Overview

Dataset statistics

Number of variables12
Number of observations30
Missing cells1
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory104.4 B

Variable types

Categorical6
Text2
Numeric4

Dataset

Description샘플 데이터
Author경기콘텐츠진흥원
URLhttps://bigdata-region.kr/#/dataset/bfb4b03c-2a67-44e3-9094-f1fe0eb5db7f

Alerts

기준년월 has constant value ""Constant
시도명 has constant value ""Constant
비교 시도명 has constant value ""Constant
비교 시군구명 is highly overall correlated with 표준편차 and 2 other fieldsHigh correlation
비교 행정동명 is highly overall correlated with 표준편차 and 2 other fieldsHigh correlation
표준편차 is highly overall correlated with 비교값 and 2 other fieldsHigh correlation
비교 행정동코드 is highly overall correlated with 비교 시군구명 and 1 other fieldsHigh correlation
비교값 is highly overall correlated with 표준편차High correlation
비교값 has 1 (3.3%) missing valuesMissing
행정동명 has unique valuesUnique
행정동 코드 has unique valuesUnique
표준편차 has 2 (6.7%) zerosZeros

Reproduction

Analysis started2023-12-10 14:18:37.741680
Analysis finished2023-12-10 14:18:40.536910
Duration2.8 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
2020-07
30 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-07
2nd row2020-07
3rd row2020-07
4th row2020-07
5th row2020-07

Common Values

ValueCountFrequency (%)
2020-07 30
100.0%

Length

2023-12-10T23:18:40.618197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:18:40.747929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-07 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

2023-12-10T23:18:40.879046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:18:41.009825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

시군구명
Text

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:18:41.202605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.1
Min length3

Characters and Unicode

Total characters93
Distinct characters28
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

Unique12 ?
Unique (%)40.0%

Sample

1st row고양시
2nd row고양시
3rd row과천시
4th row구리시
5th row광명시
ValueCountFrequency (%)
고양시 3
 
10.0%
수원시 3
 
10.0%
평택시 3
 
10.0%
부천시 3
 
10.0%
성남시 2
 
6.7%
의정부시 2
 
6.7%
용인시 2
 
6.7%
안양시 1
 
3.3%
양주시 1
 
3.3%
포천시 1
 
3.3%
Other values (9) 9
30.0%
2023-12-10T23:18:41.583580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 31
33.3%
양 6
 
6.5%
부 5
 
5.4%
천 5
 
5.4%
남 4
 
4.3%
고 3
 
3.2%
수 3
 
3.2%
원 3
 
3.2%
평 3
 
3.2%
택 3
 
3.2%
Other values (18) 27
29.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 93
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 31
33.3%
양 6
 
6.5%
부 5
 
5.4%
천 5
 
5.4%
남 4
 
4.3%
고 3
 
3.2%
수 3
 
3.2%
원 3
 
3.2%
평 3
 
3.2%
택 3
 
3.2%
Other values (18) 27
29.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 93
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 31
33.3%
양 6
 
6.5%
부 5
 
5.4%
천 5
 
5.4%
남 4
 
4.3%
고 3
 
3.2%
수 3
 
3.2%
원 3
 
3.2%
평 3
 
3.2%
택 3
 
3.2%
Other values (18) 27
29.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 93
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 31
33.3%
양 6
 
6.5%
부 5
 
5.4%
천 5
 
5.4%
남 4
 
4.3%
고 3
 
3.2%
수 3
 
3.2%
원 3
 
3.2%
평 3
 
3.2%
택 3
 
3.2%
Other values (18) 27
29.0%

행정동명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:18:41.853004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4.5
Mean length3.4666667
Min length2

Characters and Unicode

Total characters104
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 row주교동
2nd row백석2동
3rd row부림동
4th row갈매동
5th row광명6동
ValueCountFrequency (%)
주교동 1
 
3.3%
백석2동 1
 
3.3%
춘궁동 1
 
3.3%
창수면 1
 
3.3%
서탄면 1
 
3.3%
포승읍 1
 
3.3%
고덕면 1
 
3.3%
운정3동 1
 
3.3%
의정부1동 1
 
3.3%
신곡2동 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T23:18:42.322210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 26
25.0%
1 5
 
4.8%
2 5
 
4.8%
고 4
 
3.8%
백 3
 
2.9%
면 3
 
2.9%
곡 2
 
1.9%
읍 2
 
1.9%
탄 2
 
1.9%
정 2
 
1.9%
Other values (43) 50
48.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 90
86.5%
Decimal Number 14
 
13.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 26
28.9%
고 4
 
4.4%
백 3
 
3.3%
면 3
 
3.3%
곡 2
 
2.2%
읍 2
 
2.2%
탄 2
 
2.2%
정 2
 
2.2%
미 2
 
2.2%
사 2
 
2.2%
Other values (39) 42
46.7%
Decimal Number
ValueCountFrequency (%)
1 5
35.7%
2 5
35.7%
3 2
 
14.3%
6 2
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 90
86.5%
Common 14
 
13.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 26
28.9%
고 4
 
4.4%
백 3
 
3.3%
면 3
 
3.3%
곡 2
 
2.2%
읍 2
 
2.2%
탄 2
 
2.2%
정 2
 
2.2%
미 2
 
2.2%
사 2
 
2.2%
Other values (39) 42
46.7%
Common
ValueCountFrequency (%)
1 5
35.7%
2 5
35.7%
3 2
 
14.3%
6 2
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 90
86.5%
ASCII 14
 
13.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 26
28.9%
고 4
 
4.4%
백 3
 
3.3%
면 3
 
3.3%
곡 2
 
2.2%
읍 2
 
2.2%
탄 2
 
2.2%
정 2
 
2.2%
미 2
 
2.2%
사 2
 
2.2%
Other values (39) 42
46.7%
ASCII
ValueCountFrequency (%)
1 5
35.7%
2 5
35.7%
3 2
 
14.3%
6 2
 
14.3%

행정동 코드
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1285735 × 109
Minimum4.111158 × 109
Maximum4.165035 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:18:42.520896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.111158 × 109
5-th percentile4.1116524 × 109
Q14.117631 × 109
median4.1245928 × 109
Q34.138311 × 109
95-th percentile4.1562894 × 109
Maximum4.165035 × 109
Range53877000
Interquartile range (IQR)20679950

Descriptive statistics

Standard deviation14950053
Coefficient of variation (CV)0.0036211184
Kurtosis0.17624904
Mean4.1285735 × 109
Median Absolute Deviation (MAD)9538850
Skewness0.94019681
Sum1.238572 × 1011
Variance2.235041 × 1014
MonotonicityNot monotonic
2023-12-10T23:18:42.693716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4128151000 1
 
3.3%
4117156000 1
 
3.3%
4128155000 1
 
3.3%
4145059000 1
 
3.3%
4165035000 1
 
3.3%
4122032000 1
 
3.3%
4122025600 1
 
3.3%
4122033000 1
 
3.3%
4148057000 1
 
3.3%
4115051000 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
4111158000 1
3.3%
4111571000 1
3.3%
4111752000 1
3.3%
4113164000 1
3.3%
4113566500 1
3.3%
4115051000 1
3.3%
4115056800 1
3.3%
4117156000 1
3.3%
4119056000 1
3.3%
4119068000 1
3.3%
ValueCountFrequency (%)
4165035000 1
3.3%
4163025000 1
3.3%
4148057000 1
3.3%
4146555000 1
3.3%
4146358500 1
3.3%
4145059000 1
3.3%
4143051000 1
3.3%
4139063100 1
3.3%
4136054500 1
3.3%
4131051000 1
3.3%

시설 대분류
Categorical

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
생활
10 
건강
8 
문화관광
7 
안전
5 

Length

Max length4
Median length2
Mean length2.4666667
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row생활
2nd row생활
3rd row생활
4th row생활
5th row문화관광

Common Values

ValueCountFrequency (%)
생활 10
33.3%
건강 8
26.7%
문화관광 7
23.3%
안전 5
16.7%

Length

2023-12-10T23:18:42.868388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:18:43.018379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생활 10
33.3%
건강 8
26.7%
문화관광 7
23.3%
안전 5
16.7%

표준편차
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean71.718
Minimum0
Maximum414.19
Zeros2
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:18:43.140653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.747
Q110.1075
median39.92
Q389.41
95-th percentile267.012
Maximum414.19
Range414.19
Interquartile range (IQR)79.3025

Descriptive statistics

Standard deviation95.860798
Coefficient of variation (CV)1.3366351
Kurtosis5.5062806
Mean71.718
Median Absolute Deviation (MAD)36.54
Skewness2.266952
Sum2151.54
Variance9189.2925
MonotonicityNot monotonic
2023-12-10T23:18:43.271564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.0 2
 
6.7%
77.59 1
 
3.3%
77.42 1
 
3.3%
6.1 1
 
3.3%
19.91 1
 
3.3%
14.62 1
 
3.3%
10.08 1
 
3.3%
23.58 1
 
3.3%
6.79 1
 
3.3%
10.19 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
0.0 2
6.7%
1.66 1
3.3%
3.04 1
3.3%
3.72 1
3.3%
6.1 1
3.3%
6.79 1
3.3%
10.08 1
3.3%
10.19 1
3.3%
13.97 1
3.3%
14.62 1
3.3%
ValueCountFrequency (%)
414.19 1
3.3%
305.82 1
3.3%
219.58 1
3.3%
179.72 1
3.3%
108.77 1
3.3%
104.35 1
3.3%
98.49 1
3.3%
92.29 1
3.3%
80.77 1
3.3%
77.59 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

2023-12-10T23:18:43.442775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:18:43.576516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

비교 시군구명
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
파주시
6 
동두천시
6 
평택시
6 
수원시
5 
하남시
2 
Other values (5)
5 

Length

Max length4
Median length3
Mean length3.2333333
Min length3

Unique

Unique5 ?
Unique (%)16.7%

Sample

1st row파주시
2nd row파주시
3rd row파주시
4th row동두천시
5th row평택시

Common Values

ValueCountFrequency (%)
파주시 6
20.0%
동두천시 6
20.0%
평택시 6
20.0%
수원시 5
16.7%
하남시 2
 
6.7%
안양시 1
 
3.3%
연천군 1
 
3.3%
성남시 1
 
3.3%
남양주시 1
 
3.3%
구리시 1
 
3.3%

Length

2023-12-10T23:18:43.745966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:18:43.963321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
파주시 6
20.0%
동두천시 6
20.0%
평택시 6
20.0%
수원시 5
16.7%
하남시 2
 
6.7%
안양시 1
 
3.3%
연천군 1
 
3.3%
성남시 1
 
3.3%
남양주시 1
 
3.3%
구리시 1
 
3.3%

비교 행정동명
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
진서면
6 
상패동
6 
서탄면
5 
율천동
5 
호계3동
1 
Other values (7)
7 

Length

Max length4
Median length3
Mean length3.1333333
Min length3

Unique

Unique8 ?
Unique (%)26.7%

Sample

1st row진서면
2nd row진서면
3rd row진서면
4th row상패동
5th row서탄면

Common Values

ValueCountFrequency (%)
진서면 6
20.0%
상패동 6
20.0%
서탄면 5
16.7%
율천동 5
16.7%
호계3동 1
 
3.3%
군남면 1
 
3.3%
서현1동 1
 
3.3%
감북동 1
 
3.3%
화도읍 1
 
3.3%
안중읍 1
 
3.3%
Other values (2) 2
 
6.7%

Length

2023-12-10T23:18:44.184824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
진서면 6
20.0%
상패동 6
20.0%
서탄면 5
16.7%
율천동 5
16.7%
호계3동 1
 
3.3%
군남면 1
 
3.3%
서현1동 1
 
3.3%
감북동 1
 
3.3%
화도읍 1
 
3.3%
안중읍 1
 
3.3%
Other values (2) 2
 
6.7%

비교 행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1298238 × 109
Minimum4.1111566 × 109
Maximum4.180031 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:18:44.376441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.1111566 × 109
5-th percentile4.1111566 × 109
Q14.122027 × 109
median4.12506 × 109
Q34.1450568 × 109
95-th percentile4.148041 × 109
Maximum4.180031 × 109
Range68874400
Interquartile range (IQR)23029775

Descriptive statistics

Standard deviation16272180
Coefficient of variation (CV)0.0039401632
Kurtosis1.4126306
Mean4.1298238 × 109
Median Absolute Deviation (MAD)11233800
Skewness1.0966434
Sum1.2389471 × 1011
Variance2.6478383 × 1014
MonotonicityNot monotonic
2023-12-10T23:18:44.521128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4148041000 6
20.0%
4125060000 6
20.0%
4122032000 5
16.7%
4111156600 5
16.7%
4117360000 1
 
3.3%
4180031000 1
 
3.3%
4113558000 1
 
3.3%
4145058000 1
 
3.3%
4136025600 1
 
3.3%
4122025300 1
 
3.3%
Other values (2) 2
 
6.7%
ValueCountFrequency (%)
4111156600 5
16.7%
4113558000 1
 
3.3%
4117360000 1
 
3.3%
4122025300 1
 
3.3%
4122032000 5
16.7%
4125060000 6
20.0%
4131054200 1
 
3.3%
4136025600 1
 
3.3%
4145053000 1
 
3.3%
4145058000 1
 
3.3%
ValueCountFrequency (%)
4180031000 1
 
3.3%
4148041000 6
20.0%
4145058000 1
 
3.3%
4145053000 1
 
3.3%
4136025600 1
 
3.3%
4131054200 1
 
3.3%
4125060000 6
20.0%
4122032000 5
16.7%
4122025300 1
 
3.3%
4117360000 1
 
3.3%

비교값
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)100.0%
Missing1
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean1288.6583
Minimum64.99
Maximum11819.38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:18:44.693418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum64.99
5-th percentile86.11
Q1183.06
median607.16
Q31330.82
95-th percentile4491.206
Maximum11819.38
Range11754.39
Interquartile range (IQR)1147.76

Descriptive statistics

Standard deviation2317.7524
Coefficient of variation (CV)1.798578
Kurtosis15.923072
Mean1288.6583
Median Absolute Deviation (MAD)453.31
Skewness3.7516474
Sum37371.09
Variance5371976
MonotonicityNot monotonic
2023-12-10T23:18:45.041042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
64.99 1
 
3.3%
844.03 1
 
3.3%
909.39 1
 
3.3%
183.06 1
 
3.3%
2339.35 1
 
3.3%
5145.11 1
 
3.3%
607.16 1
 
3.3%
159.11 1
 
3.3%
73.45 1
 
3.3%
11819.38 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
64.99 1
3.3%
73.45 1
3.3%
105.1 1
3.3%
114.51 1
3.3%
117.99 1
3.3%
153.85 1
3.3%
159.11 1
3.3%
183.06 1
3.3%
184.13 1
3.3%
188.95 1
3.3%
ValueCountFrequency (%)
11819.38 1
3.3%
5145.11 1
3.3%
3510.35 1
3.3%
2339.35 1
3.3%
1557.05 1
3.3%
1535.79 1
3.3%
1416.17 1
3.3%
1330.82 1
3.3%
1275.47 1
3.3%
909.39 1
3.3%

Interactions

2023-12-10T23:18:39.377115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:38.121905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:38.465335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:38.923468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:39.477803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:38.193433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:38.559286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:39.048866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:39.579740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:38.266410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:38.690723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:39.153843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:39.695119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:38.367015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:38.812202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:18:39.276049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:18:45.259327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명행정동명행정동 코드시설 대분류표준편차비교 시군구명비교 행정동명비교 행정동코드비교값
시군구명1.0001.0001.0000.0000.0000.7160.6350.0000.000
행정동명1.0001.0001.0001.0001.0001.0001.0001.0001.000
행정동 코드1.0001.0001.0000.4510.1850.3650.5540.0000.000
시설 대분류0.0001.0000.4511.0000.4310.0000.0000.0000.203
표준편차0.0001.0000.1850.4311.0000.7910.8430.7200.000
비교 시군구명0.7161.0000.3650.0000.7911.0001.0001.0000.354
비교 행정동명0.6351.0000.5540.0000.8431.0001.0001.0000.595
비교 행정동코드0.0001.0000.0000.0000.7201.0001.0001.0000.170
비교값0.0001.0000.0000.2030.0000.3540.5950.1701.000
2023-12-10T23:18:45.682049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설 대분류비교 시군구명비교 행정동명
시설 대분류1.0000.0000.000
비교 시군구명0.0001.0000.949
비교 행정동명0.0000.9491.000
2023-12-10T23:18:45.879436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동 코드표준편차비교 행정동코드비교값시설 대분류비교 시군구명비교 행정동명
행정동 코드1.000-0.3980.1540.1540.2010.1590.234
표준편차-0.3981.000-0.081-0.6050.2800.5160.543
비교 행정동코드0.154-0.0811.0000.2060.0000.9330.885
비교값0.154-0.6050.2061.0000.1470.0790.299
시설 대분류0.2010.2800.0000.1471.0000.0000.000
비교 시군구명0.1590.5160.9330.0790.0001.0000.949
비교 행정동명0.2340.5430.8850.2990.0000.9491.000

Missing values

2023-12-10T23:18:40.165465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:18:40.435581image/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 코드시설 대분류표준편차비교 시도명비교 시군구명비교 행정동명비교 행정동코드비교값
02020-07경기도고양시주교동4128151000생활77.59경기도파주시진서면414804100064.99
12020-07경기도고양시백석2동4128555200생활49.85경기도파주시진서면4148041000844.03
22020-07경기도과천시부림동4129054000생활64.59경기도파주시진서면4148041000272.33
32020-07경기도구리시갈매동4131051000생활13.97경기도동두천시상패동41250600001535.79
42020-07경기도광명시광명6동4121057000문화관광0.0경기도평택시서탄면41220320001416.17
52020-07경기도남양주시다산1동4136054500생활39.06경기도동두천시상패동4125060000114.51
62020-07경기도부천시고강본동4119081000문화관광24.65경기도평택시서탄면41220320003510.35
72020-07경기도부천시원미1동4119056000건강414.19경기도안양시호계3동4117360000153.85
82020-07경기도부천시중3동4119068000안전80.77경기도동두천시상패동4125060000706.51
92020-07경기도성남시고등동4113164000건강0.0경기도연천군군남면41800310001275.47
기준년월시도명시군구명행정동명행정동 코드시설 대분류표준편차비교 시도명비교 시군구명비교 행정동명비교 행정동코드비교값
202020-07경기도의왕시고천동4143051000안전219.58경기도하남시감북동4145058000188.95
212020-07경기도의정부시신곡2동4115056800문화관광40.78경기도파주시진서면414804100011819.38
222020-07경기도의정부시의정부1동4115051000건강305.82경기도남양주시화도읍413602560073.45
232020-07경기도파주시운정3동4148057000안전10.19경기도파주시진서면4148041000<NA>
242020-07경기도평택시고덕면4122033000생활6.79경기도수원시율천동4111156600159.11
252020-07경기도평택시포승읍4122025600건강23.58경기도평택시안중읍4122025300607.16
262020-07경기도평택시서탄면4122032000건강10.08경기도하남시신장2동41450530005145.11
272020-07경기도포천시창수면4165035000생활14.62경기도구리시교문2동41310542002339.35
282020-07경기도하남시춘궁동4145059000건강19.91경기도평택시서탄면4122032000183.06
292020-07경기도고양시성사2동4128155000문화관광6.1경기도수원시율천동4111156600909.39