Overview

Dataset statistics

Number of variables11
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory96.4 B

Variable types

DateTime1
Categorical3
Text3
Numeric4

Dataset

Description샘플 데이터
Author경기콘텐츠진흥원
URLhttps://bigdata-region.kr/#/dataset/b71fde1b-d006-4cdb-a951-dd335aed930f

Alerts

기준년월 has constant value ""Constant
시도명 has constant value ""Constant
비교 시도명 has constant value ""Constant
행정동 코드 is highly overall correlated with 표준편차High correlation
표준편차 is highly overall correlated with 행정동 코드 and 1 other fieldsHigh correlation
비교 행정동코드 is highly overall correlated with 비교 시군구명High correlation
비교값 is highly overall correlated with 표준편차 and 1 other fieldsHigh correlation
비교 시군구명 is highly overall correlated with 비교 행정동코드 and 1 other fieldsHigh correlation
행정동명 has unique valuesUnique
행정동 코드 has unique valuesUnique
표준편차 has unique valuesUnique
비교값 has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:54:59.618147
Analysis finished2023-12-10 13:55:04.260038
Duration4.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2019-01-01 00:00:00
Maximum2019-01-01 00:00:00
2023-12-10T22:55:04.342632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:04.545971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기도
30 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 30
100.0%

Length

2023-12-10T22:55:04.766505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:55:04.932444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

시군구명
Text

Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:55:05.188570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.1
Min length3

Characters and Unicode

Total characters93
Distinct characters30
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

Unique17 ?
Unique (%)56.7%

Sample

1st row광명시
2nd row고양시
3rd row광주시
4th row성남시
5th row부천시
ValueCountFrequency (%)
파주시 3
 
10.0%
안성시 2
 
6.7%
오산시 2
 
6.7%
고양시 2
 
6.7%
성남시 2
 
6.7%
부천시 2
 
6.7%
화성시 1
 
3.3%
광명시 1
 
3.3%
남양주시 1
 
3.3%
과천시 1
 
3.3%
Other values (13) 13
43.3%
2023-12-10T22:55:05.862060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 30
32.3%
주 6
 
6.5%
천 6
 
6.5%
성 5
 
5.4%
양 5
 
5.4%
파 3
 
3.2%
남 3
 
3.2%
안 3
 
3.2%
부 3
 
3.2%
오 2
 
2.2%
Other values (20) 27
29.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 93
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 30
32.3%
주 6
 
6.5%
천 6
 
6.5%
성 5
 
5.4%
양 5
 
5.4%
파 3
 
3.2%
남 3
 
3.2%
안 3
 
3.2%
부 3
 
3.2%
오 2
 
2.2%
Other values (20) 27
29.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 93
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 30
32.3%
주 6
 
6.5%
천 6
 
6.5%
성 5
 
5.4%
양 5
 
5.4%
파 3
 
3.2%
남 3
 
3.2%
안 3
 
3.2%
부 3
 
3.2%
오 2
 
2.2%
Other values (20) 27
29.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 93
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 30
32.3%
주 6
 
6.5%
천 6
 
6.5%
성 5
 
5.4%
양 5
 
5.4%
파 3
 
3.2%
남 3
 
3.2%
안 3
 
3.2%
부 3
 
3.2%
오 2
 
2.2%
Other values (20) 27
29.0%

행정동명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:55:06.353068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.2333333
Min length3

Characters and Unicode

Total characters97
Distinct characters55
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

Unique30 ?
Unique (%)100.0%

Sample

1st row학온동
2nd row주교동
3rd row도척면
4th row구미동
5th row도당동
ValueCountFrequency (%)
학온동 1
 
3.3%
주교동 1
 
3.3%
중앙동 1
 
3.3%
수동면 1
 
3.3%
문원동 1
 
3.3%
금정동 1
 
3.3%
행신1동 1
 
3.3%
설악면 1
 
3.3%
봉담읍 1
 
3.3%
소흘읍 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T22:55:06.977936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 21
21.6%
면 8
 
8.2%
신 5
 
5.2%
1 5
 
5.2%
읍 2
 
2.1%
주 2
 
2.1%
교 2
 
2.1%
도 2
 
2.1%
정 2
 
2.1%
양 2
 
2.1%
Other values (45) 46
47.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 91
93.8%
Decimal Number 6
 
6.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 21
23.1%
면 8
 
8.8%
신 5
 
5.5%
읍 2
 
2.2%
주 2
 
2.2%
교 2
 
2.2%
도 2
 
2.2%
정 2
 
2.2%
양 2
 
2.2%
봉 2
 
2.2%
Other values (43) 43
47.3%
Decimal Number
ValueCountFrequency (%)
1 5
83.3%
2 1
 
16.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 91
93.8%
Common 6
 
6.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 21
23.1%
면 8
 
8.8%
신 5
 
5.5%
읍 2
 
2.2%
주 2
 
2.2%
교 2
 
2.2%
도 2
 
2.2%
정 2
 
2.2%
양 2
 
2.2%
봉 2
 
2.2%
Other values (43) 43
47.3%
Common
ValueCountFrequency (%)
1 5
83.3%
2 1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 91
93.8%
ASCII 6
 
6.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 21
23.1%
면 8
 
8.8%
신 5
 
5.5%
읍 2
 
2.2%
주 2
 
2.2%
교 2
 
2.2%
도 2
 
2.2%
정 2
 
2.2%
양 2
 
2.2%
봉 2
 
2.2%
Other values (43) 43
47.3%
ASCII
ValueCountFrequency (%)
1 5
83.3%
2 1
 
16.7%

행정동 코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1388342 × 109
Minimum4.113151 × 109
Maximum4.182031 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:55:07.230818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.113151 × 109
5-th percentile4.1142348 × 109
Q14.1228091 × 109
median4.138056 × 109
Q34.1495368 × 109
95-th percentile4.1641367 × 109
Maximum4.182031 × 109
Range68880000
Interquartile range (IQR)26727625

Descriptive statistics

Standard deviation17867239
Coefficient of variation (CV)0.0043169738
Kurtosis-0.50029415
Mean4.1388342 × 109
Median Absolute Deviation (MAD)14498800
Skewness0.36579043
Sum1.2416503 × 1011
Variance3.1923822 × 1014
MonotonicityNot monotonic
2023-12-10T22:55:07.482761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4121066000 1
 
3.3%
4150031000 1
 
3.3%
4119052000 1
 
3.3%
4125053500 1
 
3.3%
4136034000 1
 
3.3%
4129056000 1
 
3.3%
4141056000 1
 
3.3%
4128164000 1
 
3.3%
4182031000 1
 
3.3%
4159025300 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
4113151000 1
3.3%
4113567000 1
3.3%
4115051000 1
3.3%
4117352000 1
3.3%
4119052000 1
3.3%
4119060000 1
3.3%
4121066000 1
3.3%
4122061000 1
3.3%
4125053500 1
3.3%
4128151000 1
3.3%
ValueCountFrequency (%)
4182031000 1
3.3%
4165025000 1
3.3%
4163051000 1
3.3%
4161033000 1
3.3%
4159025300 1
3.3%
4155036000 1
3.3%
4155031000 1
3.3%
4150031000 1
3.3%
4148054000 1
3.3%
4148035000 1
3.3%

표준편차
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.477333
Minimum1.55
Maximum316.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:55:07.827049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.55
5-th percentile2.335
Q19.3475
median31.855
Q3111.6
95-th percentile278.1835
Maximum316.1
Range314.55
Interquartile range (IQR)102.2525

Descriptive statistics

Standard deviation93.002276
Coefficient of variation (CV)1.2321882
Kurtosis0.93513264
Mean75.477333
Median Absolute Deviation (MAD)28.14
Skewness1.4183747
Sum2264.32
Variance8649.4234
MonotonicityNot monotonic
2023-12-10T22:55:08.091781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
29.9 1
 
3.3%
8.33 1
 
3.3%
158.31 1
 
3.3%
67.45 1
 
3.3%
1.55 1
 
3.3%
12.4 1
 
3.3%
264.89 1
 
3.3%
165.95 1
 
3.3%
2.39 1
 
3.3%
22.87 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
1.55 1
3.3%
2.29 1
3.3%
2.39 1
3.3%
3.45 1
3.3%
3.98 1
3.3%
5.26 1
3.3%
7.51 1
3.3%
8.33 1
3.3%
12.4 1
3.3%
13.38 1
3.3%
ValueCountFrequency (%)
316.1 1
3.3%
289.06 1
3.3%
264.89 1
3.3%
211.56 1
3.3%
167.73 1
3.3%
165.95 1
3.3%
158.31 1
3.3%
114.5 1
3.3%
102.9 1
3.3%
67.45 1
3.3%

비교 시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
경기도
30 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 30
100.0%

Length

2023-12-10T22:55:08.325985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:55:08.504978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

비교 시군구명
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
안양시
13 
안산시
2 
파주시
2 
연천군
2 
의정부시
2 
Other values (7)
9 

Length

Max length4
Median length3
Mean length3.0666667
Min length3

Unique

Unique5 ?
Unique (%)16.7%

Sample

1st row안산시
2nd row안양시
3rd row안양시
4th row안양시
5th row파주시

Common Values

ValueCountFrequency (%)
안양시 13
43.3%
안산시 2
 
6.7%
파주시 2
 
6.7%
연천군 2
 
6.7%
의정부시 2
 
6.7%
이천시 2
 
6.7%
성남시 2
 
6.7%
용인시 1
 
3.3%
수원시 1
 
3.3%
김포시 1
 
3.3%
Other values (2) 2
 
6.7%

Length

2023-12-10T22:55:08.676967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
안양시 13
43.3%
안산시 2
 
6.7%
파주시 2
 
6.7%
연천군 2
 
6.7%
의정부시 2
 
6.7%
이천시 2
 
6.7%
성남시 2
 
6.7%
용인시 1
 
3.3%
수원시 1
 
3.3%
김포시 1
 
3.3%
Other values (2) 2
 
6.7%

비교 행정동명
Text

Distinct16
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:55:08.924556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.3
Min length2

Characters and Unicode

Total characters99
Distinct characters35
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

Unique11 ?
Unique (%)36.7%

Sample

1st row호수동
2nd row범계동
3rd row범계동
4th row범계동
5th row진서면
ValueCountFrequency (%)
범계동 8
26.7%
안양1동 5
16.7%
진서면 2
 
6.7%
중면 2
 
6.7%
관고동 2
 
6.7%
호수동 1
 
3.3%
장암동 1
 
3.3%
상갈동 1
 
3.3%
송죽동 1
 
3.3%
사동 1
 
3.3%
Other values (6) 6
20.0%
2023-12-10T22:55:09.538736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 25
25.3%
범 8
 
8.1%
계 8
 
8.1%
1 7
 
7.1%
양 6
 
6.1%
안 5
 
5.1%
면 4
 
4.0%
고 2
 
2.0%
상 2
 
2.0%
3 2
 
2.0%
Other values (25) 30
30.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89
89.9%
Decimal Number 10
 
10.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 25
28.1%
범 8
 
9.0%
계 8
 
9.0%
양 6
 
6.7%
안 5
 
5.6%
면 4
 
4.5%
고 2
 
2.2%
상 2
 
2.2%
중 2
 
2.2%
관 2
 
2.2%
Other values (22) 25
28.1%
Decimal Number
ValueCountFrequency (%)
1 7
70.0%
3 2
 
20.0%
2 1
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89
89.9%
Common 10
 
10.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 25
28.1%
범 8
 
9.0%
계 8
 
9.0%
양 6
 
6.7%
안 5
 
5.6%
면 4
 
4.5%
고 2
 
2.2%
상 2
 
2.2%
중 2
 
2.2%
관 2
 
2.2%
Other values (22) 25
28.1%
Common
ValueCountFrequency (%)
1 7
70.0%
3 2
 
20.0%
2 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89
89.9%
ASCII 10
 
10.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 25
28.1%
범 8
 
9.0%
계 8
 
9.0%
양 6
 
6.7%
안 5
 
5.6%
면 4
 
4.5%
고 2
 
2.2%
상 2
 
2.2%
중 2
 
2.2%
관 2
 
2.2%
Other values (22) 25
28.1%
ASCII
ValueCountFrequency (%)
1 7
70.0%
3 2
 
20.0%
2 1
 
10.0%

비교 행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1290233 × 109
Minimum4.1111591 × 109
Maximum4.180037 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:55:09.770866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.1111591 × 109
5-th percentile4.1132474 × 109
Q14.117151 × 109
median4.117361 × 109
Q34.1460282 × 109
95-th percentile4.1696819 × 109
Maximum4.180037 × 109
Range68877900
Interquartile range (IQR)28877250

Descriptive statistics

Standard deviation19703621
Coefficient of variation (CV)0.004771981
Kurtosis1.0327094
Mean4.1290233 × 109
Median Absolute Deviation (MAD)2306950
Skewness1.3915087
Sum1.238707 × 1011
Variance3.8823267 × 1014
MonotonicityNot monotonic
2023-12-10T22:55:10.029963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
4117361000 8
26.7%
4117151000 5
16.7%
4148041000 2
 
6.7%
4180037000 2
 
6.7%
4150053000 2
 
6.7%
4127353500 1
 
3.3%
4115052000 1
 
3.3%
4113359000 1
 
3.3%
4145054000 1
 
3.3%
4113156000 1
 
3.3%
Other values (6) 6
20.0%
ValueCountFrequency (%)
4111159100 1
 
3.3%
4113156000 1
 
3.3%
4113359000 1
 
3.3%
4115052000 1
 
3.3%
4115056100 1
 
3.3%
4117151000 5
16.7%
4117361000 8
26.7%
4119073000 1
 
3.3%
4127152500 1
 
3.3%
4127353500 1
 
3.3%
ValueCountFrequency (%)
4180037000 2
 
6.7%
4157025600 1
 
3.3%
4150053000 2
 
6.7%
4148041000 2
 
6.7%
4146353000 1
 
3.3%
4145054000 1
 
3.3%
4127353500 1
 
3.3%
4127152500 1
 
3.3%
4119073000 1
 
3.3%
4117361000 8
26.7%

비교값
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean485.85833
Minimum2.63
Maximum4503.94
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:55:10.262617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.63
5-th percentile8.3515
Q147.29
median133.95
Q3330.35
95-th percentile2389.297
Maximum4503.94
Range4501.31
Interquartile range (IQR)283.06

Descriptive statistics

Standard deviation973.02129
Coefficient of variation (CV)2.0026852
Kurtosis10.415747
Mean485.85833
Median Absolute Deviation (MAD)103.415
Skewness3.132664
Sum14575.75
Variance946770.43
MonotonicityNot monotonic
2023-12-10T22:55:10.488370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
243.14 1
 
3.3%
4503.94 1
 
3.3%
29.65 1
 
3.3%
128.5 1
 
3.3%
2027.1 1
 
3.3%
66.94 1
 
3.3%
8.61 1
 
3.3%
8.14 1
 
3.3%
1210.04 1
 
3.3%
32.84 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
2.63 1
3.3%
8.14 1
3.3%
8.61 1
3.3%
9.97 1
3.3%
20.93 1
3.3%
29.65 1
3.3%
32.84 1
3.3%
46.98 1
3.3%
48.22 1
3.3%
66.94 1
3.3%
ValueCountFrequency (%)
4503.94 1
3.3%
2685.64 1
3.3%
2027.1 1
3.3%
1210.04 1
3.3%
722.78 1
3.3%
554.97 1
3.3%
436.43 1
3.3%
359.42 1
3.3%
243.14 1
3.3%
236.48 1
3.3%

Interactions

2023-12-10T22:55:02.407772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:00.163772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:00.955910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:01.700119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:02.682386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:00.425808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:01.143322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:01.865788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:02.834048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:00.575446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:01.309193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:02.015367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:03.024095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:00.771000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:01.537055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:55:02.237821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:55:10.653309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명행정동명행정동 코드표준편차비교 시군구명비교 행정동명비교 행정동코드비교값
시군구명1.0001.0001.0000.5010.9210.9300.9010.976
행정동명1.0001.0001.0001.0001.0001.0001.0001.000
행정동 코드1.0001.0001.0000.5210.4790.6150.5960.721
표준편차0.5011.0000.5211.0000.5460.6980.0000.000
비교 시군구명0.9211.0000.4790.5461.0001.0001.0000.984
비교 행정동명0.9301.0000.6150.6981.0001.0001.0000.966
비교 행정동코드0.9011.0000.5960.0001.0001.0001.0000.865
비교값0.9761.0000.7210.0000.9840.9660.8651.000
2023-12-10T22:55:10.905423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동 코드표준편차비교 행정동코드비교값비교 시군구명
행정동 코드1.000-0.7380.0880.3160.170
표준편차-0.7381.000-0.024-0.6040.212
비교 행정동코드0.088-0.0241.0000.2560.885
비교값0.316-0.6040.2561.0000.702
비교 시군구명0.1700.2120.8850.7021.000

Missing values

2023-12-10T22:55:03.256427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:55:04.127848image/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

기준년월시도명시군구명행정동명행정동 코드표준편차비교 시도명비교 시군구명비교 행정동명비교 행정동코드비교값
02019-01경기도광명시학온동412106600029.9경기도안산시호수동4127353500243.14
12019-01경기도고양시주교동412815100038.14경기도안양시범계동4117361000188.54
22019-01경기도광주시도척면41610330005.26경기도안양시범계동411736100075.67
32019-01경기도성남시구미동4113567000114.5경기도안양시범계동4117361000140.25
42019-01경기도부천시도당동4119060000102.9경기도파주시진서면414804100070.75
52019-01경기도성남시신흥1동4113151000211.56경기도안양시범계동4117361000111.12
62019-01경기도시흥시군자동413905810031.88경기도안양시범계동4117361000126.98
72019-01경기도안성시보개면41550310002.29경기도연천군중면4180037000722.78
82019-01경기도안성시양성면41550360003.45경기도안양시안양1동4117151000359.42
92019-01경기도안양시비산2동4117352000316.1경기도의정부시장암동41150561002.63
기준년월시도명시군구명행정동명행정동 코드표준편차비교 시도명비교 시군구명비교 행정동명비교 행정동코드비교값
202019-01경기도평택시통복동4122061000167.73경기도이천시관고동4150053000554.97
212019-01경기도포천시소흘읍416502500015.01경기도안산시사동41271525002685.64
222019-01경기도화성시봉담읍415902530022.87경기도안양시안양1동411715100032.84
232019-01경기도가평군설악면41820310002.39경기도김포시양촌읍41570256001210.04
242019-01경기도고양시행신1동4128164000165.95경기도의정부시의정부2동41150520008.14
252019-01경기도군포시금정동4141056000264.89경기도부천시송내1동41190730008.61
262019-01경기도과천시문원동412905600012.4경기도성남시태평3동411315600066.94
272019-01경기도남양주시수동면41360340001.55경기도하남시덕풍1동41450540002027.1
282019-01경기도동두천시중앙동412505350067.45경기도안양시범계동4117361000128.5
292019-01경기도부천시심곡1동4119052000158.31경기도성남시상대원3동411335900029.65