Overview

Dataset statistics

Number of variables44
Number of observations10000
Missing cells117947
Missing cells (%)26.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 MiB
Average record size in memory375.0 B

Variable types

Numeric14
Categorical9
Text19
DateTime2

Dataset

Description인천광역시 미추홀구 건축물 착공신고 현황에 대한 데이터로 연번, 도로명주소, 좌표값, 대지면적 등의 항목을 제공합니다.
Author인천광역시 미추홀구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15029300&srcSe=7661IVAWM27C61E190

Alerts

건축구분 is highly imbalanced (67.8%)Imbalance
지목 is highly imbalanced (84.5%)Imbalance
구조 is highly imbalanced (66.2%)Imbalance
하수처리시설명 is highly imbalanced (74.9%)Imbalance
용도지구 is highly imbalanced (64.7%)Imbalance
용도구역 is highly imbalanced (75.2%)Imbalance
인근기계식주차장(대) is highly imbalanced (94.7%)Imbalance
증축연면적(제곱미터) has 7896 (79.0%) missing valuesMissing
최종설계변경일 has 7699 (77.0%) missing valuesMissing
착공예정일 has 688 (6.9%) missing valuesMissing
사용승인일 has 666 (6.7%) missing valuesMissing
건축허가최초접수일 has 134 (1.3%) missing valuesMissing
최대지상층수 has 165 (1.7%) missing valuesMissing
최대지하층수 has 3244 (32.4%) missing valuesMissing
최고높이(m) has 501 (5.0%) missing valuesMissing
승강기합 has 7082 (70.8%) missing valuesMissing
비상승강기합 has 9435 (94.3%) missing valuesMissing
하수처리시설용량(제곱미터) has 7383 (73.8%) missing valuesMissing
부속용도 has 2417 (24.2%) missing valuesMissing
자주식옥내주차장(대) has 6509 (65.1%) missing valuesMissing
자주식옥외주차장(대) has 3589 (35.9%) missing valuesMissing
기계식옥내주차장(대) has 9425 (94.2%) missing valuesMissing
기계식옥외주차장(대) has 9769 (97.7%) missing valuesMissing
인근자주식주차장(대) has 9800 (98.0%) missing valuesMissing
총주차대수 has 1349 (13.5%) missing valuesMissing
총주차장면적(제곱미터) has 1349 (13.5%) missing valuesMissing
세대수 has 4145 (41.4%) missing valuesMissing
호수 has 9103 (91.0%) missing valuesMissing
가구수 has 5750 (57.5%) missing valuesMissing
주건축물수 has 440 (4.4%) missing valuesMissing
부속건축물수 has 9296 (93.0%) missing valuesMissing
최대지상층수 is highly skewed (γ1 = 70.0526465)Skewed
연번 has unique valuesUnique
최대지하층수 has 4072 (40.7%) zerosZeros
동수 has 432 (4.3%) zerosZeros
승강기합 has 723 (7.2%) zerosZeros
비상승강기합 has 400 (4.0%) zerosZeros
기계식옥내주차장(대) has 165 (1.7%) zerosZeros
기계식옥외주차장(대) has 171 (1.7%) zerosZeros
인근자주식주차장(대) has 158 (1.6%) zerosZeros
세대수 has 1739 (17.4%) zerosZeros
가구수 has 1743 (17.4%) zerosZeros
부속건축물수 has 292 (2.9%) zerosZeros

Reproduction

Analysis started2024-01-28 17:00:14.611212
Analysis finished2024-01-28 17:00:16.628568
Duration2.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5371.419
Minimum1
Maximum10753
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-29T02:00:16.688101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile535.95
Q12684.75
median5368.5
Q38057.25
95-th percentile10213.05
Maximum10753
Range10752
Interquartile range (IQR)5372.5

Descriptive statistics

Standard deviation3103.6528
Coefficient of variation (CV)0.57780873
Kurtosis-1.2007946
Mean5371.419
Median Absolute Deviation (MAD)2686.5
Skewness0.00042722711
Sum53714190
Variance9632660.5
MonotonicityNot monotonic
2024-01-29T02:00:16.816381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5591 1
 
< 0.1%
4087 1
 
< 0.1%
1416 1
 
< 0.1%
5283 1
 
< 0.1%
5212 1
 
< 0.1%
1808 1
 
< 0.1%
6 1
 
< 0.1%
1322 1
 
< 0.1%
2836 1
 
< 0.1%
7718 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
10753 1
< 0.1%
10752 1
< 0.1%
10751 1
< 0.1%
10750 1
< 0.1%
10749 1
< 0.1%
10748 1
< 0.1%
10747 1
< 0.1%
10746 1
< 0.1%
10745 1
< 0.1%
10744 1
< 0.1%

건축구분
Categorical

IMBALANCE 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
신축
7538 
증축
2158 
대수선
 
259
개축
 
16
용도변경
 
15
Other values (3)
 
14

Length

Max length9
Median length2
Mean length2.0338
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row신축
2nd row신축
3rd row신축
4th row증축
5th row신축

Common Values

ValueCountFrequency (%)
신축 7538
75.4%
증축 2158
 
21.6%
대수선 259
 
2.6%
개축 16
 
0.2%
용도변경 15
 
0.1%
재축 7
 
0.1%
가설건축물축조허가 6
 
0.1%
허가/신고사항변경 1
 
< 0.1%

Length

2024-01-29T02:00:16.932584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T02:00:17.029830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신축 7538
75.4%
증축 2158
 
21.6%
대수선 259
 
2.6%
개축 16
 
0.2%
용도변경 15
 
0.1%
재축 7
 
0.1%
가설건축물축조허가 6
 
0.1%
허가/신고사항변경 1
 
< 0.1%
Distinct9800
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-29T02:00:17.213490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length22
Mean length17.0499
Min length15

Characters and Unicode

Total characters170499
Distinct characters57
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9600 ?
Unique (%)96.0%

Sample

1st row2008-건축과-신축허가-252
2nd row2004-건축과-신축허가-64
3rd row2001-종합민원과-신축허가-615
4th row2007-건축과-증축신고-10
5th row2001-종합민원과-신축허가-1399
ValueCountFrequency (%)
2007-건축과-대수선신고-2 2
 
< 0.1%
2019-건축과-신축허가-71 2
 
< 0.1%
2007-건축과-신축신고-5 2
 
< 0.1%
2007-건축과-신축허가-17 2
 
< 0.1%
2019-건축과-신축허가-97 2
 
< 0.1%
2019-건축과-신축허가-15 2
 
< 0.1%
2007-건축과-증축허가-2 2
 
< 0.1%
2018-건축과-신축허가-19 2
 
< 0.1%
2019-건축과-대수선허가-3 2
 
< 0.1%
2007-건축과-신축허가-20 2
 
< 0.1%
Other values (9790) 9980
99.8%
2024-01-29T02:00:17.533963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 30000
17.6%
0 18279
10.7%
17996
10.6%
2 14725
8.6%
1 11185
 
6.6%
9989
 
5.9%
9216
 
5.4%
8410
 
4.9%
8115
 
4.8%
8111
 
4.8%
Other values (47) 34473
20.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 75858
44.5%
Decimal Number 64537
37.9%
Dash Punctuation 30000
 
17.6%
Open Punctuation 52
 
< 0.1%
Close Punctuation 52
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
17996
23.7%
9989
13.2%
9216
12.1%
8410
11.1%
8115
10.7%
8111
10.7%
2137
 
2.8%
1788
 
2.4%
1681
 
2.2%
1681
 
2.2%
Other values (34) 6734
 
8.9%
Decimal Number
ValueCountFrequency (%)
0 18279
28.3%
2 14725
22.8%
1 11185
17.3%
3 3519
 
5.5%
9 3239
 
5.0%
4 2869
 
4.4%
5 2823
 
4.4%
6 2793
 
4.3%
8 2553
 
4.0%
7 2552
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 30000
100.0%
Open Punctuation
ValueCountFrequency (%)
( 52
100.0%
Close Punctuation
ValueCountFrequency (%)
) 52
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 94641
55.5%
Hangul 75858
44.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
17996
23.7%
9989
13.2%
9216
12.1%
8410
11.1%
8115
10.7%
8111
10.7%
2137
 
2.8%
1788
 
2.4%
1681
 
2.2%
1681
 
2.2%
Other values (34) 6734
 
8.9%
Common
ValueCountFrequency (%)
- 30000
31.7%
0 18279
19.3%
2 14725
15.6%
1 11185
 
11.8%
3 3519
 
3.7%
9 3239
 
3.4%
4 2869
 
3.0%
5 2823
 
3.0%
6 2793
 
3.0%
8 2553
 
2.7%
Other values (3) 2656
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94641
55.5%
Hangul 75858
44.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 30000
31.7%
0 18279
19.3%
2 14725
15.6%
1 11185
 
11.8%
3 3519
 
3.7%
9 3239
 
3.4%
4 2869
 
3.0%
5 2823
 
3.0%
6 2793
 
3.0%
8 2553
 
2.7%
Other values (3) 2656
 
2.8%
Hangul
ValueCountFrequency (%)
17996
23.7%
9989
13.2%
9216
12.1%
8410
11.1%
8115
10.7%
8111
10.7%
2137
 
2.8%
1788
 
2.4%
1681
 
2.2%
1681
 
2.2%
Other values (34) 6734
 
8.9%
Distinct8980
Distinct (%)89.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-29T02:00:17.851724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length36
Mean length21.6186
Min length1

Characters and Unicode

Total characters216186
Distinct characters85
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8217 ?
Unique (%)82.2%

Sample

1st row인천광역시 미추홀구 주안동 879-5 외1필지
2nd row인천광역시 미추홀구 학익동 312-50
3rd row인천광역시 미추홀구 용현동 27-13
4th row인천광역시 미추홀구 학익동 710-1
5th row인천광역시 미추홀구 문학동 338-19
ValueCountFrequency (%)
인천광역시 9999
23.7%
미추홀구 9902
23.4%
주안동 3461
 
8.2%
용현동 2226
 
5.3%
도화동 1519
 
3.6%
외1필지 1503
 
3.6%
숭의동 1218
 
2.9%
문학동 703
 
1.7%
학익동 702
 
1.7%
외2필지 327
 
0.8%
Other values (8224) 10698
25.3%
2024-01-29T02:00:18.485712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
32261
 
14.9%
10196
 
4.7%
10085
 
4.7%
10078
 
4.7%
1 10011
 
4.6%
10000
 
4.6%
10000
 
4.6%
9999
 
4.6%
9929
 
4.6%
9902
 
4.6%
Other values (75) 93725
43.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 127218
58.8%
Decimal Number 47125
 
21.8%
Space Separator 32261
 
14.9%
Dash Punctuation 9564
 
4.4%
Uppercase Letter 16
 
< 0.1%
Other Punctuation 1
 
< 0.1%
Letter Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
10196
 
8.0%
10085
 
7.9%
10078
 
7.9%
10000
 
7.9%
10000
 
7.9%
9999
 
7.9%
9929
 
7.8%
9902
 
7.8%
9902
 
7.8%
9902
 
7.8%
Other values (55) 27225
21.4%
Decimal Number
ValueCountFrequency (%)
1 10011
21.2%
2 6228
13.2%
3 4821
10.2%
6 4537
9.6%
4 4530
9.6%
5 4348
9.2%
7 3633
 
7.7%
8 3347
 
7.1%
9 3041
 
6.5%
0 2629
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
L 6
37.5%
B 3
18.8%
T 3
18.8%
X 2
 
12.5%
V 1
 
6.2%
I 1
 
6.2%
Space Separator
ValueCountFrequency (%)
32261
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9564
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 127218
58.8%
Common 88951
41.1%
Latin 17
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
10196
 
8.0%
10085
 
7.9%
10078
 
7.9%
10000
 
7.9%
10000
 
7.9%
9999
 
7.9%
9929
 
7.8%
9902
 
7.8%
9902
 
7.8%
9902
 
7.8%
Other values (55) 27225
21.4%
Common
ValueCountFrequency (%)
32261
36.3%
1 10011
 
11.3%
- 9564
 
10.8%
2 6228
 
7.0%
3 4821
 
5.4%
6 4537
 
5.1%
4 4530
 
5.1%
5 4348
 
4.9%
7 3633
 
4.1%
8 3347
 
3.8%
Other values (3) 5671
 
6.4%
Latin
ValueCountFrequency (%)
L 6
35.3%
B 3
17.6%
T 3
17.6%
X 2
 
11.8%
V 1
 
5.9%
1
 
5.9%
I 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 127218
58.8%
ASCII 88967
41.2%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
32261
36.3%
1 10011
 
11.3%
- 9564
 
10.8%
2 6228
 
7.0%
3 4821
 
5.4%
6 4537
 
5.1%
4 4530
 
5.1%
5 4348
 
4.9%
7 3633
 
4.1%
8 3347
 
3.8%
Other values (9) 5687
 
6.4%
Hangul
ValueCountFrequency (%)
10196
 
8.0%
10085
 
7.9%
10078
 
7.9%
10000
 
7.9%
10000
 
7.9%
9999
 
7.9%
9929
 
7.8%
9902
 
7.8%
9902
 
7.8%
9902
 
7.8%
Other values (55) 27225
21.4%
Number Forms
ValueCountFrequency (%)
1
100.0%

지목
Categorical

IMBALANCE 

Distinct20
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
9130 
공장용지
 
288
<NA>
 
273
잡종지
 
69
주유소용지
 
45
Other values (15)
 
195

Length

Max length5
Median length1
Mean length1.231
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row
2nd row
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
9130
91.3%
공장용지 288
 
2.9%
<NA> 273
 
2.7%
잡종지 69
 
0.7%
주유소용지 45
 
0.4%
종교용지 40
 
0.4%
39
 
0.4%
학교용지 24
 
0.2%
20
 
0.2%
임야 18
 
0.2%
Other values (10) 54
 
0.5%

Length

2024-01-29T02:00:18.632691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
9130
91.3%
공장용지 288
 
2.9%
na 273
 
2.7%
잡종지 69
 
0.7%
주유소용지 45
 
0.4%
종교용지 40
 
0.4%
39
 
0.4%
학교용지 24
 
0.2%
20
 
0.2%
임야 18
 
0.2%
Other values (10) 54
 
0.5%
Distinct4520
Distinct (%)45.2%
Missing3
Missing (%)< 0.1%
Memory size156.2 KiB
2024-01-29T02:00:18.932159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length5
Mean length4.7653296
Min length2

Characters and Unicode

Total characters47639
Distinct characters12
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

Unique2447 ?
Unique (%)24.5%

Sample

1st row336.8
2nd row188
3rd row166.9
4th row1,340
5th row165.8
ValueCountFrequency (%)
140 87
 
0.9%
149 26
 
0.3%
202 25
 
0.3%
141 23
 
0.2%
165 23
 
0.2%
198 22
 
0.2%
154 20
 
0.2%
300 19
 
0.2%
218 18
 
0.2%
159 18
 
0.2%
Other values (4510) 9716
97.2%
2024-01-29T02:00:19.373762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 7088
14.9%
1 6814
14.3%
2 5767
12.1%
3 4223
8.9%
4 3583
7.5%
5 3467
7.3%
6 3310
6.9%
0 3180
6.7%
7 3052
6.4%
8 3043
6.4%
Other values (2) 4112
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39407
82.7%
Other Punctuation 8232
 
17.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6814
17.3%
2 5767
14.6%
3 4223
10.7%
4 3583
9.1%
5 3467
8.8%
6 3310
8.4%
0 3180
8.1%
7 3052
7.7%
8 3043
7.7%
9 2968
7.5%
Other Punctuation
ValueCountFrequency (%)
. 7088
86.1%
, 1144
 
13.9%

Most occurring scripts

ValueCountFrequency (%)
Common 47639
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 7088
14.9%
1 6814
14.3%
2 5767
12.1%
3 4223
8.9%
4 3583
7.5%
5 3467
7.3%
6 3310
6.9%
0 3180
6.7%
7 3052
6.4%
8 3043
6.4%
Other values (2) 4112
8.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 47639
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 7088
14.9%
1 6814
14.3%
2 5767
12.1%
3 4223
8.9%
4 3583
7.5%
5 3467
7.3%
6 3310
6.9%
0 3180
6.7%
7 3052
6.4%
8 3043
6.4%
Other values (2) 4112
8.6%
Distinct7802
Distinct (%)78.0%
Missing3
Missing (%)< 0.1%
Memory size156.2 KiB
2024-01-29T02:00:19.697754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length5.6835051
Min length1

Characters and Unicode

Total characters56818
Distinct characters12
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

Unique6188 ?
Unique (%)61.9%

Sample

1st row181.72
2nd row110.88
3rd row97.12
4th row565
5th row99.15
ValueCountFrequency (%)
83.22 10
 
0.1%
124.32 8
 
0.1%
89.3 7
 
0.1%
96 7
 
0.1%
1,273 7
 
0.1%
83.2 6
 
0.1%
168 6
 
0.1%
477.47 6
 
0.1%
83.52 6
 
0.1%
132.6 6
 
0.1%
Other values (7792) 9928
99.3%
2024-01-29T02:00:20.128284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9663
17.0%
1 8753
15.4%
2 5533
9.7%
8 4457
7.8%
6 4373
7.7%
4 4335
7.6%
3 4179
7.4%
9 3988
7.0%
5 3918
6.9%
7 3843
 
6.8%
Other values (2) 3776
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46508
81.9%
Other Punctuation 10310
 
18.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8753
18.8%
2 5533
11.9%
8 4457
9.6%
6 4373
9.4%
4 4335
9.3%
3 4179
9.0%
9 3988
8.6%
5 3918
8.4%
7 3843
8.3%
0 3129
 
6.7%
Other Punctuation
ValueCountFrequency (%)
. 9663
93.7%
, 647
 
6.3%

Most occurring scripts

ValueCountFrequency (%)
Common 56818
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9663
17.0%
1 8753
15.4%
2 5533
9.7%
8 4457
7.8%
6 4373
7.7%
4 4335
7.6%
3 4179
7.4%
9 3988
7.0%
5 3918
6.9%
7 3843
 
6.8%
Other values (2) 3776
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 56818
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9663
17.0%
1 8753
15.4%
2 5533
9.7%
8 4457
7.8%
6 4373
7.7%
4 4335
7.6%
3 4179
7.4%
9 3988
7.0%
5 3918
6.9%
7 3843
 
6.8%
Other values (2) 3776
 
6.6%
Distinct9135
Distinct (%)91.4%
Missing3
Missing (%)< 0.1%
Memory size156.2 KiB
2024-01-29T02:00:20.416730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length6
Mean length6.2319696
Min length1

Characters and Unicode

Total characters62301
Distinct characters12
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

Unique8476 ?
Unique (%)84.8%

Sample

1st row657.2
2nd row612.24
3rd row359.18
4th row565
5th row196.38
ValueCountFrequency (%)
659.76 9
 
0.1%
659.78 8
 
0.1%
659.52 7
 
0.1%
659.94 7
 
0.1%
635.58 7
 
0.1%
659.6 6
 
0.1%
659.84 6
 
0.1%
659.4 5
 
0.1%
334.8 5
 
0.1%
659.58 5
 
0.1%
Other values (9125) 9932
99.3%
2024-01-29T02:00:20.788522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9743
15.6%
4 5846
9.4%
1 5729
9.2%
6 5621
9.0%
2 5556
8.9%
5 5483
8.8%
3 5146
8.3%
9 4927
7.9%
8 4775
7.7%
7 4260
6.8%
Other values (2) 5215
8.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50458
81.0%
Other Punctuation 11843
 
19.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 5846
11.6%
1 5729
11.4%
6 5621
11.1%
2 5556
11.0%
5 5483
10.9%
3 5146
10.2%
9 4927
9.8%
8 4775
9.5%
7 4260
8.4%
0 3115
6.2%
Other Punctuation
ValueCountFrequency (%)
. 9743
82.3%
, 2100
 
17.7%

Most occurring scripts

ValueCountFrequency (%)
Common 62301
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9743
15.6%
4 5846
9.4%
1 5729
9.2%
6 5621
9.0%
2 5556
8.9%
5 5483
8.8%
3 5146
8.3%
9 4927
7.9%
8 4775
7.7%
7 4260
6.8%
Other values (2) 5215
8.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62301
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9743
15.6%
4 5846
9.4%
1 5729
9.2%
6 5621
9.0%
2 5556
8.9%
5 5483
8.8%
3 5146
8.3%
9 4927
7.9%
8 4775
7.7%
7 4260
6.8%
Other values (2) 5215
8.4%
Distinct1899
Distinct (%)90.3%
Missing7896
Missing (%)79.0%
Memory size156.2 KiB
2024-01-29T02:00:21.051777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.0964829
Min length1

Characters and Unicode

Total characters10723
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1747 ?
Unique (%)83.0%

Sample

1st row36.96
2nd row35.11
3rd row420
4th row108.87
5th row116.24
ValueCountFrequency (%)
0 19
 
0.9%
84 7
 
0.3%
18 6
 
0.3%
23.04 5
 
0.2%
9 5
 
0.2%
19.2 5
 
0.2%
24 4
 
0.2%
30 4
 
0.2%
7.5 4
 
0.2%
58.02 3
 
0.1%
Other values (1888) 2042
97.1%
2024-01-29T02:00:21.410178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 1940
18.1%
1 1175
11.0%
2 1055
9.8%
4 1009
9.4%
6 890
8.3%
5 859
8.0%
3 855
8.0%
8 834
7.8%
7 713
 
6.6%
9 702
 
6.5%
Other values (3) 691
 
6.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8599
80.2%
Other Punctuation 2113
 
19.7%
Dash Punctuation 11
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1175
13.7%
2 1055
12.3%
4 1009
11.7%
6 890
10.4%
5 859
10.0%
3 855
9.9%
8 834
9.7%
7 713
8.3%
9 702
8.2%
0 507
5.9%
Other Punctuation
ValueCountFrequency (%)
. 1940
91.8%
, 173
 
8.2%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10723
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1940
18.1%
1 1175
11.0%
2 1055
9.8%
4 1009
9.4%
6 890
8.3%
5 859
8.0%
3 855
8.0%
8 834
7.8%
7 713
 
6.6%
9 702
 
6.5%
Other values (3) 691
 
6.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10723
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1940
18.1%
1 1175
11.0%
2 1055
9.8%
4 1009
9.4%
6 890
8.3%
5 859
8.0%
3 855
8.0%
8 834
7.8%
7 713
 
6.6%
9 702
 
6.5%
Other values (3) 691
 
6.4%
Distinct3894
Distinct (%)39.0%
Missing25
Missing (%)0.2%
Memory size156.2 KiB
2024-01-29T02:00:21.728658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.0361905
Min length1

Characters and Unicode

Total characters50236
Distinct characters12
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

Unique2401 ?
Unique (%)24.1%

Sample

1st row53.95
2nd row58.98
3rd row58.19
4th row42.16
5th row59.8
ValueCountFrequency (%)
59.91 71
 
0.7%
59.94 64
 
0.6%
59.93 61
 
0.6%
59.86 61
 
0.6%
59.87 60
 
0.6%
59.82 58
 
0.6%
59.76 56
 
0.6%
59.88 55
 
0.6%
59.96 55
 
0.6%
59.95 53
 
0.5%
Other values (3884) 9381
94.0%
2024-01-29T02:00:22.153407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9897
19.7%
5 8923
17.8%
9 6450
12.8%
7 4614
9.2%
6 3942
 
7.8%
8 3876
 
7.7%
4 3343
 
6.7%
3 2881
 
5.7%
2 2600
 
5.2%
1 2484
 
4.9%
Other values (2) 1226
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 40338
80.3%
Other Punctuation 9898
 
19.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 8923
22.1%
9 6450
16.0%
7 4614
11.4%
6 3942
9.8%
8 3876
9.6%
4 3343
 
8.3%
3 2881
 
7.1%
2 2600
 
6.4%
1 2484
 
6.2%
0 1225
 
3.0%
Other Punctuation
ValueCountFrequency (%)
. 9897
> 99.9%
, 1
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 50236
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9897
19.7%
5 8923
17.8%
9 6450
12.8%
7 4614
9.2%
6 3942
 
7.8%
8 3876
 
7.7%
4 3343
 
6.7%
3 2881
 
5.7%
2 2600
 
5.2%
1 2484
 
4.9%
Other values (2) 1226
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50236
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9897
19.7%
5 8923
17.8%
9 6450
12.8%
7 4614
9.2%
6 3942
 
7.8%
8 3876
 
7.7%
4 3343
 
6.7%
3 2881
 
5.7%
2 2600
 
5.2%
1 2484
 
4.9%
Other values (2) 1226
 
2.4%
Distinct8450
Distinct (%)84.8%
Missing34
Missing (%)0.3%
Memory size156.2 KiB
2024-01-29T02:00:22.457525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length5.8126631
Min length1

Characters and Unicode

Total characters57929
Distinct characters12
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

Unique7200 ?
Unique (%)72.2%

Sample

1st row195.13
2nd row301.61
3rd row215.21
4th row42.16
5th row118.44
ValueCountFrequency (%)
199.86 7
 
0.1%
230.79 7
 
0.1%
199.69 6
 
0.1%
129.33 5
 
0.1%
199.84 5
 
0.1%
177.4107 5
 
0.1%
59.96 5
 
0.1%
199.83 5
 
0.1%
177.65 5
 
0.1%
199.95 5
 
0.1%
Other values (8440) 9911
99.4%
2024-01-29T02:00:22.893063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 9871
17.0%
2 7497
12.9%
1 7265
12.5%
3 4833
8.3%
9 4734
8.2%
4 4531
7.8%
7 4264
7.4%
8 4099
7.1%
5 4099
7.1%
6 4026
6.9%
Other values (2) 2710
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48053
83.0%
Other Punctuation 9876
 
17.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 7497
15.6%
1 7265
15.1%
3 4833
10.1%
9 4734
9.9%
4 4531
9.4%
7 4264
8.9%
8 4099
8.5%
5 4099
8.5%
6 4026
8.4%
0 2705
 
5.6%
Other Punctuation
ValueCountFrequency (%)
. 9871
99.9%
, 5
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 57929
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 9871
17.0%
2 7497
12.9%
1 7265
12.5%
3 4833
8.3%
9 4734
8.2%
4 4531
7.8%
7 4264
7.4%
8 4099
7.1%
5 4099
7.1%
6 4026
6.9%
Other values (2) 2710
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57929
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 9871
17.0%
2 7497
12.9%
1 7265
12.5%
3 4833
8.3%
9 4734
8.2%
4 4531
7.8%
7 4264
7.4%
8 4099
7.1%
5 4099
7.1%
6 4026
6.9%
Other values (2) 2710
 
4.7%

구조
Categorical

IMBALANCE 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
철근콘크리트구조
7171 
일반철골구조
1113 
벽돌구조
 
600
경량철골구조
 
563
<NA>
 
278
Other values (19)
 
275

Length

Max length12
Median length8
Mean length7.2737
Min length3

Unique

Unique7 ?
Unique (%)0.1%

Sample

1st row철근콘크리트구조
2nd row철근콘크리트구조
3rd row철근콘크리트구조
4th row일반철골구조
5th row철근콘크리트구조

Common Values

ValueCountFrequency (%)
철근콘크리트구조 7171
71.7%
일반철골구조 1113
 
11.1%
벽돌구조 600
 
6.0%
경량철골구조 563
 
5.6%
<NA> 278
 
2.8%
철골철근콘크리트구조 62
 
0.6%
기타조적구조 57
 
0.6%
블록구조 43
 
0.4%
기타강구조 22
 
0.2%
강파이프구조 21
 
0.2%
Other values (14) 70
 
0.7%

Length

2024-01-29T02:00:23.003931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
철근콘크리트구조 7171
71.7%
일반철골구조 1113
 
11.1%
벽돌구조 600
 
6.0%
경량철골구조 563
 
5.6%
na 278
 
2.8%
철골철근콘크리트구조 62
 
0.6%
기타조적구조 57
 
0.6%
블록구조 43
 
0.4%
기타강구조 22
 
0.2%
강파이프구조 21
 
0.2%
Other values (14) 70
 
0.7%
Distinct4315
Distinct (%)43.2%
Missing5
Missing (%)< 0.1%
Memory size156.2 KiB
Minimum1982-06-16 00:00:00
Maximum2023-03-17 00:00:00
2024-01-29T02:00:23.096858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:00:23.211509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

최종설계변경일
Text

MISSING 

Distinct1697
Distinct (%)73.8%
Missing7699
Missing (%)77.0%
Memory size156.2 KiB
2024-01-29T02:00:23.473521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9982616
Min length8

Characters and Unicode

Total characters23006
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

Unique1280 ?
Unique (%)55.6%

Sample

1st row2001-10-09
2nd row2002-04-11
3rd row2020-07-10
4th row2008-05-16
5th row2002-07-19
ValueCountFrequency (%)
2001-09-25 7
 
0.3%
2001-11-09 6
 
0.3%
2002-05-09 6
 
0.3%
2003-12-02 6
 
0.3%
2002-12-12 6
 
0.3%
2015-06-17 5
 
0.2%
2002-03-13 5
 
0.2%
2016-11-16 5
 
0.2%
2001-12-07 5
 
0.2%
2001-08-31 5
 
0.2%
Other values (1687) 2245
97.6%
2024-01-29T02:00:23.844837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 6453
28.0%
- 4602
20.0%
2 4188
18.2%
1 3332
14.5%
3 821
 
3.6%
9 735
 
3.2%
6 611
 
2.7%
5 588
 
2.6%
8 566
 
2.5%
7 561
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 18404
80.0%
Dash Punctuation 4602
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 6453
35.1%
2 4188
22.8%
1 3332
18.1%
3 821
 
4.5%
9 735
 
4.0%
6 611
 
3.3%
5 588
 
3.2%
8 566
 
3.1%
7 561
 
3.0%
4 549
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 4602
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23006
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 6453
28.0%
- 4602
20.0%
2 4188
18.2%
1 3332
14.5%
3 821
 
3.6%
9 735
 
3.2%
6 611
 
2.7%
5 588
 
2.6%
8 566
 
2.5%
7 561
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23006
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 6453
28.0%
- 4602
20.0%
2 4188
18.2%
1 3332
14.5%
3 821
 
3.6%
9 735
 
3.2%
6 611
 
2.7%
5 588
 
2.6%
8 566
 
2.5%
7 561
 
2.4%
Distinct4362
Distinct (%)43.8%
Missing40
Missing (%)0.4%
Memory size156.2 KiB
2024-01-29T02:00:24.078712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9865462
Min length6

Characters and Unicode

Total characters99466
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2085 ?
Unique (%)20.9%

Sample

1st row2008-09-18
2nd row2004-05-28
3rd row2001-07-18
4th row2007-02-09
5th row2002-03-26
ValueCountFrequency (%)
2003-06-30 47
 
0.5%
2003-06-28 29
 
0.3%
2001-09-24 22
 
0.2%
2001-08-14 19
 
0.2%
2001-05-16 19
 
0.2%
2001-08-24 19
 
0.2%
2001-10-11 18
 
0.2%
2001-08-30 18
 
0.2%
2001-08-16 18
 
0.2%
2001-07-07 18
 
0.2%
Other values (4352) 9733
97.7%
2024-01-29T02:00:24.408021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 28816
29.0%
- 19920
20.0%
2 17325
17.4%
1 13810
13.9%
3 3402
 
3.4%
9 3193
 
3.2%
6 2720
 
2.7%
8 2630
 
2.6%
5 2569
 
2.6%
4 2566
 
2.6%
Other values (2) 2515
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79544
80.0%
Dash Punctuation 19920
 
20.0%
Space Separator 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 28816
36.2%
2 17325
21.8%
1 13810
17.4%
3 3402
 
4.3%
9 3193
 
4.0%
6 2720
 
3.4%
8 2630
 
3.3%
5 2569
 
3.2%
4 2566
 
3.2%
7 2513
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 19920
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 99466
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 28816
29.0%
- 19920
20.0%
2 17325
17.4%
1 13810
13.9%
3 3402
 
3.4%
9 3193
 
3.2%
6 2720
 
2.7%
8 2630
 
2.6%
5 2569
 
2.6%
4 2566
 
2.6%
Other values (2) 2515
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99466
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 28816
29.0%
- 19920
20.0%
2 17325
17.4%
1 13810
13.9%
3 3402
 
3.4%
9 3193
 
3.2%
6 2720
 
2.7%
8 2630
 
2.6%
5 2569
 
2.6%
4 2566
 
2.6%
Other values (2) 2515
 
2.5%

착공예정일
Text

MISSING 

Distinct4292
Distinct (%)46.1%
Missing688
Missing (%)6.9%
Memory size156.2 KiB
2024-01-29T02:00:24.645880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.898518
Min length6

Characters and Unicode

Total characters92175
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2154 ?
Unique (%)23.1%

Sample

1st row2008-09-18
2nd row2004-06-01
3rd row2001-07-18
4th row2007-02-10
5th row2002-03-25
ValueCountFrequency (%)
2003-06-30 66
 
0.7%
2000 28
 
0.3%
2000-09 24
 
0.3%
2000-08 19
 
0.2%
2001-11 19
 
0.2%
2001-08-30 19
 
0.2%
2001-08-16 19
 
0.2%
2003-07-01 19
 
0.2%
2001-10-24 18
 
0.2%
2001-08-24 18
 
0.2%
Other values (4283) 9065
97.3%
2024-01-29T02:00:24.982224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 26973
29.3%
- 18624
20.2%
2 16159
17.5%
1 12720
13.8%
3 3170
 
3.4%
5 2628
 
2.9%
8 2472
 
2.7%
9 2435
 
2.6%
6 2434
 
2.6%
7 2297
 
2.5%
Other values (2) 2263
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 73547
79.8%
Dash Punctuation 18624
 
20.2%
Space Separator 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 26973
36.7%
2 16159
22.0%
1 12720
17.3%
3 3170
 
4.3%
5 2628
 
3.6%
8 2472
 
3.4%
9 2435
 
3.3%
6 2434
 
3.3%
7 2297
 
3.1%
4 2259
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 18624
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 92175
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 26973
29.3%
- 18624
20.2%
2 16159
17.5%
1 12720
13.8%
3 3170
 
3.4%
5 2628
 
2.9%
8 2472
 
2.7%
9 2435
 
2.6%
6 2434
 
2.6%
7 2297
 
2.5%
Other values (2) 2263
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 92175
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 26973
29.3%
- 18624
20.2%
2 16159
17.5%
1 12720
13.8%
3 3170
 
3.4%
5 2628
 
2.9%
8 2472
 
2.7%
9 2435
 
2.6%
6 2434
 
2.6%
7 2297
 
2.5%
Other values (2) 2263
 
2.5%

사용승인일
Text

MISSING 

Distinct4239
Distinct (%)45.4%
Missing666
Missing (%)6.7%
Memory size156.2 KiB
2024-01-29T02:00:25.248004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9993572
Min length6

Characters and Unicode

Total characters93334
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

Unique2014 ?
Unique (%)21.6%

Sample

1st row2009-07-22
2nd row2004-11-09
3rd row2002-01-28
4th row2011-04-25
5th row2002-08-05
ValueCountFrequency (%)
2001-12-20 16
 
0.2%
2002-05-31 16
 
0.2%
2002-12-20 16
 
0.2%
2001-12-13 15
 
0.2%
2002-07-23 15
 
0.2%
2002-05-03 14
 
0.1%
2002-10-22 14
 
0.1%
2002-03-19 14
 
0.1%
2002-01-09 14
 
0.1%
2002-09-19 14
 
0.1%
Other values (4229) 9186
98.4%
2024-01-29T02:00:25.611560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 26372
28.3%
- 18668
20.0%
2 17609
18.9%
1 13125
14.1%
3 3178
 
3.4%
9 2759
 
3.0%
6 2377
 
2.5%
7 2370
 
2.5%
4 2313
 
2.5%
5 2282
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74666
80.0%
Dash Punctuation 18668
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 26372
35.3%
2 17609
23.6%
1 13125
17.6%
3 3178
 
4.3%
9 2759
 
3.7%
6 2377
 
3.2%
7 2370
 
3.2%
4 2313
 
3.1%
5 2282
 
3.1%
8 2281
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 18668
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 93334
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 26372
28.3%
- 18668
20.0%
2 17609
18.9%
1 13125
14.1%
3 3178
 
3.4%
9 2759
 
3.0%
6 2377
 
2.5%
7 2370
 
2.5%
4 2313
 
2.5%
5 2282
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 93334
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 26372
28.3%
- 18668
20.0%
2 17609
18.9%
1 13125
14.1%
3 3178
 
3.4%
9 2759
 
3.0%
6 2377
 
2.5%
7 2370
 
2.5%
4 2313
 
2.5%
5 2282
 
2.4%
Distinct4226
Distinct (%)42.8%
Missing134
Missing (%)1.3%
Memory size156.2 KiB
Minimum1982-06-16 00:00:00
Maximum2023-02-20 00:00:00
2024-01-29T02:00:25.723458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T02:00:25.837125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

최대지상층수
Real number (ℝ)

MISSING  SKEWED 

Distinct31
Distinct (%)0.3%
Missing165
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean4.3102186
Minimum0
Maximum523
Zeros12
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-29T02:00:25.936295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q13
median4
Q35
95-th percentile9
Maximum523
Range523
Interquartile range (IQR)2

Descriptive statistics

Standard deviation5.880829
Coefficient of variation (CV)1.3643923
Kurtosis6155.4803
Mean4.3102186
Median Absolute Deviation (MAD)1
Skewness70.052647
Sum42391
Variance34.584149
MonotonicityNot monotonic
2024-01-29T02:00:26.034039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
5 2566
25.7%
4 2079
20.8%
3 1467
14.7%
2 1407
14.1%
1 881
 
8.8%
6 498
 
5.0%
7 237
 
2.4%
8 148
 
1.5%
10 122
 
1.2%
14 106
 
1.1%
Other values (21) 324
 
3.2%
(Missing) 165
 
1.7%
ValueCountFrequency (%)
0 12
 
0.1%
1 881
 
8.8%
2 1407
14.1%
3 1467
14.7%
4 2079
20.8%
5 2566
25.7%
6 498
 
5.0%
7 237
 
2.4%
8 148
 
1.5%
9 94
 
0.9%
ValueCountFrequency (%)
523 1
< 0.1%
44 1
< 0.1%
37 1
< 0.1%
35 1
< 0.1%
32 1
< 0.1%
26 2
< 0.1%
25 1
< 0.1%
24 2
< 0.1%
23 1
< 0.1%
21 1
< 0.1%

최대지하층수
Real number (ℝ)

MISSING  ZEROS 

Distinct8
Distinct (%)0.1%
Missing3244
Missing (%)32.4%
Infinite0
Infinite (%)0.0%
Mean0.45559503
Minimum0
Maximum7
Zeros4072
Zeros (%)40.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-29T02:00:26.116706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile1
Maximum7
Range7
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.64263504
Coefficient of variation (CV)1.4105401
Kurtosis7.7721604
Mean0.45559503
Median Absolute Deviation (MAD)0
Skewness1.9184191
Sum3078
Variance0.41297979
MonotonicityNot monotonic
2024-01-29T02:00:26.192871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 4072
40.7%
1 2396
24.0%
2 220
 
2.2%
3 44
 
0.4%
4 14
 
0.1%
5 7
 
0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
(Missing) 3244
32.4%
ValueCountFrequency (%)
0 4072
40.7%
1 2396
24.0%
2 220
 
2.2%
3 44
 
0.4%
4 14
 
0.1%
5 7
 
0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
ValueCountFrequency (%)
7 1
 
< 0.1%
6 2
 
< 0.1%
5 7
 
0.1%
4 14
 
0.1%
3 44
 
0.4%
2 220
 
2.2%
1 2396
24.0%
0 4072
40.7%

최고높이(m)
Text

MISSING 

Distinct1210
Distinct (%)12.7%
Missing501
Missing (%)5.0%
Memory size156.2 KiB
2024-01-29T02:00:26.524867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.7384988
Min length1

Characters and Unicode

Total characters35512
Distinct characters12
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

Unique619 ?
Unique (%)6.5%

Sample

1st row14.4
2nd row16.2
3rd row11.7
4th row7.6
5th row7.55
ValueCountFrequency (%)
14.6 155
 
1.6%
14.4 141
 
1.5%
14.3 132
 
1.4%
14.7 125
 
1.3%
12.8 121
 
1.3%
14.5 120
 
1.3%
14.2 106
 
1.1%
14.8 105
 
1.1%
12.9 101
 
1.1%
9.1 98
 
1.0%
Other values (1200) 8295
87.3%
2024-01-29T02:00:26.950128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 8558
24.1%
1 7704
21.7%
5 3039
 
8.6%
2 2914
 
8.2%
4 2863
 
8.1%
3 2399
 
6.8%
7 1979
 
5.6%
6 1797
 
5.1%
9 1709
 
4.8%
8 1538
 
4.3%
Other values (2) 1012
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26948
75.9%
Other Punctuation 8564
 
24.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7704
28.6%
5 3039
 
11.3%
2 2914
 
10.8%
4 2863
 
10.6%
3 2399
 
8.9%
7 1979
 
7.3%
6 1797
 
6.7%
9 1709
 
6.3%
8 1538
 
5.7%
0 1006
 
3.7%
Other Punctuation
ValueCountFrequency (%)
. 8558
99.9%
, 6
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 35512
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 8558
24.1%
1 7704
21.7%
5 3039
 
8.6%
2 2914
 
8.2%
4 2863
 
8.1%
3 2399
 
6.8%
7 1979
 
5.6%
6 1797
 
5.1%
9 1709
 
4.8%
8 1538
 
4.3%
Other values (2) 1012
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 35512
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 8558
24.1%
1 7704
21.7%
5 3039
 
8.6%
2 2914
 
8.2%
4 2863
 
8.1%
3 2399
 
6.8%
7 1979
 
5.6%
6 1797
 
5.1%
9 1709
 
4.8%
8 1538
 
4.3%
Other values (2) 1012
 
2.8%

동수
Real number (ℝ)

ZEROS 

Distinct36
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2992
Minimum0
Maximum83
Zeros432
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-29T02:00:27.062443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum83
Range83
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.8140844
Coefficient of variation (CV)2.1660133
Kurtosis435.02589
Mean1.2992
Median Absolute Deviation (MAD)0
Skewness19.006796
Sum12992
Variance7.9190713
MonotonicityNot monotonic
2024-01-29T02:00:27.156847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1 8512
85.1%
2 698
 
7.0%
0 432
 
4.3%
3 138
 
1.4%
4 62
 
0.6%
5 29
 
0.3%
7 27
 
0.3%
6 17
 
0.2%
8 11
 
0.1%
13 8
 
0.1%
Other values (26) 66
 
0.7%
ValueCountFrequency (%)
0 432
 
4.3%
1 8512
85.1%
2 698
 
7.0%
3 138
 
1.4%
4 62
 
0.6%
5 29
 
0.3%
6 17
 
0.2%
7 27
 
0.3%
8 11
 
0.1%
9 5
 
0.1%
ValueCountFrequency (%)
83 1
 
< 0.1%
73 1
 
< 0.1%
72 3
< 0.1%
71 1
 
< 0.1%
70 1
 
< 0.1%
69 2
 
< 0.1%
68 1
 
< 0.1%
38 4
< 0.1%
37 2
 
< 0.1%
36 5
0.1%

승강기합
Real number (ℝ)

MISSING  ZEROS 

Distinct14
Distinct (%)0.5%
Missing7082
Missing (%)70.8%
Infinite0
Infinite (%)0.0%
Mean0.94722413
Minimum0
Maximum25
Zeros723
Zeros (%)7.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-29T02:00:27.239493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q31
95-th percentile2
Maximum25
Range25
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1093569
Coefficient of variation (CV)1.1711662
Kurtosis151.03363
Mean0.94722413
Median Absolute Deviation (MAD)0
Skewness9.1455684
Sum2764
Variance1.2306728
MonotonicityNot monotonic
2024-01-29T02:00:27.346519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 1904
 
19.0%
0 723
 
7.2%
2 194
 
1.9%
3 36
 
0.4%
4 31
 
0.3%
5 12
 
0.1%
6 8
 
0.1%
8 2
 
< 0.1%
20 2
 
< 0.1%
7 2
 
< 0.1%
Other values (4) 4
 
< 0.1%
(Missing) 7082
70.8%
ValueCountFrequency (%)
0 723
 
7.2%
1 1904
19.0%
2 194
 
1.9%
3 36
 
0.4%
4 31
 
0.3%
5 12
 
0.1%
6 8
 
0.1%
7 2
 
< 0.1%
8 2
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
25 1
 
< 0.1%
20 2
 
< 0.1%
15 1
 
< 0.1%
12 1
 
< 0.1%
10 1
 
< 0.1%
8 2
 
< 0.1%
7 2
 
< 0.1%
6 8
 
0.1%
5 12
 
0.1%
4 31
0.3%

비상승강기합
Real number (ℝ)

MISSING  ZEROS 

Distinct7
Distinct (%)1.2%
Missing9435
Missing (%)94.3%
Infinite0
Infinite (%)0.0%
Mean0.45309735
Minimum0
Maximum16
Zeros400
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-29T02:00:27.433616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile2
Maximum16
Range16
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0467842
Coefficient of variation (CV)2.3102855
Kurtosis89.352771
Mean0.45309735
Median Absolute Deviation (MAD)0
Skewness7.1008039
Sum256
Variance1.0957572
MonotonicityNot monotonic
2024-01-29T02:00:27.536710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 400
 
4.0%
1 114
 
1.1%
2 35
 
0.4%
3 7
 
0.1%
4 7
 
0.1%
16 1
 
< 0.1%
7 1
 
< 0.1%
(Missing) 9435
94.3%
ValueCountFrequency (%)
0 400
4.0%
1 114
 
1.1%
2 35
 
0.4%
3 7
 
0.1%
4 7
 
0.1%
7 1
 
< 0.1%
16 1
 
< 0.1%
ValueCountFrequency (%)
16 1
 
< 0.1%
7 1
 
< 0.1%
4 7
 
0.1%
3 7
 
0.1%
2 35
 
0.4%
1 114
 
1.1%
0 400
4.0%

하수처리시설명
Categorical

IMBALANCE 

Distinct26
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
부패탱크방법
8296 
접촉폭기방법
 
492
<NA>
 
302
현수미생물접촉방법
 
252
기타단독정화조
 
179
Other values (21)
 
479

Length

Max length13
Median length6
Mean length6.1417
Min length4

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row부패탱크방법
2nd row현수미생물접촉방법
3rd row접촉폭기방법
4th row부패탱크방법
5th row부패탱크방법

Common Values

ValueCountFrequency (%)
부패탱크방법 8296
83.0%
접촉폭기방법 492
 
4.9%
<NA> 302
 
3.0%
현수미생물접촉방법 252
 
2.5%
기타단독정화조 179
 
1.8%
기타오수처리시설 175
 
1.8%
혐기및호기성미생물조정방법 81
 
0.8%
접촉산화방법 75
 
0.8%
하수종말처리장연결 35
 
0.4%
임호프탱크방법 21
 
0.2%
Other values (16) 92
 
0.9%

Length

2024-01-29T02:00:27.642281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부패탱크방법 8296
83.0%
접촉폭기방법 492
 
4.9%
na 302
 
3.0%
현수미생물접촉방법 252
 
2.5%
기타단독정화조 179
 
1.8%
기타오수처리시설 175
 
1.8%
혐기및호기성미생물조정방법 81
 
0.8%
접촉산화방법 75
 
0.8%
하수종말처리장연결 35
 
0.4%
임호프탱크방법 21
 
0.2%
Other values (16) 92
 
0.9%
Distinct595
Distinct (%)22.7%
Missing7383
Missing (%)73.8%
Memory size156.2 KiB
2024-01-29T02:00:27.884461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length2.892243
Min length1

Characters and Unicode

Total characters7569
Distinct characters12
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

Unique361 ?
Unique (%)13.8%

Sample

1st row12
2nd row5.4
3rd row65.33
4th row13
5th row7
ValueCountFrequency (%)
0 116
 
4.4%
8 96
 
3.7%
5 91
 
3.5%
6 90
 
3.4%
12 76
 
2.9%
10 71
 
2.7%
7.2 70
 
2.7%
6.1 61
 
2.3%
2 56
 
2.1%
4 50
 
1.9%
Other values (585) 1840
70.3%
2024-01-29T02:00:28.513386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 1429
18.9%
1 916
12.1%
6 823
10.9%
2 758
10.0%
5 743
9.8%
0 687
9.1%
4 615
8.1%
8 535
 
7.1%
3 456
 
6.0%
7 405
 
5.4%
Other values (2) 202
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 6127
80.9%
Other Punctuation 1442
 
19.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 916
15.0%
6 823
13.4%
2 758
12.4%
5 743
12.1%
0 687
11.2%
4 615
10.0%
8 535
8.7%
3 456
7.4%
7 405
6.6%
9 189
 
3.1%
Other Punctuation
ValueCountFrequency (%)
. 1429
99.1%
, 13
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 7569
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 1429
18.9%
1 916
12.1%
6 823
10.9%
2 758
10.0%
5 743
9.8%
0 687
9.1%
4 615
8.1%
8 535
 
7.1%
3 456
 
6.0%
7 405
 
5.4%
Other values (2) 202
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7569
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 1429
18.9%
1 916
12.1%
6 823
10.9%
2 758
10.0%
5 743
9.8%
0 687
9.1%
4 615
8.1%
8 535
 
7.1%
3 456
 
6.0%
7 405
 
5.4%
Other values (2) 202
 
2.7%

주용도
Categorical

Distinct28
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
공동주택
3513 
단독주택
2018 
제2종근린생활시설
1540 
제1종근린생활시설
912 
공장
709 
Other values (23)
1308 

Length

Max length10
Median length4
Mean length5.2176
Min length2

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row공동주택
2nd row업무시설
3rd row공동주택
4th row제2종근린생활시설
5th row제1종근린생활시설

Common Values

ValueCountFrequency (%)
공동주택 3513
35.1%
단독주택 2018
20.2%
제2종근린생활시설 1540
15.4%
제1종근린생활시설 912
 
9.1%
공장 709
 
7.1%
업무시설 504
 
5.0%
숙박시설 133
 
1.3%
노유자시설 122
 
1.2%
자동차관련시설 95
 
0.9%
문화및집회시설 68
 
0.7%
Other values (18) 386
 
3.9%

Length

2024-01-29T02:00:28.629952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
공동주택 3513
35.1%
단독주택 2018
20.2%
제2종근린생활시설 1540
15.4%
제1종근린생활시설 912
 
9.1%
공장 709
 
7.1%
업무시설 504
 
5.0%
숙박시설 133
 
1.3%
노유자시설 122
 
1.2%
자동차관련시설 95
 
0.9%
문화및집회시설 68
 
0.7%
Other values (18) 386
 
3.9%

부속용도
Text

MISSING 

Distinct1569
Distinct (%)20.7%
Missing2417
Missing (%)24.2%
Memory size156.2 KiB
2024-01-29T02:00:28.817761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length39
Mean length6.7577476
Min length1

Characters and Unicode

Total characters51244
Distinct characters250
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1148 ?
Unique (%)15.1%

Sample

1st row다세대 주택[8세대]
2nd row오피스텔
3rd row다세대주택
4th row외1
5th row주택
ValueCountFrequency (%)
다세대주택 2065
23.2%
다가구주택 715
 
8.0%
469
 
5.3%
근린생활시설 354
 
4.0%
주택 332
 
3.7%
오피스텔 292
 
3.3%
단독주택 244
 
2.7%
다세대 190
 
2.1%
소매점 174
 
2.0%
일반음식점 171
 
1.9%
Other values (1198) 3910
43.9%
2024-01-29T02:00:29.139781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4643
 
9.1%
4582
 
8.9%
3742
 
7.3%
2681
 
5.2%
2677
 
5.2%
2093
 
4.1%
1774
 
3.5%
1727
 
3.4%
1497
 
2.9%
1389
 
2.7%
Other values (240) 24439
47.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 46034
89.8%
Space Separator 1354
 
2.6%
Other Punctuation 1350
 
2.6%
Decimal Number 907
 
1.8%
Close Punctuation 751
 
1.5%
Open Punctuation 745
 
1.5%
Dash Punctuation 77
 
0.2%
Uppercase Letter 15
 
< 0.1%
Math Symbol 10
 
< 0.1%
Modifier Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4643
 
10.1%
4582
 
10.0%
3742
 
8.1%
2681
 
5.8%
2677
 
5.8%
2093
 
4.5%
1774
 
3.9%
1727
 
3.8%
1497
 
3.3%
1389
 
3.0%
Other values (212) 19229
41.8%
Decimal Number
ValueCountFrequency (%)
2 430
47.4%
1 383
42.2%
8 29
 
3.2%
0 14
 
1.5%
3 12
 
1.3%
6 10
 
1.1%
7 8
 
0.9%
4 8
 
0.9%
5 7
 
0.8%
9 6
 
0.7%
Other Punctuation
ValueCountFrequency (%)
, 1054
78.1%
/ 214
 
15.9%
. 70
 
5.2%
: 9
 
0.7%
& 3
 
0.2%
Uppercase Letter
ValueCountFrequency (%)
P 5
33.3%
T 4
26.7%
A 4
26.7%
L 1
 
6.7%
G 1
 
6.7%
Close Punctuation
ValueCountFrequency (%)
) 745
99.2%
] 6
 
0.8%
Open Punctuation
ValueCountFrequency (%)
( 739
99.2%
[ 6
 
0.8%
Space Separator
ValueCountFrequency (%)
1354
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 77
100.0%
Math Symbol
ValueCountFrequency (%)
+ 10
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 46034
89.8%
Common 5195
 
10.1%
Latin 15
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4643
 
10.1%
4582
 
10.0%
3742
 
8.1%
2681
 
5.8%
2677
 
5.8%
2093
 
4.5%
1774
 
3.9%
1727
 
3.8%
1497
 
3.3%
1389
 
3.0%
Other values (212) 19229
41.8%
Common
ValueCountFrequency (%)
1354
26.1%
, 1054
20.3%
) 745
14.3%
( 739
14.2%
2 430
 
8.3%
1 383
 
7.4%
/ 214
 
4.1%
- 77
 
1.5%
. 70
 
1.3%
8 29
 
0.6%
Other values (13) 100
 
1.9%
Latin
ValueCountFrequency (%)
P 5
33.3%
T 4
26.7%
A 4
26.7%
L 1
 
6.7%
G 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 46034
89.8%
ASCII 5210
 
10.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
4643
 
10.1%
4582
 
10.0%
3742
 
8.1%
2681
 
5.8%
2677
 
5.8%
2093
 
4.5%
1774
 
3.9%
1727
 
3.8%
1497
 
3.3%
1389
 
3.0%
Other values (212) 19229
41.8%
ASCII
ValueCountFrequency (%)
1354
26.0%
, 1054
20.2%
) 745
14.3%
( 739
14.2%
2 430
 
8.3%
1 383
 
7.4%
/ 214
 
4.1%
- 77
 
1.5%
. 70
 
1.3%
8 29
 
0.6%
Other values (18) 115
 
2.2%

용도지역
Categorical

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
제2종일반주거지역
2218 
일반주거지역
2210 
일반상업지역
2133 
준주거지역
1831 
일반공업지역
597 
Other values (18)
1011 

Length

Max length10
Median length9
Mean length6.4895
Min length4

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st row준주거지역
2nd row준주거지역
3rd row일반주거지역
4th row제1종일반주거지역
5th row제2종일반주거지역

Common Values

ValueCountFrequency (%)
제2종일반주거지역 2218
22.2%
일반주거지역 2210
22.1%
일반상업지역 2133
21.3%
준주거지역 1831
18.3%
일반공업지역 597
 
6.0%
준공업지역 301
 
3.0%
제1종일반주거지역 232
 
2.3%
도시지역 176
 
1.8%
<NA> 130
 
1.3%
제3종일반주거지역 89
 
0.9%
Other values (13) 83
 
0.8%

Length

2024-01-29T02:00:29.247521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
제2종일반주거지역 2218
22.2%
일반주거지역 2210
22.1%
일반상업지역 2133
21.3%
준주거지역 1831
18.3%
일반공업지역 597
 
6.0%
준공업지역 301
 
3.0%
제1종일반주거지역 232
 
2.3%
도시지역 176
 
1.8%
na 130
 
1.3%
제3종일반주거지역 89
 
0.9%
Other values (13) 83
 
0.8%

용도지구
Categorical

IMBALANCE 

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
6511 
방화지구
1919 
일반미관지구
807 
최고고도지구
 
219
중심지미관지구
 
164
Other values (22)
 
380

Length

Max length11
Median length4
Mean length4.3455
Min length4

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 6511
65.1%
방화지구 1919
 
19.2%
일반미관지구 807
 
8.1%
최고고도지구 219
 
2.2%
중심지미관지구 164
 
1.6%
주거환경개선지구 113
 
1.1%
주차장정비지구 63
 
0.6%
고도지구 38
 
0.4%
기타지구 29
 
0.3%
산업단지 24
 
0.2%
Other values (17) 113
 
1.1%

Length

2024-01-29T02:00:29.347559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 6511
65.1%
방화지구 1919
 
19.2%
일반미관지구 807
 
8.1%
최고고도지구 219
 
2.2%
중심지미관지구 164
 
1.6%
주거환경개선지구 113
 
1.1%
주차장정비지구 63
 
0.6%
고도지구 38
 
0.4%
기타지구 29
 
0.3%
산업단지 24
 
0.2%
Other values (17) 113
 
1.1%

용도구역
Categorical

IMBALANCE 

Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
8328 
상대보호구역
 
534
제1종지구단위계획구역
 
414
지구단위계획구역
 
260
상대정화구역
 
120
Other values (17)
 
344

Length

Max length11
Median length4
Mean length4.5823
Min length4

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row지구단위계획구역
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 8328
83.3%
상대보호구역 534
 
5.3%
제1종지구단위계획구역 414
 
4.1%
지구단위계획구역 260
 
2.6%
상대정화구역 120
 
1.2%
상세계획구역 114
 
1.1%
기타구역 80
 
0.8%
산업시설구역 80
 
0.8%
절대정화구역 20
 
0.2%
도시계획구역 19
 
0.2%
Other values (12) 31
 
0.3%

Length

2024-01-29T02:00:29.457370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 8328
83.2%
상대보호구역 534
 
5.3%
제1종지구단위계획구역 414
 
4.1%
지구단위계획구역 260
 
2.6%
상대정화구역 120
 
1.2%
상세계획구역 114
 
1.1%
기타구역 80
 
0.8%
산업시설구역 80
 
0.8%
절대정화구역 20
 
0.2%
도시계획구역 19
 
0.2%
Other values (13) 35
 
0.3%
Distinct136
Distinct (%)3.9%
Missing6509
Missing (%)65.1%
Memory size156.2 KiB
2024-01-29T02:00:29.668617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.2368949
Min length1

Characters and Unicode

Total characters4318
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

Unique63 ?
Unique (%)1.8%

Sample

1st row2
2nd row2
3rd row0
4th row2
5th row8
ValueCountFrequency (%)
8 551
15.8%
4 496
14.2%
2 314
9.0%
3 290
 
8.3%
6 284
 
8.1%
5 261
 
7.5%
1 205
 
5.9%
7 173
 
5.0%
0 107
 
3.1%
10 85
 
2.4%
Other values (126) 725
20.8%
2024-01-29T02:00:29.995307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 744
17.2%
4 629
14.6%
8 614
14.2%
2 563
13.0%
3 420
9.7%
6 378
8.8%
5 349
8.1%
7 252
 
5.8%
0 241
 
5.6%
9 121
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4311
99.8%
Other Punctuation 7
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 744
17.3%
4 629
14.6%
8 614
14.2%
2 563
13.1%
3 420
9.7%
6 378
8.8%
5 349
8.1%
7 252
 
5.8%
0 241
 
5.6%
9 121
 
2.8%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4318
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 744
17.2%
4 629
14.6%
8 614
14.2%
2 563
13.0%
3 420
9.7%
6 378
8.8%
5 349
8.1%
7 252
 
5.8%
0 241
 
5.6%
9 121
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4318
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 744
17.2%
4 629
14.6%
8 614
14.2%
2 563
13.0%
3 420
9.7%
6 378
8.8%
5 349
8.1%
7 252
 
5.8%
0 241
 
5.6%
9 121
 
2.8%

자주식옥외주차장(대)
Real number (ℝ)

MISSING 

Distinct128
Distinct (%)2.0%
Missing3589
Missing (%)35.9%
Infinite0
Infinite (%)0.0%
Mean10.995321
Minimum0
Maximum1571
Zeros33
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-29T02:00:30.105217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median4
Q37
95-th percentile19
Maximum1571
Range1571
Interquartile range (IQR)5

Descriptive statistics

Standard deviation71.496135
Coefficient of variation (CV)6.5024148
Kurtosis317.1663
Mean10.995321
Median Absolute Deviation (MAD)2
Skewness17.178077
Sum70491
Variance5111.6973
MonotonicityNot monotonic
2024-01-29T02:00:30.218781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2 1244
 
12.4%
4 1027
 
10.3%
3 909
 
9.1%
1 752
 
7.5%
8 488
 
4.9%
5 375
 
3.8%
6 374
 
3.7%
7 256
 
2.6%
10 116
 
1.2%
12 103
 
1.0%
Other values (118) 767
 
7.7%
(Missing) 3589
35.9%
ValueCountFrequency (%)
0 33
 
0.3%
1 752
7.5%
2 1244
12.4%
3 909
9.1%
4 1027
10.3%
5 375
 
3.8%
6 374
 
3.7%
7 256
 
2.6%
8 488
 
4.9%
9 87
 
0.9%
ValueCountFrequency (%)
1571 2
< 0.1%
1505 1
 
< 0.1%
1398 1
 
< 0.1%
1391 1
 
< 0.1%
1390 1
 
< 0.1%
1378 3
< 0.1%
1327 1
 
< 0.1%
1248 1
 
< 0.1%
1246 1
 
< 0.1%
1242 1
 
< 0.1%

기계식옥내주차장(대)
Real number (ℝ)

MISSING  ZEROS 

Distinct76
Distinct (%)13.2%
Missing9425
Missing (%)94.2%
Infinite0
Infinite (%)0.0%
Mean24.947826
Minimum0
Maximum434
Zeros165
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-29T02:00:30.351068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median18
Q335
95-th percentile70
Maximum434
Range434
Interquartile range (IQR)35

Descriptive statistics

Standard deviation31.972942
Coefficient of variation (CV)1.2815923
Kurtosis49.406566
Mean24.947826
Median Absolute Deviation (MAD)18
Skewness4.9402425
Sum14345
Variance1022.2691
MonotonicityNot monotonic
2024-01-29T02:00:30.459141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 165
 
1.7%
18 23
 
0.2%
30 20
 
0.2%
26 18
 
0.2%
28 17
 
0.2%
12 16
 
0.2%
20 16
 
0.2%
44 16
 
0.2%
14 15
 
0.1%
16 15
 
0.1%
Other values (66) 254
 
2.5%
(Missing) 9425
94.2%
ValueCountFrequency (%)
0 165
1.7%
2 2
 
< 0.1%
4 2
 
< 0.1%
5 4
 
< 0.1%
6 2
 
< 0.1%
7 2
 
< 0.1%
8 6
 
0.1%
9 8
 
0.1%
10 9
 
0.1%
11 5
 
0.1%
ValueCountFrequency (%)
434 1
 
< 0.1%
196 1
 
< 0.1%
162 1
 
< 0.1%
156 1
 
< 0.1%
150 1
 
< 0.1%
148 1
 
< 0.1%
144 1
 
< 0.1%
127 1
 
< 0.1%
124 3
< 0.1%
120 1
 
< 0.1%

기계식옥외주차장(대)
Real number (ℝ)

MISSING  ZEROS 

Distinct34
Distinct (%)14.7%
Missing9769
Missing (%)97.7%
Infinite0
Infinite (%)0.0%
Mean5.991342
Minimum0
Maximum120
Zeros171
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-29T02:00:30.577592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31.5
95-th percentile35.5
Maximum120
Range120
Interquartile range (IQR)1.5

Descriptive statistics

Standard deviation14.809219
Coefficient of variation (CV)2.4717699
Kurtosis20.149359
Mean5.991342
Median Absolute Deviation (MAD)0
Skewness3.9059294
Sum1384
Variance219.31297
MonotonicityNot monotonic
2024-01-29T02:00:30.677679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 171
 
1.7%
12 8
 
0.1%
7 5
 
0.1%
8 4
 
< 0.1%
20 4
 
< 0.1%
11 3
 
< 0.1%
18 2
 
< 0.1%
1 2
 
< 0.1%
28 2
 
< 0.1%
10 2
 
< 0.1%
Other values (24) 28
 
0.3%
(Missing) 9769
97.7%
ValueCountFrequency (%)
0 171
1.7%
1 2
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
5 1
 
< 0.1%
7 5
 
0.1%
8 4
 
< 0.1%
9 1
 
< 0.1%
10 2
 
< 0.1%
11 3
 
< 0.1%
ValueCountFrequency (%)
120 1
< 0.1%
84 1
< 0.1%
68 1
< 0.1%
58 2
< 0.1%
48 1
< 0.1%
44 1
< 0.1%
42 1
< 0.1%
40 1
< 0.1%
38 1
< 0.1%
37 1
< 0.1%

인근자주식주차장(대)
Real number (ℝ)

MISSING  ZEROS 

Distinct21
Distinct (%)10.5%
Missing9800
Missing (%)98.0%
Infinite0
Infinite (%)0.0%
Mean4.915
Minimum0
Maximum150
Zeros158
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-29T02:00:30.768395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile24.1
Maximum150
Range150
Interquartile range (IQR)0

Descriptive statistics

Standard deviation19.35876
Coefficient of variation (CV)3.93871
Kurtosis34.428357
Mean4.915
Median Absolute Deviation (MAD)0
Skewness5.5599654
Sum983
Variance374.76158
MonotonicityNot monotonic
2024-01-29T02:00:30.857647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 158
 
1.6%
2 9
 
0.1%
4 4
 
< 0.1%
8 4
 
< 0.1%
7 3
 
< 0.1%
150 2
 
< 0.1%
6 2
 
< 0.1%
1 2
 
< 0.1%
24 2
 
< 0.1%
3 2
 
< 0.1%
Other values (11) 12
 
0.1%
(Missing) 9800
98.0%
ValueCountFrequency (%)
0 158
1.6%
1 2
 
< 0.1%
2 9
 
0.1%
3 2
 
< 0.1%
4 4
 
< 0.1%
5 1
 
< 0.1%
6 2
 
< 0.1%
7 3
 
< 0.1%
8 4
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
150 2
< 0.1%
91 1
< 0.1%
75 1
< 0.1%
72 1
< 0.1%
64 2
< 0.1%
52 1
< 0.1%
35 1
< 0.1%
26 1
< 0.1%
24 2
< 0.1%
23 1
< 0.1%

인근기계식주차장(대)
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9837 
0
 
160
17
 
1
150
 
1
28
 
1

Length

Max length4
Median length4
Mean length3.9515
Min length1

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row0

Common Values

ValueCountFrequency (%)
<NA> 9837
98.4%
0 160
 
1.6%
17 1
 
< 0.1%
150 1
 
< 0.1%
28 1
 
< 0.1%

Length

2024-01-29T02:00:30.957552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T02:00:31.040194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 9837
98.4%
0 160
 
1.6%
17 1
 
< 0.1%
150 1
 
< 0.1%
28 1
 
< 0.1%

총주차대수
Text

MISSING 

Distinct205
Distinct (%)2.4%
Missing1349
Missing (%)13.5%
Memory size156.2 KiB
2024-01-29T02:00:31.265137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.2390475
Min length1

Characters and Unicode

Total characters10719
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

Unique76 ?
Unique (%)0.9%

Sample

1st row8
2nd row6
3rd row2
4th row4
5th row1
ValueCountFrequency (%)
4 1200
13.9%
8 1191
13.8%
2 1065
12.3%
3 1014
11.7%
1 634
 
7.3%
6 604
 
7.0%
5 536
 
6.2%
7 411
 
4.8%
10 187
 
2.2%
12 185
 
2.1%
Other values (195) 1624
18.8%
2024-01-29T02:00:31.599651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 1929
18.0%
2 1645
15.3%
4 1528
14.3%
3 1402
13.1%
8 1354
12.6%
6 868
8.1%
5 782
7.3%
7 590
 
5.5%
0 364
 
3.4%
9 235
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10697
99.8%
Other Punctuation 22
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 1929
18.0%
2 1645
15.4%
4 1528
14.3%
3 1402
13.1%
8 1354
12.7%
6 868
8.1%
5 782
7.3%
7 590
 
5.5%
0 364
 
3.4%
9 235
 
2.2%
Other Punctuation
ValueCountFrequency (%)
, 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10719
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 1929
18.0%
2 1645
15.3%
4 1528
14.3%
3 1402
13.1%
8 1354
12.6%
6 868
8.1%
5 782
7.3%
7 590
 
5.5%
0 364
 
3.4%
9 235
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10719
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 1929
18.0%
2 1645
15.3%
4 1528
14.3%
3 1402
13.1%
8 1354
12.6%
6 868
8.1%
5 782
7.3%
7 590
 
5.5%
0 364
 
3.4%
9 235
 
2.2%
Distinct1680
Distinct (%)19.4%
Missing1349
Missing (%)13.5%
Memory size156.2 KiB
2024-01-29T02:00:31.920682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length3.3151081
Min length1

Characters and Unicode

Total characters28679
Distinct characters12
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

Unique1391 ?
Unique (%)16.1%

Sample

1st row92
2nd row69.5
3rd row23.52
4th row46
5th row11.5
ValueCountFrequency (%)
46 878
 
10.1%
92 877
 
10.1%
23 825
 
9.5%
34.5 636
 
7.4%
11.5 456
 
5.3%
69 456
 
5.3%
0 403
 
4.7%
57.5 361
 
4.2%
80.5 291
 
3.4%
35 169
 
2.0%
Other values (1670) 3299
38.1%
2024-01-29T02:00:32.367730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 4096
14.3%
5 3993
13.9%
2 3303
11.5%
1 3063
10.7%
3 2833
9.9%
4 2687
9.4%
6 2275
7.9%
9 2114
7.4%
0 1569
 
5.5%
7 1383
 
4.8%
Other values (2) 1363
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24392
85.1%
Other Punctuation 4287
 
14.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 3993
16.4%
2 3303
13.5%
1 3063
12.6%
3 2833
11.6%
4 2687
11.0%
6 2275
9.3%
9 2114
8.7%
0 1569
 
6.4%
7 1383
 
5.7%
8 1172
 
4.8%
Other Punctuation
ValueCountFrequency (%)
. 4096
95.5%
, 191
 
4.5%

Most occurring scripts

ValueCountFrequency (%)
Common 28679
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 4096
14.3%
5 3993
13.9%
2 3303
11.5%
1 3063
10.7%
3 2833
9.9%
4 2687
9.4%
6 2275
7.9%
9 2114
7.4%
0 1569
 
5.5%
7 1383
 
4.8%
Other values (2) 1363
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28679
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 4096
14.3%
5 3993
13.9%
2 3303
11.5%
1 3063
10.7%
3 2833
9.9%
4 2687
9.4%
6 2275
7.9%
9 2114
7.4%
0 1569
 
5.5%
7 1383
 
4.8%
Other values (2) 1363
 
4.8%

세대수
Real number (ℝ)

MISSING  ZEROS 

Distinct84
Distinct (%)1.4%
Missing4145
Missing (%)41.4%
Infinite0
Infinite (%)0.0%
Mean7.6421862
Minimum0
Maximum280
Zeros1739
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-29T02:00:32.516163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q310
95-th percentile19
Maximum280
Range280
Interquartile range (IQR)10

Descriptive statistics

Standard deviation12.309688
Coefficient of variation (CV)1.6107548
Kurtosis129.82296
Mean7.6421862
Median Absolute Deviation (MAD)5
Skewness8.7550547
Sum44745
Variance151.52842
MonotonicityNot monotonic
2024-01-29T02:00:32.628965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1739
17.4%
8 1346
 
13.5%
1 329
 
3.3%
10 320
 
3.2%
12 302
 
3.0%
6 248
 
2.5%
7 190
 
1.9%
9 185
 
1.8%
16 174
 
1.7%
11 145
 
1.5%
Other values (74) 877
 
8.8%
(Missing) 4145
41.4%
ValueCountFrequency (%)
0 1739
17.4%
1 329
 
3.3%
2 49
 
0.5%
3 63
 
0.6%
4 128
 
1.3%
5 51
 
0.5%
6 248
 
2.5%
7 190
 
1.9%
8 1346
13.5%
9 185
 
1.8%
ValueCountFrequency (%)
280 1
< 0.1%
254 1
< 0.1%
243 1
< 0.1%
186 1
< 0.1%
171 1
< 0.1%
158 1
< 0.1%
149 1
< 0.1%
144 1
< 0.1%
140 2
< 0.1%
129 2
< 0.1%

호수
Text

MISSING 

Distinct110
Distinct (%)12.3%
Missing9103
Missing (%)91.0%
Memory size156.2 KiB
2024-01-29T02:00:32.850484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length1.6109253
Min length1

Characters and Unicode

Total characters1445
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

Unique49 ?
Unique (%)5.5%

Sample

1st row11
2nd row2
3rd row10
4th row17
5th row12
ValueCountFrequency (%)
4 88
 
9.8%
2 72
 
8.0%
1 47
 
5.2%
3 44
 
4.9%
6 41
 
4.6%
12 37
 
4.1%
8 33
 
3.7%
16 28
 
3.1%
14 26
 
2.9%
24 24
 
2.7%
Other values (100) 457
50.9%
2024-01-29T02:00:33.169728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 335
23.2%
2 257
17.8%
4 199
13.8%
3 158
10.9%
6 115
 
8.0%
8 96
 
6.6%
5 84
 
5.8%
0 77
 
5.3%
9 63
 
4.4%
7 60
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1444
99.9%
Other Punctuation 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 335
23.2%
2 257
17.8%
4 199
13.8%
3 158
10.9%
6 115
 
8.0%
8 96
 
6.6%
5 84
 
5.8%
0 77
 
5.3%
9 63
 
4.4%
7 60
 
4.2%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1445
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 335
23.2%
2 257
17.8%
4 199
13.8%
3 158
10.9%
6 115
 
8.0%
8 96
 
6.6%
5 84
 
5.8%
0 77
 
5.3%
9 63
 
4.4%
7 60
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1445
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 335
23.2%
2 257
17.8%
4 199
13.8%
3 158
10.9%
6 115
 
8.0%
8 96
 
6.6%
5 84
 
5.8%
0 77
 
5.3%
9 63
 
4.4%
7 60
 
4.2%

가구수
Real number (ℝ)

MISSING  ZEROS 

Distinct23
Distinct (%)0.5%
Missing5750
Missing (%)57.5%
Infinite0
Infinite (%)0.0%
Mean2.9576471
Minimum0
Maximum98
Zeros1743
Zeros (%)17.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-29T02:00:33.279282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile16
Maximum98
Range98
Interquartile range (IQR)3

Descriptive statistics

Standard deviation5.1060851
Coefficient of variation (CV)1.7264011
Kurtosis30.56074
Mean2.9576471
Median Absolute Deviation (MAD)1
Skewness3.3137181
Sum12570
Variance26.072106
MonotonicityNot monotonic
2024-01-29T02:00:33.376989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 1743
 
17.4%
1 1019
 
10.2%
2 308
 
3.1%
3 272
 
2.7%
5 129
 
1.3%
19 95
 
0.9%
13 70
 
0.7%
12 62
 
0.6%
9 61
 
0.6%
4 59
 
0.6%
Other values (13) 432
 
4.3%
(Missing) 5750
57.5%
ValueCountFrequency (%)
0 1743
17.4%
1 1019
10.2%
2 308
 
3.1%
3 272
 
2.7%
4 59
 
0.6%
5 129
 
1.3%
6 44
 
0.4%
7 58
 
0.6%
8 54
 
0.5%
9 61
 
0.6%
ValueCountFrequency (%)
98 1
 
< 0.1%
31 1
 
< 0.1%
27 1
 
< 0.1%
19 95
0.9%
18 48
0.5%
17 33
 
0.3%
16 38
 
0.4%
15 44
0.4%
14 37
 
0.4%
13 70
0.7%

주건축물수
Real number (ℝ)

MISSING 

Distinct35
Distinct (%)0.4%
Missing440
Missing (%)4.4%
Infinite0
Infinite (%)0.0%
Mean1.289749
Minimum0
Maximum83
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-29T02:00:33.470008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q31
95-th percentile2
Maximum83
Range83
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.7335064
Coefficient of variation (CV)2.1194097
Kurtosis471.66941
Mean1.289749
Median Absolute Deviation (MAD)0
Skewness19.965034
Sum12330
Variance7.4720573
MonotonicityNot monotonic
2024-01-29T02:00:33.564505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
1 8826
88.3%
2 465
 
4.7%
3 83
 
0.8%
4 50
 
0.5%
7 22
 
0.2%
5 20
 
0.2%
6 17
 
0.2%
8 9
 
0.1%
13 8
 
0.1%
9 5
 
0.1%
Other values (25) 55
 
0.5%
(Missing) 440
 
4.4%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 8826
88.3%
2 465
 
4.7%
3 83
 
0.8%
4 50
 
0.5%
5 20
 
0.2%
6 17
 
0.2%
7 22
 
0.2%
8 9
 
0.1%
9 5
 
0.1%
ValueCountFrequency (%)
83 1
 
< 0.1%
73 1
 
< 0.1%
72 3
< 0.1%
71 1
 
< 0.1%
70 1
 
< 0.1%
69 1
 
< 0.1%
68 1
 
< 0.1%
38 4
< 0.1%
37 2
 
< 0.1%
36 5
0.1%

부속건축물수
Real number (ℝ)

MISSING  ZEROS 

Distinct13
Distinct (%)1.8%
Missing9296
Missing (%)93.0%
Infinite0
Infinite (%)0.0%
Mean0.94034091
Minimum0
Maximum69
Zeros292
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-29T02:00:33.641466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum69
Range69
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.9076576
Coefficient of variation (CV)3.0921313
Kurtosis429.78759
Mean0.94034091
Median Absolute Deviation (MAD)1
Skewness18.838296
Sum662
Variance8.4544727
MonotonicityNot monotonic
2024-01-29T02:00:33.725389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 338
 
3.4%
0 292
 
2.9%
2 41
 
0.4%
3 15
 
0.1%
4 5
 
0.1%
11 4
 
< 0.1%
5 2
 
< 0.1%
7 2
 
< 0.1%
69 1
 
< 0.1%
8 1
 
< 0.1%
Other values (3) 3
 
< 0.1%
(Missing) 9296
93.0%
ValueCountFrequency (%)
0 292
2.9%
1 338
3.4%
2 41
 
0.4%
3 15
 
0.1%
4 5
 
0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
7 2
 
< 0.1%
8 1
 
< 0.1%
10 1
 
< 0.1%
ValueCountFrequency (%)
69 1
 
< 0.1%
16 1
 
< 0.1%
11 4
 
< 0.1%
10 1
 
< 0.1%
8 1
 
< 0.1%
7 2
 
< 0.1%
6 1
 
< 0.1%
5 2
 
< 0.1%
4 5
 
0.1%
3 15
0.1%

Sample

연번건축구분허가번호대지위치지목대지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)증축연면적(제곱미터)건폐율용적률구조허가일최종설계변경일착공처리일착공예정일사용승인일건축허가최초접수일최대지상층수최대지하층수최고높이(m)동수승강기합비상승강기합하수처리시설명하수처리시설용량(제곱미터)주용도부속용도용도지역용도지구용도구역자주식옥내주차장(대)자주식옥외주차장(대)기계식옥내주차장(대)기계식옥외주차장(대)인근자주식주차장(대)인근기계식주차장(대)총주차대수총주차장면적(제곱미터)세대수호수가구수주건축물수부속건축물수
55905591신축2008-건축과-신축허가-252인천광역시 미추홀구 주안동 879-5 외1필지336.8181.72657.2<NA>53.95195.13철근콘크리트구조2008-09-02<NA>2008-09-182008-09-182009-07-222008-08-275014.40<NA><NA>부패탱크방법<NA>공동주택다세대 주택[8세대]준주거지역<NA><NA><NA>8<NA><NA><NA><NA>8928<NA><NA><NA><NA>
64426443신축2004-건축과-신축허가-64인천광역시 미추홀구 학익동 312-50188110.88612.24<NA>58.98301.61철근콘크리트구조2004-05-17<NA>2004-05-282004-06-012004-11-092004-05-066<NA>16.21<NA><NA>현수미생물접촉방법12업무시설오피스텔준주거지역<NA><NA>24<NA><NA><NA><NA>669.50<NA>01<NA>
91959196신축2001-종합민원과-신축허가-615인천광역시 미추홀구 용현동 27-13166.997.12359.18<NA>58.19215.21철근콘크리트구조2001-07-022001-10-092001-07-182001-07-182002-01-282001-06-254<NA>11.71<NA><NA>접촉폭기방법<NA>공동주택다세대주택일반주거지역<NA><NA>2<NA><NA><NA><NA><NA>223.5212<NA><NA>1<NA>
868869증축2007-건축과-증축신고-10인천광역시 미추홀구 학익동 710-11,34056556536.9642.1642.16일반철골구조2007-02-07<NA>2007-02-092007-02-102011-04-252007-02-051<NA>7.62<NA><NA>부패탱크방법<NA>제2종근린생활시설외1제1종일반주거지역<NA>지구단위계획구역<NA>4<NA><NA><NA><NA>446<NA><NA>02<NA>
84698470신축2001-종합민원과-신축허가-1399인천광역시 미추홀구 문학동 338-19165.899.15196.38<NA>59.8118.44철근콘크리트구조2001-09-262002-04-112002-03-262002-03-252002-08-052001-07-262<NA>7.55100부패탱크방법5.4제1종근린생활시설주택제2종일반주거지역<NA><NA>010000111.5<NA><NA>110
94079408신축2001-종합민원과-신축허가-417인천광역시 미추홀구 도화동 498-4157.893.18470.01<NA>59.05231.1철근콘크리트구조2001-05-24<NA>2001-05-262001-05-262001-10-242001-05-164112.451<NA><NA>부패탱크방법<NA>공동주택다세대주택준주거지역<NA><NA>21<NA><NA><NA><NA>334.59<NA><NA>1<NA>
74037404신축2002-건축과-신축허가-495인천광역시 미추홀구 주안동 503-1235140.94560.48<NA>59.97238.5철근콘크리트구조2002-06-17<NA>2002-06-292002-07-012002-11-302002-06-055014.81<NA><NA>부패탱크방법<NA>공동주택다세대주택일반주거지역<NA><NA>8<NA><NA><NA><NA><NA>8116.848<NA>01<NA>
33753376신축2016-건축과-신축허가-107인천광역시 미추홀구 주안동 1506-17 외1필지346.8196.351,630.09<NA>56.62458.66철근콘크리트구조2016-03-292020-07-102019-01-072018-12-252021-08-272016-03-1510033.611<NA>부패탱크방법<NA>업무시설업무시설(오피스텔) 및 공동주택(다세대주택),제1종근린생활시설(사무소)준주거지역<NA><NA>6<NA>10<NA><NA><NA>16109.64611<NA>1<NA>
59315932신축2007-건축과-신축허가-67인천광역시 미추홀구 주안동 197-6508.7282.392,615.36<NA>55.51480.54철근콘크리트구조2007-08-242008-05-162007-10-022007-10-052008-07-022007-08-0110131.411<NA>부패탱크방법<NA>공동주택업무시설일반상업지역방화지구<NA>10<NA>22<NA><NA><NA>32215.819<NA><NA>1<NA>
24832484신축2019-건축과-신축허가-10인천광역시 미추홀구 숭의동 42-173276160.18608.06<NA>58.04220.31철근콘크리트구조2019-07-25<NA>2019-08-132019-08-082020-02-202019-07-115016.711<NA>부패탱크방법<NA>공동주택다세대주택제2종일반주거지역<NA><NA><NA>8<NA><NA><NA><NA>81008<NA><NA>1<NA>
연번건축구분허가번호대지위치지목대지면적(제곱미터)건축면적(제곱미터)연면적(제곱미터)증축연면적(제곱미터)건폐율용적률구조허가일최종설계변경일착공처리일착공예정일사용승인일건축허가최초접수일최대지상층수최대지하층수최고높이(m)동수승강기합비상승강기합하수처리시설명하수처리시설용량(제곱미터)주용도부속용도용도지역용도지구용도구역자주식옥내주차장(대)자주식옥외주차장(대)기계식옥내주차장(대)기계식옥외주차장(대)인근자주식주차장(대)인근기계식주차장(대)총주차대수총주차장면적(제곱미터)세대수호수가구수주건축물수부속건축물수
99989999신축2000-종합민원과-신축허가-8인천광역시 미추홀구 학익동 579 외1필지617298.71298.71<NA>48.4148.41경량철골구조2000-10-022001-12-01<NA><NA>2001-12-012000-09-261<NA>6.21<NA><NA>부패탱크방법<NA>공장<NA>일반공업지역<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1<NA>
76757676신축2002-건축과-신축허가-210인천광역시 미추홀구 주안동 402-1143.885.86411.8<NA>59.7286.37철근콘크리트구조2002-03-282002-11-132002-07-102002-07-102003-04-162002-03-025015.11<NA><NA>접촉폭기방법<NA>업무시설<NA>일반주거지역<NA><NA><NA>3<NA><NA><NA><NA>334.5<NA><NA><NA>1<NA>
73897390신축2002-건축과-신축허가-508인천광역시 미추홀구 숭의동 17-1189112.82436.85<NA>59.69231.13철근콘크리트구조2002-06-212002-09-062002-06-272002-06-282002-11-072002-06-125012.61<NA><NA>부패탱크방법<NA>공동주택다세대주택준주거지역<NA><NA>42<NA><NA><NA><NA>6698<NA>01<NA>
93799380신축2001-종합민원과-신축허가-434인천광역시 미추홀구 도화동 14-16271.4160.46659.36<NA>59.81242.94철근콘크리트구조2001-05-30<NA>2001-06-042001-06-042002-02-092001-05-155<NA>16.61<NA><NA>부패탱크방법7.56공동주택다세대주택준주거지역<NA><NA>5<NA><NA><NA><NA><NA>557.512<NA><NA>1<NA>
19351936증축1990-건축과-증축신고-4인천광역시 미추홀구 숭의동 248-21717175.07128.13128.1343.900643.9006벽돌구조1990-04-25<NA>1990-10-081990-10-082016-05-131990-04-25116.82<NA><NA>부패탱크방법3.4단독주택근린생활시설(제과점)준주거지역<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1<NA>111
335336증축2014-건축과-증축신고-37인천광역시 미추홀구 주안동 1571-30177.389.66324.7938.1650.5697143.6435철근콘크리트구조2014-10-072015-01-052014-10-102014-10-102015-01-122014-09-24319.710<NA><NA><NA>제2종근린생활시설<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>1<NA>
20272028신축2022-건축과-신축허가-17인천광역시 미추홀구 용현동 624-84257198.78855.45<NA>77.35332.86철근콘크리트구조2022-02-28<NA>2022-05-192022-05-172022-11-252022-01-255016.611<NA>부패탱크방법<NA>업무시설오피스텔일반상업지역방화지구<NA><NA>8<NA><NA><NA><NA>8100<NA>9<NA>1<NA>
1017010171신축2000-건축지적과-신축허가-164인천광역시 미추홀구 도화동 51-6411.9246.6272.49<NA>59.868966.1544일반철골구조2000-06-10<NA>2000-06-132000-06-2000-08-052000-06-052<NA>10.751<NA><NA>부패탱크방법<NA>제2종근린생활시설수리점일반주거지역일반미관지구<NA><NA>1<NA><NA><NA><NA>111.5<NA><NA><NA>1<NA>
23442345신축2020-건축과-신축허가-45인천광역시 미추홀구 학익동 587-90잡종지331228.46228.46<NA>69.0269.02일반철골구조2020-04-102020-05-112020-05-282020-05-252020-08-202020-03-25108.11<NA><NA>부패탱크방법1.14제2종근린생활시설수리점준공업지역<NA><NA>11<NA><NA><NA><NA>225<NA><NA><NA>1<NA>
82518252신축2001-종합민원과-신축허가-1621인천광역시 미추홀구 용현동 25-10175.2104.68292.22<NA>59.74166.79철근콘크리트구조2001-10-112002-05-092002-04-192002-04-202002-09-022001-09-213<NA>9.11<NA><NA>부패탱크방법<NA>단독주택다가구주택일반주거지역<NA><NA><NA>2<NA><NA><NA><NA>2240<NA>51<NA>