Overview

Dataset statistics

Number of variables8
Number of observations10000
Missing cells232
Missing cells (%)0.3%
Duplicate rows172
Duplicate rows (%)1.7%
Total size in memory742.2 KiB
Average record size in memory76.0 B

Variable types

Numeric4
Categorical2
Text2

Dataset

Description배전회선 정보 및 배전 상황보고를 제공하는 시스템
Author한국전력공사
URLhttps://www.data.go.kr/data/3068753/fileData.do

Alerts

Dataset has 172 (1.7%) duplicate rowsDuplicates
1차사업소코드 is highly overall correlated with 2차사업소코드 and 1 other fieldsHigh correlation
2차사업소코드 is highly overall correlated with 1차사업소코드 and 1 other fieldsHigh correlation
산업분류코드 is highly overall correlated with 산업분류High correlation
1차사업소명 is highly overall correlated with 1차사업소코드 and 1 other fieldsHigh correlation
산업분류 is highly overall correlated with 산업분류코드High correlation
주생산품 has 232 (2.3%) missing valuesMissing
계약전력 is highly skewed (γ1 = 50.14352897)Skewed

Reproduction

Analysis started2023-12-12 04:52:16.067470
Analysis finished2023-12-12 04:52:19.914006
Duration3.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

1차사업소코드
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4632.626
Minimum3400
Maximum5900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:52:19.992170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3400
5-th percentile3400
Q13900
median4600
Q35200
95-th percentile5700
Maximum5900
Range2500
Interquartile range (IQR)1300

Descriptive statistics

Standard deviation714.95607
Coefficient of variation (CV)0.15433063
Kurtosis-1.2982066
Mean4632.626
Median Absolute Deviation (MAD)630
Skewness-0.013429644
Sum46326260
Variance511162.18
MonotonicityNot monotonic
2023-12-12T13:52:20.143613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
3900 1825
18.2%
5500 1188
11.9%
5200 1135
11.3%
4000 1024
10.2%
4600 949
9.5%
5700 762
7.6%
3400 666
 
6.7%
5000 646
 
6.5%
4500 527
 
5.3%
4800 415
 
4.2%
Other values (5) 863
8.6%
ValueCountFrequency (%)
3400 666
 
6.7%
3850 122
 
1.2%
3900 1825
18.2%
3970 58
 
0.6%
4000 1024
10.2%
4200 254
 
2.5%
4500 527
 
5.3%
4600 949
9.5%
4800 415
 
4.2%
5000 646
 
6.5%
ValueCountFrequency (%)
5900 116
 
1.2%
5700 762
7.6%
5500 1188
11.9%
5300 313
 
3.1%
5200 1135
11.3%
5000 646
6.5%
4800 415
 
4.2%
4600 949
9.5%
4500 527
5.3%
4200 254
 
2.5%

1차사업소명
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경기본부
1825 
부산울산본부
1188 
대구본부
1135 
인천본부
1024 
대전세종충남본부
949 
Other values (10)
3879 

Length

Max length8
Median length4
Mean length4.8918
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row경기본부
2nd row대구본부
3rd row대전세종충남본부
4th row전북본부
5th row경기본부

Common Values

ValueCountFrequency (%)
경기본부 1825
18.2%
부산울산본부 1188
11.9%
대구본부 1135
11.3%
인천본부 1024
10.2%
대전세종충남본부 949
9.5%
경남본부 762
7.6%
경기북부본부 666
 
6.7%
광주전남본부 646
 
6.5%
충북본부 527
 
5.3%
전북본부 415
 
4.2%
Other values (5) 863
8.6%

Length

2023-12-12T13:52:20.338569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기본부 1825
18.2%
부산울산본부 1188
11.9%
대구본부 1135
11.3%
인천본부 1024
10.2%
대전세종충남본부 949
9.5%
경남본부 762
7.6%
경기북부본부 666
 
6.7%
광주전남본부 646
 
6.5%
충북본부 527
 
5.3%
전북본부 415
 
4.2%
Other values (5) 863
8.6%

2차사업소코드
Real number (ℝ)

HIGH CORRELATION 

Distinct198
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4794.3738
Minimum2930
Maximum7130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:52:20.537833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2930
5-th percentile3520
Q14009
median4690
Q35477
95-th percentile5920
Maximum7130
Range4200
Interquartile range (IQR)1468

Descriptive statistics

Standard deviation890.81097
Coefficient of variation (CV)0.1858034
Kurtosis-0.060459445
Mean4794.3738
Median Absolute Deviation (MAD)732
Skewness0.41318807
Sum47943738
Variance793544.18
MonotonicityNot monotonic
2023-12-12T13:52:20.794041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3922 440
 
4.4%
5560 385
 
3.9%
4046 281
 
2.8%
5445 246
 
2.5%
4009 245
 
2.5%
7048 226
 
2.3%
3950 205
 
2.1%
4063 202
 
2.0%
4620 191
 
1.9%
3530 184
 
1.8%
Other values (188) 7395
74.0%
ValueCountFrequency (%)
2930 93
0.9%
3010 10
 
0.1%
3033 13
 
0.1%
3050 39
0.4%
3110 10
 
0.1%
3120 23
 
0.2%
3130 7
 
0.1%
3134 15
 
0.1%
3410 3
 
< 0.1%
3414 56
0.6%
ValueCountFrequency (%)
7130 34
 
0.3%
7048 226
2.3%
7046 53
 
0.5%
6990 166
1.7%
5920 72
 
0.7%
5910 44
 
0.4%
5840 24
 
0.2%
5791 129
1.3%
5783 14
 
0.1%
5782 9
 
0.1%

2차사업소명
Text

Distinct197
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T13:52:21.154493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length4
Mean length4.4296
Min length4

Characters and Unicode

Total characters44296
Distinct characters129
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row성남지사
2nd row남대구지사
3rd row세종지사
4th row고창지사
5th row여주지사
ValueCountFrequency (%)
안산지사 440
 
4.4%
김해지사 385
 
3.9%
김포지사 281
 
2.8%
남대구지사 246
 
2.5%
남인천지사 245
 
2.5%
서평택지사 226
 
2.3%
오산지사 205
 
2.1%
서인천지사 202
 
2.0%
천안지사 191
 
1.9%
파주지사 184
 
1.8%
Other values (187) 7395
74.0%
2023-12-12T13:52:21.738489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 9378
21.2%
지 9319
21.0%
산 2203
 
5.0%
천 1731
 
3.9%
부 1328
 
3.0%
주 1124
 
2.5%
안 1041
 
2.4%
서 1028
 
2.3%
남 846
 
1.9%
김 729
 
1.6%
Other values (119) 15569
35.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 44296
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 9378
21.2%
지 9319
21.0%
산 2203
 
5.0%
천 1731
 
3.9%
부 1328
 
3.0%
주 1124
 
2.5%
안 1041
 
2.4%
서 1028
 
2.3%
남 846
 
1.9%
김 729
 
1.6%
Other values (119) 15569
35.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 44296
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 9378
21.2%
지 9319
21.0%
산 2203
 
5.0%
천 1731
 
3.9%
부 1328
 
3.0%
주 1124
 
2.5%
안 1041
 
2.4%
서 1028
 
2.3%
남 846
 
1.9%
김 729
 
1.6%
Other values (119) 15569
35.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 44296
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 9378
21.2%
지 9319
21.0%
산 2203
 
5.0%
천 1731
 
3.9%
부 1328
 
3.0%
주 1124
 
2.5%
안 1041
 
2.4%
서 1028
 
2.3%
남 846
 
1.9%
김 729
 
1.6%
Other values (119) 15569
35.1%

계약전력
Real number (ℝ)

SKEWED 

Distinct520
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean802.0092
Minimum104
Maximum240000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:52:21.936197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum104
5-th percentile150
Q1250
median350
Q3700
95-th percentile2500
Maximum240000
Range239896
Interquartile range (IQR)450

Descriptive statistics

Standard deviation3079.7423
Coefficient of variation (CV)3.8400336
Kurtosis3679.941
Mean802.0092
Median Absolute Deviation (MAD)150
Skewness50.143529
Sum8020092
Variance9484812.7
MonotonicityNot monotonic
2023-12-12T13:52:22.169420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 1135
 
11.3%
290 841
 
8.4%
250 832
 
8.3%
300 752
 
7.5%
150 662
 
6.6%
500 626
 
6.3%
950 561
 
5.6%
400 541
 
5.4%
600 374
 
3.7%
450 237
 
2.4%
Other values (510) 3439
34.4%
ValueCountFrequency (%)
104 1
 
< 0.1%
105 2
 
< 0.1%
106 2
 
< 0.1%
109 1
 
< 0.1%
110 6
 
0.1%
113 1
 
< 0.1%
114 1
 
< 0.1%
115 2
 
< 0.1%
119 1
 
< 0.1%
120 19
0.2%
ValueCountFrequency (%)
240000 1
 
< 0.1%
60000 1
 
< 0.1%
50857 1
 
< 0.1%
40000 5
0.1%
34000 1
 
< 0.1%
31050 1
 
< 0.1%
30000 2
 
< 0.1%
29400 1
 
< 0.1%
29000 1
 
< 0.1%
21000 1
 
< 0.1%

주생산품
Text

MISSING 

Distinct3974
Distinct (%)40.7%
Missing232
Missing (%)2.3%
Memory size156.2 KiB
2023-12-12T13:52:22.575197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length4.4438984
Min length1

Characters and Unicode

Total characters43408
Distinct characters612
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2975 ?
Unique (%)30.5%

Sample

1st row주차장
2nd row전자부품
3rd row레미콘제조
4th row양수장
5th row전자부품
ValueCountFrequency (%)
자동차부품 415
 
4.2%
전자부품 204
 
2.0%
제조 168
 
1.7%
금형 156
 
1.6%
합성수지 135
 
1.4%
자동차부품제조 107
 
1.1%
플라스틱 106
 
1.1%
기계부품 74
 
0.7%
인쇄 73
 
0.7%
기계 70
 
0.7%
Other values (3932) 8449
84.9%
2023-12-12T13:52:23.163805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
제 2376
 
5.5%
조 2252
 
5.2%
품 1708
 
3.9%
기 1335
 
3.1%
부 1267
 
2.9%
자 1257
 
2.9%
동 840
 
1.9%
차 774
 
1.8%
수 735
 
1.7%
장 729
 
1.7%
Other values (602) 30135
69.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 42731
98.4%
Space Separator 189
 
0.4%
Other Punctuation 153
 
0.4%
Uppercase Letter 125
 
0.3%
Open Punctuation 122
 
0.3%
Close Punctuation 50
 
0.1%
Decimal Number 18
 
< 0.1%
Math Symbol 14
 
< 0.1%
Lowercase Letter 5
 
< 0.1%
Other Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 2376
 
5.6%
조 2252
 
5.3%
품 1708
 
4.0%
기 1335
 
3.1%
부 1267
 
3.0%
자 1257
 
2.9%
동 840
 
2.0%
차 774
 
1.8%
수 735
 
1.7%
장 729
 
1.7%
Other values (565) 29458
68.9%
Uppercase Letter
ValueCountFrequency (%)
P 29
23.2%
C 24
19.2%
D 16
12.8%
V 14
11.2%
L 13
10.4%
E 9
 
7.2%
B 4
 
3.2%
T 2
 
1.6%
M 2
 
1.6%
O 2
 
1.6%
Other values (8) 10
 
8.0%
Decimal Number
ValueCountFrequency (%)
1 7
38.9%
2 6
33.3%
0 2
 
11.1%
3 2
 
11.1%
7 1
 
5.6%
Other Punctuation
ValueCountFrequency (%)
, 123
80.4%
. 15
 
9.8%
/ 14
 
9.2%
· 1
 
0.7%
Lowercase Letter
ValueCountFrequency (%)
p 3
60.0%
c 1
 
20.0%
v 1
 
20.0%
Space Separator
ValueCountFrequency (%)
188
99.5%
　 1
 
0.5%
Open Punctuation
ValueCountFrequency (%)
( 121
99.2%
{ 1
 
0.8%
Close Punctuation
ValueCountFrequency (%)
) 50
100.0%
Math Symbol
ValueCountFrequency (%)
+ 14
100.0%
Other Symbol
ValueCountFrequency (%)
■ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 42731
98.4%
Common 547
 
1.3%
Latin 130
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 2376
 
5.6%
조 2252
 
5.3%
품 1708
 
4.0%
기 1335
 
3.1%
부 1267
 
3.0%
자 1257
 
2.9%
동 840
 
2.0%
차 774
 
1.8%
수 735
 
1.7%
장 729
 
1.7%
Other values (565) 29458
68.9%
Latin
ValueCountFrequency (%)
P 29
22.3%
C 24
18.5%
D 16
12.3%
V 14
10.8%
L 13
10.0%
E 9
 
6.9%
B 4
 
3.1%
p 3
 
2.3%
T 2
 
1.5%
M 2
 
1.5%
Other values (11) 14
10.8%
Common
ValueCountFrequency (%)
188
34.4%
, 123
22.5%
( 121
22.1%
) 50
 
9.1%
. 15
 
2.7%
+ 14
 
2.6%
/ 14
 
2.6%
1 7
 
1.3%
2 6
 
1.1%
0 2
 
0.4%
Other values (6) 7
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 42728
98.4%
ASCII 674
 
1.6%
Compat Jamo 3
 
< 0.1%
None 2
 
< 0.1%
Geometric Shapes 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
제 2376
 
5.6%
조 2252
 
5.3%
품 1708
 
4.0%
기 1335
 
3.1%
부 1267
 
3.0%
자 1257
 
2.9%
동 840
 
2.0%
차 774
 
1.8%
수 735
 
1.7%
장 729
 
1.7%
Other values (562) 29455
68.9%
ASCII
ValueCountFrequency (%)
188
27.9%
, 123
18.2%
( 121
18.0%
) 50
 
7.4%
P 29
 
4.3%
C 24
 
3.6%
D 16
 
2.4%
. 15
 
2.2%
+ 14
 
2.1%
/ 14
 
2.1%
Other values (24) 80
11.9%
None
ValueCountFrequency (%)
· 1
50.0%
　 1
50.0%
Compat Jamo
ValueCountFrequency (%)
ㄹ 1
33.3%
ㅈ 1
33.3%
ㆍ 1
33.3%
Geometric Shapes
ValueCountFrequency (%)
■ 1
100.0%

산업분류코드
Real number (ℝ)

HIGH CORRELATION 

Distinct670
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25060.544
Minimum1110
Maximum55909
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T13:52:23.364854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1110
5-th percentile1152
Q118123.5
median25921
Q330310
95-th percentile52102
Maximum55909
Range54799
Interquartile range (IQR)12186.5

Descriptive statistics

Standard deviation12818.102
Coefficient of variation (CV)0.51148537
Kurtosis0.32637026
Mean25060.544
Median Absolute Deviation (MAD)5498
Skewness0.24283465
Sum2.5060544 × 108
Variance1.6430374 × 108
MonotonicityNot monotonic
2023-12-12T13:52:23.533526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30399 288
 
2.9%
29294 265
 
2.6%
26299 242
 
2.4%
33999 196
 
2.0%
1110 194
 
1.9%
29199 188
 
1.9%
30310 172
 
1.7%
20202 159
 
1.6%
52102 128
 
1.3%
1412 122
 
1.2%
Other values (660) 8046
80.5%
ValueCountFrequency (%)
1110 194
1.9%
1121 108
1.1%
1122 74
 
0.7%
1123 34
 
0.3%
1131 57
 
0.6%
1132 1
 
< 0.1%
1140 14
 
0.1%
1151 5
 
0.1%
1152 101
1.0%
1159 44
 
0.4%
ValueCountFrequency (%)
55909 26
0.3%
55901 18
 
0.2%
55221 2
 
< 0.1%
55219 8
 
0.1%
55214 1
 
< 0.1%
55119 8
 
0.1%
55114 5
 
0.1%
55113 7
 
0.1%
55112 45
0.4%
55111 20
0.2%

산업분류
Categorical

HIGH CORRELATION 

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
기계 및 장비
1133 
축산업
929 
플라스틱제품
781 
제재 및 목재
680 
사무기계
623 
Other values (33)
5854 

Length

Max length10
Median length8
Mean length5.6261
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row소매,소비용품 수선
2nd row유리 및 유리제품
3rd row석유 정제
4th row축산업
5th row영상,음향

Common Values

ValueCountFrequency (%)
기계 및 장비 1133
 
11.3%
축산업 929
 
9.3%
플라스틱제품 781
 
7.8%
제재 및 목재 680
 
6.8%
사무기계 623
 
6.2%
석탄광업 604
 
6.0%
출판,인쇄 518
 
5.2%
유리 및 유리제품 455
 
4.5%
석유 정제 423
 
4.2%
화학제품 419
 
4.2%
Other values (28) 3435
34.4%

Length

2023-12-12T13:52:23.723043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
및 2733
 
15.8%
기계 1133
 
6.6%
장비 1133
 
6.6%
축산업 929
 
5.4%
플라스틱제품 781
 
4.5%
제재 680
 
3.9%
목재 680
 
3.9%
사무기계 623
 
3.6%
석탄광업 604
 
3.5%
출판,인쇄 518
 
3.0%
Other values (43) 7465
43.2%

Interactions

2023-12-12T13:52:19.109112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:17.575200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:18.097461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:18.582392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:19.242706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:17.700292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:18.225914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:18.727238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:19.368295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:17.831001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:18.324748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:18.856852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:19.487343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:17.985073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:18.464080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:52:18.996531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:52:23.886040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1차사업소코드1차사업소명2차사업소코드계약전력산업분류코드산업분류
1차사업소코드1.0001.0000.9450.0000.3110.497
1차사업소명1.0001.0000.9590.0310.4190.548
2차사업소코드0.9450.9591.0000.0000.2920.463
계약전력0.0000.0310.0001.0000.0000.083
산업분류코드0.3110.4190.2920.0001.0000.999
산업분류0.4970.5480.4630.0830.9991.000
2023-12-12T13:52:24.053656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1차사업소명산업분류
1차사업소명1.0000.187
산업분류0.1871.000
2023-12-12T13:52:24.176963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1차사업소코드2차사업소코드계약전력산업분류코드1차사업소명산업분류
1차사업소코드1.0000.827-0.037-0.0081.0000.198
2차사업소코드0.8271.000-0.0430.0080.8150.185
계약전력-0.037-0.0431.000-0.0050.0170.042
산업분류코드-0.0080.008-0.0051.0000.1700.988
1차사업소명1.0000.8150.0170.1701.0000.187
산업분류0.1980.1850.0420.9880.1871.000

Missing values

2023-12-12T13:52:19.636107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:52:19.817302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

1차사업소코드1차사업소명2차사업소코드2차사업소명계약전력주생산품산업분류코드산업분류
896943900경기본부3120성남지사250주차장52915소매,소비용품 수선
546775200대구본부5445남대구지사300전자부품26310유리 및 유리제품
357174600대전세종충남본부4632세종지사900레미콘제조23322석유 정제
89664800전북본부4822고창지사1250양수장1110축산업
776803900경기본부3958여주지사450전자부품32199영상,음향
498515500부산울산본부5521북부산지사290도금25921플라스틱제품
588445500부산울산본부5560김해지사250산업기계28909조립금속
752794000인천본부4009남인천지사290슬라이딩레일30203사무기계
342225300경북본부5280구미지사200고무제품22199출판,인쇄
819133900경기본부3922안산지사150오수펌프장36010가구 및 기타
1차사업소코드1차사업소명2차사업소코드2차사업소명계약전력주생산품산업분류코드산업분류
844714600대전세종충남본부4632세종지사500<NA>41226수도사업
542945200대구본부5378칠곡지사900전자정밀26299유리 및 유리제품
84115900제주본부5920서귀포지사400밀감하우스1131축산업
664944000인천본부4063서인천지사990PVC29174기계 및 장비
640144800전북본부4910군산지사500전자응용공작기계29221기계 및 장비
240493900경기본부3963광주지사450비료 및 질소화20209제재 및 목재
644895700경남본부5791경남본부직할200금형프레스가공29294기계 및 장비
124684200강원본부4358양양지사300게사료제조10800석탄광업
921624600대전세종충남본부4671당진지사250숙박업55102숙박 및 음식점업
79515500부산울산본부5545양산지사3100배수장1110축산업

Duplicate rows

Most frequently occurring

1차사업소코드1차사업소명2차사업소코드2차사업소명계약전력주생산품산업분류코드산업분류# duplicates
604000인천본부4046김포지사290<NA>33999의료,광학기기7
283900경기본부3922안산지사950자동차부품30399사무기계4
574000인천본부4046김포지사200합성수지20202제재 및 목재4
1055200대구본부5445남대구지사200자동차부품30399사무기계4
1315500부산울산본부5560김해지사200자동차부품30310사무기계4
1325500부산울산본부5560김해지사200전자부품32199영상,음향4
73400경기북부본부3598고양지사200인쇄18111의복 제조업3
173900경기본부3922안산지사150기계29199기계 및 장비3
203900경기본부3922안산지사250기계29199기계 및 장비3
373900경기본부3950오산지사200금형29294기계 및 장비3