.4%
1001487 1
0.4%
1001486 1
0.4%
1001485 1
0.4%
1001484 1
0.4%
1001483 1
0.4%
1001482 1
0.4%
1001481 1
0.4%
Distinct227
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2023-12-11T00:01:02.176480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length5.4361233
Min length4

Characters and Unicode

Total characters1234
Distinct characters191
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique227 ?
Unique (%)100.0%

Sample

1st row통인시장
2nd row광장시장
3rd row동대문종합시장
4th row종로신진시장
5th row충신시장
ValueCountFrequency (%)
통인시장 1
 
0.4%
등마루시장 1
 
0.4%
신월중앙시장 1
 
0.4%
화곡중앙골목시장 1
 
0.4%
대원종합시장 1
 
0.4%
남부골목시장 1
 
0.4%
화곡본동시장 1
 
0.4%
까치산시장 1
 
0.4%
송화골목시장 1
 
0.4%
방신전통시장 1
 
0.4%
Other values (218) 218
95.6%
2023-12-11T00:01:03.104609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
230
 
18.6%
226
 
18.3%
42
 
3.4%
36
 
2.9%
33
 
2.7%
29
 
2.4%
17
 
1.4%
16
 
1.3%
15
 
1.2%
15
 
1.2%
Other values (181) 575
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1218
98.7%
Decimal Number 10
 
0.8%
Connector Punctuation 2
 
0.2%
Space Separator 1
 
0.1%
Uppercase Letter 1
 
0.1%
Close Punctuation 1
 
0.1%
Open Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
230
18.9%
226
18.6%
42
 
3.4%
36
 
3.0%
33
 
2.7%
29
 
2.4%
17
 
1.4%
16
 
1.3%
15
 
1.2%
15
 
1.2%
Other values (170) 559
45.9%
Decimal Number
ValueCountFrequency (%)
3 3
30.0%
4 2
20.0%
2 2
20.0%
7 1
 
10.0%
1 1
 
10.0%
6 1
 
10.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1218
98.7%
Common 15
 
1.2%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
230
18.9%
226
18.6%
42
 
3.4%
36
 
3.0%
33
 
2.7%
29
 
2.4%
17
 
1.4%
16
 
1.3%
15
 
1.2%
15
 
1.2%
Other values (170) 559
45.9%
Common
ValueCountFrequency (%)
3 3
20.0%
_ 2
13.3%
4 2
13.3%
2 2
13.3%
1
 
6.7%
7 1
 
6.7%
) 1
 
6.7%
1 1
 
6.7%
( 1
 
6.7%
6 1
 
6.7%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1218
98.7%
ASCII 16
 
1.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
230
18.9%
226
18.6%
42
 
3.4%
36
 
3.0%
33
 
2.7%
29
 
2.4%
17
 
1.4%
16
 
1.3%
15
 
1.2%
15
 
1.2%
Other values (170) 559
45.9%
ASCII
ValueCountFrequency (%)
3 3
18.8%
_ 2
12.5%
4 2
12.5%
2 2
12.5%
1
 
6.2%
A 1
 
6.2%
7 1
 
6.2%
) 1
 
6.2%
1 1
 
6.2%
( 1
 
6.2%

X중심좌표(XCNTS_VALU)
Real number (ℝ)

HIGH CORRELATION 

Distinct224
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198610.48
Minimum183061
Maximum214971
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-11T00:01:03.438662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum183061
5-th percentile186155.4
Q1192631
median200011
Q3204026
95-th percentile210457
Maximum214971
Range31910
Interquartile range (IQR)11395

Descriptive statistics

Standard deviation7303.041
Coefficient of variation (CV)0.036770674
Kurtosis-0.85573112
Mean198610.48
Median Absolute Deviation (MAD)5826
Skewness-0.099689539
Sum45084578
Variance53334407
MonotonicityNot monotonic
2023-12-11T00:01:03.771758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
190850 2
 
0.9%
201018 2
 
0.9%
203566 2
 
0.9%
187073 1
 
0.4%
185547 1
 
0.4%
187642 1
 
0.4%
186203 1
 
0.4%
186493 1
 
0.4%
185468 1
 
0.4%
183436 1
 
0.4%
Other values (214) 214
94.3%
ValueCountFrequency (%)
183061 1
0.4%
183436 1
0.4%
184671 1
0.4%
185257 1
0.4%
185337 1
0.4%
185468 1
0.4%
185488 1
0.4%
185547 1
0.4%
185783 1
0.4%
186077 1
0.4%
ValueCountFrequency (%)
214971 1
0.4%
213336 1
0.4%
212839 1
0.4%
212698 1
0.4%
212119 1
0.4%
211974 1
0.4%
211664 1
0.4%
211411 1
0.4%
211326 1
0.4%
211283 1
0.4%

Y중심좌표(YDNTS_VALU)
Real number (ℝ)

HIGH CORRELATION 

Distinct226
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean450180.67
Minimum437870
Maximum463403
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-11T00:01:04.232668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum437870
5-th percentile442077
Q1446130
median450197
Q3453382.5
95-th percentile459809.3
Maximum463403
Range25533
Interquartile range (IQR)7252.5

Descriptive statistics

Standard deviation5361.3918
Coefficient of variation (CV)0.011909422
Kurtosis-0.45651125
Mean450180.67
Median Absolute Deviation (MAD)3424
Skewness0.14700849
Sum1.0219101 × 108
Variance28744522
MonotonicityNot monotonic
2023-12-11T00:01:04.591220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
450932 2
 
0.9%
453459 1
 
0.4%
450807 1
 
0.4%
448352 1
 
0.4%
449458 1
 
0.4%
448174 1
 
0.4%
449328 1
 
0.4%
448134 1
 
0.4%
449994 1
 
0.4%
452447 1
 
0.4%
Other values (216) 216
95.2%
ValueCountFrequency (%)
437870 1
0.4%
439104 1
0.4%
439419 1
0.4%
439748 1
0.4%
440835 1
0.4%
441202 1
0.4%
441257 1
0.4%
441265 1
0.4%
441586 1
0.4%
441651 1
0.4%
ValueCountFrequency (%)
463403 1
0.4%
462853 1
0.4%
462814 1
0.4%
462240 1
0.4%
461795 1
0.4%
460978 1
0.4%
460755 1
0.4%
460598 1
0.4%
460301 1
0.4%
460080 1
0.4%

X최소좌표(XCNTS_MIN)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct227
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198501.52
Minimum183013
Maximum214765
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-11T00:01:04.904570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum183013
5-th percentile186080.4
Q1192544.5
median199818
Q3203942.5
95-th percentile210223.2
Maximum214765
Range31752
Interquartile range (IQR)11398

Descriptive statistics

Standard deviation7297.3782
Coefficient of variation (CV)0.036762328
Kurtosis-0.85888154
Mean198501.52
Median Absolute Deviation (MAD)5883
Skewness-0.10268312
Sum45059846
Variance53251729
MonotonicityNot monotonic
2023-12-11T00:01:05.367837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
197215 1
 
0.4%
187611 1
 
0.4%
185630 1
 
0.4%
185505 1
 
0.4%
187396 1
 
0.4%
186100 1
 
0.4%
186360 1
 
0.4%
185334 1
 
0.4%
183327 1
 
0.4%
183013 1
 
0.4%
Other values (217) 217
95.6%
ValueCountFrequency (%)
183013 1
0.4%
183327 1
0.4%
184624 1
0.4%
185150 1
0.4%
185216 1
0.4%
185334 1
0.4%
185349 1
0.4%
185505 1
0.4%
185630 1
0.4%
185968 1
0.4%
ValueCountFrequency (%)
214765 1
0.4%
213191 1
0.4%
212748 1
0.4%
212534 1
0.4%
212058 1
0.4%
211869 1
0.4%
211578 1
0.4%
211271 1
0.4%
211255 1
0.4%
211180 1
0.4%

Y최소좌표(YDNTS_MIN)
Real number (ℝ)

HIGH CORRELATION 

Distinct225
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean450073.43
Minimum437844
Maximum463366
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-11T00:01:06.010340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum437844
5-th percentile442012.7
Q1445757.5
median450141
Q3453268
95-th percentile459676.2
Maximum463366
Range25522
Interquartile range (IQR)7510.5

Descriptive statistics

Standard deviation5362.2865
Coefficient of variation (CV)0.011914248
Kurtosis-0.45825461
Mean450073.43
Median Absolute Deviation (MAD)3407
Skewness0.14801518
Sum1.0216667 × 108
Variance28754116
MonotonicityNot monotonic
2023-12-11T00:01:06.465298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
452234 2
 
0.9%
445352 2
 
0.9%
453357 1
 
0.4%
444674 1
 
0.4%
448171 1
 
0.4%
449423 1
 
0.4%
448029 1
 
0.4%
449233 1
 
0.4%
447958 1
 
0.4%
449841 1
 
0.4%
Other values (215) 215
94.7%
ValueCountFrequency (%)
437844 1
0.4%
439031 1
0.4%
439373 1
0.4%
439475 1
0.4%
440629 1
0.4%
441095 1
0.4%
441146 1
0.4%
441208 1
0.4%
441445 1
0.4%
441449 1
0.4%
ValueCountFrequency (%)
463366 1
0.4%
462769 1
0.4%
462706 1
0.4%
462074 1
0.4%
461690 1
0.4%
460859 1
0.4%
460591 1
0.4%
460487 1
0.4%
460171 1
0.4%
460047 1
0.4%

X최대좌표(XCNTS_MAX)
Real number (ℝ)

HIGH CORRELATION 

Distinct225
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean198718.9
Minimum183099
Maximum215152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-11T00:01:06.743637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum183099
5-th percentile186252.3
Q1192717.5
median200143
Q3204107.5
95-th percentile210659.9
Maximum215152
Range32053
Interquartile range (IQR)11390

Descriptive statistics

Standard deviation7309.1948
Coefficient of variation (CV)0.036781579
Kurtosis-0.85278715
Mean198718.9
Median Absolute Deviation (MAD)5819
Skewness-0.097791312
Sum45109190
Variance53424328
MonotonicityNot monotonic
2023-12-11T00:01:07.283308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
201938 2
 
0.9%
201828 2
 
0.9%
197460 1
 
0.4%
187104 1
 
0.4%
185596 1
 
0.4%
187888 1
 
0.4%
186309 1
 
0.4%
186662 1
 
0.4%
185537 1
 
0.4%
183515 1
 
0.4%
Other values (215) 215
94.7%
ValueCountFrequency (%)
183099 1
0.4%
183515 1
0.4%
184738 1
0.4%
185344 1
0.4%
185462 1
0.4%
185537 1
0.4%
185596 1
0.4%
185625 1
0.4%
185934 1
0.4%
186186 1
0.4%
ValueCountFrequency (%)
215152 1
0.4%
213467 1
0.4%
212937 1
0.4%
212866 1
0.4%
212184 1
0.4%
212075 1
0.4%
211766 1
0.4%
211527 1
0.4%
211424 1
0.4%
211408 1
0.4%

Y최대좌표(YDNTS_MAX)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct227
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean450288.9
Minimum437894
Maximum463439
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-11T00:01:07.699030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum437894
5-th percentile442120.5
Q1446375
median450322
Q3453482.5
95-th percentile459946.2
Maximum463439
Range25545
Interquartile range (IQR)7107.5

Descriptive statistics

Standard deviation5364.1805
Coefficient of variation (CV)0.011912753
Kurtosis-0.45463751
Mean450288.9
Median Absolute Deviation (MAD)3475
Skewness0.14564383
Sum1.0221558 × 108
Variance28774433
MonotonicityNot monotonic
2023-12-11T00:01:08.579470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
453547 1
 
0.4%
450843 1
 
0.4%
448538 1
 
0.4%
449521 1
 
0.4%
448335 1
 
0.4%
449433 1
 
0.4%
448317 1
 
0.4%
450157 1
 
0.4%
452566 1
 
0.4%
451498 1
 
0.4%
Other values (217) 217
95.6%
ValueCountFrequency (%)
437894 1
0.4%
439154 1
0.4%
439492 1
0.4%
440069 1
0.4%
440967 1
0.4%
441267 1
0.4%
441309 1
0.4%
441406 1
0.4%
441764 1
0.4%
441766 1
0.4%
ValueCountFrequency (%)
463439 1
0.4%
462987 1
0.4%
462860 1
0.4%
462384 1
0.4%
461991 1
0.4%
461124 1
0.4%
460930 1
0.4%
460747 1
0.4%
460407 1
0.4%
460205 1
0.4%

등록일자(DW_REGIST)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size1.9 KiB
2018/10/15
227 

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 (%)
2018/10/15 227
100.0%

Length

2023-12-11T00:01:08.950533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T00:01:09.204746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018/10/15 227
100.0%

시군구코드(SIGNGU_CD)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11401.806
Minimum11110
Maximum11740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-11T00:01:09.414474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11140
Q111230
median11410
Q311560
95-th percentile11710
Maximum11740
Range630
Interquartile range (IQR)330

Descriptive statistics

Standard deviation185.31496
Coefficient of variation (CV)0.016253123
Kurtosis-1.2012479
Mean11401.806
Median Absolute Deviation (MAD)180
Skewness0.14417426
Sum2588210
Variance34341.635
MonotonicityIncreasing
2023-12-11T00:01:09.666782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11230 18
 
7.9%
11620 16
 
7.0%
11140 13
 
5.7%
11470 13
 
5.7%
11215 12
 
5.3%
11590 12
 
5.3%
11440 11
 
4.8%
11305 11
 
4.8%
11740 10
 
4.4%
11290 10
 
4.4%
Other values (15) 101
44.5%
ValueCountFrequency (%)
11110 9
4.0%
11140 13
5.7%
11170 8
3.5%
11200 5
 
2.2%
11215 12
5.3%
11230 18
7.9%
11260 9
4.0%
11290 10
4.4%
11305 11
4.8%
11320 6
 
2.6%
ValueCountFrequency (%)
11740 10
4.4%
11710 5
 
2.2%
11680 6
 
2.6%
11650 2
 
0.9%
11620 16
7.0%
11590 12
5.3%
11560 10
4.4%
11545 7
3.1%
11530 6
 
2.6%
11500 10
4.4%

행정동코드(ADSTRD_CD)
Real number (ℝ)

HIGH CORRELATION 

Distinct187
Distinct (%)82.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11402433
Minimum11110515
Maximum11740685
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-11T00:01:10.037834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110515
5-th percentile11140590
Q111230580
median11410520
Q311560608
95-th percentile11710589
Maximum11740685
Range630170
Interquartile range (IQR)330027.5

Descriptive statistics

Standard deviation185302.06
Coefficient of variation (CV)0.016251098
Kurtosis-1.2011999
Mean11402433
Median Absolute Deviation (MAD)179810
Skewness0.1441769
Sum2.5883523 × 109
Variance3.4336853 × 1010
MonotonicityIncreasing
2023-12-11T00:01:10.469466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11230545 6
 
2.6%
11140615 4
 
1.8%
11215847 4
 
1.8%
11290810 3
 
1.3%
11110630 3
 
1.3%
11230536 3
 
1.3%
11140590 3
 
1.3%
11440555 3
 
1.3%
11545630 2
 
0.9%
11590650 2
 
0.9%
Other values (177) 194
85.5%
ValueCountFrequency (%)
11110515 1
 
0.4%
11110615 1
 
0.4%
11110630 3
1.3%
11110670 1
 
0.4%
11110680 1
 
0.4%
11110710 2
0.9%
11140540 1
 
0.4%
11140590 3
1.3%
11140605 1
 
0.4%
11140615 4
1.8%
ValueCountFrequency (%)
11740685 1
0.4%
11740660 1
0.4%
11740650 1
0.4%
11740610 2
0.9%
11740600 1
0.4%
11740590 1
0.4%
11740570 1
0.4%
11740560 1
0.4%
11740530 1
0.4%
11710650 1
0.4%

면적(RELM_AR)
Real number (ℝ)

UNIQUE 

Distinct227
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29055.643
Minimum1461
Maximum376969
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.1 KiB
2023-12-11T00:01:10.866876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1461
5-th percentile3740.1
Q17785.5
median19387
Q339142.5
95-th percentile74969.7
Maximum376969
Range375508
Interquartile range (IQR)31357

Descriptive statistics

Standard deviation37654.009
Coefficient of variation (CV)1.2959276
Kurtosis40.017913
Mean29055.643
Median Absolute Deviation (MAD)13072
Skewness5.2693233
Sum6595631
Variance1.4178244 × 109
MonotonicityNot monotonic
2023-12-11T00:01:11.188750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33789 1
 
0.4%
2665 1
 
0.4%
34641 1
 
0.4%
3018 1
 
0.4%
66099 1
 
0.4%
21476 1
 
0.4%
40528 1
 
0.4%
30128 1
 
0.4%
24095 1
 
0.4%
4144 1
 
0.4%
Other values (217) 217
95.6%
ValueCountFrequency (%)
1461 1
0.4%
1991 1
0.4%
2219 1
0.4%
2406 1
0.4%
2573 1
0.4%
2665 1
0.4%
2983 1
0.4%
3018 1
0.4%
3239 1
0.4%
3363 1
0.4%
ValueCountFrequency (%)
376969 1
0.4%
275758 1
0.4%
157840 1
0.4%
136819 1
0.4%
136629 1
0.4%
98642 1
0.4%
98009 1
0.4%
94018 1
0.4%
89211 1
0.4%
88666 1
0.4%

Interactions

2023-12-11T00:00:57.772372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:34.507560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:36.743068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:39.441243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:42.248512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:44.638350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:47.981167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:50.691881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:52.765395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:54.922707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:57.968505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:34.744746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:37.016516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:40.126770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:42.451564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:44.841784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:48.373386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:50.973997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:52.984738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:55.110204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:58.211398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:34.983804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:37.245347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:40.347091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:42.699491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:45.212207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:48.611359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:51.169760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:53.193888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:55.346438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:58.450112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:35.178647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:37.450647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:40.551100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:42.908788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:45.697304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:48.862604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:51.345371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:53.409372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:56.234416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:58.693564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:35.458936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:37.766267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:40.764605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:43.149596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:45.934711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:49.082695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:51.535556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:53.637261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:56.461493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:58.909508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:35.648094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:37.973025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:41.008410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:43.361827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:46.243040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:49.362524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:51.715294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:53.826151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:56.647912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:59.117791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:35.833181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:38.269827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:41.266706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:43.629472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:46.621252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:49.608146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:51.904002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:54.049280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:56.872863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:59.426201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:36.043144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:38.516726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:41.486423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:43.859110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:46.870716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:49.895733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:52.093798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:54.263239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:57.078663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:59.638539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:36.261914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:38.779569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:41.714809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:44.182394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:47.296850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:50.152297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:52.318533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:54.491989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:57.340169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:59.924514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:36.479849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:39.157350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:42.042189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:44.415298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:47.637634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:50.391188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:52.549793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:54.707780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T00:00:57.566308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T00:01:11.480848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전통시장코드(TRDIT_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)
전통시장코드(TRDIT_TRDA)1.0000.9100.9080.9090.9090.9110.9110.9810.9820.099
X중심좌표(XCNTS_VALU)0.9101.0000.6341.0000.6581.0000.6470.9000.9050.136
Y중심좌표(YDNTS_VALU)0.9080.6341.0000.6391.0000.6311.0000.9120.9160.000
X최소좌표(XCNTS_MIN)0.9091.0000.6391.0000.6620.9990.6510.9020.9060.142
Y최소좌표(YDNTS_MIN)0.9090.6581.0000.6621.0000.6530.9990.9130.9160.000
X최대좌표(XCNTS_MAX)0.9111.0000.6310.9990.6531.0000.6450.9000.9040.126
Y최대좌표(YDNTS_MAX)0.9110.6471.0000.6510.9990.6451.0000.9120.9160.000
시군구코드(SIGNGU_CD)0.9810.9000.9120.9020.9130.9000.9121.0001.0000.132
행정동코드(ADSTRD_CD)0.9820.9050.9160.9060.9160.9040.9161.0001.0000.134
면적(RELM_AR)0.0990.1360.0000.1420.0000.1260.0000.1320.1341.000
2023-12-11T00:01:11.865876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
전통시장코드(TRDIT_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)
전통시장코드(TRDIT_TRDA)1.000-0.267-0.612-0.267-0.615-0.268-0.6110.9991.000-0.027
X중심좌표(XCNTS_VALU)-0.2671.0000.4051.0000.4051.0000.406-0.268-0.2670.153
Y중심좌표(YDNTS_VALU)-0.6120.4051.0000.4041.0000.4061.000-0.611-0.6120.093
X최소좌표(XCNTS_MIN)-0.2671.0000.4041.0000.4041.0000.405-0.268-0.2670.145
Y최소좌표(YDNTS_MIN)-0.6150.4051.0000.4041.0000.4050.999-0.613-0.6150.082
X최대좌표(XCNTS_MAX)-0.2681.0000.4061.0000.4051.0000.407-0.269-0.2680.160
Y최대좌표(YDNTS_MAX)-0.6110.4061.0000.4050.9990.4071.000-0.609-0.6110.104
시군구코드(SIGNGU_CD)0.999-0.268-0.611-0.268-0.613-0.269-0.6091.0000.999-0.021
행정동코드(ADSTRD_CD)1.000-0.267-0.612-0.267-0.615-0.268-0.6110.9991.000-0.027
면적(RELM_AR)-0.0270.1530.0930.1450.0820.1600.104-0.021-0.0271.000

Missing values

2023-12-11T00:01:00.330580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T00:01:00.802788image/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

전통시장코드(TRDIT_TRDA)전통시장명(TRDIT_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)
01001264통인시장1973424534591972154533571974604535472018/10/15111101111051533789
11001265광장시장1999484522701998034521502001434523742018/10/15111101111061550438
21001266동대문종합시장2007154523202005914522342008474524212018/10/15111101111063039184
31001267종로신진시장2004684523142003434522342005924523962018/10/15111101111063039286
41001268충신시장2005054527292004034526622005974528442018/10/15111101111063015656
51001269동문시장2009834523332009474523082010084523552018/10/1511110111106701991
61001270창신골목시장2010184525902009374524782011114526992018/10/15111101111068022735
71001271동묘시장2016454525492013914523832019384527122018/10/15111101111071076653
81001272신설종합시장2018544525692017594525232019384526152018/10/1511110111107109843
91001273남대문시장1980914511491978484508831984164513632018/10/151114011140540136819
전통시장코드(TRDIT_TRDA)전통시장명(TRDIT_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)
2171001481명일골목시장2128394499772127484498862129374500832018/10/15117401174053027660
2181001482고덕전통시장2149714509322147654508252151524510982018/10/15117401174056042950
2191001483암사종합시장2114114501652112714500152115274503222018/10/15117401174057035724
2201001484양지골목시장2121194506782120584505872121844507682018/10/15117401174059021891
2211001485고분다리골목시장2116644492272115784491802117664493012018/10/15117401174060011985
2221001486동서울시장2113264491832112554491122114084492642018/10/15117401174061010257
2231001487천호시장2112834490782111574490042114244491732018/10/15117401174061028311
2241001488성내골목시장2112774482702111804480912113584484082018/10/15117401174065023607
2251001489둔촌역전통시장2119744475902118694474372120754477522018/10/15117401174066036303
2261001490길동복조리시장2126984487812125344485092128664490522018/10/15117401174068594018