Overview

Dataset statistics

Number of variables6
Number of observations263
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.7 KiB
Average record size in memory53.5 B

Variable types

Text1
Numeric5

Dataset

Description2014년도 지자체별 도로조명(가로등) 정기점검업무 결과 및 부적합설비 개수현황 등
Author한국전기안전공사
URLhttps://www.data.go.kr/data/15043804/fileData.do

Alerts

정기점검 적합 is highly overall correlated with 정기점검 부적합 and 1 other fieldsHigh correlation
정기점검 부적합 is highly overall correlated with 정기점검 적합 and 2 other fieldsHigh correlation
재점검 적합 is highly overall correlated with 정기점검 적합 and 1 other fieldsHigh correlation
재점검 부적합 is highly overall correlated with 정기점검 부적합High correlation
정기점검 부적합 has 9 (3.4%) zerosZeros
재점검 적합 has 36 (13.7%) zerosZeros
재점검 부적합 has 54 (20.5%) zerosZeros
개수확인 적합 has 147 (55.9%) zerosZeros

Reproduction

Analysis started2023-12-12 03:05:48.254891
Analysis finished2023-12-12 03:05:52.044532
Duration3.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역
Text

Distinct233
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2023-12-12T12:05:52.362154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.5285171
Min length2

Characters and Unicode

Total characters928
Distinct characters146
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique219 ?
Unique (%)83.3%

Sample

1st row강릉시
2nd row고성군
3rd row동해시
4th row삼척시
5th row삼척시
ValueCountFrequency (%)
동구 6
 
2.0%
남구 6
 
2.0%
중구 6
 
2.0%
서구 5
 
1.7%
북구 5
 
1.7%
창원시 5
 
1.7%
용인시 4
 
1.3%
수원시 4
 
1.3%
청주시 4
 
1.3%
부천시 4
 
1.3%
Other values (232) 251
83.7%
2023-12-12T12:05:52.940705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 111
 
12.0%
시 107
 
11.5%
군 91
 
9.8%
37
 
4.0%
천 26
 
2.8%
주 25
 
2.7%
산 24
 
2.6%
양 22
 
2.4%
성 21
 
2.3%
원 21
 
2.3%
Other values (136) 443
47.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 891
96.0%
Space Separator 37
 
4.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 111
 
12.5%
시 107
 
12.0%
군 91
 
10.2%
천 26
 
2.9%
주 25
 
2.8%
산 24
 
2.7%
양 22
 
2.5%
성 21
 
2.4%
원 21
 
2.4%
동 20
 
2.2%
Other values (135) 423
47.5%
Space Separator
ValueCountFrequency (%)
37
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 891
96.0%
Common 37
 
4.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 111
 
12.5%
시 107
 
12.0%
군 91
 
10.2%
천 26
 
2.9%
주 25
 
2.8%
산 24
 
2.7%
양 22
 
2.5%
성 21
 
2.4%
원 21
 
2.4%
동 20
 
2.2%
Other values (135) 423
47.5%
Common
ValueCountFrequency (%)
37
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 891
96.0%
ASCII 37
 
4.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 111
 
12.5%
시 107
 
12.0%
군 91
 
10.2%
천 26
 
2.9%
주 25
 
2.8%
산 24
 
2.7%
양 22
 
2.5%
성 21
 
2.4%
원 21
 
2.4%
동 20
 
2.2%
Other values (135) 423
47.5%
ASCII
ValueCountFrequency (%)
37
100.0%

정기점검 적합
Real number (ℝ)

HIGH CORRELATION 

Distinct206
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219.79087
Minimum2
Maximum891
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2023-12-12T12:05:53.156926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile23.1
Q196.5
median188
Q3312.5
95-th percentile535.4
Maximum891
Range889
Interquartile range (IQR)216

Descriptive statistics

Standard deviation160.4069
Coefficient of variation (CV)0.72981599
Kurtosis2.3721252
Mean219.79087
Median Absolute Deviation (MAD)103
Skewness1.3038548
Sum57805
Variance25730.372
MonotonicityNot monotonic
2023-12-12T12:05:53.354319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
242 4
 
1.5%
312 3
 
1.1%
73 3
 
1.1%
161 3
 
1.1%
51 3
 
1.1%
93 3
 
1.1%
46 3
 
1.1%
128 2
 
0.8%
48 2
 
0.8%
270 2
 
0.8%
Other values (196) 235
89.4%
ValueCountFrequency (%)
2 2
0.8%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
11 1
0.4%
13 1
0.4%
17 1
0.4%
18 1
0.4%
20 1
0.4%
ValueCountFrequency (%)
891 1
0.4%
878 1
0.4%
830 1
0.4%
681 1
0.4%
674 1
0.4%
646 1
0.4%
645 1
0.4%
618 1
0.4%
595 1
0.4%
573 1
0.4%

정기점검 부적합
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct81
Distinct (%)30.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.30038
Minimum0
Maximum339
Zeros9
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2023-12-12T12:05:53.548494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.1
Q18
median17
Q334
95-th percentile93.5
Maximum339
Range339
Interquartile range (IQR)26

Descriptive statistics

Standard deviation36.239622
Coefficient of variation (CV)1.2805348
Kurtosis27.798601
Mean28.30038
Median Absolute Deviation (MAD)11
Skewness4.2353033
Sum7443
Variance1313.3102
MonotonicityNot monotonic
2023-12-12T12:05:53.738296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 12
 
4.6%
16 11
 
4.2%
13 11
 
4.2%
3 10
 
3.8%
0 9
 
3.4%
6 9
 
3.4%
5 8
 
3.0%
15 8
 
3.0%
4 8
 
3.0%
12 7
 
2.7%
Other values (71) 170
64.6%
ValueCountFrequency (%)
0 9
3.4%
1 5
1.9%
2 6
2.3%
3 10
3.8%
4 8
3.0%
5 8
3.0%
6 9
3.4%
7 6
2.3%
8 7
2.7%
9 12
4.6%
ValueCountFrequency (%)
339 1
0.4%
272 1
0.4%
135 1
0.4%
131 1
0.4%
123 1
0.4%
121 1
0.4%
115 1
0.4%
109 1
0.4%
107 1
0.4%
102 1
0.4%

재점검 적합
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct49
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.562738
Minimum0
Maximum98
Zeros36
Zeros (%)13.7%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2023-12-12T12:05:53.933254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median8
Q317
95-th percentile39.8
Maximum98
Range98
Interquartile range (IQR)15

Descriptive statistics

Standard deviation15.586831
Coefficient of variation (CV)1.2407193
Kurtosis8.8612315
Mean12.562738
Median Absolute Deviation (MAD)7
Skewness2.5538896
Sum3304
Variance242.94929
MonotonicityNot monotonic
2023-12-12T12:05:54.471317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0 36
 
13.7%
1 23
 
8.7%
4 14
 
5.3%
8 14
 
5.3%
2 14
 
5.3%
3 13
 
4.9%
7 12
 
4.6%
11 10
 
3.8%
12 9
 
3.4%
6 8
 
3.0%
Other values (39) 110
41.8%
ValueCountFrequency (%)
0 36
13.7%
1 23
8.7%
2 14
 
5.3%
3 13
 
4.9%
4 14
 
5.3%
5 8
 
3.0%
6 8
 
3.0%
7 12
 
4.6%
8 14
 
5.3%
9 7
 
2.7%
ValueCountFrequency (%)
98 1
0.4%
94 1
0.4%
85 1
0.4%
80 1
0.4%
76 1
0.4%
52 1
0.4%
51 1
0.4%
49 1
0.4%
47 2
0.8%
45 1
0.4%

재점검 부적합
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct59
Distinct (%)22.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.494297
Minimum0
Maximum311
Zeros54
Zeros (%)20.5%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2023-12-12T12:05:54.673663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q317
95-th percentile60.7
Maximum311
Range311
Interquartile range (IQR)16

Descriptive statistics

Standard deviation31.477469
Coefficient of variation (CV)2.031552
Kurtosis45.640979
Mean15.494297
Median Absolute Deviation (MAD)5
Skewness5.7848479
Sum4075
Variance990.83107
MonotonicityNot monotonic
2023-12-12T12:05:54.894363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 54
20.5%
1 23
 
8.7%
2 18
 
6.8%
4 17
 
6.5%
5 14
 
5.3%
6 10
 
3.8%
7 10
 
3.8%
3 10
 
3.8%
9 8
 
3.0%
15 7
 
2.7%
Other values (49) 92
35.0%
ValueCountFrequency (%)
0 54
20.5%
1 23
8.7%
2 18
 
6.8%
3 10
 
3.8%
4 17
 
6.5%
5 14
 
5.3%
6 10
 
3.8%
7 10
 
3.8%
8 5
 
1.9%
9 8
 
3.0%
ValueCountFrequency (%)
311 1
0.4%
271 1
0.4%
112 1
0.4%
107 1
0.4%
90 2
0.8%
85 1
0.4%
81 1
0.4%
80 1
0.4%
79 1
0.4%
74 1
0.4%

개수확인 적합
Real number (ℝ)

ZEROS 

Distinct34
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7148289
Minimum0
Maximum271
Zeros147
Zeros (%)55.9%
Negative0
Negative (%)0.0%
Memory size2.4 KiB
2023-12-12T12:05:55.083440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile30.5
Maximum271
Range271
Interquartile range (IQR)4

Descriptive statistics

Standard deviation19.379655
Coefficient of variation (CV)3.3911173
Kurtosis135.30463
Mean5.7148289
Median Absolute Deviation (MAD)0
Skewness10.322494
Sum1503
Variance375.57104
MonotonicityNot monotonic
2023-12-12T12:05:55.243783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 147
55.9%
1 25
 
9.5%
2 11
 
4.2%
3 10
 
3.8%
7 7
 
2.7%
4 7
 
2.7%
5 5
 
1.9%
6 5
 
1.9%
8 4
 
1.5%
11 4
 
1.5%
Other values (24) 38
 
14.4%
ValueCountFrequency (%)
0 147
55.9%
1 25
 
9.5%
2 11
 
4.2%
3 10
 
3.8%
4 7
 
2.7%
5 5
 
1.9%
6 5
 
1.9%
7 7
 
2.7%
8 4
 
1.5%
9 1
 
0.4%
ValueCountFrequency (%)
271 1
 
0.4%
76 1
 
0.4%
53 1
 
0.4%
47 1
 
0.4%
45 1
 
0.4%
44 1
 
0.4%
41 1
 
0.4%
38 1
 
0.4%
35 2
0.8%
32 3
1.1%

Interactions

2023-12-12T12:05:51.168509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:48.546733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:49.229711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:49.882315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:50.494037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:51.307105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:48.665477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:49.365196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:49.995664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:50.613881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:51.441553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:48.783185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:49.484530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:50.150333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:50.758408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:51.567364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:48.917099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:49.592659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:50.260024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:50.897620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:51.694046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:49.105477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:49.733135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:50.373811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T12:05:51.024777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T12:05:55.371025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정기점검 적합정기점검 부적합재점검 적합재점검 부적합개수확인 적합
정기점검 적합1.0000.5070.6900.3380.388
정기점검 부적합0.5071.0000.5840.9750.788
재점검 적합0.6900.5841.0000.0560.285
재점검 부적합0.3380.9750.0561.0000.808
개수확인 적합0.3880.7880.2850.8081.000
2023-12-12T12:05:55.513936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
정기점검 적합정기점검 부적합재점검 적합재점검 부적합개수확인 적합
정기점검 적합1.0000.5920.6610.2100.348
정기점검 부적합0.5921.0000.6440.6990.441
재점검 적합0.6610.6441.0000.0470.297
재점검 부적합0.2100.6990.0471.0000.427
개수확인 적합0.3480.4410.2970.4271.000

Missing values

2023-12-12T12:05:51.846182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T12:05:51.989041image/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

지역정기점검 적합정기점검 부적합재점검 적합재점검 부적합개수확인 적합
0강릉시31264243232
1고성군9312751
2동해시211232121
3삼척시2223614220
4삼척시21100
5속초시1768645
6양구군335140
7양양군128184130
8영월군70000
9영월군93171250
지역정기점검 적합정기점검 부적합재점검 적합재점검 부적합개수확인 적합
253중구56416974
254강화군1572815130
255계양구314151413
256남동구68113853
257부평구397191722
258연수구595191099
259옹진군1465665145
260세종특별자치시735330
261제주시362339363110
262서귀포시425131171122