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/c082ef64-6959-451b-9ade-637231d7c2e4

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 1 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
비교 행정동코드 is highly overall correlated with 비교 시군구명 and 1 other fieldsHigh correlation
표준편차 is highly overall correlated with 행정동 코드High correlation
행정동명 has unique valuesUnique
행정동 코드 has unique valuesUnique
표준편차 has unique valuesUnique
비교값 has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:56:16.185377
Analysis finished2023-12-10 13:56:19.995487
Duration3.81 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

2023-12-10T22:56:20.094716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:56:20.281613image/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

2023-12-10T22:56:20.438801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:56:20.614387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

시군구명
Text

Distinct22
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:56:20.980730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length5.1666667
Min length3

Characters and Unicode

Total characters155
Distinct characters41
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

Unique15 ?
Unique (%)50.0%

Sample

1st row광주시
2nd row고양시 덕양구
3rd row구리시
4th row김포시
5th row군포시
ValueCountFrequency (%)
성남시 4
 
8.7%
안양시 4
 
8.7%
고양시 3
 
6.5%
용인시 3
 
6.5%
덕양구 3
 
6.5%
수원시 2
 
4.3%
만안구 2
 
4.3%
처인구 2
 
4.3%
동안구 2
 
4.3%
파주시 2
 
4.3%
Other values (17) 19
41.3%
2023-12-10T22:56:21.848835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 28
18.1%
구 17
 
11.0%
16
 
10.3%
양 11
 
7.1%
안 8
 
5.2%
남 6
 
3.9%
성 5
 
3.2%
인 5
 
3.2%
수 4
 
2.6%
고 3
 
1.9%
Other values (31) 52
33.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 139
89.7%
Space Separator 16
 
10.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 28
20.1%
구 17
 
12.2%
양 11
 
7.9%
안 8
 
5.8%
남 6
 
4.3%
성 5
 
3.6%
인 5
 
3.6%
수 4
 
2.9%
고 3
 
2.2%
군 3
 
2.2%
Other values (30) 49
35.3%
Space Separator
ValueCountFrequency (%)
16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 139
89.7%
Common 16
 
10.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 28
20.1%
구 17
 
12.2%
양 11
 
7.9%
안 8
 
5.8%
남 6
 
4.3%
성 5
 
3.6%
인 5
 
3.6%
수 4
 
2.9%
고 3
 
2.2%
군 3
 
2.2%
Other values (30) 49
35.3%
Common
ValueCountFrequency (%)
16
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 139
89.7%
ASCII 16
 
10.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 28
20.1%
구 17
 
12.2%
양 11
 
7.9%
안 8
 
5.8%
남 6
 
4.3%
성 5
 
3.6%
인 5
 
3.6%
수 4
 
2.9%
고 3
 
2.2%
군 3
 
2.2%
Other values (30) 49
35.3%
ASCII
ValueCountFrequency (%)
16
100.0%

행정동명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T22:56:22.176128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.3666667
Min length3

Characters and Unicode

Total characters101
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 row오포읍
2nd row행주동
3rd row수택2동
4th row월곶면
5th row오금동
ValueCountFrequency (%)
오포읍 1
 
3.3%
행주동 1
 
3.3%
화정2동 1
 
3.3%
창릉동 1
 
3.3%
장안면 1
 
3.3%
설악면 1
 
3.3%
춘궁동 1
 
3.3%
감북동 1
 
3.3%
조리읍 1
 
3.3%
운정3동 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T22:56:22.784381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 22
21.8%
면 6
 
5.9%
정 4
 
4.0%
1 4
 
4.0%
2 4
 
4.0%
읍 3
 
3.0%
원 2
 
2.0%
안 2
 
2.0%
달 2
 
2.0%
부 2
 
2.0%
Other values (45) 50
49.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 91
90.1%
Decimal Number 10
 
9.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 22
24.2%
면 6
 
6.6%
정 4
 
4.4%
읍 3
 
3.3%
원 2
 
2.2%
안 2
 
2.2%
달 2
 
2.2%
부 2
 
2.2%
남 2
 
2.2%
성 2
 
2.2%
Other values (42) 44
48.4%
Decimal Number
ValueCountFrequency (%)
1 4
40.0%
2 4
40.0%
3 2
20.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 91
90.1%
Common 10
 
9.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 22
24.2%
면 6
 
6.6%
정 4
 
4.4%
읍 3
 
3.3%
원 2
 
2.2%
안 2
 
2.2%
달 2
 
2.2%
부 2
 
2.2%
남 2
 
2.2%
성 2
 
2.2%
Other values (42) 44
48.4%
Common
ValueCountFrequency (%)
1 4
40.0%
2 4
40.0%
3 2
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 91
90.1%
ASCII 10
 
9.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 22
24.2%
면 6
 
6.6%
정 4
 
4.4%
읍 3
 
3.3%
원 2
 
2.2%
안 2
 
2.2%
달 2
 
2.2%
부 2
 
2.2%
남 2
 
2.2%
성 2
 
2.2%
Other values (42) 44
48.4%
ASCII
ValueCountFrequency (%)
1 4
40.0%
2 4
40.0%
3 2
20.0%

행정동 코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1354504 × 109
Minimum4.111356 × 109
Maximum4.183038 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:56:23.013931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.111356 × 109
5-th percentile4.1122834 × 109
Q14.1171615 × 109
median4.130057 × 109
Q34.1476594 × 109
95-th percentile4.1725783 × 109
Maximum4.183038 × 109
Range71682000
Interquartile range (IQR)30497900

Descriptive statistics

Standard deviation20562715
Coefficient of variation (CV)0.0049723036
Kurtosis-0.21148619
Mean4.1354504 × 109
Median Absolute Deviation (MAD)16287000
Skewness0.66734748
Sum1.2406351 × 1011
Variance4.2282525 × 1014
MonotonicityNot monotonic
2023-12-10T22:56:23.284796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4161025000 1
 
3.3%
4146559000 1
 
3.3%
4129056000 1
 
3.3%
4128162200 1
 
3.3%
4128158000 1
 
3.3%
4159037000 1
 
3.3%
4182031000 1
 
3.3%
4145059000 1
 
3.3%
4145058000 1
 
3.3%
4148026200 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
4111356000 1
3.3%
4111571000 1
3.3%
4113154000 1
3.3%
4113351000 1
3.3%
4113555000 1
3.3%
4113560000 1
3.3%
4115052000 1
3.3%
4117161000 1
3.3%
4117163000 1
3.3%
4117354600 1
3.3%
ValueCountFrequency (%)
4183038000 1
3.3%
4182031000 1
3.3%
4161025000 1
3.3%
4159037000 1
3.3%
4157035000 1
3.3%
4150025300 1
3.3%
4148057000 1
3.3%
4148026200 1
3.3%
4146559000 1
3.3%
4146134000 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

2023-12-10T22:56:23.503287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:56:23.673427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

비교 시군구명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
이천시
10 
파주시
9 
성남시 수정구
7 
군포시
 
1
김포시
 
1
Other values (2)
2 

Length

Max length7
Median length3
Mean length3.9333333
Min length3

Unique

Unique4 ?
Unique (%)13.3%

Sample

1st row성남시 수정구
2nd row파주시
3rd row이천시
4th row성남시 수정구
5th row군포시

Common Values

ValueCountFrequency (%)
이천시 10
33.3%
파주시 9
30.0%
성남시 수정구 7
23.3%
군포시 1
 
3.3%
김포시 1
 
3.3%
양주시 1
 
3.3%
연천군 1
 
3.3%

Length

2023-12-10T22:56:23.856612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:56:24.050834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
이천시 10
27.0%
파주시 9
24.3%
성남시 7
18.9%
수정구 7
18.9%
군포시 1
 
2.7%
김포시 1
 
2.7%
양주시 1
 
2.7%
연천군 1
 
2.7%

비교 행정동명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
창전동
10 
태평4동
7 
장단면
5 
진동면
4 
광정동
 
1
Other values (3)
3 

Length

Max length4
Median length3
Mean length3.2
Min length2

Unique

Unique4 ?
Unique (%)13.3%

Sample

1st row태평4동
2nd row진동면
3rd row창전동
4th row태평4동
5th row광정동

Common Values

ValueCountFrequency (%)
창전동 10
33.3%
태평4동 7
23.3%
장단면 5
16.7%
진동면 4
 
13.3%
광정동 1
 
3.3%
구래동 1
 
3.3%
장흥면 1
 
3.3%
중면 1
 
3.3%

Length

2023-12-10T22:56:24.276386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:56:24.502569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
창전동 10
33.3%
태평4동 7
23.3%
장단면 5
16.7%
진동면 4
 
13.3%
광정동 1
 
3.3%
구래동 1
 
3.3%
장흥면 1
 
3.3%
중면 1
 
3.3%

비교 행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)26.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1422049 × 109
Minimum4.1131561 × 109
Maximum4.180037 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:56:24.690270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.1131561 × 109
5-th percentile4.1131561 × 109
Q14.1428062 × 109
median4.14804 × 109
Q34.150051 × 109
95-th percentile4.1603444 × 109
Maximum4.180037 × 109
Range66880900
Interquartile range (IQR)7244750

Descriptive statistics

Standard deviation17534036
Coefficient of variation (CV)0.0042330199
Kurtosis-0.053056514
Mean4.1422049 × 109
Median Absolute Deviation (MAD)2011000
Skewness-0.67514902
Sum1.2426615 × 1011
Variance3.0744241 × 1014
MonotonicityNot monotonic
2023-12-10T22:56:24.882220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4150051000 10
33.3%
4113156100 7
23.3%
4148039000 5
16.7%
4148040000 4
 
13.3%
4141062000 1
 
3.3%
4157057000 1
 
3.3%
4163034000 1
 
3.3%
4180037000 1
 
3.3%
ValueCountFrequency (%)
4113156100 7
23.3%
4141062000 1
 
3.3%
4148039000 5
16.7%
4148040000 4
 
13.3%
4150051000 10
33.3%
4157057000 1
 
3.3%
4163034000 1
 
3.3%
4180037000 1
 
3.3%
ValueCountFrequency (%)
4180037000 1
 
3.3%
4163034000 1
 
3.3%
4157057000 1
 
3.3%
4150051000 10
33.3%
4148040000 4
 
13.3%
4148039000 5
16.7%
4141062000 1
 
3.3%
4113156100 7
23.3%

표준편차
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93.435333
Minimum16.64
Maximum422.44
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:56:25.087606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16.64
5-th percentile17.618
Q141.24
median62.535
Q3118.0425
95-th percentile218.5155
Maximum422.44
Range405.8
Interquartile range (IQR)76.8025

Descriptive statistics

Standard deviation85.959613
Coefficient of variation (CV)0.91999044
Kurtosis6.4855463
Mean93.435333
Median Absolute Deviation (MAD)32.905
Skewness2.2599461
Sum2803.06
Variance7389.0551
MonotonicityNot monotonic
2023-12-10T22:56:25.374135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
112.92 1
 
3.3%
119.75 1
 
3.3%
17.06 1
 
3.3%
422.44 1
 
3.3%
40.79 1
 
3.3%
26.03 1
 
3.3%
30.16 1
 
3.3%
49.75 1
 
3.3%
53.03 1
 
3.3%
47.92 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
16.64 1
3.3%
17.06 1
3.3%
18.3 1
3.3%
26.03 1
3.3%
30.16 1
3.3%
31.01 1
3.3%
39.38 1
3.3%
40.79 1
3.3%
42.59 1
3.3%
47.92 1
3.3%
ValueCountFrequency (%)
422.44 1
3.3%
223.47 1
3.3%
212.46 1
3.3%
208.19 1
3.3%
184.86 1
3.3%
128.06 1
3.3%
124.43 1
3.3%
119.75 1
3.3%
112.92 1
3.3%
102.94 1
3.3%

비교값
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean183.45733
Minimum17.86
Maximum567.16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T22:56:25.647622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17.86
5-th percentile31.3195
Q190.445
median148.885
Q3263.305
95-th percentile442.95
Maximum567.16
Range549.3
Interquartile range (IQR)172.86

Descriptive statistics

Standard deviation135.9741
Coefficient of variation (CV)0.74117561
Kurtosis1.8891816
Mean183.45733
Median Absolute Deviation (MAD)83.17
Skewness1.30923
Sum5503.72
Variance18488.956
MonotonicityNot monotonic
2023-12-10T22:56:25.909767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
114.22 1
 
3.3%
93.61 1
 
3.3%
540.69 1
 
3.3%
32.26 1
 
3.3%
32.57 1
 
3.3%
187.29 1
 
3.3%
30.55 1
 
3.3%
297.94 1
 
3.3%
131.83 1
 
3.3%
267.18 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
17.86 1
3.3%
30.55 1
3.3%
32.26 1
3.3%
32.57 1
3.3%
62.99 1
3.3%
68.44 1
3.3%
84.72 1
3.3%
89.39 1
3.3%
93.61 1
3.3%
99.28 1
3.3%
ValueCountFrequency (%)
567.16 1
3.3%
540.69 1
3.3%
323.49 1
3.3%
300.21 1
3.3%
297.94 1
3.3%
291.49 1
3.3%
287.82 1
3.3%
267.18 1
3.3%
251.68 1
3.3%
245.48 1
3.3%

Interactions

2023-12-10T22:56:18.881809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:56:16.770477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:56:17.537292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:56:18.281673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:56:19.027973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:56:16.970495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:56:17.775335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:56:18.440727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:56:19.171534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:56:17.122130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:56:17.955470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:56:18.590891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:56:19.305473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:56:17.356272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:56:18.125629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:56:18.748769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:56:26.096068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명행정동명행정동 코드비교 시군구명비교 행정동명비교 행정동코드표준편차비교값
시군구명1.0001.0001.0000.8580.8530.8860.3790.933
행정동명1.0001.0001.0001.0001.0001.0001.0001.000
행정동 코드1.0001.0001.0000.4160.4380.1330.3400.000
비교 시군구명0.8581.0000.4161.0001.0001.0000.0000.327
비교 행정동명0.8531.0000.4381.0001.0001.0000.0000.176
비교 행정동코드0.8861.0000.1331.0001.0001.0000.0000.569
표준편차0.3791.0000.3400.0000.0000.0001.0000.332
비교값0.9331.0000.0000.3270.1760.5690.3321.000
2023-12-10T22:56:26.356332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비교 행정동명비교 시군구명
비교 행정동명1.0000.978
비교 시군구명0.9781.000
2023-12-10T22:56:26.498806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동 코드비교 행정동코드표준편차비교값비교 시군구명비교 행정동명
행정동 코드1.000-0.159-0.5010.1050.2000.191
비교 행정동코드-0.1591.000-0.051-0.2000.9790.957
표준편차-0.501-0.0511.000-0.3200.0000.000
비교값0.105-0.200-0.3201.0000.0750.000
비교 시군구명0.2000.9790.0000.0751.0000.978
비교 행정동명0.1910.9570.0000.0000.9781.000

Missing values

2023-12-10T22:56:19.623629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:56:19.896120image/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 코드비교 시도명비교 시군구명비교 행정동명비교 행정동코드표준편차비교값
02019-01경기도광주시오포읍4161025000경기도성남시 수정구태평4동4113156100112.92114.22
12019-01경기도고양시 덕양구행주동4128163000경기도파주시진동면414804000068.017.86
22019-01경기도구리시수택2동4131058000경기도이천시창전동4150051000223.47300.21
32019-01경기도김포시월곶면4157035000경기도성남시 수정구태평4동411315610056.34147.64
42019-01경기도군포시오금동4141058000경기도군포시광정동414106200031.01567.16
52019-01경기도성남시 분당구이매1동4113560000경기도이천시창전동415005100064.14245.48
62019-01경기도성남시 분당구정자1동4113555000경기도파주시장단면4148039000128.06173.18
72019-01경기도성남시 수정구태평1동4113154000경기도이천시창전동415005100095.97214.57
82019-01경기도성남시 중원구성남동4113351000경기도이천시창전동4150051000124.43150.79
92019-01경기도수원시 권선구서둔동4111356000경기도성남시 수정구태평4동4113156100208.19150.13
기준년월시도명시군구명행정동명행정동 코드비교 시도명비교 시군구명비교 행정동명비교 행정동코드표준편차비교값
202019-01경기도이천시부발읍4150025300경기도성남시 수정구태평4동411315610060.93323.49
212019-01경기도파주시운정3동4148057000경기도이천시창전동415005100087.44119.19
222019-01경기도파주시조리읍4148026200경기도성남시 수정구태평4동411315610047.92267.18
232019-01경기도하남시감북동4145058000경기도이천시창전동415005100053.03131.83
242019-01경기도하남시춘궁동4145059000경기도이천시창전동415005100049.75297.94
252019-01경기도가평군설악면4182031000경기도이천시창전동415005100030.1630.55
262019-01경기도화성시장안면4159037000경기도파주시장단면414803900026.03187.29
272019-01경기도고양시 덕양구창릉동4128158000경기도연천군중면418003700040.7932.57
282019-01경기도고양시 덕양구화정2동4128162200경기도파주시진동면4148040000422.4432.26
292019-01경기도과천시문원동4129056000경기도성남시 수정구태평4동411315610017.06540.69