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/b3f69806-2bb3-4199-9109-0b58d2b4668b

Alerts

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

Reproduction

Analysis started2023-12-10 14:20:06.589893
Analysis finished2023-12-10 14:20:08.592714
Duration2 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
2020-02
30 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-02
2nd row2020-02
3rd row2020-02
4th row2020-02
5th row2020-02

Common Values

ValueCountFrequency (%)
2020-02 30
100.0%

Length

2023-12-10T23:20:08.666892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:20:08.764754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-02 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

2023-12-10T23:20:08.861181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:20:08.954220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

시군구명
Text

Distinct16
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:20:09.122275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.1
Min length3

Characters and Unicode

Total characters93
Distinct characters26
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

Unique7 ?
Unique (%)23.3%

Sample

1st row구리시
2nd row고양시
3rd row부천시
4th row성남시
5th row부천시
ValueCountFrequency (%)
부천시 4
13.3%
성남시 4
13.3%
안산시 3
10.0%
수원시 2
 
6.7%
시흥시 2
 
6.7%
이천시 2
 
6.7%
파주시 2
 
6.7%
화성시 2
 
6.7%
동두천시 2
 
6.7%
구리시 1
 
3.3%
Other values (6) 6
20.0%
2023-12-10T23:20:09.419868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 32
34.4%
천 8
 
8.6%
성 6
 
6.5%
남 6
 
6.5%
부 4
 
4.3%
산 4
 
4.3%
주 3
 
3.2%
안 3
 
3.2%
양 2
 
2.2%
두 2
 
2.2%
Other values (16) 23
24.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 93
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 32
34.4%
천 8
 
8.6%
성 6
 
6.5%
남 6
 
6.5%
부 4
 
4.3%
산 4
 
4.3%
주 3
 
3.2%
안 3
 
3.2%
양 2
 
2.2%
두 2
 
2.2%
Other values (16) 23
24.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 93
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 32
34.4%
천 8
 
8.6%
성 6
 
6.5%
남 6
 
6.5%
부 4
 
4.3%
산 4
 
4.3%
주 3
 
3.2%
안 3
 
3.2%
양 2
 
2.2%
두 2
 
2.2%
Other values (16) 23
24.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 93
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 32
34.4%
천 8
 
8.6%
성 6
 
6.5%
남 6
 
6.5%
부 4
 
4.3%
산 4
 
4.3%
주 3
 
3.2%
안 3
 
3.2%
양 2
 
2.2%
두 2
 
2.2%
Other values (16) 23
24.7%

행정동명
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:20:09.624065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3.5
Mean length3.3666667
Min length2

Characters and Unicode

Total characters101
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 row교문2동
2nd row대덕동
3rd row심곡1동
4th row야탑1동
5th row역곡3동
ValueCountFrequency (%)
교문2동 1
 
3.3%
대덕동 1
 
3.3%
영통2동 1
 
3.3%
태평4동 1
 
3.3%
춘의동 1
 
3.3%
산성동 1
 
3.3%
심곡2동 1
 
3.3%
중앙동 1
 
3.3%
불현동 1
 
3.3%
양정동 1
 
3.3%
Other values (20) 20
66.7%
2023-12-10T23:20:09.949898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 27
26.7%
2 6
 
5.9%
1 4
 
4.0%
곡 3
 
3.0%
면 3
 
3.0%
본 3
 
3.0%
교 2
 
2.0%
평 2
 
2.0%
대 2
 
2.0%
장 2
 
2.0%
Other values (43) 47
46.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 89
88.1%
Decimal Number 12
 
11.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 27
30.3%
곡 3
 
3.4%
면 3
 
3.4%
본 3
 
3.4%
교 2
 
2.2%
평 2
 
2.2%
대 2
 
2.2%
장 2
 
2.2%
심 2
 
2.2%
오 2
 
2.2%
Other values (39) 41
46.1%
Decimal Number
ValueCountFrequency (%)
2 6
50.0%
1 4
33.3%
4 1
 
8.3%
3 1
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 89
88.1%
Common 12
 
11.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 27
30.3%
곡 3
 
3.4%
면 3
 
3.4%
본 3
 
3.4%
교 2
 
2.2%
평 2
 
2.2%
대 2
 
2.2%
장 2
 
2.2%
심 2
 
2.2%
오 2
 
2.2%
Other values (39) 41
46.1%
Common
ValueCountFrequency (%)
2 6
50.0%
1 4
33.3%
4 1
 
8.3%
3 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 89
88.1%
ASCII 12
 
11.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 27
30.3%
곡 3
 
3.4%
면 3
 
3.4%
본 3
 
3.4%
교 2
 
2.2%
평 2
 
2.2%
대 2
 
2.2%
장 2
 
2.2%
심 2
 
2.2%
오 2
 
2.2%
Other values (39) 41
46.1%
ASCII
ValueCountFrequency (%)
2 6
50.0%
1 4
33.3%
4 1
 
8.3%
3 1
 
8.3%

행정동 코드
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1307442 × 109
Minimum4.111355 × 109
Maximum4.159054 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:20:10.079078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.111355 × 109
5-th percentile4.1123871 × 109
Q14.1190538 × 109
median4.1271545 × 109
Q34.1435599 × 109
95-th percentile4.1549839 × 109
Maximum4.159054 × 109
Range47699000
Interquartile range (IQR)24506125

Descriptive statistics

Standard deviation14703584
Coefficient of variation (CV)0.0035595484
Kurtosis-1.0061787
Mean4.1307442 × 109
Median Absolute Deviation (MAD)11904700
Skewness0.4121285
Sum1.2392233 × 1011
Variance2.1619538 × 1014
MonotonicityNot monotonic
2023-12-10T23:20:10.223251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4131054200 1
 
3.3%
4148025300 1
 
3.3%
4139059500 1
 
3.3%
4111758000 1
 
3.3%
4113156100 1
 
3.3%
4119059000 1
 
3.3%
4113160000 1
 
3.3%
4119051000 1
 
3.3%
4125053500 1
 
3.3%
4125056500 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
4111355000 1
3.3%
4111758000 1
3.3%
4113156100 1
3.3%
4113160000 1
3.3%
4113562000 1
3.3%
4113565000 1
3.3%
4119051000 1
3.3%
4119052000 1
3.3%
4119059000 1
3.3%
4119079000 1
3.3%
ValueCountFrequency (%)
4159054000 1
3.3%
4159033000 1
3.3%
4150035000 1
3.3%
4150034000 1
3.3%
4148051000 1
3.3%
4148025300 1
3.3%
4146154000 1
3.3%
4145060000 1
3.3%
4139059500 1
3.3%
4139058900 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

2023-12-10T23:20:10.351300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:20:10.449541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

비교 시군구명
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
성남시
13 
하남시
3 
고양시
3 
용인시
3 
부천시
2 
Other values (4)
6 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique2 ?
Unique (%)6.7%

Sample

1st row부천시
2nd row성남시
3rd row여주시
4th row안양시
5th row성남시

Common Values

ValueCountFrequency (%)
성남시 13
43.3%
하남시 3
 
10.0%
고양시 3
 
10.0%
용인시 3
 
10.0%
부천시 2
 
6.7%
안양시 2
 
6.7%
양평군 2
 
6.7%
여주시 1
 
3.3%
파주시 1
 
3.3%

Length

2023-12-10T23:20:10.546106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:20:10.666581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
성남시 13
43.3%
하남시 3
 
10.0%
고양시 3
 
10.0%
용인시 3
 
10.0%
부천시 2
 
6.7%
안양시 2
 
6.7%
양평군 2
 
6.7%
여주시 1
 
3.3%
파주시 1
 
3.3%

비교 행정동명
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
태평4동
9 
백현동
4 
상현2동
3 
고강본동
2 
안양9동
2 
Other values (7)
10 

Length

Max length4
Median length4
Mean length3.6
Min length3

Unique

Unique4 ?
Unique (%)13.3%

Sample

1st row고강본동
2nd row태평4동
3rd row흥천면
4th row안양9동
5th row태평4동

Common Values

ValueCountFrequency (%)
태평4동 9
30.0%
백현동 4
13.3%
상현2동 3
 
10.0%
고강본동 2
 
6.7%
안양9동 2
 
6.7%
위례동 2
 
6.7%
송포동 2
 
6.7%
지평면 2
 
6.7%
흥천면 1
 
3.3%
행신1동 1
 
3.3%
Other values (2) 2
 
6.7%

Length

2023-12-10T23:20:10.802756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
태평4동 9
30.0%
백현동 4
13.3%
상현2동 3
 
10.0%
고강본동 2
 
6.7%
안양9동 2
 
6.7%
위례동 2
 
6.7%
송포동 2
 
6.7%
지평면 2
 
6.7%
흥천면 1
 
3.3%
행신1동 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%
Mean4.1295613 × 109
Minimum4.1131561 × 109
Maximum4.1830395 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:20:10.917466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.1131561 × 109
5-th percentile4.1131561 × 109
Q14.1131561 × 109
median4.1181196 × 109
Q34.1450596 × 109
95-th percentile4.1758361 × 109
Maximum4.1830395 × 109
Range69883400
Interquartile range (IQR)31903525

Descriptive statistics

Standard deviation21196263
Coefficient of variation (CV)0.0051328122
Kurtosis0.83522212
Mean4.1295613 × 109
Median Absolute Deviation (MAD)4963450
Skewness1.2743981
Sum1.2388684 × 1011
Variance4.4928155 × 1014
MonotonicityNot monotonic
2023-12-10T23:20:11.353957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
4113156100 9
30.0%
4113565700 4
13.3%
4146558000 3
 
10.0%
4119081000 2
 
6.7%
4117158100 2
 
6.7%
4145058500 2
 
6.7%
4128758000 2
 
6.7%
4183039500 2
 
6.7%
4167032000 1
 
3.3%
4128164000 1
 
3.3%
Other values (2) 2
 
6.7%
ValueCountFrequency (%)
4113156100 9
30.0%
4113565700 4
13.3%
4117158100 2
 
6.7%
4119081000 2
 
6.7%
4128164000 1
 
3.3%
4128758000 2
 
6.7%
4145058500 2
 
6.7%
4145060000 1
 
3.3%
4146558000 3
 
10.0%
4148051000 1
 
3.3%
ValueCountFrequency (%)
4183039500 2
6.7%
4167032000 1
 
3.3%
4148051000 1
 
3.3%
4146558000 3
10.0%
4145060000 1
 
3.3%
4145058500 2
6.7%
4128758000 2
6.7%
4128164000 1
 
3.3%
4119081000 2
6.7%
4117158100 2
6.7%

표준편차
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150.852
Minimum61.42
Maximum748.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:20:11.540734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum61.42
5-th percentile82.976
Q1101.2275
median114.335
Q3139.825
95-th percentile332.705
Maximum748.3
Range686.88
Interquartile range (IQR)38.5975

Descriptive statistics

Standard deviation130.52735
Coefficient of variation (CV)0.86526764
Kurtosis16.262034
Mean150.852
Median Absolute Deviation (MAD)17.32
Skewness3.8801272
Sum4525.56
Variance17037.39
MonotonicityNot monotonic
2023-12-10T23:20:11.660536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
114.01 1
 
3.3%
79.25 1
 
3.3%
92.45 1
 
3.3%
107.61 1
 
3.3%
748.3 1
 
3.3%
149.36 1
 
3.3%
434.9 1
 
3.3%
117.48 1
 
3.3%
102.45 1
 
3.3%
114.66 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
61.42 1
3.3%
79.25 1
3.3%
87.53 1
3.3%
92.45 1
3.3%
92.88 1
3.3%
93.39 1
3.3%
98.99 1
3.3%
100.82 1
3.3%
102.45 1
3.3%
104.99 1
3.3%
ValueCountFrequency (%)
748.3 1
3.3%
434.9 1
3.3%
207.8 1
3.3%
196.43 1
3.3%
170.36 1
3.3%
149.36 1
3.3%
143.2 1
3.3%
142.26 1
3.3%
132.52 1
3.3%
130.79 1
3.3%

비교값
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean105.04067
Minimum21.08
Maximum199.28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:20:11.783142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum21.08
5-th percentile34.9035
Q186.7975
median96.89
Q3125.7825
95-th percentile166.0165
Maximum199.28
Range178.2
Interquartile range (IQR)38.985

Descriptive statistics

Standard deviation40.43021
Coefficient of variation (CV)0.38490055
Kurtosis0.2718852
Mean105.04067
Median Absolute Deviation (MAD)20.805
Skewness0.12520988
Sum3151.22
Variance1634.6019
MonotonicityNot monotonic
2023-12-10T23:20:11.917516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
89.45 1
 
3.3%
112.67 1
 
3.3%
117.88 1
 
3.3%
157.71 1
 
3.3%
21.08 1
 
3.3%
91.12 1
 
3.3%
35.91 1
 
3.3%
116.76 1
 
3.3%
96.2 1
 
3.3%
154.86 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
21.08 1
3.3%
34.08 1
3.3%
35.91 1
3.3%
68.01 1
3.3%
68.57 1
3.3%
76.27 1
3.3%
83.43 1
3.3%
86.04 1
3.3%
89.07 1
3.3%
89.45 1
3.3%
ValueCountFrequency (%)
199.28 1
3.3%
172.6 1
3.3%
157.97 1
3.3%
157.71 1
3.3%
154.86 1
3.3%
143.29 1
3.3%
132.95 1
3.3%
127.38 1
3.3%
120.99 1
3.3%
117.88 1
3.3%

Interactions

2023-12-10T23:20:07.986514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:06.888073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:07.263975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:07.628011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:08.071391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:06.977236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:07.349277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:07.704183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:08.173986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:07.075666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:07.448803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:07.794162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:08.257288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:07.170641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:07.540212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:20:07.897508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:20:12.027017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명행정동명행정동 코드비교 시군구명비교 행정동명비교 행정동코드표준편차비교값
시군구명1.0001.0001.0000.6220.5530.7290.0000.607
행정동명1.0001.0001.0001.0001.0001.0001.0001.000
행정동 코드1.0001.0001.0000.6070.5390.6200.0000.146
비교 시군구명0.6221.0000.6071.0001.0001.0000.1750.000
비교 행정동명0.5531.0000.5391.0001.0001.0000.2950.000
비교 행정동코드0.7291.0000.6201.0001.0001.0000.4580.000
표준편차0.0001.0000.0000.1750.2950.4581.0000.514
비교값0.6071.0000.1460.0000.0000.0000.5141.000
2023-12-10T23:20:12.158070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비교 시군구명비교 행정동명
비교 시군구명1.0000.926
비교 행정동명0.9261.000
2023-12-10T23:20:12.241343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동 코드비교 행정동코드표준편차비교값비교 시군구명비교 행정동명
행정동 코드1.0000.301-0.4750.0250.2490.273
비교 행정동코드0.3011.0000.053-0.1440.9170.849
표준편차-0.4750.0531.000-0.5980.0000.077
비교값0.025-0.144-0.5981.0000.0000.000
비교 시군구명0.2490.9170.0000.0001.0000.926
비교 행정동명0.2730.8490.0770.0000.9261.000

Missing values

2023-12-10T23:20:08.380266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:20:08.534507image/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
02020-02경기도구리시교문2동4131054200경기도부천시고강본동4119081000114.0189.45
12020-02경기도고양시대덕동4128167000경기도성남시태평4동4113156100170.3668.57
22020-02경기도부천시심곡1동4119052000경기도여주시흥천면4167032000108.1389.07
32020-02경기도성남시야탑1동4113562000경기도안양시안양9동4117158100207.890.34
42020-02경기도부천시역곡3동4119079000경기도성남시태평4동4113156100110.23120.99
52020-02경기도성남시판교동4113565000경기도하남시위례동4145058500196.4334.08
62020-02경기도수원시평동4111355000경기도고양시송포동4128758000130.79172.6
72020-02경기도시흥시정왕본동4139058900경기도고양시행신1동4128164000143.268.01
82020-02경기도안산시본오1동4127154000경기도성남시태평4동4113156100142.2686.04
92020-02경기도안산시본오2동4127155000경기도성남시백현동411356570061.42199.28
기준년월시도명시군구명행정동명행정동 코드비교 시도명비교 시군구명비교 행정동명비교 행정동코드표준편차비교값
202020-02경기도화성시병점2동4159054000경기도성남시태평4동411315610092.8893.66
212020-02경기도남양주시양정동4136054000경기도용인시상현2동4146558000128.37143.29
222020-02경기도동두천시불현동4125056500경기도안양시안양9동4117158100114.66154.86
232020-02경기도동두천시중앙동4125053500경기도하남시위례동4145058500102.4596.2
242020-02경기도부천시심곡2동4119051000경기도성남시태평4동4113156100117.48116.76
252020-02경기도성남시산성동4113160000경기도고양시송포동4128758000434.935.91
262020-02경기도부천시춘의동4119059000경기도성남시태평4동4113156100149.3691.12
272020-02경기도성남시태평4동4113156100경기도성남시백현동4113565700748.321.08
282020-02경기도수원시영통2동4111758000경기도성남시태평4동4113156100107.61157.71
292020-02경기도시흥시배곧동4139059500경기도성남시백현동411356570092.45117.88