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/eb8ac1b7-9cb2-4ba7-ad30-c4242dd0f2ce

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 13:51:42.998681
Analysis finished2023-12-10 13:51:46.756769
Duration3.76 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
2023-12-10T22:51:46.819748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:47.023975image/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

2023-12-10T22:51:47.262487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:51:47.409341image/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
2023-12-10T22:51:47.620548image/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%
2023-12-10T22:51:48.091432image/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
2023-12-10T22:51:48.401680image/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%
2023-12-10T22:51:49.003982image/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
2023-12-10T22:51:49.210172image/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
2023-12-10T22:51:49.457445image/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%
Mean60.059333
Minimum0.15
Maximum395.46
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:51:49.675658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.15
5-th percentile0.713
Q12.215
median13.415
Q391.705
95-th percentile255.2435
Maximum395.46
Range395.31
Interquartile range (IQR)89.49

Descriptive statistics

Standard deviation96.652325
Coefficient of variation (CV)1.6092807
Kurtosis5.1056982
Mean60.059333
Median Absolute Deviation (MAD)12.105
Skewness2.2566714
Sum1801.78
Variance9341.6719
MonotonicityNot monotonic
2023-12-10T22:51:49.912592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4.92 1
 
3.3%
1.12 1
 
3.3%
104.03 1
 
3.3%
185.63 1
 
3.3%
0.38 1
 
3.3%
4.56 1
 
3.3%
184.47 1
 
3.3%
114.09 1
 
3.3%
0.15 1
 
3.3%
5.13 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
0.15 1
3.3%
0.38 1
3.3%
1.12 1
3.3%
1.5 1
3.3%
1.77 1
3.3%
1.9 1
3.3%
2.02 1
3.3%
2.18 1
3.3%
2.32 1
3.3%
4.56 1
3.3%
ValueCountFrequency (%)
395.46 1
3.3%
312.2 1
3.3%
185.63 1
3.3%
184.47 1
3.3%
114.09 1
3.3%
111.41 1
3.3%
104.03 1
3.3%
99.12 1
3.3%
69.46 1
3.3%
47.99 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

2023-12-10T22:51:50.121563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:51:50.285651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

비교 시군구명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
안산시
11 
수원시
5 
성남시
3 
파주시
3 
양평군
2 
Other values (6)
6 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique6 ?
Unique (%)20.0%

Sample

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

Common Values

ValueCountFrequency (%)
안산시 11
36.7%
수원시 5
16.7%
성남시 3
 
10.0%
파주시 3
 
10.0%
양평군 2
 
6.7%
평택시 1
 
3.3%
고양시 1
 
3.3%
과천시 1
 
3.3%
광주시 1
 
3.3%
화성시 1
 
3.3%

Length

2023-12-10T22:51:50.430502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
안산시 11
36.7%
수원시 5
16.7%
성남시 3
 
10.0%
파주시 3
 
10.0%
양평군 2
 
6.7%
평택시 1
 
3.3%
고양시 1
 
3.3%
과천시 1
 
3.3%
광주시 1
 
3.3%
화성시 1
 
3.3%

비교 행정동명
Text

Distinct16
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:51:50.666883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.1333333
Min length2

Characters and Unicode

Total characters94
Distinct characters31
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 row수진2동
2nd row원곡동
3rd row원곡동
4th row원곡동
5th row진서면
ValueCountFrequency (%)
원곡동 8
26.7%
매산동 5
16.7%
진서면 2
 
6.7%
청운면 2
 
6.7%
대부동 2
 
6.7%
수진2동 1
 
3.3%
운정2동 1
 
3.3%
세교동 1
 
3.3%
정자3동 1
 
3.3%
주교동 1
 
3.3%
Other values (6) 6
20.0%
2023-12-10T22:51:51.131147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 25
26.6%
원 8
 
8.5%
곡 8
 
8.5%
매 5
 
5.3%
산 5
 
5.3%
면 5
 
5.3%
진 3
 
3.2%
서 3
 
3.2%
운 3
 
3.2%
2 2
 
2.1%
Other values (21) 27
28.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89
94.7%
Decimal Number 5
 
5.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 25
28.1%
원 8
 
9.0%
곡 8
 
9.0%
매 5
 
5.6%
산 5
 
5.6%
면 5
 
5.6%
진 3
 
3.4%
서 3
 
3.4%
운 3
 
3.4%
교 2
 
2.2%
Other values (18) 22
24.7%
Decimal Number
ValueCountFrequency (%)
2 2
40.0%
1 2
40.0%
3 1
20.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89
94.7%
Common 5
 
5.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 25
28.1%
원 8
 
9.0%
곡 8
 
9.0%
매 5
 
5.6%
산 5
 
5.6%
면 5
 
5.6%
진 3
 
3.4%
서 3
 
3.4%
운 3
 
3.4%
교 2
 
2.2%
Other values (18) 22
24.7%
Common
ValueCountFrequency (%)
2 2
40.0%
1 2
40.0%
3 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89
94.7%
ASCII 5
 
5.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 25
28.1%
원 8
 
9.0%
곡 8
 
9.0%
매 5
 
5.6%
산 5
 
5.6%
면 5
 
5.6%
진 3
 
3.4%
서 3
 
3.4%
운 3
 
3.4%
교 2
 
2.2%
Other values (18) 22
24.7%
ASCII
ValueCountFrequency (%)
2 2
40.0%
1 2
40.0%
3 1
20.0%

비교 행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1309151 × 109
Minimum4.111566 × 109
Maximum4.183037 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:51:51.313656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.111566 × 109
5-th percentile4.111566 × 109
Q14.1149401 × 109
median4.1273545 × 109
Q34.1288275 × 109
95-th percentile4.1731442 × 109
Maximum4.183037 × 109
Range71471000
Interquartile range (IQR)13887375

Descriptive statistics

Standard deviation19628570
Coefficient of variation (CV)0.0047516276
Kurtosis1.6575376
Mean4.1309151 × 109
Median Absolute Deviation (MAD)11040750
Skewness1.4300929
Sum1.2392745 × 1011
Variance3.8528076 × 1014
MonotonicityNot monotonic
2023-12-10T22:51:51.479680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
4127354500 8
26.7%
4111566000 5
16.7%
4148041000 2
 
6.7%
4183037000 2
 
6.7%
4127361000 2
 
6.7%
4113158000 1
 
3.3%
4129053000 1
 
3.3%
4119061000 1
 
3.3%
4159035000 1
 
3.3%
4113566500 1
 
3.3%
Other values (6) 6
20.0%
ValueCountFrequency (%)
4111566000 5
16.7%
4113158000 1
 
3.3%
4113557000 1
 
3.3%
4113566500 1
 
3.3%
4119061000 1
 
3.3%
4122064000 1
 
3.3%
4127154000 1
 
3.3%
4127354500 8
26.7%
4127361000 2
 
6.7%
4128151000 1
 
3.3%
ValueCountFrequency (%)
4183037000 2
 
6.7%
4161053000 1
 
3.3%
4159035000 1
 
3.3%
4148056000 1
 
3.3%
4148041000 2
 
6.7%
4129053000 1
 
3.3%
4128151000 1
 
3.3%
4127361000 2
 
6.7%
4127354500 8
26.7%
4127154000 1
 
3.3%

비교값
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1291.9163
Minimum6.3
Maximum24527.12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:51:51.704911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.3
5-th percentile9.338
Q146.5925
median81.41
Q3308.0725
95-th percentile3580.385
Maximum24527.12
Range24520.82
Interquartile range (IQR)261.48

Descriptive statistics

Standard deviation4487.7812
Coefficient of variation (CV)3.4737398
Kurtosis27.154176
Mean1291.9163
Median Absolute Deviation (MAD)71.92
Skewness5.1209193
Sum38757.49
Variance20140180
MonotonicityNot monotonic
2023-12-10T22:51:51.902170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
885.77 1
 
3.3%
24527.12 1
 
3.3%
25.75 1
 
3.3%
24.26 1
 
3.3%
3772.22 1
 
3.3%
26.54 1
 
3.3%
28.03 1
 
3.3%
6.3 1
 
3.3%
1122.11 1
 
3.3%
50.49 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
6.3 1
3.3%
7.97 1
3.3%
11.01 1
3.3%
24.26 1
3.3%
25.75 1
3.3%
26.54 1
3.3%
28.03 1
3.3%
46.31 1
3.3%
47.44 1
3.3%
48.63 1
3.3%
ValueCountFrequency (%)
24527.12 1
3.3%
3772.22 1
3.3%
3345.92 1
3.3%
1880.43 1
3.3%
1122.11 1
3.3%
1081.49 1
3.3%
885.77 1
3.3%
321.67 1
3.3%
267.28 1
3.3%
257.34 1
3.3%

Interactions

2023-12-10T22:51:45.801610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:43.506508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:44.223400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:45.175070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:45.965564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:43.702744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:44.385747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:45.321048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:46.106645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:43.896038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:44.869171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:45.475443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:46.251696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:44.059339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:45.040206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:51:45.622141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:51:52.040089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명행정동명행정동 코드표준편차비교 시군구명비교 행정동명비교 행정동코드비교값
시군구명1.0001.0001.0000.8350.7480.9300.9001.000
행정동명1.0001.0001.0001.0001.0001.0001.0001.000
행정동 코드1.0001.0001.0000.5650.0000.6150.0580.000
표준편차0.8351.0000.5651.0000.0000.0000.0000.000
비교 시군구명0.7481.0000.0000.0001.0001.0001.0000.812
비교 행정동명0.9301.0000.6150.0001.0001.0001.0001.000
비교 행정동코드0.9001.0000.0580.0001.0001.0001.0000.568
비교값1.0001.0000.0000.0000.8121.0000.5681.000
2023-12-10T22:51:52.208150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동 코드표준편차비교 행정동코드비교값비교 시군구명
행정동 코드1.000-0.744-0.1000.3290.000
표준편차-0.7441.0000.221-0.5840.000
비교 행정동코드-0.1000.2211.000-0.0090.890
비교값0.329-0.584-0.0091.0000.577
비교 시군구명0.0000.0000.8900.5771.000

Missing values

2023-12-10T22:51:46.432227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:51:46.672104image/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
02019-01경기도광명시학온동41210660004.92경기도성남시수진2동4113158000885.77
12019-01경기도고양시주교동412815100019.08경기도안산시원곡동4127354500171.49
22019-01경기도광주시도척면41610330002.32경기도안산시원곡동412735450076.29
32019-01경기도성남시구미동411356700047.99경기도안산시원곡동4127354500267.28
42019-01경기도부천시도당동411906000099.12경기도파주시진서면414804100086.53
52019-01경기도성남시신흥1동4113151000312.2경기도안산시원곡동412735450061.54
62019-01경기도시흥시군자동413905810028.42경기도안산시원곡동412735450046.31
72019-01경기도안성시보개면41550310001.5경기도양평군청운면4183037000321.67
82019-01경기도안성시양성면41550360001.77경기도수원시매산동4111566000198.87
92019-01경기도안양시비산2동411735200069.46경기도파주시운정2동414805600011.01
기준년월시도명시군구명행정동명행정동 코드표준편차비교 시도명비교 시군구명비교 행정동명비교 행정동코드비교값
202019-01경기도평택시통복동4122061000395.46경기도안산시대부동41273610001081.49
212019-01경기도포천시소흘읍41650250001.9경기도고양시주교동41281510003345.92
222019-01경기도화성시봉담읍41590253005.13경기도수원시매산동411156600050.49
232019-01경기도가평군설악면41820310000.15경기도안산시본오1동41271540001122.11
242019-01경기도고양시행신1동4128164000114.09경기도과천시별양동41290530006.3
252019-01경기도군포시금정동4141056000184.47경기도광주시광남동416105300028.03
262019-01경기도과천시문원동41290560004.56경기도성남시구미1동411356650026.54
272019-01경기도남양주시수동면41360340000.38경기도화성시서신면41590350003772.22
282019-01경기도동두천시중앙동4125053500185.63경기도안산시원곡동412735450024.26
292019-01경기도부천시심곡1동4119052000104.03경기도부천시중동411906100025.75