Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory996.1 KiB
Average record size in memory102.0 B

Variable types

Categorical4
Numeric6
Text1

Dataset

Description대용량고객 직거래수용가 도로명주소 , 업종구분 분류
Author한국전력공사
URLhttps://www.data.go.kr/data/15069021/fileData.do

Alerts

년 is highly overall correlated with 구군 and 1 other fieldsHigh correlation
시도 is highly overall correlated with 구군High correlation
구군 is highly overall correlated with 년 and 1 other fieldsHigh correlation
대분류 is highly overall correlated with 소분류High correlation
소분류 is highly overall correlated with 년 and 1 other fieldsHigh correlation
읍면일련번호 has 4607 (46.1%) zerosZeros

Reproduction

Analysis started2023-12-12 22:55:16.225174
Analysis finished2023-12-12 22:55:22.353569
Duration6.13 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
서울
3859 
부산
2181 
인천
1596 
대구
1035 
광주
938 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row부산
2nd row부산
3rd row서울
4th row부산
5th row부산

Common Values

ValueCountFrequency (%)
서울 3859
38.6%
부산 2181
21.8%
인천 1596
16.0%
대구 1035
 
10.3%
광주 938
 
9.4%
대전 391
 
3.9%

Length

2023-12-13T07:55:22.425307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:55:22.544203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울 3859
38.6%
부산 2181
21.8%
인천 1596
16.0%
대구 1035
 
10.3%
광주 938
 
9.4%
대전 391
 
3.9%

구군
Categorical

HIGH CORRELATION 

Distinct50
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
동구
 
686
중구
 
654
남구
 
636
서구
 
599
북구
 
524
Other values (45)
6901 

Length

Max length4
Median length3
Mean length2.777
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서구
2nd row중구
3rd row강남구
4th row북구
5th row강서구

Common Values

ValueCountFrequency (%)
동구 686
 
6.9%
중구 654
 
6.5%
남구 636
 
6.4%
서구 599
 
6.0%
북구 524
 
5.2%
강서구 330
 
3.3%
마포구 233
 
2.3%
성북구 221
 
2.2%
영등포구 218
 
2.2%
부산진구 218
 
2.2%
Other values (40) 5681
56.8%

Length

2023-12-13T07:55:22.738001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
동구 686
 
6.9%
중구 654
 
6.5%
남구 636
 
6.4%
서구 599
 
6.0%
북구 524
 
5.2%
강서구 330
 
3.3%
마포구 233
 
2.3%
성북구 221
 
2.2%
영등포구 218
 
2.2%
부산진구 218
 
2.2%
Other values (40) 5681
56.8%

도로명번호
Real number (ℝ)

Distinct9106
Distinct (%)91.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4058826.7
Minimum1000021
Maximum4652582
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:55:22.906862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000021
5-th percentile3123006.7
Q14124354
median4181246
Q34244497
95-th percentile4286818.4
Maximum4652582
Range3652561
Interquartile range (IQR)120143

Descriptive statistics

Standard deviation401215.02
Coefficient of variation (CV)0.098849999
Kurtosis8.4035446
Mean4058826.7
Median Absolute Deviation (MAD)60045.5
Skewness-2.845019
Sum4.0588267 × 1010
Variance1.6097349 × 1011
MonotonicityNot monotonic
2023-12-13T07:55:23.102433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3100021 7
 
0.1%
2007002 6
 
0.1%
3007001 6
 
0.1%
2000008 6
 
0.1%
2000010 6
 
0.1%
3005016 5
 
0.1%
3000028 5
 
0.1%
3009006 5
 
0.1%
3009020 4
 
< 0.1%
3103010 4
 
< 0.1%
Other values (9096) 9946
99.5%
ValueCountFrequency (%)
1000021 1
 
< 0.1%
1000028 1
 
< 0.1%
1000036 1
 
< 0.1%
1000037 1
 
< 0.1%
2000003 3
< 0.1%
2000006 2
 
< 0.1%
2000008 6
0.1%
2000010 6
0.1%
2000011 1
 
< 0.1%
2000013 2
 
< 0.1%
ValueCountFrequency (%)
4652582 1
< 0.1%
4301428 1
< 0.1%
4298413 1
< 0.1%
4298411 1
< 0.1%
4298170 1
< 0.1%
4298167 1
< 0.1%
4298165 1
< 0.1%
4298162 1
< 0.1%
4298159 1
< 0.1%
4298154 1
< 0.1%

읍면일련번호
Real number (ℝ)

ZEROS 

Distinct18
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7093
Minimum0
Maximum20
Zeros4607
Zeros (%)46.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:55:23.276447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q31
95-th percentile2
Maximum20
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.0243527
Coefficient of variation (CV)1.4441741
Kurtosis53.750414
Mean0.7093
Median Absolute Deviation (MAD)1
Skewness5.168652
Sum7093
Variance1.0492984
MonotonicityNot monotonic
2023-12-13T07:55:23.436744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 4607
46.1%
1 4533
45.3%
2 537
 
5.4%
3 151
 
1.5%
4 62
 
0.6%
5 35
 
0.4%
6 29
 
0.3%
7 15
 
0.1%
9 8
 
0.1%
8 8
 
0.1%
Other values (8) 15
 
0.1%
ValueCountFrequency (%)
0 4607
46.1%
1 4533
45.3%
2 537
 
5.4%
3 151
 
1.5%
4 62
 
0.6%
5 35
 
0.4%
6 29
 
0.3%
7 15
 
0.1%
8 8
 
0.1%
9 8
 
0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
17 2
 
< 0.1%
16 1
 
< 0.1%
14 1
 
< 0.1%
13 2
 
< 0.1%
12 2
 
< 0.1%
11 1
 
< 0.1%
10 5
0.1%
9 8
0.1%
8 8
0.1%

도로명
Text

Distinct9038
Distinct (%)90.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T07:55:23.712826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10
Mean length6.6289
Min length2

Characters and Unicode

Total characters66289
Distinct characters453
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8196 ?
Unique (%)82.0%

Sample

1st row까치고개로108번가길
2nd row초량중로6번길
3rd row영동대로85길
4th row구포시장1길
5th row대저로109번길
ValueCountFrequency (%)
중앙로 9
 
0.1%
청계천로 7
 
0.1%
중앙대로 7
 
0.1%
당산로 6
 
0.1%
국채보상로 6
 
0.1%
달구벌대로 6
 
0.1%
천호대로 6
 
0.1%
경인로 5
 
< 0.1%
제봉로 5
 
< 0.1%
동천로 5
 
< 0.1%
Other values (9028) 9938
99.4%
2023-12-13T07:55:24.112005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
길 9083
 
13.7%
로 8853
 
13.4%
번 3969
 
6.0%
1 3522
 
5.3%
2 2656
 
4.0%
3 2142
 
3.2%
4 1796
 
2.7%
5 1583
 
2.4%
6 1510
 
2.3%
7 1396
 
2.1%
Other values (443) 29779
44.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 48229
72.8%
Decimal Number 18058
 
27.2%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
길 9083
18.8%
로 8853
18.4%
번 3969
 
8.2%
대 1379
 
2.9%
동 859
 
1.8%
가 697
 
1.4%
산 671
 
1.4%
천 520
 
1.1%
서 482
 
1.0%
남 397
 
0.8%
Other values (431) 21319
44.2%
Decimal Number
ValueCountFrequency (%)
1 3522
19.5%
2 2656
14.7%
3 2142
11.9%
4 1796
9.9%
5 1583
8.8%
6 1510
8.4%
7 1396
 
7.7%
9 1193
 
6.6%
8 1145
 
6.3%
0 1115
 
6.2%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 48229
72.8%
Common 18060
 
27.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
길 9083
18.8%
로 8853
18.4%
번 3969
 
8.2%
대 1379
 
2.9%
동 859
 
1.8%
가 697
 
1.4%
산 671
 
1.4%
천 520
 
1.1%
서 482
 
1.0%
남 397
 
0.8%
Other values (431) 21319
44.2%
Common
ValueCountFrequency (%)
1 3522
19.5%
2 2656
14.7%
3 2142
11.9%
4 1796
9.9%
5 1583
8.8%
6 1510
8.4%
7 1396
 
7.7%
9 1193
 
6.6%
8 1145
 
6.3%
0 1115
 
6.2%
Other values (2) 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 48229
72.8%
ASCII 18060
 
27.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
길 9083
18.8%
로 8853
18.4%
번 3969
 
8.2%
대 1379
 
2.9%
동 859
 
1.8%
가 697
 
1.4%
산 671
 
1.4%
천 520
 
1.1%
서 482
 
1.0%
남 397
 
0.8%
Other values (431) 21319
44.2%
ASCII
ValueCountFrequency (%)
1 3522
19.5%
2 2656
14.7%
3 2142
11.9%
4 1796
9.9%
5 1583
8.8%
6 1510
8.4%
7 1396
 
7.7%
9 1193
 
6.6%
8 1145
 
6.3%
0 1115
 
6.2%
Other values (2) 2
 
< 0.1%

년
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2009.9072
Minimum2007
Maximum2012
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:55:24.256926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2007
5-th percentile2008
Q12008
median2010
Q32012
95-th percentile2012
Maximum2012
Range5
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.727393
Coefficient of variation (CV)0.00085943918
Kurtosis-1.6195323
Mean2009.9072
Median Absolute Deviation (MAD)2
Skewness0.011999329
Sum20099072
Variance2.9838865
MonotonicityNot monotonic
2023-12-13T07:55:24.371147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2012 3076
30.8%
2008 3048
30.5%
2009 1471
14.7%
2011 1354
13.5%
2010 762
 
7.6%
2007 289
 
2.9%
ValueCountFrequency (%)
2007 289
 
2.9%
2008 3048
30.5%
2009 1471
14.7%
2010 762
 
7.6%
2011 1354
13.5%
2012 3076
30.8%
ValueCountFrequency (%)
2012 3076
30.8%
2011 1354
13.5%
2010 762
 
7.6%
2009 1471
14.7%
2008 3048
30.5%
2007 289
 
2.9%

월
Real number (ℝ)

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.6537
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:55:24.485006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile12
Maximum12
Range11
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.3534909
Coefficient of variation (CV)0.50400392
Kurtosis-1.1438358
Mean6.6537
Median Absolute Deviation (MAD)3
Skewness-0.033350536
Sum66537
Variance11.245901
MonotonicityNot monotonic
2023-12-13T07:55:24.609025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
5 1141
11.4%
7 986
9.9%
11 938
9.4%
8 936
9.4%
2 834
8.3%
10 832
8.3%
12 802
8.0%
4 792
7.9%
6 723
7.2%
9 713
7.1%
Other values (2) 1303
13.0%
ValueCountFrequency (%)
1 660
6.6%
2 834
8.3%
3 643
6.4%
4 792
7.9%
5 1141
11.4%
6 723
7.2%
7 986
9.9%
8 936
9.4%
9 713
7.1%
10 832
8.3%
ValueCountFrequency (%)
12 802
8.0%
11 938
9.4%
10 832
8.3%
9 713
7.1%
8 936
9.4%
7 986
9.9%
6 723
7.2%
5 1141
11.4%
4 792
7.9%
3 643
6.4%

일
Real number (ℝ)

Distinct31
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.796
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:55:24.730633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q18
median16
Q323
95-th percentile29
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.8229796
Coefficient of variation (CV)0.55855784
Kurtosis-1.2041918
Mean15.796
Median Absolute Deviation (MAD)8
Skewness-0.0035521234
Sum157960
Variance77.844968
MonotonicityNot monotonic
2023-12-13T07:55:24.877025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
4 369
 
3.7%
27 351
 
3.5%
28 350
 
3.5%
10 345
 
3.5%
25 344
 
3.4%
5 343
 
3.4%
23 339
 
3.4%
18 338
 
3.4%
21 336
 
3.4%
12 336
 
3.4%
Other values (21) 6549
65.5%
ValueCountFrequency (%)
1 301
3.0%
2 334
3.3%
3 334
3.3%
4 369
3.7%
5 343
3.4%
6 305
3.0%
7 331
3.3%
8 307
3.1%
9 294
2.9%
10 345
3.5%
ValueCountFrequency (%)
31 195
1.9%
30 294
2.9%
29 318
3.2%
28 350
3.5%
27 351
3.5%
26 308
3.1%
25 344
3.4%
24 300
3.0%
23 339
3.4%
22 335
3.4%

대분류
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
전주부속품(I)
2040 
금구류
1574 
볼트
1247 
전주
1002 
절연자재
952 
Other values (9)
3185 

Length

Max length8
Median length5
Mean length4.2826
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row보호장치
2nd row전주부속품(I)
3rd row전주
4th row전주부속품(I)
5th row볼트

Common Values

ValueCountFrequency (%)
전주부속품(I) 2040
20.4%
금구류 1574
15.7%
볼트 1247
12.5%
전주 1002
10.0%
절연자재 952
9.5%
개폐장치 595
 
5.9%
보호장치 581
 
5.8%
전선부속품 539
 
5.4%
충전기 351
 
3.5%
전력용변압기 349
 
3.5%
Other values (4) 770
 
7.7%

Length

2023-12-13T07:55:25.011763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전주부속품(i 2040
20.4%
금구류 1574
15.7%
볼트 1247
12.5%
전주 1002
10.0%
절연자재 952
9.5%
개폐장치 595
 
5.9%
보호장치 581
 
5.8%
전선부속품 539
 
5.4%
충전기 351
 
3.5%
전력용변압기 349
 
3.5%
Other values (4) 770
 
7.7%

소분류
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
금속
3613 
기본일반
2678 
전기전자
372 
전기전자 기술자학회
 
302
기계
 
277
Other values (19)
2758 

Length

Max length10
Median length9
Mean length3.4692
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row절연자재
2nd row금속
3rd row금속
4th row기본일반
5th row금속

Common Values

ValueCountFrequency (%)
금속 3613
36.1%
기본일반 2678
26.8%
전기전자 372
 
3.7%
전기전자 기술자학회 302
 
3.0%
기계 277
 
2.8%
국제전기 기술위원회 268
 
2.7%
변성기 240
 
2.4%
보호장치 237
 
2.4%
건설 237
 
2.4%
절연자재 230
 
2.3%
Other values (14) 1546
15.5%

Length

2023-12-13T07:55:25.160324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
금속 3613
33.1%
기본일반 2678
24.6%
전기전자 674
 
6.2%
기술자학회 302
 
2.8%
기계 277
 
2.5%
국제전기 268
 
2.5%
기술위원회 268
 
2.5%
변성기 240
 
2.2%
보호장치 237
 
2.2%
건설 237
 
2.2%
Other values (17) 2110
19.4%

카운트
Real number (ℝ)

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4794
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T07:55:25.288704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q35
95-th percentile11
Maximum27
Range26
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.6658313
Coefficient of variation (CV)1.0535815
Kurtosis7.0887449
Mean3.4794
Median Absolute Deviation (MAD)1
Skewness2.3752631
Sum34794
Variance13.438319
MonotonicityNot monotonic
2023-12-13T07:55:25.407322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 4086
40.9%
2 1598
 
16.0%
3 1167
 
11.7%
5 622
 
6.2%
4 607
 
6.1%
7 402
 
4.0%
6 376
 
3.8%
9 259
 
2.6%
8 227
 
2.3%
10 135
 
1.4%
Other values (13) 521
 
5.2%
ValueCountFrequency (%)
1 4086
40.9%
2 1598
 
16.0%
3 1167
 
11.7%
4 607
 
6.1%
5 622
 
6.2%
6 376
 
3.8%
7 402
 
4.0%
8 227
 
2.3%
9 259
 
2.6%
10 135
 
1.4%
ValueCountFrequency (%)
27 18
 
0.2%
25 3
 
< 0.1%
21 9
 
0.1%
20 27
 
0.3%
19 36
0.4%
18 23
 
0.2%
17 23
 
0.2%
16 63
0.6%
15 83
0.8%
14 33
 
0.3%

Interactions

2023-12-13T07:55:21.008478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:17.619510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:18.179030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:18.902895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:19.580479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:20.286532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:21.122929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:17.707942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:18.293211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:19.026515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:19.695492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:20.436731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:21.514598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:17.808161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:18.433787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:19.151782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:19.816374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:20.573819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:21.619485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:17.906782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:18.564478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:19.260140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:19.938437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:20.675278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:21.743350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:17.990695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:18.664085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:19.354982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:20.052122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:20.782039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:21.882807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:18.079249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:18.778819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:19.467933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:20.170990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:55:20.893688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:55:25.497779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도구군도로명번호읍면일련번호년월일대분류소분류카운트
시도1.0000.9380.5230.0720.5730.5700.1190.2560.6580.168
구군0.9381.0000.5750.2500.9470.8510.5160.5210.7490.398
도로명번호0.5230.5751.0000.3370.2740.2330.0630.1370.2850.052
읍면일련번호0.0720.2500.3371.0000.0710.0590.0000.0250.1270.040
년0.5730.9470.2740.0711.0000.2280.0160.6330.9220.317
월0.5700.8510.2330.0590.2281.0000.0650.1860.2540.236
일0.1190.5160.0630.0000.0160.0651.0000.0000.0000.000
대분류0.2560.5210.1370.0250.6330.1860.0001.0000.9060.563
소분류0.6580.7490.2850.1270.9220.2540.0000.9061.0000.557
카운트0.1680.3980.0520.0400.3170.2360.0000.5630.5571.000
2023-12-13T07:55:25.622313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도구군대분류소분류
시도1.0000.7380.1280.328
구군0.7381.0000.1760.254
대분류0.1280.1761.0000.571
소분류0.3280.2540.5711.000
2023-12-13T07:55:25.739408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로명번호읍면일련번호년월일카운트시도구군대분류소분류
도로명번호1.000-0.1020.462-0.010-0.0000.0220.3450.2690.0500.125
읍면일련번호-0.1021.0000.0210.004-0.0110.0020.0300.0540.0130.068
년0.4620.0211.0000.0280.0010.0550.3910.7400.3610.707
월-0.0100.0040.0281.0000.0020.0330.3440.4610.0760.095
일-0.000-0.0110.0010.0021.000-0.0010.0640.1830.0120.000
카운트0.0220.0020.0550.033-0.0011.0000.0870.1520.2790.247
시도0.3450.0300.3910.3440.0640.0871.0000.7380.1280.328
구군0.2690.0540.7400.4610.1830.1520.7381.0000.1760.254
대분류0.0500.0130.3610.0760.0120.2790.1280.1761.0000.571
소분류0.1250.0680.7070.0950.0000.2470.3280.2540.5711.000

Missing values

2023-12-13T07:55:22.019921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:55:22.243412image/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

시도구군도로명번호읍면일련번호도로명년월일대분류소분류카운트
49409부산서구41780640까치고개로108번가길2011210보호장치절연자재2
47960부산중구41752770초량중로6번길2011113전주부속품(I)금속1
48236서울강남구41667010영동대로85길2011121전주금속2
63147부산북구41960260구포시장1길2012617전주부속품(I)기본일반2
18243부산강서구42082430대저로109번길200856볼트금속1
37180서울강남구41662390도곡로64길2009822충전기전기전자4
6814서울광진구41121691능동로53길2008115전주부속품(I)금속4
32560대구달성군42443481삼강5길200937전주전기전자6
11596서울중랑구41185401중랑역로14길2008311전주커넥터5
72549부산사하구42021651다대동로29번길201219금구류기본일반4
시도구군도로명번호읍면일련번호도로명년월일대분류소분류카운트
9603서울성북구41210841동소문로10길2008219볼트금속1
27380인천남구42535071인주대로104번길2009126개폐장치변성기15
5229서울성동구41090701금호산2나길2008105볼트금속1
52432광주동구42772781화산로335번길201152단말장치차단기3
28993서울영등포구41547091영등포로29길20091117충전기전기전자1
39817서울관악구41604090봉천로21가길20101019전주건설3
53392광주동구42771922중앙로160번길2011617단말장치기본일반2
9401서울은평구41330500갈현로45가길2008217볼트금속1
6462서울광진구41123900용마산로28가길20081127금구류금속3
66683대구북구42352761연암로34길2012811전주부속품(I)기본일반1