Overview

Dataset statistics

Number of variables7
Number of observations25
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory67.3 B

Variable types

Numeric6
Text1

Dataset

Description고유번호,구명,구코드,2007년환산량,2008년환산량,X 좌표,Y 좌표
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-342/S/1/datasetView.do

Alerts

고유번호 is highly overall correlated with 구코드 and 1 other fieldsHigh correlation
구코드 is highly overall correlated with 고유번호 and 1 other fieldsHigh correlation
2007년환산량 is highly overall correlated with 2008년환산량High correlation
2008년환산량 is highly overall correlated with 2007년환산량High correlation
Y 좌표 is highly overall correlated with 고유번호 and 1 other fieldsHigh correlation
고유번호 has unique valuesUnique
구명 has unique valuesUnique
구코드 has unique valuesUnique
2007년환산량 has unique valuesUnique
2008년환산량 has unique valuesUnique
X 좌표 has unique valuesUnique
Y 좌표 has unique valuesUnique

Reproduction

Analysis started2023-12-11 09:27:23.973428
Analysis finished2023-12-11 09:27:27.962742
Duration3.99 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

고유번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-11T18:27:28.321935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.2
Q17
median13
Q319
95-th percentile23.8
Maximum25
Range24
Interquartile range (IQR)12

Descriptive statistics

Standard deviation7.3598007
Coefficient of variation (CV)0.56613852
Kurtosis-1.2
Mean13
Median Absolute Deviation (MAD)6
Skewness0
Sum325
Variance54.166667
MonotonicityStrictly decreasing
2023-12-11T18:27:28.457832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
25 1
 
4.0%
24 1
 
4.0%
1 1
 
4.0%
2 1
 
4.0%
3 1
 
4.0%
4 1
 
4.0%
5 1
 
4.0%
6 1
 
4.0%
7 1
 
4.0%
8 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
1 1
4.0%
2 1
4.0%
3 1
4.0%
4 1
4.0%
5 1
4.0%
6 1
4.0%
7 1
4.0%
8 1
4.0%
9 1
4.0%
10 1
4.0%
ValueCountFrequency (%)
25 1
4.0%
24 1
4.0%
23 1
4.0%
22 1
4.0%
21 1
4.0%
20 1
4.0%
19 1
4.0%
18 1
4.0%
17 1
4.0%
16 1
4.0%

구명
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-11T18:27:28.671698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.08
Min length2

Characters and Unicode

Total characters77
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st row광진구
2nd row영등포구
3rd row동작구
4th row송파구
5th row양천구
ValueCountFrequency (%)
광진구 1
 
4.0%
마포구 1
 
4.0%
종로구 1
 
4.0%
동대문구 1
 
4.0%
성동구 1
 
4.0%
중랑구 1
 
4.0%
강북구 1
 
4.0%
성북구 1
 
4.0%
도봉구 1
 
4.0%
노원구 1
 
4.0%
Other values (15) 15
60.0%
2023-12-11T18:27:29.049617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26
33.8%
4
 
5.2%
4
 
5.2%
3
 
3.9%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
Other values (26) 28
36.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 77
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
26
33.8%
4
 
5.2%
4
 
5.2%
3
 
3.9%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
Other values (26) 28
36.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 77
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
26
33.8%
4
 
5.2%
4
 
5.2%
3
 
3.9%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
Other values (26) 28
36.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 77
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
26
33.8%
4
 
5.2%
4
 
5.2%
3
 
3.9%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
Other values (26) 28
36.4%

구코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean416.6
Minimum110
Maximum740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-11T18:27:29.204754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile146
Q1260
median410
Q3560
95-th percentile704
Maximum740
Range630
Interquartile range (IQR)300

Descriptive statistics

Standard deviation190.18478
Coefficient of variation (CV)0.45651651
Kurtosis-1.1960489
Mean416.6
Median Absolute Deviation (MAD)150
Skewness0.083150093
Sum10415
Variance36170.25
MonotonicityNot monotonic
2023-12-11T18:27:29.356696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
215 1
 
4.0%
560 1
 
4.0%
140 1
 
4.0%
110 1
 
4.0%
230 1
 
4.0%
200 1
 
4.0%
260 1
 
4.0%
305 1
 
4.0%
290 1
 
4.0%
320 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
110 1
4.0%
140 1
4.0%
170 1
4.0%
200 1
4.0%
215 1
4.0%
230 1
4.0%
260 1
4.0%
290 1
4.0%
305 1
4.0%
320 1
4.0%
ValueCountFrequency (%)
740 1
4.0%
710 1
4.0%
680 1
4.0%
650 1
4.0%
620 1
4.0%
590 1
4.0%
560 1
4.0%
545 1
4.0%
530 1
4.0%
500 1
4.0%

2007년환산량
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean356.64394
Minimum218.36237
Maximum933.44868
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-11T18:27:29.473963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum218.36237
5-th percentile237.83014
Q1283.43158
median315.14619
Q3377.9155
95-th percentile532.71212
Maximum933.44868
Range715.0863
Interquartile range (IQR)94.483928

Descriptive statistics

Standard deviation145.43307
Coefficient of variation (CV)0.40778225
Kurtosis10.162425
Mean356.64394
Median Absolute Deviation (MAD)50.934986
Skewness2.8419945
Sum8916.0984
Variance21150.777
MonotonicityNot monotonic
2023-12-11T18:27:29.620615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
320.69791189 1
 
4.0%
433.13303608 1
 
4.0%
368.81795546 1
 
4.0%
286.7344084 1
 
4.0%
283.79486235 1
 
4.0%
307.98711328 1
 
4.0%
246.74889224 1
 
4.0%
218.36237455 1
 
4.0%
315.14618766 1
 
4.0%
264.21120164 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
218.36237455 1
4.0%
235.60045054 1
4.0%
246.74889224 1
4.0%
264.00241543 1
4.0%
264.21120164 1
4.0%
281.12072032 1
4.0%
283.43157513 1
4.0%
283.79486235 1
4.0%
286.7344084 1
4.0%
287.07023383 1
4.0%
ValueCountFrequency (%)
933.44867755 1
4.0%
543.33105576 1
4.0%
490.23639292 1
4.0%
471.25270575 1
4.0%
433.13303608 1
4.0%
401.12320936 1
4.0%
377.91550332 1
4.0%
368.81795546 1
4.0%
351.3716842 1
4.0%
338.95147784 1
4.0%

2008년환산량
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean363.76979
Minimum219.95591
Maximum954.24895
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-11T18:27:29.787233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum219.95591
5-th percentile240.2707
Q1286.29954
median323.92509
Q3381.95568
95-th percentile546.49576
Maximum954.24895
Range734.29304
Interquartile range (IQR)95.656131

Descriptive statistics

Standard deviation149.2165
Coefficient of variation (CV)0.41019487
Kurtosis10.061966
Mean363.76979
Median Absolute Deviation (MAD)54.049911
Skewness2.8296566
Sum9094.2447
Variance22265.564
MonotonicityNot monotonic
2023-12-11T18:27:29.911534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
327.23130497 1
 
4.0%
438.57081075 1
 
4.0%
381.95567513 1
 
4.0%
309.27982133 1
 
4.0%
293.72594133 1
 
4.0%
313.4605054 1
 
4.0%
255.17664825 1
 
4.0%
219.95590981 1
 
4.0%
323.92508608 1
 
4.0%
266.62862083 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
219.95590981 1
4.0%
236.54420669 1
4.0%
255.17664825 1
4.0%
266.62862083 1
4.0%
269.87517491 1
4.0%
281.20785211 1
4.0%
286.29954438 1
4.0%
286.44809309 1
4.0%
288.11756336 1
4.0%
293.72594133 1
4.0%
ValueCountFrequency (%)
954.24894691 1
4.0%
553.23875171 1
4.0%
519.52380768 1
4.0%
476.06275108 1
4.0%
438.57081075 1
4.0%
393.73895483 1
4.0%
381.95567513 1
4.0%
379.42654129 1
4.0%
358.1567129 1
4.0%
343.41516646 1
4.0%

X 좌표
Real number (ℝ)

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean199118.68
Minimum184329.33
Maximum212941.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-11T18:27:30.031707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum184329.33
5-th percentile187318.53
Q1193173.93
median199487.03
Q3204477.71
95-th percentile209756.43
Maximum212941.3
Range28611.971
Interquartile range (IQR)11303.783

Descriptive statistics

Standard deviation7674.8559
Coefficient of variation (CV)0.038544128
Kurtosis-0.8265811
Mean199118.68
Median Absolute Deviation (MAD)6313.097
Skewness-0.15939425
Sum4977967
Variance58903413
MonotonicityNot monotonic
2023-12-11T18:27:30.147518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
207579.005 1
 
4.0%
192393.039 1
 
4.0%
199487.028 1
 
4.0%
198780.238 1
 
4.0%
204477.714 1
 
4.0%
203619.334 1
 
4.0%
208297.55 1
 
4.0%
201299.277 1
 
4.0%
202052.468 1
 
4.0%
202805.836 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
184329.333 1
4.0%
187284.922 1
4.0%
187452.985 1
4.0%
191167.747 1
4.0%
191941.166 1
4.0%
192393.039 1
4.0%
193173.931 1
4.0%
194316.924 1
4.0%
195029.548 1
4.0%
195074.546 1
4.0%
ValueCountFrequency (%)
212941.304 1
4.0%
210121.155 1
4.0%
208297.55 1
4.0%
207579.005 1
4.0%
206807.137 1
4.0%
205874.547 1
4.0%
204477.714 1
4.0%
203619.334 1
4.0%
203344.759 1
4.0%
202805.836 1
4.0%

Y 좌표
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean450555.23
Minimum440078.42
Maximum462913.04
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-11T18:27:30.273092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum440078.42
5-th percentile440922.22
Q1445049.16
median450129.15
Q3455094.43
95-th percentile461554.67
Maximum462913.04
Range22834.616
Interquartile range (IQR)10045.276

Descriptive statistics

Standard deviation6465.8637
Coefficient of variation (CV)0.014350879
Kurtosis-0.70453285
Mean450555.23
Median Absolute Deviation (MAD)5079.988
Skewness0.22656312
Sum11263881
Variance41807394
MonotonicityNot monotonic
2023-12-11T18:27:30.392414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
449851.374 1
 
4.0%
446800.622 1
 
4.0%
450935.422 1
 
4.0%
455503.482 1
 
4.0%
453806.678 1
 
4.0%
450129.147 1
 
4.0%
455094.435 1
 
4.0%
460818.584 1
 
4.0%
456378.766 1
 
4.0%
462913.039 1
 
4.0%
Other values (15) 15
60.0%
ValueCountFrequency (%)
440078.423 1
4.0%
440666.05 1
4.0%
441946.916 1
4.0%
444035.495 1
4.0%
444060.673 1
4.0%
444117.29 1
4.0%
445049.159 1
4.0%
446800.622 1
4.0%
447515.498 1
4.0%
447956.789 1
4.0%
ValueCountFrequency (%)
462913.039 1
4.0%
461738.689 1
4.0%
460818.584 1
4.0%
457560.435 1
4.0%
456378.766 1
4.0%
455503.482 1
4.0%
455094.435 1
4.0%
453806.678 1
4.0%
453703.074 1
4.0%
451818.06 1
4.0%

Interactions

2023-12-11T18:27:27.212116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:24.231445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:24.822922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:25.498993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:26.080800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:26.649681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:27.284553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:24.325423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:24.921247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:25.604750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:26.189001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:26.748339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:27.383938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:24.425857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:25.029330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:25.717322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:26.288659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:26.850660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:27.492012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:24.529989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:25.146126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:25.807870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:26.382258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:26.947225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:27.574338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:24.639028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:25.284914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:25.898529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:26.472066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:27.037533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:27.650671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:24.730768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:25.409490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:25.990657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:26.568359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T18:27:27.133948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T18:27:30.480097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호구명구코드2007년환산량2008년환산량X 좌표Y 좌표
고유번호1.0001.0000.8480.0000.0000.3790.459
구명1.0001.0001.0001.0001.0001.0001.000
구코드0.8481.0001.0000.2760.3620.5770.765
2007년환산량0.0001.0000.2761.0000.9780.0000.635
2008년환산량0.0001.0000.3620.9781.0000.0000.000
X 좌표0.3791.0000.5770.0000.0001.0000.000
Y 좌표0.4591.0000.7650.6350.0000.0001.000
2023-12-11T18:27:30.601801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고유번호구코드2007년환산량2008년환산량X 좌표Y 좌표
고유번호1.0000.6660.3450.287-0.302-0.631
구코드0.6661.0000.4280.376-0.073-0.596
2007년환산량0.3450.4281.0000.9920.000-0.371
2008년환산량0.2870.3760.9921.0000.023-0.370
X 좌표-0.302-0.0730.0000.0231.0000.202
Y 좌표-0.631-0.596-0.371-0.3700.2021.000

Missing values

2023-12-11T18:27:27.777879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T18:27:27.920418image/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

고유번호구명구코드2007년환산량2008년환산량X 좌표Y 좌표
025광진구215320.697912327.231305207579.005449851.374
124영등포구560433.133036438.570811192393.039446800.622
223동작구590287.070234288.117563195074.546444117.29
322송파구710490.236393519.523808210121.155445049.159
421양천구470338.951478343.415166187284.922447515.498
520금천구545235.600451236.544207191167.747440078.423
619구로구530351.371684358.156713187452.985444035.495
718관악구620377.915503379.426541195029.548440666.05
817강서구500401.123209393.738955184329.333451818.06
916은평구380287.309668286.448093193173.931457560.435
고유번호구명구코드2007년환산량2008년환산량X 좌표Y 좌표
1510용산구170264.002415269.875175198315.522447956.789
169노원구350471.252706476.062751206807.137461738.689
178도봉구320264.211202266.628621202805.836462913.039
187성북구290315.146188323.925086202052.468456378.766
196강북구305218.362375219.95591201299.277460818.584
205중랑구260246.748892255.176648208297.55455094.435
214성동구200307.987113313.460505203619.334450129.147
223동대문구230283.794862293.725941204477.714453806.678
232종로구110286.734408309.279821198780.238455503.482
241중구140368.817955381.955675199487.028450935.422