Overview

Dataset statistics

Number of variables7
Number of observations83
Missing cells3
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.9 KiB
Average record size in memory60.6 B

Variable types

Categorical3
Numeric3
Text1

Dataset

Description경기도 광주시 읍면동 코드에 대한 데이터로 행정동명, 행정동 코드, 법정동명, 법정동 코드, 법정동리, 법정동리 코드를 제공합니다
URLhttps://www.data.go.kr/data/15101142/fileData.do

Alerts

데이터기준일 has constant value ""Constant
행정동명 is highly overall correlated with 행정동 코드 and 3 other fieldsHigh correlation
법정동명 is highly overall correlated with 행정동 코드 and 3 other fieldsHigh correlation
행정동 코드 is highly overall correlated with 행정동명 and 1 other fieldsHigh correlation
법정동 코드 is highly overall correlated with 법정동리 코드 and 2 other fieldsHigh correlation
법정동리 코드 is highly overall correlated with 법정동 코드 and 2 other fieldsHigh correlation
법정동 코드 has 1 (1.2%) missing valuesMissing
법정동리 has 1 (1.2%) missing valuesMissing
법정동리 코드 has 1 (1.2%) missing valuesMissing

Reproduction

Analysis started2023-12-12 22:54:49.863959
Analysis finished2023-12-12 22:54:51.003831
Duration1.14 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)19.3%
Missing0
Missing (%)0.0%
Memory size796.0 B
곤지암읍
16 
초월읍
12 
퇴촌면
10 
도척면
8 
남종면
8 
Other values (11)
29 

Length

Max length5
Median length3
Mean length3.5301205
Min length3

Unique

Unique5 ?
Unique (%)6.0%

Sample

1st row경안동
2nd row쌍령동
3rd row송정동
4th row탄벌동
5th row탄벌동

Common Values

ValueCountFrequency (%)
곤지암읍 16
19.3%
초월읍 12
14.5%
퇴촌면 10
12.0%
도척면 8
9.6%
남종면 8
9.6%
남한산성면 8
9.6%
광남1동 6
 
7.2%
탄벌동 3
 
3.6%
오포1동 3
 
3.6%
경안동 2
 
2.4%
Other values (6) 7
8.4%

Length

2023-12-13T07:54:51.291892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
곤지암읍 16
19.3%
초월읍 12
14.5%
퇴촌면 10
12.0%
도척면 8
9.6%
남종면 8
9.6%
남한산성면 8
9.6%
광남1동 6
 
7.2%
탄벌동 3
 
3.6%
오포1동 3
 
3.6%
경안동 2
 
2.4%
Other values (6) 7
8.4%

행정동 코드
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)19.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1610371 × 109
Minimum4.1610253 × 109
Maximum4.161061 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size879.0 B
2023-12-13T07:54:51.379984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.1610253 × 109
5-th percentile4.1610253 × 109
Q14.1610259 × 109
median4.161034 × 109
Q34.161044 × 109
95-th percentile4.161058 × 109
Maximum4.161061 × 109
Range35700
Interquartile range (IQR)18100

Descriptive statistics

Standard deviation11946.837
Coefficient of variation (CV)2.87112 × 10-6
Kurtosis-0.81371255
Mean4.1610371 × 109
Median Absolute Deviation (MAD)8100
Skewness0.81354964
Sum3.4536608 × 1011
Variance1.4272691 × 108
MonotonicityNot monotonic
2023-12-13T07:54:51.469246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
4161025900 16
19.3%
4161025300 12
14.5%
4161034000 10
12.0%
4161037000 8
9.6%
4161035000 8
9.6%
4161033000 8
9.6%
4161056000 6
 
7.2%
4161055000 3
 
3.6%
4161058000 3
 
3.6%
4161051000 2
 
2.4%
Other values (6) 7
8.4%
ValueCountFrequency (%)
4161025300 12
14.5%
4161025900 16
19.3%
4161033000 8
9.6%
4161034000 10
12.0%
4161035000 8
9.6%
4161037000 8
9.6%
4161051000 2
 
2.4%
4161052000 1
 
1.2%
4161054000 1
 
1.2%
4161055000 3
 
3.6%
ValueCountFrequency (%)
4161061000 1
 
1.2%
4161060000 1
 
1.2%
4161059000 2
 
2.4%
4161058000 3
3.6%
4161057000 1
 
1.2%
4161056000 6
7.2%
4161055000 3
3.6%
4161054000 1
 
1.2%
4161052000 1
 
1.2%
4161051000 2
 
2.4%

법정동명
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)32.5%
Missing0
Missing (%)0.0%
Memory size796.0 B
곤지암읍
16 
초월읍
12 
퇴촌면
10 
남한산성면
8 
남종면
8 
Other values (22)
29 

Length

Max length5
Median length3
Mean length3.3493976
Min length2

Unique

Unique21 ?
Unique (%)25.3%

Sample

1st row경안동
2nd row쌍령동
3rd row송정동
4th row회덕동
5th row탄벌동

Common Values

ValueCountFrequency (%)
곤지암읍 16
19.3%
초월읍 12
14.5%
퇴촌면 10
12.0%
남한산성면 8
9.6%
남종면 8
9.6%
도척면 8
9.6%
역동 1
 
1.2%
송정동 1
 
1.2%
회덕동 1
 
1.2%
탄벌동 1
 
1.2%
Other values (17) 17
20.5%

Length

2023-12-13T07:54:51.596945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
곤지암읍 16
19.3%
초월읍 12
14.5%
퇴촌면 10
12.0%
남한산성면 8
9.6%
남종면 8
9.6%
도척면 8
9.6%
고산동 1
 
1.2%
경안동 1
 
1.2%
신현동 1
 
1.2%
양벌동 1
 
1.2%
Other values (17) 17
20.5%

법정동 코드
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)31.7%
Missing1
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean4.1610258 × 109
Minimum4.1610101 × 109
Maximum4.161037 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size879.0 B
2023-12-13T07:54:51.696666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.1610101 × 109
5-th percentile4.1610105 × 109
Q14.1610253 × 109
median4.1610259 × 109
Q34.161034 × 109
95-th percentile4.161037 × 109
Maximum4.161037 × 109
Range26900
Interquartile range (IQR)8700

Descriptive statistics

Standard deviation9381.2783
Coefficient of variation (CV)2.254559 × 10-6
Kurtosis-1.0344932
Mean4.1610258 × 109
Median Absolute Deviation (MAD)8100
Skewness-0.60379857
Sum3.4120412 × 1011
Variance88008383
MonotonicityIncreasing
2023-12-13T07:54:51.795298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
4161025900 16
19.3%
4161025300 12
14.5%
4161034000 10
12.0%
4161037000 8
9.6%
4161035000 8
9.6%
4161033000 8
9.6%
4161010100 1
 
1.2%
4161011500 1
 
1.2%
4161012000 1
 
1.2%
4161011900 1
 
1.2%
Other values (16) 16
19.3%
ValueCountFrequency (%)
4161010100 1
1.2%
4161010200 1
1.2%
4161010300 1
1.2%
4161010400 1
1.2%
4161010500 1
1.2%
4161010600 1
1.2%
4161010700 1
1.2%
4161010800 1
1.2%
4161010900 1
1.2%
4161011000 1
1.2%
ValueCountFrequency (%)
4161037000 8
9.6%
4161035000 8
9.6%
4161034000 10
12.0%
4161033000 8
9.6%
4161025900 16
19.3%
4161025300 12
14.5%
4161012000 1
 
1.2%
4161011900 1
 
1.2%
4161011800 1
 
1.2%
4161011700 1
 
1.2%

법정동리
Text

MISSING 

Distinct82
Distinct (%)100.0%
Missing1
Missing (%)1.2%
Memory size796.0 B
2023-12-13T07:54:52.026646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.9878049
Min length2

Characters and Unicode

Total characters245
Distinct characters93
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

Unique82 ?
Unique (%)100.0%

Sample

1st row경안동
2nd row쌍령동
3rd row송정동
4th row회덕동
5th row탄벌동
ValueCountFrequency (%)
경안동 1
 
1.2%
정지리 1
 
1.2%
광동리 1
 
1.2%
영동리 1
 
1.2%
우산리 1
 
1.2%
관음리 1
 
1.2%
진우리 1
 
1.2%
방도리 1
 
1.2%
궁평리 1
 
1.2%
상림리 1
 
1.2%
Other values (72) 72
87.8%
2023-12-13T07:54:52.457663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 62
25.3%
동 25
 
10.2%
산 5
 
2.0%
도 5
 
2.0%
수 5
 
2.0%
지 5
 
2.0%
신 4
 
1.6%
천 4
 
1.6%
현 4
 
1.6%
삼 4
 
1.6%
Other values (83) 122
49.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 245
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 62
25.3%
동 25
 
10.2%
산 5
 
2.0%
도 5
 
2.0%
수 5
 
2.0%
지 5
 
2.0%
신 4
 
1.6%
천 4
 
1.6%
현 4
 
1.6%
삼 4
 
1.6%
Other values (83) 122
49.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 245
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 62
25.3%
동 25
 
10.2%
산 5
 
2.0%
도 5
 
2.0%
수 5
 
2.0%
지 5
 
2.0%
신 4
 
1.6%
천 4
 
1.6%
현 4
 
1.6%
삼 4
 
1.6%
Other values (83) 122
49.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 245
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 62
25.3%
동 25
 
10.2%
산 5
 
2.0%
도 5
 
2.0%
수 5
 
2.0%
지 5
 
2.0%
신 4
 
1.6%
천 4
 
1.6%
현 4
 
1.6%
삼 4
 
1.6%
Other values (83) 122
49.8%

법정동리 코드
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct82
Distinct (%)100.0%
Missing1
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean4.1610259 × 109
Minimum4.1610101 × 109
Maximum4.161037 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size879.0 B
2023-12-13T07:54:52.587722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.1610101 × 109
5-th percentile4.1610105 × 109
Q14.1610253 × 109
median4.1610259 × 109
Q34.161034 × 109
95-th percentile4.161037 × 109
Maximum4.161037 × 109
Range26928
Interquartile range (IQR)8704.5

Descriptive statistics

Standard deviation9390.914
Coefficient of variation (CV)2.2568747 × 10-6
Kurtosis-1.0333564
Mean4.1610259 × 109
Median Absolute Deviation (MAD)8096
Skewness-0.60527544
Sum3.4120412 × 1011
Variance88189265
MonotonicityNot monotonic
2023-12-13T07:54:52.718644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4161010100 1
 
1.2%
4161034029 1
 
1.2%
4161034026 1
 
1.2%
4161034021 1
 
1.2%
4161034024 1
 
1.2%
4161034023 1
 
1.2%
4161034022 1
 
1.2%
4161033028 1
 
1.2%
4161033023 1
 
1.2%
4161033027 1
 
1.2%
Other values (72) 72
86.7%
ValueCountFrequency (%)
4161010100 1
1.2%
4161010200 1
1.2%
4161010300 1
1.2%
4161010400 1
1.2%
4161010500 1
1.2%
4161010600 1
1.2%
4161010700 1
1.2%
4161010800 1
1.2%
4161010900 1
1.2%
4161011000 1
1.2%
ValueCountFrequency (%)
4161037028 1
1.2%
4161037027 1
1.2%
4161037026 1
1.2%
4161037025 1
1.2%
4161037024 1
1.2%
4161037023 1
1.2%
4161037022 1
1.2%
4161037021 1
1.2%
4161035028 1
1.2%
4161035027 1
1.2%

데이터기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size796.0 B
2023-06-14
83 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-06-14
2nd row2023-06-14
3rd row2023-06-14
4th row2023-06-14
5th row2023-06-14

Common Values

ValueCountFrequency (%)
2023-06-14 83
100.0%

Length

2023-12-13T07:54:52.838141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:54:52.936465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-06-14 83
100.0%

Interactions

2023-12-13T07:54:50.567752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:50.114753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:50.346113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:50.639133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:50.201593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:50.421527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:50.702575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:50.272810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:54:50.502713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:54:52.996952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동명행정동 코드법정동명법정동 코드법정동리법정동리 코드
행정동명1.0001.0001.0001.0001.0001.000
행정동 코드1.0001.0001.0000.9641.0000.964
법정동명1.0001.0001.0001.0001.0001.000
법정동 코드1.0000.9641.0001.0001.0001.000
법정동리1.0001.0001.0001.0001.0001.000
법정동리 코드1.0000.9641.0001.0001.0001.000
2023-12-13T07:54:53.096745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동명법정동명
행정동명1.0000.914
법정동명0.9141.000
2023-12-13T07:54:53.165847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동 코드법정동 코드법정동리 코드행정동명법정동명
행정동 코드1.000-0.126-0.1250.9330.858
법정동 코드-0.1261.0000.9920.9270.847
법정동리 코드-0.1250.9921.0000.9270.847
행정동명0.9330.9270.9271.0000.914
법정동명0.8580.8470.8470.9141.000

Missing values

2023-12-13T07:54:50.791035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:54:50.879317image/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.
2023-12-13T07:54:50.957913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

행정동명행정동 코드법정동명법정동 코드법정동리법정동리 코드데이터기준일
0경안동4161051000경안동4161010100경안동41610101002023-06-14
1쌍령동4161054000쌍령동4161010200쌍령동41610102002023-06-14
2송정동4161052000송정동4161010300송정동41610103002023-06-14
3탄벌동4161055000회덕동4161010400회덕동41610104002023-06-14
4탄벌동4161055000탄벌동4161010500탄벌동41610105002023-06-14
5탄벌동4161055000목현동4161010600목현동41610106002023-06-14
6광남1동4161056000삼동4161010700삼동41610107002023-06-14
7광남1동4161056000중대동4161010800중대동41610108002023-06-14
8광남1동4161056000직동4161010900직동41610109002023-06-14
9광남1동4161056000태전동4161011000태전동41610110002023-06-14
행정동명행정동 코드법정동명법정동 코드법정동리법정동리 코드데이터기준일
73남종면4161035000남종면4161035000이석리41610350232023-06-14
74남한산성면4161037000남한산성면4161037000검복리41610370282023-06-14
75남한산성면4161037000남한산성면4161037000오전리41610370262023-06-14
76남한산성면4161037000남한산성면4161037000불당리41610370272023-06-14
77남한산성면4161037000남한산성면4161037000산성리41610370212023-06-14
78남한산성면4161037000남한산성면4161037000광지원리41610370232023-06-14
79남한산성면4161037000남한산성면4161037000하번천리41610370252023-06-14
80남한산성면4161037000남한산성면4161037000엄미리41610370222023-06-14
81남한산성면4161037000남한산성면4161037000상번천리41610370242023-06-14
82광남2동4161057000<NA><NA><NA><NA>2023-06-14