Overview

Dataset statistics

Number of variables9
Number of observations2355
Missing cells1112
Missing cells (%)5.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory177.2 KiB
Average record size in memory77.1 B

Variable types

Numeric5
Categorical2
Text2

Dataset

Description시군구코드,법정동코드,행정동코드,시도명,시군구명,법정동명,행정동명,적용시작일,적용만료일
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15410/S/1/datasetView.do

Alerts

시도명 has constant value ""Constant
시군구코드 is highly overall correlated with 시군구명High correlation
시군구명 is highly overall correlated with 시군구코드High correlation
행정동명 has 1112 (47.2%) missing valuesMissing
법정동코드 has 34 (1.4%) zerosZeros
행정동코드 has 1112 (47.2%) zerosZeros

Reproduction

Analysis started2024-05-18 06:45:53.974962
Analysis finished2024-05-18 06:46:04.103558
Duration10.13 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11372.762
Minimum11000
Maximum11740
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2024-05-18T15:46:04.385657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11000
5-th percentile11110
Q111200
median11350
Q311560
95-th percentile11710
Maximum11740
Range740
Interquartile range (IQR)360

Descriptive statistics

Standard deviation197.80669
Coefficient of variation (CV)0.017393021
Kurtosis-1.2584248
Mean11372.762
Median Absolute Deviation (MAD)180
Skewness0.26074139
Sum26782855
Variance39127.488
MonotonicityNot monotonic
2024-05-18T15:46:04.980058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
11110 231
 
9.8%
11140 215
 
9.1%
11290 172
 
7.3%
11560 166
 
7.0%
11200 161
 
6.8%
11680 124
 
5.3%
11410 123
 
5.2%
11440 109
 
4.6%
11170 101
 
4.3%
11230 92
 
3.9%
Other values (16) 861
36.6%
ValueCountFrequency (%)
11000 1
 
< 0.1%
11110 231
9.8%
11140 215
9.1%
11170 101
4.3%
11200 161
6.8%
11215 34
 
1.4%
11230 92
 
3.9%
11260 48
 
2.0%
11290 172
7.3%
11305 39
 
1.7%
ValueCountFrequency (%)
11740 58
 
2.5%
11710 65
 
2.8%
11680 124
5.3%
11650 68
2.9%
11620 86
3.7%
11590 50
 
2.1%
11560 166
7.0%
11545 18
 
0.8%
11530 83
3.5%
11500 77
3.3%

법정동코드
Real number (ℝ)

ZEROS 

Distinct172
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21669.427
Minimum0
Maximum92800
Zeros34
Zeros (%)1.4%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2024-05-18T15:46:05.645007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile800
Q110300
median11000
Q313500
95-th percentile91200
Maximum92800
Range92800
Interquartile range (IQR)3200

Descriptive statistics

Standard deviation28397.617
Coefficient of variation (CV)1.3104923
Kurtosis2.0891126
Mean21669.427
Median Absolute Deviation (MAD)900
Skewness1.9830475
Sum51031500
Variance8.0642466 × 108
MonotonicityNot monotonic
2024-05-18T15:46:06.217029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10100 143
 
6.1%
10200 139
 
5.9%
10300 128
 
5.4%
10500 92
 
3.9%
10400 86
 
3.7%
10700 81
 
3.4%
10600 75
 
3.2%
10800 67
 
2.8%
10900 61
 
2.6%
11000 52
 
2.2%
Other values (162) 1431
60.8%
ValueCountFrequency (%)
0 34
1.4%
100 17
0.7%
200 12
 
0.5%
300 13
 
0.6%
400 12
 
0.5%
500 10
 
0.4%
600 9
 
0.4%
700 9
 
0.4%
800 8
 
0.3%
900 8
 
0.3%
ValueCountFrequency (%)
92800 1
 
< 0.1%
92700 1
 
< 0.1%
92600 3
0.1%
92500 3
0.1%
92400 5
0.2%
92300 5
0.2%
92200 5
0.2%
92100 7
0.3%
92000 7
0.3%
91900 7
0.3%

행정동코드
Real number (ℝ)

ZEROS 

Distinct117
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean321.24331
Minimum0
Maximum870
Zeros1112
Zeros (%)47.2%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2024-05-18T15:46:06.847661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median515
Q3610
95-th percentile710
Maximum870
Range870
Interquartile range (IQR)610

Descriptive statistics

Standard deviation309.30079
Coefficient of variation (CV)0.96282406
Kurtosis-1.8631361
Mean321.24331
Median Absolute Deviation (MAD)235
Skewness-0.011741972
Sum756528
Variance95666.979
MonotonicityNot monotonic
2024-05-18T15:46:07.571285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1112
47.2%
550 58
 
2.5%
610 56
 
2.4%
600 49
 
2.1%
620 47
 
2.0%
530 47
 
2.0%
520 46
 
2.0%
510 46
 
2.0%
590 45
 
1.9%
540 45
 
1.9%
Other values (107) 804
34.1%
ValueCountFrequency (%)
0 1112
47.2%
11 3
 
0.1%
385 1
 
< 0.1%
501 5
 
0.2%
502 3
 
0.1%
510 46
 
2.0%
511 1
 
< 0.1%
512 1
 
< 0.1%
513 1
 
< 0.1%
514 1
 
< 0.1%
ValueCountFrequency (%)
870 4
0.2%
860 4
0.2%
850 2
0.1%
847 2
0.1%
846 1
 
< 0.1%
845 1
 
< 0.1%
840 3
0.1%
830 2
0.1%
820 2
0.1%
810 4
0.2%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.5 KiB
서울특별시
2355 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 2355
100.0%

Length

2024-05-18T15:46:08.059075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T15:46:08.468125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 2355
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size18.5 KiB
종로구
231 
중구
215 
성북구
172 
영등포구
166 
성동구
161 
Other values (21)
1410 

Length

Max length4
Median length3
Mean length3.070913
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row강남구
2nd row강남구
3rd row강남구
4th row강동구
5th row강동구

Common Values

ValueCountFrequency (%)
종로구 231
 
9.8%
중구 215
 
9.1%
성북구 172
 
7.3%
영등포구 166
 
7.0%
성동구 161
 
6.8%
강남구 124
 
5.3%
서대문구 123
 
5.2%
마포구 109
 
4.6%
용산구 101
 
4.3%
동대문구 92
 
3.9%
Other values (16) 861
36.6%

Length

2024-05-18T15:46:09.131899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
종로구 231
 
9.8%
중구 215
 
9.1%
성북구 172
 
7.3%
영등포구 166
 
7.0%
성동구 161
 
6.8%
강남구 124
 
5.3%
서대문구 123
 
5.2%
마포구 109
 
4.6%
용산구 101
 
4.3%
동대문구 92
 
3.9%
Other values (16) 861
36.6%

법정동명
Text

Distinct835
Distinct (%)35.5%
Missing0
Missing (%)0.0%
Memory size18.5 KiB
2024-05-18T15:46:10.111566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.4985138
Min length2

Characters and Unicode

Total characters8239
Distinct characters222
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

Unique344 ?
Unique (%)14.6%

Sample

1st row일원동
2nd row개포동
3rd row대치동
4th row상일동
5th row상일동
ValueCountFrequency (%)
신림동 27
 
1.1%
미아동 27
 
1.1%
봉천동 24
 
1.0%
수유동 20
 
0.8%
상계동 16
 
0.7%
독산동 15
 
0.6%
시흥동 15
 
0.6%
면목동 15
 
0.6%
신사동 15
 
0.6%
신정동 14
 
0.6%
Other values (825) 2167
92.0%
2024-05-18T15:46:11.801707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 2255
27.4%
가 429
 
5.2%
1 211
 
2.6%
2 203
 
2.5%
신 193
 
2.3%
로 136
 
1.7%
문 122
 
1.5%
3 115
 
1.4%
산 97
 
1.2%
곡 90
 
1.1%
Other values (212) 4388
53.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7521
91.3%
Decimal Number 718
 
8.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 2255
30.0%
가 429
 
5.7%
신 193
 
2.6%
로 136
 
1.8%
문 122
 
1.6%
산 97
 
1.3%
곡 90
 
1.2%
수 88
 
1.2%
봉 87
 
1.2%
구 86
 
1.1%
Other values (202) 3938
52.4%
Decimal Number
ValueCountFrequency (%)
1 211
29.4%
2 203
28.3%
3 115
16.0%
4 74
 
10.3%
5 55
 
7.7%
6 32
 
4.5%
7 17
 
2.4%
8 7
 
1.0%
0 2
 
0.3%
9 2
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7521
91.3%
Common 718
 
8.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 2255
30.0%
가 429
 
5.7%
신 193
 
2.6%
로 136
 
1.8%
문 122
 
1.6%
산 97
 
1.3%
곡 90
 
1.2%
수 88
 
1.2%
봉 87
 
1.2%
구 86
 
1.1%
Other values (202) 3938
52.4%
Common
ValueCountFrequency (%)
1 211
29.4%
2 203
28.3%
3 115
16.0%
4 74
 
10.3%
5 55
 
7.7%
6 32
 
4.5%
7 17
 
2.4%
8 7
 
1.0%
0 2
 
0.3%
9 2
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7521
91.3%
ASCII 718
 
8.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 2255
30.0%
가 429
 
5.7%
신 193
 
2.6%
로 136
 
1.8%
문 122
 
1.6%
산 97
 
1.3%
곡 90
 
1.2%
수 88
 
1.2%
봉 87
 
1.2%
구 86
 
1.1%
Other values (202) 3938
52.4%
ASCII
ValueCountFrequency (%)
1 211
29.4%
2 203
28.3%
3 115
16.0%
4 74
 
10.3%
5 55
 
7.7%
6 32
 
4.5%
7 17
 
2.4%
8 7
 
1.0%
0 2
 
0.3%
9 2
 
0.3%

행정동명
Text

MISSING 

Distinct666
Distinct (%)53.6%
Missing1112
Missing (%)47.2%
Memory size18.5 KiB
2024-05-18T15:46:12.702466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length4.5502816
Min length2

Characters and Unicode

Total characters5656
Distinct characters197
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

Unique444 ?
Unique (%)35.7%

Sample

1st row개포3동
2nd row개포3동
3rd row개포3동
4th row상일제1동
5th row상일제2동
ValueCountFrequency (%)
종로1.2.3.4가동 29
 
2.3%
명동 23
 
1.9%
종로1.2가동 17
 
1.4%
사직동 12
 
1.0%
소공동 12
 
1.0%
필동 11
 
0.9%
종로3.4가동 11
 
0.9%
회현동 11
 
0.9%
태평로1가동 11
 
0.9%
광희동 10
 
0.8%
Other values (656) 1096
88.2%
2024-05-18T15:46:14.004651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 1270
22.5%
제 592
 
10.5%
1 268
 
4.7%
2 257
 
4.5%
. 152
 
2.7%
3 149
 
2.6%
로 143
 
2.5%
가 136
 
2.4%
4 112
 
2.0%
신 87
 
1.5%
Other values (187) 2490
44.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4617
81.6%
Decimal Number 886
 
15.7%
Other Punctuation 153
 
2.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1270
27.5%
제 592
 
12.8%
로 143
 
3.1%
가 136
 
2.9%
신 87
 
1.9%
종 71
 
1.5%
문 49
 
1.1%
곡 46
 
1.0%
수 44
 
1.0%
산 43
 
0.9%
Other values (175) 2136
46.3%
Decimal Number
ValueCountFrequency (%)
1 268
30.2%
2 257
29.0%
3 149
16.8%
4 112
12.6%
5 48
 
5.4%
6 23
 
2.6%
7 13
 
1.5%
8 8
 
0.9%
9 5
 
0.6%
0 3
 
0.3%
Other Punctuation
ValueCountFrequency (%)
. 152
99.3%
, 1
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4617
81.6%
Common 1039
 
18.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1270
27.5%
제 592
 
12.8%
로 143
 
3.1%
가 136
 
2.9%
신 87
 
1.9%
종 71
 
1.5%
문 49
 
1.1%
곡 46
 
1.0%
수 44
 
1.0%
산 43
 
0.9%
Other values (175) 2136
46.3%
Common
ValueCountFrequency (%)
1 268
25.8%
2 257
24.7%
. 152
14.6%
3 149
14.3%
4 112
10.8%
5 48
 
4.6%
6 23
 
2.2%
7 13
 
1.3%
8 8
 
0.8%
9 5
 
0.5%
Other values (2) 4
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4617
81.6%
ASCII 1039
 
18.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 1270
27.5%
제 592
 
12.8%
로 143
 
3.1%
가 136
 
2.9%
신 87
 
1.9%
종 71
 
1.5%
문 49
 
1.1%
곡 46
 
1.0%
수 44
 
1.0%
산 43
 
0.9%
Other values (175) 2136
46.3%
ASCII
ValueCountFrequency (%)
1 268
25.8%
2 257
24.7%
. 152
14.6%
3 149
14.3%
4 112
10.8%
5 48
 
4.6%
6 23
 
2.2%
7 13
 
1.3%
8 8
 
0.8%
9 5
 
0.5%
Other values (2) 4
 
0.4%

적용시작일
Real number (ℝ)

Distinct66
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19907542
Minimum19880423
Maximum20221223
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2024-05-18T15:46:14.630412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19880423
5-th percentile19880423
Q119880423
median19880423
Q319880423
95-th percentile20080630
Maximum20221223
Range340800
Interquartile range (IQR)0

Descriptive statistics

Standard deviation63246.296
Coefficient of variation (CV)0.0031770018
Kurtosis5.4378013
Mean19907542
Median Absolute Deviation (MAD)0
Skewness2.4931099
Sum4.6882261 × 1010
Variance4.0000939 × 109
MonotonicityDecreasing
2024-05-18T15:46:15.364893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
19880423 1795
76.2%
19950301 73
 
3.1%
19980914 61
 
2.6%
20080901 40
 
1.7%
19981201 28
 
1.2%
19920416 27
 
1.1%
20071231 26
 
1.1%
19880701 21
 
0.9%
20080630 20
 
0.8%
19890901 20
 
0.8%
Other values (56) 244
 
10.4%
ValueCountFrequency (%)
19880423 1795
76.2%
19880501 14
 
0.6%
19880701 21
 
0.9%
19890101 15
 
0.6%
19890427 6
 
0.3%
19890601 8
 
0.3%
19890901 20
 
0.8%
19910603 1
 
< 0.1%
19910907 12
 
0.5%
19911204 1
 
< 0.1%
ValueCountFrequency (%)
20221223 3
0.1%
20210701 2
 
0.1%
20210315 1
 
< 0.1%
20200101 1
 
< 0.1%
20190101 6
0.3%
20161109 1
 
< 0.1%
20160928 4
0.2%
20150723 1
 
< 0.1%
20150706 3
0.1%
20150508 1
 
< 0.1%

적용만료일
Real number (ℝ)

Distinct66
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64162867
Minimum19880423
Maximum99991231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size20.8 KiB
2024-05-18T15:46:16.078733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19880423
5-th percentile19880423
Q119880701
median99991231
Q399991231
95-th percentile99991231
Maximum99991231
Range80110808
Interquartile range (IQR)80110530

Descriptive statistics

Standard deviation39814199
Coefficient of variation (CV)0.62051776
Kurtosis-1.9571041
Mean64162867
Median Absolute Deviation (MAD)0
Skewness-0.21107031
Sum1.5110355 × 1011
Variance1.5851704 × 1015
MonotonicityNot monotonic
2024-05-18T15:46:16.806189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99991231 1301
55.2%
19880423 564
23.9%
19950301 70
 
3.0%
20071230 37
 
1.6%
19981201 37
 
1.6%
20080831 25
 
1.1%
19980914 20
 
0.8%
20080505 19
 
0.8%
20080810 14
 
0.6%
19881231 13
 
0.6%
Other values (56) 255
 
10.8%
ValueCountFrequency (%)
19880423 564
23.9%
19880430 1
 
< 0.1%
19880501 6
 
0.3%
19880601 1
 
< 0.1%
19880630 12
 
0.5%
19880701 12
 
0.5%
19880831 7
 
0.3%
19881231 13
 
0.6%
19890101 10
 
0.4%
19890427 9
 
0.4%
ValueCountFrequency (%)
99991231 1301
55.2%
20221222 3
 
0.1%
20210630 2
 
0.1%
20191231 1
 
< 0.1%
20181231 6
 
0.3%
20161128 1
 
< 0.1%
20150712 1
 
< 0.1%
20130719 7
 
0.3%
20090503 11
 
0.5%
20090419 2
 
0.1%

Interactions

2024-05-18T15:46:00.927008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:45:55.039686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:45:56.393292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:45:57.890146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:45:59.265028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:46:01.411113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:45:55.298667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:45:56.690665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:45:58.178611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:45:59.583195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:46:01.807893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:45:55.525918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:45:56.958293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:45:58.435739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:45:59.860402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:46:02.139389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:45:55.795523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:45:57.220692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:45:58.686837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:46:00.227295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:46:02.844619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:45:56.098144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:45:57.582373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:45:58.970792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T15:46:00.504661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T15:46:17.280504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구코드법정동코드행정동코드시군구명적용시작일적용만료일
시군구코드1.0000.2840.2591.0000.5840.217
법정동코드0.2841.0000.4810.4830.0000.296
행정동코드0.2590.4811.0000.5070.3780.254
시군구명1.0000.4830.5071.0000.8210.387
적용시작일0.5840.0000.3780.8211.0000.530
적용만료일0.2170.2960.2540.3870.5301.000
2024-05-18T15:46:17.840277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구코드법정동코드행정동코드적용시작일적용만료일시군구명
시군구코드1.000-0.2860.021-0.053-0.1630.997
법정동코드-0.2861.000-0.071-0.120-0.0440.274
행정동코드0.021-0.0711.0000.3760.3360.242
적용시작일-0.053-0.1200.3761.0000.3380.286
적용만료일-0.163-0.0440.3360.3381.0000.372
시군구명0.9970.2740.2420.2860.3721.000

Missing values

2024-05-18T15:46:03.352375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T15:46:03.842497image/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

시군구코드법정동코드행정동코드시도명시군구명법정동명행정동명적용시작일적용만료일
01168011400675서울특별시강남구일원동개포3동2022122399991231
11168010300675서울특별시강남구개포동개포3동2022122399991231
21168010600675서울특별시강남구대치동개포3동2022122399991231
31174010300525서울특별시강동구상일동상일제1동2021070199991231
41174010300526서울특별시강동구상일동상일제2동2021070199991231
51129010300620서울특별시성북구돈암동정릉제1동2021031599991231
61153011200800서울특별시구로구항동항동2020010199991231
71130510300625서울특별시강북구수유동수유2동2019010199991231
81130510200595서울특별시강북구번동번1동2019010199991231
91130510300635서울특별시강북구수유동수유3동2019010199991231
시군구코드법정동코드행정동코드시도명시군구명법정동명행정동명적용시작일적용만료일
23451111010400520서울특별시종로구효자동효자동1988042320081031
23461111010500520서울특별시종로구창성동효자동1988042320081031
23471111010800520서울특별시종로구통인동효자동1988042320081031
23481111010900520서울특별시종로구누상동효자동1988042320081031
23491111011000520서울특별시종로구누하동효자동1988042320081031
23501111011100520서울특별시종로구옥인동효자동1988042320081031
23511123010500600서울특별시동대문구답십리동답십리제1동1988042399991231
23521111011600590서울특별시종로구도렴동세종로동1988042319981201
23531111011700590서울특별시종로구당주동세종로동1988042319981201
23541111011800590서울특별시종로구내수동세종로동1988042319981201