Overview

Dataset statistics

Number of variables11
Number of observations2707
Missing cells2331
Missing cells (%)7.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory251.3 KiB
Average record size in memory95.0 B

Variable types

Text4
Numeric7

Dataset

Description관리_공작물_종류_pk,관리_허가대장_pk,공작물_종류_코드,기타_종류,길이,높이,면적,구조_코드,건폐_율,기타_구조,작업_일자
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15663/S/1/datasetView.do

Alerts

공작물_종류_코드 is highly overall correlated with 구조_코드High correlation
구조_코드 is highly overall correlated with 공작물_종류_코드High correlation
기타_종류 has 1571 (58.0%) missing valuesMissing
구조_코드 has 159 (5.9%) missing valuesMissing
기타_구조 has 589 (21.8%) missing valuesMissing
길이 is highly skewed (γ1 = 47.31560339)Skewed
건폐_율 is highly skewed (γ1 = 29.96397725)Skewed
관리_공작물_종류_pk has unique valuesUnique
길이 has 392 (14.5%) zerosZeros
높이 has 69 (2.5%) zerosZeros
면적 has 1231 (45.5%) zerosZeros
건폐_율 has 2276 (84.1%) zerosZeros

Reproduction

Analysis started2024-05-11 00:12:48.687853
Analysis finished2024-05-11 00:13:04.109027
Duration15.42 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct2707
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size21.3 KiB
2024-05-11T00:13:04.371029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length15
Mean length14.738825
Min length7

Characters and Unicode

Total characters39898
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

Unique2707 ?
Unique (%)100.0%

Sample

1st row11110-1
2nd row11110-10
3rd row11110-1000000000000000011764
4th row11110-1000000000000000039507
5th row11110-1000000000000000059657
ValueCountFrequency (%)
11110-1 1
 
< 0.1%
11545-100001685 1
 
< 0.1%
11545-100001844 1
 
< 0.1%
11545-100001584 1
 
< 0.1%
11545-100001585 1
 
< 0.1%
11545-100001604 1
 
< 0.1%
11545-100001624 1
 
< 0.1%
11545-100001644 1
 
< 0.1%
11545-100001664 1
 
< 0.1%
11545-100001684 1
 
< 0.1%
Other values (2697) 2697
99.6%
2024-05-11T00:13:05.122507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15271
38.3%
1 10464
26.2%
- 2707
 
6.8%
4 2317
 
5.8%
5 2067
 
5.2%
2 1919
 
4.8%
6 1533
 
3.8%
8 1102
 
2.8%
7 981
 
2.5%
3 798
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37191
93.2%
Dash Punctuation 2707
 
6.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15271
41.1%
1 10464
28.1%
4 2317
 
6.2%
5 2067
 
5.6%
2 1919
 
5.2%
6 1533
 
4.1%
8 1102
 
3.0%
7 981
 
2.6%
3 798
 
2.1%
9 739
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 2707
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39898
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15271
38.3%
1 10464
26.2%
- 2707
 
6.8%
4 2317
 
5.8%
5 2067
 
5.2%
2 1919
 
4.8%
6 1533
 
3.8%
8 1102
 
2.8%
7 981
 
2.5%
3 798
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15271
38.3%
1 10464
26.2%
- 2707
 
6.8%
4 2317
 
5.8%
5 2067
 
5.2%
2 1919
 
4.8%
6 1533
 
3.8%
8 1102
 
2.8%
7 981
 
2.5%
3 798
 
2.0%
Distinct2232
Distinct (%)82.5%
Missing0
Missing (%)0.0%
Memory size21.3 KiB
2024-05-11T00:13:05.488470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length15
Mean length15.206502
Min length9

Characters and Unicode

Total characters41164
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

Unique1969 ?
Unique (%)72.7%

Sample

1st row11110-6778
2nd row11110-6788
3rd row11110-1000000000000000030294
4th row11110-1000000000000000112636
5th row11110-1000000000000000175101
ValueCountFrequency (%)
11215-100026953 13
 
0.5%
11170-100087134 11
 
0.4%
11215-100083658 10
 
0.4%
11215-100033370 10
 
0.4%
11590-100099281 9
 
0.3%
11740-100012224 8
 
0.3%
11170-100093897 8
 
0.3%
11545-1000000000000000062954 8
 
0.3%
11215-1000000000000000184650 7
 
0.3%
11380-100083607 7
 
0.3%
Other values (2222) 2616
96.6%
2024-05-11T00:13:06.171291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 13154
32.0%
1 10068
24.5%
- 2707
 
6.6%
5 2439
 
5.9%
2 2213
 
5.4%
4 1964
 
4.8%
3 1870
 
4.5%
6 1836
 
4.5%
7 1725
 
4.2%
8 1604
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 38457
93.4%
Dash Punctuation 2707
 
6.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13154
34.2%
1 10068
26.2%
5 2439
 
6.3%
2 2213
 
5.8%
4 1964
 
5.1%
3 1870
 
4.9%
6 1836
 
4.8%
7 1725
 
4.5%
8 1604
 
4.2%
9 1584
 
4.1%
Dash Punctuation
ValueCountFrequency (%)
- 2707
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 41164
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13154
32.0%
1 10068
24.5%
- 2707
 
6.6%
5 2439
 
5.9%
2 2213
 
5.4%
4 1964
 
4.8%
3 1870
 
4.5%
6 1836
 
4.5%
7 1725
 
4.2%
8 1604
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 41164
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13154
32.0%
1 10068
24.5%
- 2707
 
6.6%
5 2439
 
5.9%
2 2213
 
5.4%
4 1964
 
4.8%
3 1870
 
4.5%
6 1836
 
4.5%
7 1725
 
4.2%
8 1604
 
3.9%

공작물_종류_코드
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)0.8%
Missing12
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean18.020408
Minimum1
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2024-05-11T00:13:06.534912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q12
median5
Q36
95-th percentile99
Maximum99
Range98
Interquartile range (IQR)4

Descriptive statistics

Standard deviation33.391293
Coefficient of variation (CV)1.8529709
Kurtosis2.0321844
Mean18.020408
Median Absolute Deviation (MAD)2
Skewness1.9948576
Sum48565
Variance1114.9784
MonotonicityNot monotonic
2024-05-11T00:13:06.916178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2 740
27.3%
3 567
20.9%
99 389
14.4%
6 380
14.0%
5 333
12.3%
7 90
 
3.3%
14 34
 
1.3%
9 25
 
0.9%
17 24
 
0.9%
1 22
 
0.8%
Other values (11) 91
 
3.4%
ValueCountFrequency (%)
1 22
 
0.8%
2 740
27.3%
3 567
20.9%
4 2
 
0.1%
5 333
12.3%
6 380
14.0%
7 90
 
3.3%
8 12
 
0.4%
9 25
 
0.9%
10 16
 
0.6%
ValueCountFrequency (%)
99 389
14.4%
21 2
 
0.1%
19 3
 
0.1%
18 9
 
0.3%
17 24
 
0.9%
16 10
 
0.4%
15 12
 
0.4%
14 34
 
1.3%
13 6
 
0.2%
12 15
 
0.6%

기타_종류
Text

MISSING 

Distinct680
Distinct (%)59.9%
Missing1571
Missing (%)58.0%
Memory size21.3 KiB
2024-05-11T00:13:07.347359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length49
Median length32
Mean length6.1355634
Min length1

Characters and Unicode

Total characters6970
Distinct characters394
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique546 ?
Unique (%)48.1%

Sample

1st row장식탑, 기념탑, 그 밖에 이와 비슷한 것/기타
2nd row등대
3rd row옥외조형물
4th row체육시설(야구장)
5th row석탑
ValueCountFrequency (%)
방음벽 63
 
4.5%
가설울타리 39
 
2.8%
담장 34
 
2.4%
수직순환식 27
 
1.9%
옹벽 22
 
1.6%
주차장 21
 
1.5%
태양광 15
 
1.1%
8대 14
 
1.0%
l형 12
 
0.8%
l형옹벽 12
 
0.8%
Other values (711) 1153
81.7%
2024-05-11T00:13:08.157391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
276
 
4.0%
250
 
3.6%
234
 
3.4%
196
 
2.8%
( 182
 
2.6%
) 182
 
2.6%
175
 
2.5%
141
 
2.0%
119
 
1.7%
110
 
1.6%
Other values (384) 5105
73.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5700
81.8%
Decimal Number 316
 
4.5%
Space Separator 276
 
4.0%
Open Punctuation 184
 
2.6%
Close Punctuation 184
 
2.6%
Uppercase Letter 166
 
2.4%
Other Punctuation 51
 
0.7%
Dash Punctuation 49
 
0.7%
Lowercase Letter 33
 
0.5%
Math Symbol 11
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
250
 
4.4%
234
 
4.1%
196
 
3.4%
175
 
3.1%
141
 
2.5%
119
 
2.1%
110
 
1.9%
105
 
1.8%
100
 
1.8%
99
 
1.7%
Other values (330) 4171
73.2%
Uppercase Letter
ValueCountFrequency (%)
L 38
22.9%
P 24
14.5%
B 16
9.6%
R 15
 
9.0%
O 11
 
6.6%
A 8
 
4.8%
C 7
 
4.2%
N 7
 
4.2%
I 6
 
3.6%
E 5
 
3.0%
Other values (10) 29
17.5%
Decimal Number
ValueCountFrequency (%)
1 71
22.5%
2 53
16.8%
8 51
16.1%
3 39
12.3%
4 21
 
6.6%
6 21
 
6.6%
5 21
 
6.6%
0 16
 
5.1%
9 14
 
4.4%
7 9
 
2.8%
Lowercase Letter
ValueCountFrequency (%)
m 15
45.5%
n 6
 
18.2%
o 6
 
18.2%
a 1
 
3.0%
b 1
 
3.0%
i 1
 
3.0%
c 1
 
3.0%
v 1
 
3.0%
p 1
 
3.0%
Other Punctuation
ValueCountFrequency (%)
. 16
31.4%
, 16
31.4%
' 10
19.6%
: 6
 
11.8%
/ 2
 
3.9%
* 1
 
2.0%
Math Symbol
ValueCountFrequency (%)
+ 6
54.5%
~ 4
36.4%
= 1
 
9.1%
Open Punctuation
ValueCountFrequency (%)
( 182
98.9%
[ 2
 
1.1%
Close Punctuation
ValueCountFrequency (%)
) 182
98.9%
] 2
 
1.1%
Space Separator
ValueCountFrequency (%)
276
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5700
81.8%
Common 1071
 
15.4%
Latin 199
 
2.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
250
 
4.4%
234
 
4.1%
196
 
3.4%
175
 
3.1%
141
 
2.5%
119
 
2.1%
110
 
1.9%
105
 
1.8%
100
 
1.8%
99
 
1.7%
Other values (330) 4171
73.2%
Latin
ValueCountFrequency (%)
L 38
19.1%
P 24
12.1%
B 16
 
8.0%
R 15
 
7.5%
m 15
 
7.5%
O 11
 
5.5%
A 8
 
4.0%
C 7
 
3.5%
N 7
 
3.5%
n 6
 
3.0%
Other values (19) 52
26.1%
Common
ValueCountFrequency (%)
276
25.8%
( 182
17.0%
) 182
17.0%
1 71
 
6.6%
2 53
 
4.9%
8 51
 
4.8%
- 49
 
4.6%
3 39
 
3.6%
4 21
 
2.0%
6 21
 
2.0%
Other values (15) 126
11.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5699
81.8%
ASCII 1270
 
18.2%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
276
21.7%
( 182
14.3%
) 182
14.3%
1 71
 
5.6%
2 53
 
4.2%
8 51
 
4.0%
- 49
 
3.9%
3 39
 
3.1%
L 38
 
3.0%
P 24
 
1.9%
Other values (44) 305
24.0%
Hangul
ValueCountFrequency (%)
250
 
4.4%
234
 
4.1%
196
 
3.4%
175
 
3.1%
141
 
2.5%
119
 
2.1%
110
 
1.9%
105
 
1.8%
100
 
1.8%
99
 
1.7%
Other values (329) 4170
73.2%
Compat Jamo
ValueCountFrequency (%)
1
100.0%

길이
Real number (ℝ)

SKEWED  ZEROS 

Distinct1097
Distinct (%)40.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.04357
Minimum0
Maximum150858
Zeros392
Zeros (%)14.5%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2024-05-11T00:13:08.527277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.4
median12.4
Q327.15
95-th percentile109.64
Maximum150858
Range150858
Interquartile range (IQR)21.75

Descriptive statistics

Standard deviation3001.193
Coefficient of variation (CV)23.438842
Kurtosis2357.4569
Mean128.04357
Median Absolute Deviation (MAD)9.7
Skewness47.315603
Sum346613.94
Variance9007159.5
MonotonicityNot monotonic
2024-05-11T00:13:08.971080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 392
 
14.5%
6.3 46
 
1.7%
6.0 43
 
1.6%
6.2 32
 
1.2%
6.4 26
 
1.0%
7.0 25
 
0.9%
6.1 23
 
0.8%
20.0 22
 
0.8%
10.0 21
 
0.8%
12.0 21
 
0.8%
Other values (1087) 2056
76.0%
ValueCountFrequency (%)
0.0 392
14.5%
0.2 1
 
< 0.1%
0.21 1
 
< 0.1%
0.425 1
 
< 0.1%
0.5 1
 
< 0.1%
0.6 1
 
< 0.1%
0.7 2
 
0.1%
0.74 1
 
< 0.1%
0.75 1
 
< 0.1%
0.77 1
 
< 0.1%
ValueCountFrequency (%)
150858.0 1
< 0.1%
29737.0 1
< 0.1%
17550.0 1
< 0.1%
8154.0 1
< 0.1%
7680.0 1
< 0.1%
6500.0 1
< 0.1%
6300.0 1
< 0.1%
6200.0 1
< 0.1%
6160.0 2
0.1%
5970.0 1
< 0.1%

높이
Real number (ℝ)

ZEROS 

Distinct488
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.72042
Minimum-12.6
Maximum7990
Zeros69
Zeros (%)2.5%
Negative4
Negative (%)0.1%
Memory size23.9 KiB
2024-05-11T00:13:09.411767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-12.6
5-th percentile1.3
Q13
median4.64
Q37.95
95-th percentile16
Maximum7990
Range8002.6
Interquartile range (IQR)4.95

Descriptive statistics

Standard deviation391.21518
Coefficient of variation (CV)14.112888
Kurtosis380.6249
Mean27.72042
Median Absolute Deviation (MAD)2.14
Skewness19.291128
Sum75039.176
Variance153049.31
MonotonicityNot monotonic
2024-05-11T00:13:09.878270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.0 177
 
6.5%
7.99 113
 
4.2%
4.0 109
 
4.0%
6.0 104
 
3.8%
8.0 99
 
3.7%
2.0 73
 
2.7%
0.0 69
 
2.5%
2.5 58
 
2.1%
3.5 56
 
2.1%
4.5 54
 
2.0%
Other values (478) 1795
66.3%
ValueCountFrequency (%)
-12.6 1
 
< 0.1%
-5.1 1
 
< 0.1%
-4.7 1
 
< 0.1%
-4.3 1
 
< 0.1%
0.0 69
2.5%
0.45 2
 
0.1%
0.5 6
 
0.2%
0.6 1
 
< 0.1%
0.62 1
 
< 0.1%
0.7 2
 
0.1%
ValueCountFrequency (%)
7990.0 4
0.1%
7950.0 1
 
< 0.1%
7880.0 1
 
< 0.1%
4100.0 1
 
< 0.1%
3300.0 1
 
< 0.1%
2750.0 1
 
< 0.1%
305.0 1
 
< 0.1%
91.0 1
 
< 0.1%
80.0 2
0.1%
68.0 1
 
< 0.1%

면적
Real number (ℝ)

ZEROS 

Distinct1187
Distinct (%)43.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean280.33256
Minimum0
Maximum24919.05
Zeros1231
Zeros (%)45.5%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2024-05-11T00:13:10.170239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6.3
Q361.325
95-th percentile1609.502
Maximum24919.05
Range24919.05
Interquartile range (IQR)61.325

Descriptive statistics

Standard deviation1241.8495
Coefficient of variation (CV)4.4299152
Kurtosis125.99126
Mean280.33256
Median Absolute Deviation (MAD)6.3
Skewness9.5989035
Sum758860.24
Variance1542190.1
MonotonicityNot monotonic
2024-05-11T00:13:10.619923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1231
45.5%
34.56 12
 
0.4%
30.0 11
 
0.4%
4.0 10
 
0.4%
18.0 10
 
0.4%
29.26 10
 
0.4%
36.0 9
 
0.3%
1.0 8
 
0.3%
31.5 8
 
0.3%
144.0 7
 
0.3%
Other values (1177) 1391
51.4%
ValueCountFrequency (%)
0.0 1231
45.5%
0.2 1
 
< 0.1%
0.2175 1
 
< 0.1%
0.56 1
 
< 0.1%
0.58 1
 
< 0.1%
0.6 1
 
< 0.1%
0.64 1
 
< 0.1%
0.7 1
 
< 0.1%
0.7025 1
 
< 0.1%
0.76 1
 
< 0.1%
ValueCountFrequency (%)
24919.05 1
< 0.1%
19392.52 1
< 0.1%
18356.94 1
< 0.1%
15436.8 1
< 0.1%
12310.48 1
< 0.1%
11869.0 1
< 0.1%
11454.09 1
< 0.1%
11422.2 1
< 0.1%
11400.0 1
< 0.1%
10176.84 2
0.1%

구조_코드
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct33
Distinct (%)1.3%
Missing159
Missing (%)5.9%
Infinite0
Infinite (%)0.0%
Mean28.091837
Minimum10
Maximum99
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2024-05-11T00:13:11.024393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile17
Q121
median31
Q331
95-th percentile39
Maximum99
Range89
Interquartile range (IQR)10

Descriptive statistics

Standard deviation9.8281366
Coefficient of variation (CV)0.34985739
Kurtosis22.064195
Mean28.091837
Median Absolute Deviation (MAD)1
Skewness3.2823923
Sum71578
Variance96.592269
MonotonicityNot monotonic
2024-05-11T00:13:11.423522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
31 880
32.5%
21 795
29.4%
32 279
 
10.3%
30 117
 
4.3%
39 112
 
4.1%
33 78
 
2.9%
11 55
 
2.0%
20 46
 
1.7%
12 42
 
1.6%
99 21
 
0.8%
Other values (23) 123
 
4.5%
(Missing) 159
 
5.9%
ValueCountFrequency (%)
10 19
 
0.7%
11 55
 
2.0%
12 42
 
1.6%
13 9
 
0.3%
17 4
 
0.1%
19 6
 
0.2%
20 46
 
1.7%
21 795
29.4%
22 2
 
0.1%
23 5
 
0.2%
ValueCountFrequency (%)
99 21
0.8%
90 2
 
0.1%
63 3
 
0.1%
61 7
 
0.3%
60 3
 
0.1%
52 1
 
< 0.1%
51 8
 
0.3%
50 1
 
< 0.1%
49 3
 
0.1%
43 1
 
< 0.1%

건폐_율
Real number (ℝ)

SKEWED  ZEROS 

Distinct353
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.0383702
Minimum0
Maximum4001
Zeros2276
Zeros (%)84.1%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2024-05-11T00:13:11.773793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile21.507
Maximum4001
Range4001
Interquartile range (IQR)0

Descriptive statistics

Standard deviation102.86765
Coefficient of variation (CV)14.615265
Kurtosis1001.4769
Mean7.0383702
Median Absolute Deviation (MAD)0
Skewness29.963977
Sum19052.868
Variance10581.753
MonotonicityNot monotonic
2024-05-11T00:13:12.366654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2276
84.1%
0.01 8
 
0.3%
0.2 7
 
0.3%
72.24 6
 
0.2%
1.0 5
 
0.2%
1.4 4
 
0.1%
0.3 4
 
0.1%
0.1 4
 
0.1%
0.4 3
 
0.1%
0.0005 3
 
0.1%
Other values (343) 387
 
14.3%
ValueCountFrequency (%)
0.0 2276
84.1%
0.0004 2
 
0.1%
0.0005 3
 
0.1%
0.0006 1
 
< 0.1%
0.0009 1
 
< 0.1%
0.001 2
 
0.1%
0.0015 1
 
< 0.1%
0.0019 1
 
< 0.1%
0.002 1
 
< 0.1%
0.003 1
 
< 0.1%
ValueCountFrequency (%)
4001.0 1
< 0.1%
2021.0 1
< 0.1%
2019.0 2
0.1%
152.04 1
< 0.1%
100.0 1
< 0.1%
92.46 1
< 0.1%
88.66 1
< 0.1%
88.02 1
< 0.1%
86.97 1
< 0.1%
86.49 1
< 0.1%

기타_구조
Text

MISSING 

Distinct121
Distinct (%)5.7%
Missing589
Missing (%)21.8%
Memory size21.3 KiB
2024-05-11T00:13:12.789814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length6.667611
Min length2

Characters and Unicode

Total characters14122
Distinct characters151
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique84 ?
Unique (%)4.0%

Sample

1st row경량철골구조
2nd row경량철골구조
3rd row기타조적구조
4th row석구조
5th row일반목구조
ValueCountFrequency (%)
철근콘크리트구조 759
35.3%
일반철골구조 672
31.2%
경량철골구조 237
 
11.0%
기타강구조 77
 
3.6%
강파이프구조 73
 
3.4%
벽돌구조 45
 
2.1%
블록구조 30
 
1.4%
기타구조 15
 
0.7%
철골콘크리트구조 14
 
0.7%
기타콘크리트구조 9
 
0.4%
Other values (120) 220
 
10.2%
2024-05-11T00:13:13.668720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2073
14.7%
2024
14.3%
1758
12.4%
971
 
6.9%
832
 
5.9%
813
 
5.8%
810
 
5.7%
810
 
5.7%
775
 
5.5%
692
 
4.9%
Other values (141) 2564
18.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13953
98.8%
Uppercase Letter 67
 
0.5%
Space Separator 33
 
0.2%
Other Punctuation 16
 
0.1%
Decimal Number 16
 
0.1%
Lowercase Letter 10
 
0.1%
Open Punctuation 9
 
0.1%
Close Punctuation 9
 
0.1%
Math Symbol 9
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2073
14.9%
2024
14.5%
1758
12.6%
971
7.0%
832
 
6.0%
813
 
5.8%
810
 
5.8%
810
 
5.8%
775
 
5.6%
692
 
5.0%
Other values (108) 2395
17.2%
Uppercase Letter
ValueCountFrequency (%)
P 24
35.8%
R 15
22.4%
F 6
 
9.0%
G 5
 
7.5%
E 4
 
6.0%
I 4
 
6.0%
C 3
 
4.5%
S 2
 
3.0%
T 1
 
1.5%
U 1
 
1.5%
Other values (2) 2
 
3.0%
Decimal Number
ValueCountFrequency (%)
3 3
18.8%
1 3
18.8%
6 2
12.5%
7 2
12.5%
2 2
12.5%
4 2
12.5%
0 1
 
6.2%
8 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
, 9
56.2%
. 5
31.2%
: 1
 
6.2%
/ 1
 
6.2%
Lowercase Letter
ValueCountFrequency (%)
p 4
40.0%
m 3
30.0%
r 2
20.0%
c 1
 
10.0%
Math Symbol
ValueCountFrequency (%)
+ 7
77.8%
~ 2
 
22.2%
Space Separator
ValueCountFrequency (%)
33
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13953
98.8%
Common 92
 
0.7%
Latin 77
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2073
14.9%
2024
14.5%
1758
12.6%
971
7.0%
832
 
6.0%
813
 
5.8%
810
 
5.8%
810
 
5.8%
775
 
5.6%
692
 
5.0%
Other values (108) 2395
17.2%
Common
ValueCountFrequency (%)
33
35.9%
( 9
 
9.8%
) 9
 
9.8%
, 9
 
9.8%
+ 7
 
7.6%
. 5
 
5.4%
3 3
 
3.3%
1 3
 
3.3%
6 2
 
2.2%
7 2
 
2.2%
Other values (7) 10
 
10.9%
Latin
ValueCountFrequency (%)
P 24
31.2%
R 15
19.5%
F 6
 
7.8%
G 5
 
6.5%
p 4
 
5.2%
E 4
 
5.2%
I 4
 
5.2%
m 3
 
3.9%
C 3
 
3.9%
r 2
 
2.6%
Other values (6) 7
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13953
98.8%
ASCII 169
 
1.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
2073
14.9%
2024
14.5%
1758
12.6%
971
7.0%
832
 
6.0%
813
 
5.8%
810
 
5.8%
810
 
5.8%
775
 
5.6%
692
 
5.0%
Other values (108) 2395
17.2%
ASCII
ValueCountFrequency (%)
33
19.5%
P 24
14.2%
R 15
 
8.9%
( 9
 
5.3%
) 9
 
5.3%
, 9
 
5.3%
+ 7
 
4.1%
F 6
 
3.6%
. 5
 
3.0%
G 5
 
3.0%
Other values (23) 47
27.8%

작업_일자
Real number (ℝ)

Distinct606
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20165587
Minimum20111227
Maximum20240510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.9 KiB
2024-05-11T00:13:14.083044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20111227
5-th percentile20111227
Q120130522
median20161029
Q320201007
95-th percentile20230411
Maximum20240510
Range129283
Interquartile range (IQR)70484.5

Descriptive statistics

Standard deviation40158.585
Coefficient of variation (CV)0.0019914414
Kurtosis-1.3220236
Mean20165587
Median Absolute Deviation (MAD)39687
Skewness0.098354849
Sum5.4588244 × 1010
Variance1.612712 × 109
MonotonicityNot monotonic
2024-05-11T00:13:14.553132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20111227 483
 
17.8%
20211029 162
 
6.0%
20201103 53
 
2.0%
20170929 45
 
1.7%
20201007 45
 
1.7%
20220621 44
 
1.6%
20191026 34
 
1.3%
20170406 27
 
1.0%
20201111 26
 
1.0%
20150402 24
 
0.9%
Other values (596) 1764
65.2%
ValueCountFrequency (%)
20111227 483
17.8%
20120102 1
 
< 0.1%
20120111 1
 
< 0.1%
20120112 1
 
< 0.1%
20120113 2
 
0.1%
20120117 2
 
0.1%
20120118 1
 
< 0.1%
20120125 2
 
0.1%
20120128 1
 
< 0.1%
20120203 2
 
0.1%
ValueCountFrequency (%)
20240510 2
 
0.1%
20240507 5
0.2%
20240420 1
 
< 0.1%
20240411 2
 
0.1%
20240406 6
0.2%
20240402 4
0.1%
20240330 2
 
0.1%
20240327 2
 
0.1%
20240309 4
0.1%
20240227 1
 
< 0.1%

Interactions

2024-05-11T00:13:01.526456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:50.289818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:52.171980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:54.118151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:55.922657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:57.811250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:59.687039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:13:01.796733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:50.546354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:52.440241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:54.395344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:56.181560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:58.084546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:59.890115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:13:02.088174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:50.825819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:52.724997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:54.648968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:56.455861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:58.447606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:13:00.138609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:13:02.371940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:51.112948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:53.053083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:54.850600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:56.739958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:58.658809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:13:00.400745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:13:02.554807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:51.373055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:53.318547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:55.079307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:56.999868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:58.925603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:13:00.672614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:13:02.731946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:51.612870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:53.569775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:55.344630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:57.252701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:59.124467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:13:00.940714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:13:03.046353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:51.897067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:53.835560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:55.635831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:57.533107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:12:59.367815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T00:13:01.240149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T00:13:14.856918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공작물_종류_코드길이높이면적구조_코드건폐_율작업_일자
공작물_종류_코드1.0000.0000.0000.0000.3700.0110.282
길이0.0001.0000.0000.0000.0000.0000.000
높이0.0000.0001.0000.2710.0000.0000.000
면적0.0000.0000.2711.0000.0980.0000.087
구조_코드0.3700.0000.0000.0981.0000.0000.164
건폐_율0.0110.0000.0000.0000.0001.0000.097
작업_일자0.2820.0000.0000.0870.1640.0971.000
2024-05-11T00:13:15.062999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공작물_종류_코드길이높이면적구조_코드건폐_율작업_일자
공작물_종류_코드1.000-0.2270.3790.3310.5780.241-0.012
길이-0.2271.000-0.1030.089-0.159-0.0260.128
높이0.379-0.1031.0000.1700.4240.095-0.090
면적0.3310.0890.1701.0000.2070.345-0.050
구조_코드0.578-0.1590.4240.2071.0000.136-0.101
건폐_율0.241-0.0260.0950.3450.1361.0000.020
작업_일자-0.0120.128-0.090-0.050-0.1010.0201.000

Missing values

2024-05-11T00:13:03.405326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T00:13:03.727921image/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.
2024-05-11T00:13:03.974940image/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_공작물_종류_pk관리_허가대장_pk공작물_종류_코드기타_종류길이높이면적구조_코드건폐_율기타_구조작업_일자
011110-111110-67783<NA>30.03.090.0120.0<NA>20160716
111110-1011110-67885<NA>11.46.628.5310.0<NA>20200515
211110-100000000000000001176411110-10000000000000000302943<NA>14.72.130.87300.0경량철골구조20220916
311110-100000000000000003950711110-10000000000000001126362<NA>19.25.2557.37<NA>2021.0<NA>20221108
411110-100000000000000005965711110-10000000000000001751013<NA>54.22.70.0300.0경량철골구조20221126
511110-100000000000000007411211110-10000000000000001934223<NA>7.33.00.0100.0기타조적구조20221217
611110-100000000000000008875911110-10000000000000002115213<NA>10.24.213.26100.0석구조20230113
711110-100000000000000013001011110-1000000000000000279888<NA>장식탑, 기념탑, 그 밖에 이와 비슷한 것/기타9.95.924.37500.25일반목구조20230429
811110-100000000000000015231511110-10000000000000003339502<NA>11.712.550.0200.0철근콘크리트구조20230719
911110-100000000000000015361111110-10000000000000003364527등대2.411.310.082030.77철근콘크리트구조20230719
관리_공작물_종류_pk관리_허가대장_pk공작물_종류_코드기타_종류길이높이면적구조_코드건폐_율기타_구조작업_일자
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270011740-311740-895199야구연습장철탑0.08.50.0330.0<NA>20150623
270111740-411740-895212증축0.036.00.0<NA>0.0<NA>20191120
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270611740-911740-89576<NA>0.07.952100.6310.0<NA>20131031