Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells1025
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory761.7 KiB
Average record size in memory78.0 B

Variable types

Text2
Categorical1
Numeric5

Dataset

Description관리_대지_위치_PK,관리_허가대장_PK,대표_여부,시군구_코드,법정동_코드,대지_구분_코드,번,지
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15403/S/1/datasetView.do

Alerts

관리_허가대장_PK has 775 (7.8%) missing valuesMissing
대지_구분_코드 has 247 (2.5%) missing valuesMissing
관리_대지_위치_PK has unique valuesUnique
대지_구분_코드 has 9629 (96.3%) zerosZeros
번 has 311 (3.1%) zerosZeros
지 has 1187 (11.9%) zerosZeros

Reproduction

Analysis started2024-05-18 04:39:56.563173
Analysis finished2024-05-18 04:40:09.850462
Duration13.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리_대지_위치_PK
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T13:40:10.366472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length15
Mean length20.7762
Min length8

Characters and Unicode

Total characters207762
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st row11740-100062677
2nd row11170-100093867
3rd row11320-1000000000000000046122
4th row11000-1367
5th row11170-100098583
ValueCountFrequency (%)
11740-100062677 1
 
< 0.1%
11560-1000000000000004450593 1
 
< 0.1%
11740-1000000000000004051460 1
 
< 0.1%
11740-1000000000000004768298 1
 
< 0.1%
11170-100109803 1
 
< 0.1%
11170-1000000000000000961300 1
 
< 0.1%
11320-100082588 1
 
< 0.1%
11320-100071591 1
 
< 0.1%
11170-1000000000000004824889 1
 
< 0.1%
11170-100007541 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-05-18T13:40:11.764500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 96361
46.4%
1 40954
19.7%
7 10296
 
5.0%
4 10072
 
4.8%
- 10000
 
4.8%
5 7134
 
3.4%
3 6871
 
3.3%
2 6723
 
3.2%
8 6647
 
3.2%
9 6492
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 197762
95.2%
Dash Punctuation 10000
 
4.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 96361
48.7%
1 40954
20.7%
7 10296
 
5.2%
4 10072
 
5.1%
5 7134
 
3.6%
3 6871
 
3.5%
2 6723
 
3.4%
8 6647
 
3.4%
9 6492
 
3.3%
6 6212
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 207762
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 96361
46.4%
1 40954
19.7%
7 10296
 
5.0%
4 10072
 
4.8%
- 10000
 
4.8%
5 7134
 
3.4%
3 6871
 
3.3%
2 6723
 
3.2%
8 6647
 
3.2%
9 6492
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 207762
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 96361
46.4%
1 40954
19.7%
7 10296
 
5.0%
4 10072
 
4.8%
- 10000
 
4.8%
5 7134
 
3.4%
3 6871
 
3.3%
2 6723
 
3.2%
8 6647
 
3.2%
9 6492
 
3.1%

관리_허가대장_PK
Text

MISSING 

Distinct6115
Distinct (%)66.3%
Missing775
Missing (%)7.8%
Memory size156.2 KiB
2024-05-18T13:40:12.531281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length15
Mean length19.53897
Min length7

Characters and Unicode

Total characters180247
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4967 ?
Unique (%)53.8%

Sample

1st row11740-100081951
2nd row11170-100068812
3rd row11320-100068905
4th row11000-135
5th row11170-100073052
ValueCountFrequency (%)
11650-1000000000000000499102 105
 
1.1%
11740-1000000000000000062646 101
 
1.1%
11170-100063630 95
 
1.0%
11740-100059186 93
 
1.0%
11170-100020545 54
 
0.6%
11170-100023536 49
 
0.5%
11000-100004105 46
 
0.5%
11140-100047972 45
 
0.5%
11320-100069125 36
 
0.4%
11140-100077178 32
 
0.3%
Other values (6105) 8569
92.9%
2024-05-18T13:40:13.891613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 81668
45.3%
1 36380
20.2%
7 9730
 
5.4%
- 9225
 
5.1%
4 7733
 
4.3%
2 6867
 
3.8%
3 6606
 
3.7%
5 6553
 
3.6%
6 5734
 
3.2%
8 5120
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 171022
94.9%
Dash Punctuation 9225
 
5.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 81668
47.8%
1 36380
21.3%
7 9730
 
5.7%
4 7733
 
4.5%
2 6867
 
4.0%
3 6606
 
3.9%
5 6553
 
3.8%
6 5734
 
3.4%
8 5120
 
3.0%
9 4631
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 9225
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 180247
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 81668
45.3%
1 36380
20.2%
7 9730
 
5.4%
- 9225
 
5.1%
4 7733
 
4.3%
2 6867
 
3.8%
3 6606
 
3.7%
5 6553
 
3.6%
6 5734
 
3.2%
8 5120
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 180247
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 81668
45.3%
1 36380
20.2%
7 9730
 
5.4%
- 9225
 
5.1%
4 7733
 
4.3%
2 6867
 
3.8%
3 6606
 
3.7%
5 6553
 
3.6%
6 5734
 
3.2%
8 5120
 
2.8%

대표_여부
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
5745 
0
4255 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 5745
57.5%
0 4255
42.5%

Length

2024-05-18T13:40:14.452447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T13:40:14.747656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 5745
57.5%
0 4255
42.5%

시군구_코드
Real number (ℝ)

Distinct25
Distinct (%)0.3%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean11400.573
Minimum11110
Maximum11740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T13:40:15.036018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11170
Q111170
median11320
Q311680
95-th percentile11740
Maximum11740
Range630
Interquartile range (IQR)510

Descriptive statistics

Standard deviation237.26905
Coefficient of variation (CV)0.020812028
Kurtosis-1.5562453
Mean11400.573
Median Absolute Deviation (MAD)150
Skewness0.37770878
Sum1.1399433 × 108
Variance56296.601
MonotonicityNot monotonic
2024-05-18T13:40:15.490308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
11170 3210
32.1%
11740 2050
20.5%
11320 1053
 
10.5%
11680 369
 
3.7%
11650 352
 
3.5%
11140 261
 
2.6%
11710 202
 
2.0%
11260 202
 
2.0%
11500 198
 
2.0%
11110 190
 
1.9%
Other values (15) 1912
19.1%
ValueCountFrequency (%)
11110 190
 
1.9%
11140 261
 
2.6%
11170 3210
32.1%
11200 168
 
1.7%
11215 179
 
1.8%
11230 114
 
1.1%
11260 202
 
2.0%
11290 150
 
1.5%
11305 82
 
0.8%
11320 1053
 
10.5%
ValueCountFrequency (%)
11740 2050
20.5%
11710 202
 
2.0%
11680 369
 
3.7%
11650 352
 
3.5%
11620 163
 
1.6%
11590 106
 
1.1%
11560 135
 
1.4%
11545 103
 
1.0%
11530 152
 
1.5%
11500 198
 
2.0%

법정동_코드
Real number (ℝ)

Distinct77
Distinct (%)0.8%
Missing2
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean11336.907
Minimum0
Maximum18700
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T13:40:16.086281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10100
Q110500
median10800
Q311900
95-th percentile13200
Maximum18700
Range18700
Interquartile range (IQR)1400

Descriptive statistics

Standard deviation1342.2476
Coefficient of variation (CV)0.11839627
Kurtosis5.3811342
Mean11336.907
Median Absolute Deviation (MAD)500
Skewness1.8932233
Sum1.133464 × 108
Variance1801628.6
MonotonicityNot monotonic
2024-05-18T13:40:16.718716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10900 1048
 
10.5%
10100 878
 
8.8%
10700 845
 
8.5%
10800 781
 
7.8%
10500 751
 
7.5%
10200 750
 
7.5%
13100 535
 
5.3%
10600 532
 
5.3%
13000 505
 
5.1%
10300 296
 
3.0%
Other values (67) 3077
30.8%
ValueCountFrequency (%)
0 1
 
< 0.1%
10100 878
8.8%
10200 750
7.5%
10300 296
 
3.0%
10400 258
 
2.6%
10500 751
7.5%
10600 532
5.3%
10700 845
8.5%
10800 781
7.8%
10900 1048
10.5%
ValueCountFrequency (%)
18700 1
 
< 0.1%
18600 1
 
< 0.1%
18500 1
 
< 0.1%
18400 4
 
< 0.1%
18300 13
0.1%
18200 5
 
0.1%
18100 1
 
< 0.1%
17500 17
0.2%
17400 8
0.1%
17200 2
 
< 0.1%

대지_구분_코드
Real number (ℝ)

MISSING  ZEROS 

Distinct6
Distinct (%)0.1%
Missing247
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean0.021839434
Minimum0
Maximum9
Zeros9629
Zeros (%)96.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T13:40:17.258767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9
Range9
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.22831466
Coefficient of variation (CV)10.454239
Kurtosis470.90973
Mean0.021839434
Median Absolute Deviation (MAD)0
Skewness17.473287
Sum213
Variance0.052127584
MonotonicityNot monotonic
2024-05-18T13:40:17.718922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 9629
96.3%
2 66
 
0.7%
1 54
 
0.5%
7 2
 
< 0.1%
4 1
 
< 0.1%
9 1
 
< 0.1%
(Missing) 247
 
2.5%
ValueCountFrequency (%)
0 9629
96.3%
1 54
 
0.5%
2 66
 
0.7%
4 1
 
< 0.1%
7 2
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
9 1
 
< 0.1%
7 2
 
< 0.1%
4 1
 
< 0.1%
2 66
 
0.7%
1 54
 
0.5%
0 9629
96.3%

번
Real number (ℝ)

ZEROS 

Distinct1015
Distinct (%)10.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean304.5908
Minimum0
Maximum4677
Zeros311
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T13:40:18.207049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q159
median229
Q3450
95-th percentile797
Maximum4677
Range4677
Interquartile range (IQR)391

Descriptive statistics

Standard deviation309.84837
Coefficient of variation (CV)1.0172611
Kurtosis8.8075937
Mean304.5908
Median Absolute Deviation (MAD)189
Skewness1.9611255
Sum3045908
Variance96006.013
MonotonicityNot monotonic
2024-05-18T13:40:18.666865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 366
 
3.7%
0 311
 
3.1%
423 124
 
1.2%
5 120
 
1.2%
40 107
 
1.1%
2 74
 
0.7%
11 55
 
0.5%
98 49
 
0.5%
63 48
 
0.5%
315 47
 
0.5%
Other values (1005) 8699
87.0%
ValueCountFrequency (%)
0 311
3.1%
1 366
3.7%
2 74
 
0.7%
3 45
 
0.4%
4 36
 
0.4%
5 120
 
1.2%
6 24
 
0.2%
7 31
 
0.3%
8 37
 
0.4%
9 29
 
0.3%
ValueCountFrequency (%)
4677 1
 
< 0.1%
3581 1
 
< 0.1%
2533 1
 
< 0.1%
2473 1
 
< 0.1%
2252 1
 
< 0.1%
2092 1
 
< 0.1%
1762 1
 
< 0.1%
1736 23
0.2%
1732 1
 
< 0.1%
1720 2
 
< 0.1%

지
Real number (ℝ)

ZEROS 

Distinct553
Distinct (%)5.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.6278
Minimum0
Maximum3249
Zeros1187
Zeros (%)11.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T13:40:19.108598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median12
Q337
95-th percentile253
Maximum3249
Range3249
Interquartile range (IQR)34

Descriptive statistics

Standard deviation209.49787
Coefficient of variation (CV)3.3451259
Kurtosis90.503155
Mean62.6278
Median Absolute Deviation (MAD)11
Skewness8.2652721
Sum626278
Variance43889.36
MonotonicityNot monotonic
2024-05-18T13:40:19.632265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1187
 
11.9%
1 674
 
6.7%
2 561
 
5.6%
3 467
 
4.7%
5 386
 
3.9%
4 350
 
3.5%
6 264
 
2.6%
7 252
 
2.5%
8 223
 
2.2%
9 212
 
2.1%
Other values (543) 5424
54.2%
ValueCountFrequency (%)
0 1187
11.9%
1 674
6.7%
2 561
5.6%
3 467
 
4.7%
4 350
 
3.5%
5 386
 
3.9%
6 264
 
2.6%
7 252
 
2.5%
8 223
 
2.2%
9 212
 
2.1%
ValueCountFrequency (%)
3249 1
< 0.1%
3196 1
< 0.1%
3195 1
< 0.1%
3194 1
< 0.1%
3187 1
< 0.1%
3185 1
< 0.1%
3146 1
< 0.1%
3136 1
< 0.1%
3128 1
< 0.1%
3118 1
< 0.1%

Interactions

2024-05-18T13:40:06.572526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:39:59.148938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:40:01.201786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:40:03.045828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:40:04.879339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:40:06.944761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:39:59.511834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:40:01.705514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:40:03.340664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:40:05.200176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:40:07.453117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:39:59.887135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:40:02.098952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:40:03.610811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:40:05.583366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:40:07.727319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:40:00.381620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:40:02.398790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:40:03.917568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:40:05.847096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:40:07.988928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:40:00.755178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:40:02.736643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:40:04.265914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T13:40:06.211879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T13:40:19.909067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대표_여부시군구_코드법정동_코드대지_구분_코드번지
대표_여부1.0000.1010.1560.0820.1430.080
시군구_코드0.1011.0000.5430.1140.4340.206
법정동_코드0.1560.5431.0000.0370.2560.115
대지_구분_코드0.0820.1140.0371.0000.0150.065
번0.1430.4340.2560.0151.0000.000
지0.0800.2060.1150.0650.0001.000
2024-05-18T13:40:20.211103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구_코드법정동_코드대지_구분_코드번지대표_여부
시군구_코드1.000-0.4970.0800.304-0.0530.077
법정동_코드-0.4971.000-0.088-0.1450.0640.112
대지_구분_코드0.080-0.0881.000-0.153-0.1130.059
번0.304-0.145-0.1531.000-0.0040.107
지-0.0530.064-0.113-0.0041.0000.080
대표_여부0.0770.1120.0590.1070.0801.000

Missing values

2024-05-18T13:40:08.503169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T13:40:09.108458image/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.
2024-05-18T13:40:09.594609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

관리_대지_위치_PK관리_허가대장_PK대표_여부시군구_코드법정동_코드대지_구분_코드번지
999111740-10006267711740-10008195111174010500025211
840811170-10009386711170-10006881201117010800033239
286211320-100000000000000004612211320-1000689051113201050005321
36311000-136711000-13501153010200<NA>00
1603911170-10009858311170-10007305211117012800065375
571611170-100000000000000441724811170-10000000000000004206771111701310001149
1085811320-100000000000000004828711320-10007124501132010700066269
534911680-100000000000000496766211680-100000000000000050215301168010700058213
504611545-10000568211545-1000046761115451010006028
1321011740-10006269311740-1000883911117401090004255
관리_대지_위치_PK관리_허가대장_PK대표_여부시군구_코드법정동_코드대지_구분_코드번지
1525811305-100000000000000223378711305-10008794101130510100023315
239911000-10000665711000-100004105011215105000659
54311320-100000000000000494422311320-10000000000000002254181113201080003512
124811320-10006959111320-10006006501132010500070718
1073911170-100000000000000325695511170-10008529211117012100012
392611680-100000000000000493682911680-100000000000000049823101168010500012822
858611305-10009643611305-1000909410113051030004756
796611170-100000000000000073260011170-1000906331111701310002672
1223711170-337511170-3263111170112000850
604011650-100000000000000366867711650-10000000000000002829521116501030004932