Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells11019
Missing cells (%)13.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory752.0 KiB
Average record size in memory77.0 B

Variable types

Text3
Categorical1
Numeric4

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15657/S/1/datasetView.do

Alerts

대표_여부 is highly imbalanced (71.1%)Imbalance
형식_코드 has 5512 (55.1%) missing valuesMissing
기타_형식 has 5507 (55.1%) missing valuesMissing
용량_인용 is highly skewed (γ1 = 33.64771194)Skewed
용량_루베 is highly skewed (γ1 = 40.50018066)Skewed
관리_오수정화시설 has unique valuesUnique
용량_인용 has 5624 (56.2%) zerosZeros
용량_루베 has 9900 (99.0%) zerosZeros

Reproduction

Analysis started2024-05-18 03:32:04.487823
Analysis finished2024-05-18 03:32:13.915143
Duration9.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

관리_오수정화시설
Text

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T12:32:14.482381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length10.7626
Min length7

Characters and Unicode

Total characters107626
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10000 ?
Unique (%)100.0%

Sample

1st row11170-9424
2nd row11140-25114
3rd row11260-9690
4th row11710-24461
5th row11140-24923
ValueCountFrequency (%)
11170-9424 1
 
< 0.1%
11530-23722 1
 
< 0.1%
11620-24241 1
 
< 0.1%
11530-5150 1
 
< 0.1%
11530-22765 1
 
< 0.1%
11620-24426 1
 
< 0.1%
11380-594 1
 
< 0.1%
11320-16400 1
 
< 0.1%
11260-29928 1
 
< 0.1%
11260-20431 1
 
< 0.1%
Other values (9990) 9990
99.9%
2024-05-18T12:32:15.776234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 30565
28.4%
0 13747
12.8%
2 10593
 
9.8%
- 10000
 
9.3%
5 7866
 
7.3%
6 7378
 
6.9%
3 7167
 
6.7%
4 6239
 
5.8%
7 4853
 
4.5%
9 4613
 
4.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 97626
90.7%
Dash Punctuation 10000
 
9.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 30565
31.3%
0 13747
14.1%
2 10593
 
10.9%
5 7866
 
8.1%
6 7378
 
7.6%
3 7167
 
7.3%
4 6239
 
6.4%
7 4853
 
5.0%
9 4613
 
4.7%
8 4605
 
4.7%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 107626
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 30565
28.4%
0 13747
12.8%
2 10593
 
9.8%
- 10000
 
9.3%
5 7866
 
7.3%
6 7378
 
6.9%
3 7167
 
6.7%
4 6239
 
5.8%
7 4853
 
4.5%
9 4613
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 107626
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 30565
28.4%
0 13747
12.8%
2 10593
 
9.8%
- 10000
 
9.3%
5 7866
 
7.3%
6 7378
 
6.9%
3 7167
 
6.7%
4 6239
 
5.8%
7 4853
 
4.5%
9 4613
 
4.3%

관리_건축물대장
Text

Distinct9946
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-18T12:32:16.327908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length11
Mean length10.5812
Min length7

Characters and Unicode

Total characters105812
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

Unique9898 ?
Unique (%)99.0%

Sample

1st row11170-9844
2nd row11140-726
3rd row11260-10444
4th row11710-271
5th row11140-457
ValueCountFrequency (%)
11500-233 6
 
0.1%
11500-820 3
 
< 0.1%
11470-100211867 3
 
< 0.1%
11290-2419 2
 
< 0.1%
11500-100248705 2
 
< 0.1%
11170-154 2
 
< 0.1%
11530-792 2
 
< 0.1%
11110-26854 2
 
< 0.1%
11290-100189596 2
 
< 0.1%
11350-184 2
 
< 0.1%
Other values (9936) 9974
99.7%
2024-05-18T12:32:17.170434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 30577
28.9%
0 13534
12.8%
2 10597
 
10.0%
- 10000
 
9.5%
5 7649
 
7.2%
6 7221
 
6.8%
3 6781
 
6.4%
4 5930
 
5.6%
7 4666
 
4.4%
9 4462
 
4.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 95812
90.5%
Dash Punctuation 10000
 
9.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 30577
31.9%
0 13534
14.1%
2 10597
 
11.1%
5 7649
 
8.0%
6 7221
 
7.5%
3 6781
 
7.1%
4 5930
 
6.2%
7 4666
 
4.9%
9 4462
 
4.7%
8 4395
 
4.6%
Dash Punctuation
ValueCountFrequency (%)
- 10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 105812
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 30577
28.9%
0 13534
12.8%
2 10597
 
10.0%
- 10000
 
9.5%
5 7649
 
7.2%
6 7221
 
6.8%
3 6781
 
6.4%
4 5930
 
5.6%
7 4666
 
4.4%
9 4462
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 105812
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 30577
28.9%
0 13534
12.8%
2 10597
 
10.0%
- 10000
 
9.5%
5 7649
 
7.2%
6 7221
 
6.8%
3 6781
 
6.4%
4 5930
 
5.6%
7 4666
 
4.4%
9 4462
 
4.2%

대표_여부
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
9495 
0
 
505

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
1 9495
95.0%
0 505
 
5.1%

Length

2024-05-18T12:32:17.581572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T12:32:17.829844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 9495
95.0%
0 505
 
5.1%

형식_코드
Real number (ℝ)

MISSING 

Distinct21
Distinct (%)0.5%
Missing5512
Missing (%)55.1%
Infinite0
Infinite (%)0.0%
Mean195.63614
Minimum0
Maximum300
Zeros18
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T12:32:18.241687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile117
Q1201
median201
Q3201
95-th percentile205
Maximum300
Range300
Interquartile range (IQR)0

Descriptive statistics

Standard deviation29.661484
Coefficient of variation (CV)0.15161556
Kurtosis12.070234
Mean195.63614
Median Absolute Deviation (MAD)0
Skewness-2.1527481
Sum878015
Variance879.80361
MonotonicityNot monotonic
2024-05-18T12:32:18.591932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
201 3354
33.5%
202 231
 
2.3%
117 167
 
1.7%
204 167
 
1.7%
199 100
 
1.0%
205 96
 
1.0%
113 91
 
0.9%
209 84
 
0.8%
101 62
 
0.6%
299 57
 
0.6%
Other values (11) 79
 
0.8%
(Missing) 5512
55.1%
ValueCountFrequency (%)
0 18
 
0.2%
11 1
 
< 0.1%
101 62
 
0.6%
102 3
 
< 0.1%
103 6
 
0.1%
107 1
 
< 0.1%
113 91
0.9%
117 167
1.7%
199 100
1.0%
200 1
 
< 0.1%
ValueCountFrequency (%)
300 16
 
0.2%
299 57
 
0.6%
212 1
 
< 0.1%
211 5
 
0.1%
209 84
 
0.8%
206 26
 
0.3%
205 96
1.0%
204 167
1.7%
203 1
 
< 0.1%
202 231
2.3%

기타_형식
Text

MISSING 

Distinct490
Distinct (%)10.9%
Missing5507
Missing (%)55.1%
Memory size156.2 KiB
2024-05-18T12:32:19.108337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length34
Mean length5.8744714
Min length1

Characters and Unicode

Total characters26394
Distinct characters129
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique334 ?
Unique (%)7.4%

Sample

1st row에프.알.피
2nd row살수형부패탱크
3rd row부패탱크방법
4th row콘크리트각형
5th row부패탱크방법
ValueCountFrequency (%)
부패탱크방법 898
18.8%
f.r.p 646
 
13.5%
콘크리트각형 267
 
5.6%
p.e 208
 
4.4%
에프알피 207
 
4.3%
부패탱크식 181
 
3.8%
f.r.p원형 171
 
3.6%
frp 132
 
2.8%
에프.알.피 127
 
2.7%
임호프방식 119
 
2.5%
Other values (387) 1820
38.1%
2024-05-18T12:32:20.357474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 2460
 
9.3%
크 1863
 
7.1%
P 1561
 
5.9%
탱 1487
 
5.6%
부 1414
 
5.4%
패 1395
 
5.3%
방 1317
 
5.0%
R 1200
 
4.5%
F 1199
 
4.5%
법 1156
 
4.4%
Other values (119) 11342
43.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18341
69.5%
Uppercase Letter 4782
 
18.1%
Other Punctuation 2596
 
9.8%
Space Separator 311
 
1.2%
Open Punctuation 94
 
0.4%
Close Punctuation 93
 
0.4%
Decimal Number 91
 
0.3%
Dash Punctuation 75
 
0.3%
Lowercase Letter 6
 
< 0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
크 1863
 
10.2%
탱 1487
 
8.1%
부 1414
 
7.7%
패 1395
 
7.6%
방 1317
 
7.2%
법 1156
 
6.3%
형 1025
 
5.6%
프 750
 
4.1%
식 744
 
4.1%
각 534
 
2.9%
Other values (85) 6656
36.3%
Uppercase Letter
ValueCountFrequency (%)
P 1561
32.6%
R 1200
25.1%
F 1199
25.1%
E 346
 
7.2%
C 298
 
6.2%
N 82
 
1.7%
O 81
 
1.7%
V 14
 
0.3%
A 1
 
< 0.1%
Decimal Number
ValueCountFrequency (%)
3 48
52.7%
0 17
 
18.7%
2 10
 
11.0%
1 7
 
7.7%
5 4
 
4.4%
4 3
 
3.3%
7 1
 
1.1%
9 1
 
1.1%
Other Punctuation
ValueCountFrequency (%)
. 2460
94.8%
, 113
 
4.4%
' 19
 
0.7%
/ 2
 
0.1%
: 1
 
< 0.1%
* 1
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
f 3
50.0%
p 1
 
16.7%
r 1
 
16.7%
c 1
 
16.7%
Math Symbol
ValueCountFrequency (%)
= 2
66.7%
+ 1
33.3%
Space Separator
ValueCountFrequency (%)
311
100.0%
Open Punctuation
ValueCountFrequency (%)
( 94
100.0%
Close Punctuation
ValueCountFrequency (%)
) 93
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 75
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 18341
69.5%
Latin 4788
 
18.1%
Common 3265
 
12.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
크 1863
 
10.2%
탱 1487
 
8.1%
부 1414
 
7.7%
패 1395
 
7.6%
방 1317
 
7.2%
법 1156
 
6.3%
형 1025
 
5.6%
프 750
 
4.1%
식 744
 
4.1%
각 534
 
2.9%
Other values (85) 6656
36.3%
Common
ValueCountFrequency (%)
. 2460
75.3%
311
 
9.5%
, 113
 
3.5%
( 94
 
2.9%
) 93
 
2.8%
- 75
 
2.3%
3 48
 
1.5%
' 19
 
0.6%
0 17
 
0.5%
2 10
 
0.3%
Other values (11) 25
 
0.8%
Latin
ValueCountFrequency (%)
P 1561
32.6%
R 1200
25.1%
F 1199
25.0%
E 346
 
7.2%
C 298
 
6.2%
N 82
 
1.7%
O 81
 
1.7%
V 14
 
0.3%
f 3
 
0.1%
A 1
 
< 0.1%
Other values (3) 3
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 18341
69.5%
ASCII 8053
30.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2460
30.5%
P 1561
19.4%
R 1200
14.9%
F 1199
14.9%
E 346
 
4.3%
311
 
3.9%
C 298
 
3.7%
, 113
 
1.4%
( 94
 
1.2%
) 93
 
1.2%
Other values (24) 378
 
4.7%
Hangul
ValueCountFrequency (%)
크 1863
 
10.2%
탱 1487
 
8.1%
부 1414
 
7.7%
패 1395
 
7.6%
방 1317
 
7.2%
법 1156
 
6.3%
형 1025
 
5.6%
프 750
 
4.1%
식 744
 
4.1%
각 534
 
2.9%
Other values (85) 6656
36.3%

용량_인용
Real number (ℝ)

SKEWED  ZEROS 

Distinct148
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.1609
Minimum0
Maximum10530
Zeros5624
Zeros (%)56.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T12:32:20.685466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q320
95-th percentile56.05
Maximum10530
Range10530
Interquartile range (IQR)20

Descriptive statistics

Standard deviation177.94798
Coefficient of variation (CV)7.0724011
Kurtosis1636.7937
Mean25.1609
Median Absolute Deviation (MAD)0
Skewness33.647712
Sum251609
Variance31665.483
MonotonicityNot monotonic
2024-05-18T12:32:21.125204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5624
56.2%
10 854
 
8.5%
20 738
 
7.4%
25 692
 
6.9%
15 574
 
5.7%
30 357
 
3.6%
40 206
 
2.1%
50 146
 
1.5%
35 108
 
1.1%
5 105
 
1.1%
Other values (138) 596
 
6.0%
ValueCountFrequency (%)
0 5624
56.2%
1 1
 
< 0.1%
2 3
 
< 0.1%
3 4
 
< 0.1%
5 105
 
1.1%
7 1
 
< 0.1%
10 854
 
8.5%
12 1
 
< 0.1%
15 574
 
5.7%
16 1
 
< 0.1%
ValueCountFrequency (%)
10530 1
< 0.1%
7520 1
< 0.1%
5410 1
< 0.1%
2575 1
< 0.1%
2250 1
< 0.1%
2130 1
< 0.1%
2050 1
< 0.1%
2000 1
< 0.1%
1900 1
< 0.1%
1800 2
< 0.1%

용량_루베
Real number (ℝ)

SKEWED  ZEROS 

Distinct59
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1012786
Minimum0
Maximum121
Zeros9900
Zeros (%)99.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T12:32:21.550339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum121
Range121
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.0418329
Coefficient of variation (CV)20.160556
Kurtosis2007.3805
Mean0.1012786
Median Absolute Deviation (MAD)0
Skewness40.500181
Sum1012.786
Variance4.1690817
MonotonicityNot monotonic
2024-05-18T12:32:22.010461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 9900
99.0%
3.0 9
 
0.1%
2.5 5
 
0.1%
5.5 4
 
< 0.1%
3.5 4
 
< 0.1%
2.0 4
 
< 0.1%
9.0 4
 
< 0.1%
4.0 4
 
< 0.1%
6.0 3
 
< 0.1%
5.0 3
 
< 0.1%
Other values (49) 60
 
0.6%
ValueCountFrequency (%)
0.0 9900
99.0%
1.15 2
 
< 0.1%
1.25 1
 
< 0.1%
1.55 2
 
< 0.1%
2.0 4
 
< 0.1%
2.224 1
 
< 0.1%
2.5 5
 
0.1%
2.52 1
 
< 0.1%
2.65 1
 
< 0.1%
2.75 2
 
< 0.1%
ValueCountFrequency (%)
121.0 1
< 0.1%
101.0 1
< 0.1%
64.73 1
< 0.1%
50.0 1
< 0.1%
41.0 2
< 0.1%
29.6 1
< 0.1%
26.0 1
< 0.1%
25.0 1
< 0.1%
22.05 1
< 0.1%
21.5 1
< 0.1%

작업_일자
Real number (ℝ)

Distinct416
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20120985
Minimum20111227
Maximum20150905
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T12:32:22.482613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20111227
5-th percentile20111227
Q120111227
median20111227
Q320130207
95-th percentile20150507
Maximum20150905
Range39678
Interquartile range (IQR)18980

Descriptive statistics

Standard deviation14158.981
Coefficient of variation (CV)0.00070369222
Kurtosis-0.23603783
Mean20120985
Median Absolute Deviation (MAD)0
Skewness1.1597771
Sum2.0120985 × 1011
Variance2.0047674 × 108
MonotonicityNot monotonic
2024-05-18T12:32:23.009264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20111227 5885
58.9%
20120825 956
 
9.6%
20150116 159
 
1.6%
20121222 121
 
1.2%
20150402 87
 
0.9%
20141115 82
 
0.8%
20120920 73
 
0.7%
20150107 73
 
0.7%
20150522 65
 
0.7%
20141203 57
 
0.6%
Other values (406) 2442
24.4%
ValueCountFrequency (%)
20111227 5885
58.9%
20120102 5
 
0.1%
20120104 3
 
< 0.1%
20120110 2
 
< 0.1%
20120112 7
 
0.1%
20120113 2
 
< 0.1%
20120117 1
 
< 0.1%
20120118 1
 
< 0.1%
20120119 3
 
< 0.1%
20120128 1
 
< 0.1%
ValueCountFrequency (%)
20150905 17
0.2%
20150826 1
 
< 0.1%
20150825 8
0.1%
20150822 6
 
0.1%
20150821 2
 
< 0.1%
20150820 1
 
< 0.1%
20150819 2
 
< 0.1%
20150818 3
 
< 0.1%
20150815 3
 
< 0.1%
20150813 2
 
< 0.1%

Interactions

2024-05-18T12:32:11.442265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:32:06.812620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:32:08.518073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:32:09.991860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:32:11.737984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:32:07.219369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:32:08.980926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:32:10.349080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:32:12.045739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:32:07.620518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:32:09.281527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:32:10.626879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:32:12.376442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:32:08.067708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:32:09.641907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T12:32:11.053056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T12:32:23.304633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대표_여부형식_코드용량_인용용량_루베작업_일자
대표_여부1.0000.1710.0950.0250.140
형식_코드0.1711.0000.0970.0000.242
용량_인용0.0950.0971.0000.1210.000
용량_루베0.0250.0000.1211.0000.032
작업_일자0.1400.2420.0000.0321.000
2024-05-18T12:32:23.579151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
형식_코드용량_인용용량_루베작업_일자대표_여부
형식_코드1.000-0.090-0.0020.0190.209
용량_인용-0.0901.0000.1220.0470.068
용량_루베-0.0020.1221.0000.0390.019
작업_일자0.0190.0470.0391.0000.119
대표_여부0.2090.0680.0190.1191.000

Missing values

2024-05-18T12:32:12.984943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T12:32:13.441968image/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.
2024-05-18T12:32:13.769920image/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_오수정화시설관리_건축물대장대표_여부형식_코드기타_형식용량_인용용량_루베작업_일자
2177011170-942411170-98441201에프.알.피200.020140121
1555311140-2511411140-7261206살수형부패탱크00.020111227
964311260-969011260-104441<NA><NA>00.020111227
193811710-2446111710-2710<NA><NA>00.020111227
1708311140-2492311140-4570201<NA>00.020120825
2723511110-1557111110-166711<NA><NA>00.020150717
1650711290-3038011290-335501<NA><NA>00.020120518
2187811170-2253511170-229771<NA><NA>300.020140125
1719011440-3274011440-14841<NA><NA>00.020120825
1718711500-10001358511500-1002155081201부패탱크방법10011.0120120825
관리_오수정화시설관리_건축물대장대표_여부형식_코드기타_형식용량_인용용량_루베작업_일자
575611530-1705011530-184791201F.R.P200.020111227
1293811260-1746611260-183651<NA><NA>00.020111227
2255211110-1959811110-206961<NA><NA>00.020140611
2591511620-1710211620-182321<NA><NA>00.020150415
2256011545-818411545-93241299분뇨정화조100.020140614
1119511545-529411545-64121201분뇨정화조100.020111227
1256211545-515911545-62761201콘크리트각형400.020111227
2551011260-970211260-104561<NA><NA>00.020150402
1320911260-1925711260-201631201F.R.P250.020111227
2171111170-1786411170-183001204피이100.020140121