Overview

Dataset statistics

Number of variables10
Number of observations1311
Missing cells3
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory106.4 KiB
Average record size in memory83.1 B

Variable types

Categorical6
DateTime1
Numeric2
Text1

Dataset

Description2014년 전북지역 도로조명, 보안등,공원등에 대한 정기점검 상세현황을 제공
Author한국전기안전공사
URLhttps://www.data.go.kr/data/15043803/fileData.do

Alerts

주기 is highly overall correlated with 분류 and 2 other fieldsHigh correlation
분류 is highly overall correlated with 구분표 and 2 other fieldsHigh correlation
구분표 is highly overall correlated with 분류 and 1 other fieldsHigh correlation
점검시기 is highly overall correlated with 주기High correlation
점검구분 is highly overall correlated with 구분표 and 1 other fieldsHigh correlation
부적합사유 is highly overall correlated with 주기High correlation
점검구분 is highly imbalanced (93.0%)Imbalance
부적합사유 is highly imbalanced (52.6%)Imbalance

Reproduction

Analysis started2023-12-12 18:09:30.481803
Analysis finished2023-12-12 18:09:32.183611
Duration1.7 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

분류
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
가로등등주
607 
보안등
536 
가로등 분전함
131 
경보등/경광등
 
16
차량용
 
8
Other values (2)
 
13

Length

Max length7
Median length5
Mean length4.3806255
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row보안등
2nd row보안등
3rd row보안등
4th row보안등
5th row공원등

Common Values

ValueCountFrequency (%)
가로등등주 607
46.3%
보안등 536
40.9%
가로등 분전함 131
 
10.0%
경보등/경광등 16
 
1.2%
차량용 8
 
0.6%
보행자용 8
 
0.6%
공원등 5
 
0.4%

Length

2023-12-13T03:09:32.280282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:09:32.425963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가로등등주 607
42.1%
보안등 536
37.2%
가로등 131
 
9.1%
분전함 131
 
9.1%
경보등/경광등 16
 
1.1%
차량용 8
 
0.6%
보행자용 8
 
0.6%
공원등 5
 
0.3%

관할사업소
Categorical

Distinct5
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
전북지역본부
839 
전북서부지사
279 
남원순창지사
134 
군산지사
 
38
익산지사
 
21

Length

Max length6
Median length6
Mean length5.9099924
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전북지역본부
2nd row전북서부지사
3rd row남원순창지사
4th row전북지역본부
5th row남원순창지사

Common Values

ValueCountFrequency (%)
전북지역본부 839
64.0%
전북서부지사 279
 
21.3%
남원순창지사 134
 
10.2%
군산지사 38
 
2.9%
익산지사 21
 
1.6%

Length

2023-12-13T03:09:32.580767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:09:32.763137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전북지역본부 839
64.0%
전북서부지사 279
 
21.3%
남원순창지사 134
 
10.2%
군산지사 38
 
2.9%
익산지사 21
 
1.6%

점검시기
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
14-Jun
363 
14-May
250 
14-Apr
148 
14-Jul
131 
14-Mar
130 
Other values (10)
289 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row14-May
2nd row14-Aug
3rd row14-May
4th row14-Mar
5th row14-May

Common Values

ValueCountFrequency (%)
14-Jun 363
27.7%
14-May 250
19.1%
14-Apr 148
11.3%
14-Jul 131
 
10.0%
14-Mar 130
 
9.9%
14-Feb 96
 
7.3%
14-Aug 79
 
6.0%
13-Dec 39
 
3.0%
13-Oct 25
 
1.9%
14-Sep 23
 
1.8%
Other values (5) 27
 
2.1%

Length

2023-12-13T03:09:32.948100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
14-jun 363
27.7%
14-may 250
19.1%
14-apr 148
11.3%
14-jul 131
 
10.0%
14-mar 130
 
9.9%
14-feb 96
 
7.3%
14-aug 79
 
6.0%
13-dec 39
 
3.0%
13-oct 25
 
1.9%
14-sep 23
 
1.8%
Other values (5) 27
 
2.1%

점검일
Date

Distinct81
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
Minimum2014-02-20 00:00:00
Maximum2014-12-26 00:00:00
2023-12-13T03:09:33.110072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:33.327908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

고객번호
Real number (ℝ)

Distinct1310
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5290278 × 109
Minimum6043297
Maximum9.2000712 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2023-12-13T03:09:33.528281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6043297
5-th percentile6158788
Q12.0032473 × 109
median4.0031035 × 109
Q36.0028319 × 109
95-th percentile9.0028215 × 109
Maximum9.2000712 × 109
Range9.1940279 × 109
Interquartile range (IQR)3.9995846 × 109

Descriptive statistics

Standard deviation2.8484695 × 109
Coefficient of variation (CV)0.62893618
Kurtosis-1.0365961
Mean4.5290278 × 109
Median Absolute Deviation (MAD)1.9997423 × 109
Skewness0.02787052
Sum5.9375555 × 1012
Variance8.1137783 × 1018
MonotonicityNot monotonic
2023-12-13T03:09:33.710981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2003229004 2
 
0.2%
1004489200 1
 
0.1%
4003083511 1
 
0.1%
6199994 1
 
0.1%
6199993 1
 
0.1%
1006738939 1
 
0.1%
1006738938 1
 
0.1%
1006738941 1
 
0.1%
1006738940 1
 
0.1%
4003083512 1
 
0.1%
Other values (1300) 1300
99.2%
ValueCountFrequency (%)
6043297 1
0.1%
6047640 1
0.1%
6049843 1
0.1%
6049950 1
0.1%
6049954 1
0.1%
6049955 1
0.1%
6050912 1
0.1%
6052336 1
0.1%
6052338 1
0.1%
6053195 1
0.1%
ValueCountFrequency (%)
9200071163 1
0.1%
9200071162 1
0.1%
9200071039 1
0.1%
9200070564 1
0.1%
9200069074 1
0.1%
9200058407 1
0.1%
9200042567 1
0.1%
9200042566 1
0.1%
9200042549 1
0.1%
9200042548 1
0.1%

구분표
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)1.5%
Missing2
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean9.6267456 × 1014
Minimum1.35 × 1014
Maximum9.86 × 1014
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size11.7 KiB
2023-12-13T03:09:33.894794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.35 × 1014
5-th percentile9.51 × 1014
Q19.57 × 1014
median9.58 × 1014
Q39.82 × 1014
95-th percentile9.83 × 1014
Maximum9.86 × 1014
Range8.51 × 1014
Interquartile range (IQR)2.5 × 1013

Descriptive statistics

Standard deviation5.6802729 × 1013
Coefficient of variation (CV)0.059005121
Kurtosis172.58993
Mean9.6267456 × 1014
Median Absolute Deviation (MAD)3 × 1012
Skewness-12.718781
Sum1.260141 × 1018
Variance3.22655 × 1027
MonotonicityNot monotonic
2023-12-13T03:09:34.047932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
957000000000000 344
26.2%
983000000000000 292
22.3%
981000000000000 126
 
9.6%
958000000000000 106
 
8.1%
955000000000000 104
 
7.9%
961000000000000 85
 
6.5%
977000000000000 71
 
5.4%
956000000000000 48
 
3.7%
951000000000000 44
 
3.4%
982000000000000 31
 
2.4%
Other values (10) 58
 
4.4%
ValueCountFrequency (%)
135000000000000 3
 
0.2%
205000000000000 2
 
0.2%
235000000000000 1
 
0.1%
605000000000000 2
 
0.2%
907000000000000 1
 
0.1%
950000000000000 29
 
2.2%
951000000000000 44
3.4%
954000000000000 7
 
0.5%
955000000000000 104
7.9%
956000000000000 48
3.7%
ValueCountFrequency (%)
986000000000000 9
 
0.7%
984000000000000 2
 
0.2%
983000000000000 292
22.3%
982000000000000 31
 
2.4%
981000000000000 126
 
9.6%
980000000000000 2
 
0.2%
977000000000000 71
 
5.4%
961000000000000 85
 
6.5%
958000000000000 106
 
8.1%
957000000000000 344
26.2%

주소
Text

Distinct237
Distinct (%)18.1%
Missing1
Missing (%)0.1%
Memory size10.4 KiB
2023-12-13T03:09:34.488358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length15.479389
Min length11

Characters and Unicode

Total characters20278
Distinct characters163
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique96 ?
Unique (%)7.3%

Sample

1st row전라북도 전주시 완산구 태평동
2nd row전라북도 고창군 부안면 오산리
3rd row전라북도 남원시 이백면 서곡리
4th row전라북도 완주군 상관면 마치리
5th row전라북도 순창군 팔덕면 청계리
ValueCountFrequency (%)
전라북도 1310
26.0%
완주군 480
 
9.5%
봉동읍 240
 
4.8%
전주시 235
 
4.7%
완산구 227
 
4.5%
정읍시 202
 
4.0%
남원시 101
 
2.0%
둔산리 87
 
1.7%
장기리 79
 
1.6%
상관면 76
 
1.5%
Other values (300) 1992
39.6%
2023-12-13T03:09:35.101635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3719
18.3%
전 1570
 
7.7%
도 1343
 
6.6%
북 1326
 
6.5%
라 1310
 
6.5%
리 869
 
4.3%
주 758
 
3.7%
동 732
 
3.6%
완 727
 
3.6%
군 705
 
3.5%
Other values (153) 7219
35.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16482
81.3%
Space Separator 3719
 
18.3%
Decimal Number 77
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 1570
 
9.5%
도 1343
 
8.1%
북 1326
 
8.0%
라 1310
 
7.9%
리 869
 
5.3%
주 758
 
4.6%
동 732
 
4.4%
완 727
 
4.4%
군 705
 
4.3%
시 652
 
4.0%
Other values (148) 6490
39.4%
Decimal Number
ValueCountFrequency (%)
2 39
50.6%
3 33
42.9%
4 3
 
3.9%
1 2
 
2.6%
Space Separator
ValueCountFrequency (%)
3719
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 16482
81.3%
Common 3796
 
18.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 1570
 
9.5%
도 1343
 
8.1%
북 1326
 
8.0%
라 1310
 
7.9%
리 869
 
5.3%
주 758
 
4.6%
동 732
 
4.4%
완 727
 
4.4%
군 705
 
4.3%
시 652
 
4.0%
Other values (148) 6490
39.4%
Common
ValueCountFrequency (%)
3719
98.0%
2 39
 
1.0%
3 33
 
0.9%
4 3
 
0.1%
1 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 16482
81.3%
ASCII 3796
 
18.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3719
98.0%
2 39
 
1.0%
3 33
 
0.9%
4 3
 
0.1%
1 2
 
0.1%
Hangul
ValueCountFrequency (%)
전 1570
 
9.5%
도 1343
 
8.1%
북 1326
 
8.0%
라 1310
 
7.9%
리 869
 
5.3%
주 758
 
4.6%
동 732
 
4.4%
완 727
 
4.4%
군 705
 
4.3%
시 652
 
4.0%
Other values (148) 6490
39.4%

점검구분
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
재점검 전산외
1300 
재점검
 
11

Length

Max length7
Median length7
Mean length6.9664378
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
재점검 전산외 1300
99.2%
재점검 11
 
0.8%

Length

2023-12-13T03:09:35.281744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:09:35.396700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재점검 1311
50.2%
전산외 1300
49.8%

주기
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
1
770 
3
541 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
1 770
58.7%
3 541
41.3%

Length

2023-12-13T03:09:35.501012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:09:35.585729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 770
58.7%
3 541
41.3%

부적합사유
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct7
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size10.4 KiB
누전차단기
732 
절연저항
506 
접지상태
 
31
개폐기차단기
 
28
인입구배선
 
8
Other values (2)
 
6

Length

Max length6
Median length5
Mean length4.6094584
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row누전차단기
2nd row누전차단기
3rd row누전차단기
4th row누전차단기
5th row절연저항

Common Values

ValueCountFrequency (%)
누전차단기 732
55.8%
절연저항 506
38.6%
접지상태 31
 
2.4%
개폐기차단기 28
 
2.1%
인입구배선 8
 
0.6%
<NA> 3
 
0.2%
옥내외배선 3
 
0.2%

Length

2023-12-13T03:09:35.701012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:09:35.810405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
누전차단기 732
55.8%
절연저항 506
38.6%
접지상태 31
 
2.4%
개폐기차단기 28
 
2.1%
인입구배선 8
 
0.6%
na 3
 
0.2%
옥내외배선 3
 
0.2%

Interactions

2023-12-13T03:09:31.447424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:31.173001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:31.641199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:09:31.330719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:09:36.236826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류관할사업소점검시기점검일고객번호구분표점검구분주기부적합사유
분류1.0000.2710.5710.8610.3560.6370.5021.0000.548
관할사업소0.2711.0000.7440.8910.5630.0540.0760.2270.279
점검시기0.5710.7441.0000.9750.6390.0000.0000.7630.631
점검일0.8610.8910.9751.0000.8550.9420.6330.9710.820
고객번호0.3560.5630.6390.8551.0000.1290.1110.5250.270
구분표0.6370.0540.0000.9420.1291.0000.9450.0860.000
점검구분0.5020.0760.0000.6330.1110.9451.0000.1520.000
주기1.0000.2270.7630.9710.5250.0860.1521.0000.849
부적합사유0.5480.2790.6310.8200.2700.0000.0000.8491.000
2023-12-13T03:09:36.341231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주기점검구분관할사업소점검시기부적합사유분류
주기1.0000.0970.2770.7130.6550.998
점검구분0.0971.0000.0930.0000.0000.538
관할사업소0.2770.0931.0000.4150.1930.177
점검시기0.7130.0000.4151.0000.3540.300
부적합사유0.6550.0000.1930.3541.0000.366
분류0.9980.5380.1770.3000.3661.000
2023-12-13T03:09:36.433734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
고객번호구분표분류관할사업소점검시기점검구분주기부적합사유
고객번호1.0000.0840.1890.2660.2960.0850.4030.144
구분표0.0841.0000.5260.0930.0000.7850.0810.000
분류0.1890.5261.0000.1770.3000.5380.9980.366
관할사업소0.2660.0930.1771.0000.4150.0930.2770.193
점검시기0.2960.0000.3000.4151.0000.0000.7130.354
점검구분0.0850.7850.5380.0930.0001.0000.0970.000
주기0.4030.0810.9980.2770.7130.0971.0000.655
부적합사유0.1440.0000.3660.1930.3540.0000.6551.000

Missing values

2023-12-13T03:09:31.799467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:09:31.961628image/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.
2023-12-13T03:09:32.101547image/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

분류관할사업소점검시기점검일고객번호구분표주소점검구분주기부적합사유
0보안등전북지역본부14-May2014-07-211004489200605000000000000전라북도 전주시 완산구 태평동재점검3누전차단기
1보안등전북서부지사14-Aug2014-12-181004675867605000000000000전라북도 고창군 부안면 오산리재점검3누전차단기
2보안등남원순창지사14-May2014-06-278000933361205000000000000전라북도 남원시 이백면 서곡리재점검3누전차단기
3보안등전북지역본부14-Mar2014-04-219004102208<NA>전라북도 완주군 상관면 마치리재점검3누전차단기
4공원등남원순창지사14-May2014-08-229000944443135000000000000전라북도 순창군 팔덕면 청계리재점검3절연저항
5공원등남원순창지사14-May2014-08-229000944444135000000000000전라북도 순창군 팔덕면 청계리재점검3절연저항
6공원등남원순창지사14-Apr2014-05-249000960242205000000000000전라북도 남원시 노암동재점검3절연저항
7공원등전북지역본부14-Jul2014-10-278000909875235000000000000전라북도 전주시 완산구 서신동재점검3누전차단기
8보안등전북지역본부14-May2014-07-241006731336983000000000000전라북도 전주시 완산구 원당동재점검 전산외3누전차단기
9보안등전북지역본부14-May2014-07-296180046983000000000000전라북도 전주시 완산구 태평동재점검 전산외3누전차단기
분류관할사업소점검시기점검일고객번호구분표주소점검구분주기부적합사유
1301차량용전북지역본부14-Apr2014-05-316002719967957000000000000전라북도 전주시 완산구 서신동재점검 전산외1접지상태
1302차량용전북지역본부14-Apr2014-05-316002719968957000000000000전라북도 전주시 완산구 서신동재점검 전산외1접지상태
1303보행자용남원순창지사14-May2014-06-279002844258983000000000000전라북도 순창군 순창읍 남계리재점검 전산외1누전차단기
1304보행자용남원순창지사14-May2014-06-279002844259983000000000000전라북도 순창군 순창읍 남계리재점검 전산외1누전차단기
1305보행자용남원순창지사14-May2014-06-279002844260983000000000000전라북도 순창군 순창읍 남계리재점검 전산외1누전차단기
1306보행자용남원순창지사14-May2014-06-279002844261983000000000000전라북도 순창군 순창읍 남계리재점검 전산외1누전차단기
1307보행자용전북지역본부14-Apr2014-05-316002711335957000000000000전라북도 전주시 완산구 효자동3가재점검 전산외1접지상태
1308보행자용전북지역본부14-Apr2014-05-316002711337957000000000000전라북도 전주시 완산구 효자동3가재점검 전산외1접지상태
1309보행자용전북지역본부14-Apr2014-05-316002719965957000000000000전라북도 전주시 완산구 서신동재점검 전산외1접지상태
1310보행자용전북지역본부14-Apr2014-05-316002719966957000000000000전라북도 전주시 완산구 서신동재점검 전산외1접지상태