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

Categorical5
Text2
Numeric4

Dataset

Description샘플 데이터
Author경기콘텐츠진흥원
URLhttps://bigdata-region.kr/#/dataset/f3123025-e5eb-4109-819a-54037fb15a68

Alerts

기준년월 has constant value ""Constant
시도명 has constant value ""Constant
비교 시도명 has constant value ""Constant
비교 행정동명 is highly overall correlated with 비교 행정동코드 and 2 other fieldsHigh correlation
비교 시군구명 is highly overall correlated with 비교 행정동코드 and 1 other fieldsHigh correlation
행정동 코드 is highly overall correlated with 표준편차High correlation
표준편차 is highly overall correlated with 행정동 코드High correlation
비교 행정동코드 is highly overall correlated with 비교 시군구명 and 1 other fieldsHigh correlation
비교값 is highly overall correlated with 비교 행정동명High correlation
행정동명 has unique valuesUnique
행정동 코드 has unique valuesUnique
표준편차 has unique valuesUnique
비교값 has unique valuesUnique

Reproduction

Analysis started2023-12-10 14:09:25.250856
Analysis finished2023-12-10 14:09:28.633627
Duration3.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2019-01
30 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-01
2nd row2019-01
3rd row2019-01
4th row2019-01
5th row2019-01

Common Values

ValueCountFrequency (%)
2019-01 30
100.0%

Length

2023-12-10T23:09:28.757855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:09:28.920067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-01 30
100.0%

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

2023-12-10T23:09:29.076092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:09:29.397792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

시군구명
Text

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:09:29.606531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.1
Min length3

Characters and Unicode

Total characters93
Distinct characters29
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

Unique14 ?
Unique (%)46.7%

Sample

1st row고양시
2nd row고양시
3rd row광명시
4th row김포시
5th row광주시
ValueCountFrequency (%)
부천시 5
16.7%
고양시 3
 
10.0%
수원시 2
 
6.7%
안산시 2
 
6.7%
안양시 2
 
6.7%
광명시 2
 
6.7%
여주시 1
 
3.3%
이천시 1
 
3.3%
남양주시 1
 
3.3%
군포시 1
 
3.3%
Other values (10) 10
33.3%
2023-12-10T23:09:30.160616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 29
31.2%
천 7
 
7.5%
양 7
 
7.5%
부 6
 
6.5%
안 4
 
4.3%
주 4
 
4.3%
고 3
 
3.2%
광 3
 
3.2%
군 2
 
2.2%
평 2
 
2.2%
Other values (19) 26
28.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 93
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 29
31.2%
천 7
 
7.5%
양 7
 
7.5%
부 6
 
6.5%
안 4
 
4.3%
주 4
 
4.3%
고 3
 
3.2%
광 3
 
3.2%
군 2
 
2.2%
평 2
 
2.2%
Other values (19) 26
28.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 93
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 29
31.2%
천 7
 
7.5%
양 7
 
7.5%
부 6
 
6.5%
안 4
 
4.3%
주 4
 
4.3%
고 3
 
3.2%
광 3
 
3.2%
군 2
 
2.2%
평 2
 
2.2%
Other values (19) 26
28.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 93
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 29
31.2%
천 7
 
7.5%
양 7
 
7.5%
부 6
 
6.5%
안 4
 
4.3%
주 4
 
4.3%
고 3
 
3.2%
광 3
 
3.2%
군 2
 
2.2%
평 2
 
2.2%
Other values (19) 26
28.0%

행정동명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:09:30.499789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.3333333
Min length3

Characters and Unicode

Total characters100
Distinct characters53
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 row정발산동
2nd row삼송동
3rd row소하2동
4th row운양동
5th row남종면
ValueCountFrequency (%)
정발산동 1
 
3.3%
삼송동 1
 
3.3%
수동면 1
 
3.3%
금정동 1
 
3.3%
행신1동 1
 
3.3%
광명4동 1
 
3.3%
설악면 1
 
3.3%
남양읍 1
 
3.3%
송북동 1
 
3.3%
대월면 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T23:09:31.029104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 24
24.0%
면 6
 
6.0%
1 6
 
6.0%
송 4
 
4.0%
산 3
 
3.0%
2 3
 
3.0%
양 3
 
3.0%
정 2
 
2.0%
갈 2
 
2.0%
신 2
 
2.0%
Other values (43) 45
45.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 90
90.0%
Decimal Number 10
 
10.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 24
26.7%
면 6
 
6.7%
송 4
 
4.4%
산 3
 
3.3%
양 3
 
3.3%
정 2
 
2.2%
갈 2
 
2.2%
신 2
 
2.2%
중 2
 
2.2%
남 2
 
2.2%
Other values (40) 40
44.4%
Decimal Number
ValueCountFrequency (%)
1 6
60.0%
2 3
30.0%
4 1
 
10.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 90
90.0%
Common 10
 
10.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 24
26.7%
면 6
 
6.7%
송 4
 
4.4%
산 3
 
3.3%
양 3
 
3.3%
정 2
 
2.2%
갈 2
 
2.2%
신 2
 
2.2%
중 2
 
2.2%
남 2
 
2.2%
Other values (40) 40
44.4%
Common
ValueCountFrequency (%)
1 6
60.0%
2 3
30.0%
4 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 90
90.0%
ASCII 10
 
10.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 24
26.7%
면 6
 
6.7%
송 4
 
4.4%
산 3
 
3.3%
양 3
 
3.3%
정 2
 
2.2%
갈 2
 
2.2%
신 2
 
2.2%
중 2
 
2.2%
남 2
 
2.2%
Other values (40) 40
44.4%
ASCII
ValueCountFrequency (%)
1 6
60.0%
2 3
30.0%
4 1
 
10.0%

행정동 코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1324299 × 109
Minimum4.1111591 × 109
Maximum4.182031 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:09:31.292914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.1111591 × 109
5-th percentile4.1124626 × 109
Q14.1190638 × 109
median4.126203 × 109
Q34.1450272 × 109
95-th percentile4.1652326 × 109
Maximum4.182031 × 109
Range70871900
Interquartile range (IQR)25963500

Descriptive statistics

Standard deviation19238820
Coefficient of variation (CV)0.0046555708
Kurtosis0.058051167
Mean4.1324299 × 109
Median Absolute Deviation (MAD)8844000
Skewness1.057093
Sum1.239729 × 1011
Variance3.701322 × 1014
MonotonicityNot monotonic
2023-12-10T23:09:31.505604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4128553000 1
 
3.3%
4117355000 1
 
3.3%
4125053500 1
 
3.3%
4136034000 1
 
3.3%
4141056000 1
 
3.3%
4128164000 1
 
3.3%
4121055000 1
 
3.3%
4182031000 1
 
3.3%
4159026200 1
 
3.3%
4122056000 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
4111159100 1
3.3%
4111572000 1
3.3%
4113551000 1
3.3%
4115057300 1
3.3%
4117355000 1
3.3%
4117363000 1
3.3%
4119056000 1
3.3%
4119063000 1
3.3%
4119066000 1
3.3%
4119071000 1
3.3%
ValueCountFrequency (%)
4182031000 1
3.3%
4167034000 1
3.3%
4163031000 1
3.3%
4161035000 1
3.3%
4159026200 1
3.3%
4157058000 1
3.3%
4150035000 1
3.3%
4146351000 1
3.3%
4141056000 1
3.3%
4136034000 1
3.3%

표준편차
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean111.87867
Minimum0.49
Maximum485.45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:09:31.705387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.49
5-th percentile1.2965
Q129.07
median77.48
Q3164.0025
95-th percentile336.6975
Maximum485.45
Range484.96
Interquartile range (IQR)134.9325

Descriptive statistics

Standard deviation119.65252
Coefficient of variation (CV)1.0694847
Kurtosis2.9046594
Mean111.87867
Median Absolute Deviation (MAD)68.315
Skewness1.6556521
Sum3356.36
Variance14316.725
MonotonicityNot monotonic
2023-12-10T23:09:31.935642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
180.25 1
 
3.3%
99.92 1
 
3.3%
76.89 1
 
3.3%
2.05 1
 
3.3%
157.68 1
 
3.3%
229.74 1
 
3.3%
485.45 1
 
3.3%
0.68 1
 
3.3%
11.09 1
 
3.3%
28.94 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
0.49 1
3.3%
0.68 1
3.3%
2.05 1
3.3%
2.22 1
3.3%
4.56 1
3.3%
7.24 1
3.3%
11.09 1
3.3%
28.94 1
3.3%
29.46 1
3.3%
38.62 1
3.3%
ValueCountFrequency (%)
485.45 1
3.3%
417.54 1
3.3%
237.89 1
3.3%
231.59 1
3.3%
229.74 1
3.3%
221.86 1
3.3%
180.25 1
3.3%
166.11 1
3.3%
157.68 1
3.3%
114.11 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

2023-12-10T23:09:32.245096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:09:32.444518image/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
안양시
9 
파주시
6 
연천군
4 
김포시
2 
구리시
2 
Other values (6)
7 

Length

Max length4
Median length3
Mean length3.0333333
Min length3

Unique

Unique5 ?
Unique (%)16.7%

Sample

1st row안양시
2nd row김포시
3rd row안양시
4th row연천군
5th row김포시

Common Values

ValueCountFrequency (%)
안양시 9
30.0%
파주시 6
20.0%
연천군 4
13.3%
김포시 2
 
6.7%
구리시 2
 
6.7%
과천시 2
 
6.7%
의왕시 1
 
3.3%
의정부시 1
 
3.3%
안산시 1
 
3.3%
광명시 1
 
3.3%

Length

2023-12-10T23:09:32.932837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
안양시 9
30.0%
파주시 6
20.0%
연천군 4
13.3%
김포시 2
 
6.7%
구리시 2
 
6.7%
과천시 2
 
6.7%
의왕시 1
 
3.3%
의정부시 1
 
3.3%
안산시 1
 
3.3%
광명시 1
 
3.3%

비교 행정동명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
범계동
6 
진서면
6 
중면
4 
안양1동
2 
수택1동
2 
Other values (9)
10 

Length

Max length4
Median length3
Mean length3.1
Min length2

Unique

Unique8 ?
Unique (%)26.7%

Sample

1st row안양1동
2nd row통진읍
3rd row범계동
4th row중면
5th row운양동

Common Values

ValueCountFrequency (%)
범계동 6
20.0%
진서면 6
20.0%
중면 4
13.3%
안양1동 2
 
6.7%
수택1동 2
 
6.7%
갈현동 2
 
6.7%
통진읍 1
 
3.3%
운양동 1
 
3.3%
부곡동 1
 
3.3%
신곡2동 1
 
3.3%
Other values (4) 4
13.3%

Length

2023-12-10T23:09:33.145668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
범계동 6
20.0%
진서면 6
20.0%
중면 4
13.3%
안양1동 2
 
6.7%
수택1동 2
 
6.7%
갈현동 2
 
6.7%
통진읍 1
 
3.3%
운양동 1
 
3.3%
부곡동 1
 
3.3%
신곡2동 1
 
3.3%
Other values (4) 4
13.3%

비교 행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)46.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.138489 × 109
Minimum4.1150568 × 109
Maximum4.180037 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:09:33.348440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.1150568 × 109
5-th percentile4.117151 × 109
Q14.117361 × 109
median4.131057 × 109
Q34.148041 × 109
95-th percentile4.180037 × 109
Maximum4.180037 × 109
Range64980200
Interquartile range (IQR)30680000

Descriptive statistics

Standard deviation21662827
Coefficient of variation (CV)0.0052344773
Kurtosis-0.54924725
Mean4.138489 × 109
Median Absolute Deviation (MAD)13906000
Skewness0.72202326
Sum1.2415467 × 1011
Variance4.6927806 × 1014
MonotonicityNot monotonic
2023-12-10T23:09:33.535774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
4117361000 6
20.0%
4148041000 6
20.0%
4180037000 4
13.3%
4117151000 2
 
6.7%
4131057000 2
 
6.7%
4129052000 2
 
6.7%
4157025000 1
 
3.3%
4157058000 1
 
3.3%
4143052000 1
 
3.3%
4115056800 1
 
3.3%
Other values (4) 4
13.3%
ValueCountFrequency (%)
4115056800 1
 
3.3%
4117151000 2
 
6.7%
4117155000 1
 
3.3%
4117361000 6
20.0%
4121056000 1
 
3.3%
4127153200 1
 
3.3%
4129052000 2
 
6.7%
4131057000 2
 
6.7%
4143052000 1
 
3.3%
4148041000 6
20.0%
ValueCountFrequency (%)
4180037000 4
13.3%
4157058000 1
 
3.3%
4157025000 1
 
3.3%
4150034000 1
 
3.3%
4148041000 6
20.0%
4143052000 1
 
3.3%
4131057000 2
 
6.7%
4129052000 2
 
6.7%
4127153200 1
 
3.3%
4121056000 1
 
3.3%

비교값
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean281.32167
Minimum0.46
Maximum3171.86
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:09:33.736513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.46
5-th percentile11.4095
Q145.4025
median88.73
Q3160.51
95-th percentile1028.6135
Maximum3171.86
Range3171.4
Interquartile range (IQR)115.1075

Descriptive statistics

Standard deviation611.61059
Coefficient of variation (CV)2.1740615
Kurtosis18.195017
Mean281.32167
Median Absolute Deviation (MAD)53.945
Skewness4.0538312
Sum8439.65
Variance374067.52
MonotonicityNot monotonic
2023-12-10T23:09:33.951648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
33.9 1
 
3.3%
141.79 1
 
3.3%
116.74 1
 
3.3%
684.88 1
 
3.3%
58.31 1
 
3.3%
5.87 1
 
3.3%
88.33 1
 
3.3%
1309.85 1
 
3.3%
79.71 1
 
3.3%
18.87 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
0.46 1
3.3%
5.87 1
3.3%
18.18 1
3.3%
18.87 1
3.3%
24.89 1
3.3%
24.96 1
3.3%
33.9 1
3.3%
45.12 1
3.3%
46.25 1
3.3%
57.06 1
3.3%
ValueCountFrequency (%)
3171.86 1
3.3%
1309.85 1
3.3%
684.88 1
3.3%
581.3 1
3.3%
552.89 1
3.3%
389.13 1
3.3%
170.27 1
3.3%
166.75 1
3.3%
141.79 1
3.3%
122.04 1
3.3%

Interactions

2023-12-10T23:09:27.522320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:09:25.730279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:09:26.315616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:09:26.920464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:09:27.676367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:09:25.885585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:09:26.454487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:09:27.075448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:09:27.820758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:09:26.025185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:09:26.595885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:09:27.228074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:09:28.070210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:09:26.183030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:09:26.740325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:09:27.380199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:09:34.125815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명행정동명행정동 코드표준편차비교 시군구명비교 행정동명비교 행정동코드비교값
시군구명1.0001.0001.0000.0000.6340.7220.4990.996
행정동명1.0001.0001.0001.0001.0001.0001.0001.000
행정동 코드1.0001.0001.0000.0000.2760.7500.3560.873
표준편차0.0001.0000.0001.0000.5520.7450.6420.000
비교 시군구명0.6341.0000.2760.5521.0001.0001.0000.000
비교 행정동명0.7221.0000.7500.7451.0001.0001.0000.884
비교 행정동코드0.4991.0000.3560.6421.0001.0001.0000.349
비교값0.9961.0000.8730.0000.0000.8840.3491.000
2023-12-10T23:09:34.314744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비교 행정동명비교 시군구명
비교 행정동명1.0000.918
비교 시군구명0.9181.000
2023-12-10T23:09:34.455716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동 코드표준편차비교 행정동코드비교값비교 시군구명비교 행정동명
행정동 코드1.000-0.5340.2150.0630.1770.403
표준편차-0.5341.000-0.221-0.4470.2600.285
비교 행정동코드0.215-0.2211.0000.2030.9090.834
비교값0.063-0.4470.2031.0000.0000.563
비교 시군구명0.1770.2600.9090.0001.0000.918
비교 행정동명0.4030.2850.8340.5630.9181.000

Missing values

2023-12-10T23:09:28.263965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:09:28.536749image/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
02019-01경기도고양시정발산동4128553000180.25경기도안양시안양1동411715100033.9
12019-01경기도고양시삼송동412815750038.62경기도김포시통진읍4157025000581.3
22019-01경기도광명시소하2동412106500059.7경기도안양시범계동4117361000170.27
32019-01경기도김포시운양동415705800055.19경기도연천군중면418003700024.89
42019-01경기도광주시남종면41610350000.49경기도김포시운양동41570580003171.86
52019-01경기도부천시범박동411907800078.07경기도파주시진서면414804100094.5
62019-01경기도부천시상1동4119063000221.86경기도안양시범계동411736100057.06
72019-01경기도부천시심곡본동4119071000237.89경기도파주시진서면414804100092.13
82019-01경기도부천시원미1동411905600098.12경기도연천군중면4180037000122.04
92019-01경기도부천시중1동4119066000417.54경기도의왕시부곡동4143052000166.75
기준년월시도명시군구명행정동명행정동 코드표준편차비교 시도명비교 시군구명비교 행정동명비교 행정동코드비교값
202019-01경기도의정부시송산1동411505730029.46경기도안양시범계동4117361000552.89
212019-01경기도이천시대월면41500350007.24경기도연천군중면4180037000389.13
222019-01경기도평택시송북동412205600028.94경기도안산시사이동412715320018.87
232019-01경기도화성시남양읍415902620011.09경기도연천군중면418003700079.71
242019-01경기도가평군설악면41820310000.68경기도과천시갈현동41290520001309.85
252019-01경기도광명시광명4동4121055000485.45경기도파주시진서면414804100088.33
262019-01경기도고양시행신1동4128164000229.74경기도광명시광명5동41210560005.87
272019-01경기도군포시금정동4141056000157.68경기도이천시마장면415003400058.31
282019-01경기도남양주시수동면41360340002.05경기도안양시안양5동4117155000684.88
292019-01경기도동두천시중앙동412505350076.89경기도안양시범계동4117361000116.74