Overview

Dataset statistics

Number of variables13
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory116.4 B

Variable types

DateTime1
Categorical4
Text1
Numeric7

Dataset

Description샘플 데이터
Author경기콘텐츠진흥원
URLhttps://bigdata-region.kr/#/dataset/64f3c95d-3cdb-4562-bec3-c2711300845a

Alerts

기준년월 has constant value ""Constant
시도명 has constant value ""Constant
인구 1천명당시설물개수 표준편차 is highly overall correlated with 대분류High correlation
대분류 is highly overall correlated with 인구 1천명당시설물개수 표준편차High correlation
행정동 코드 is highly overall correlated with 시군구명High correlation
시설물 개수 is highly overall correlated with 인구 1천명당시설물개수 and 3 other fieldsHigh correlation
인구 1천명당시설물개수 is highly overall correlated with 시설물 개수 and 3 other fieldsHigh correlation
인구 1천명당시설물개수순위 is highly overall correlated with 시설물 개수 and 3 other fieldsHigh correlation
인구 1천명당시설물개수백분위 is highly overall correlated with 시설물 개수 and 3 other fieldsHigh correlation
유동 인구 생활여건지수 is highly overall correlated with 시설물 개수 and 3 other fieldsHigh correlation
시군구명 is highly overall correlated with 행정동 코드High correlation
인구 1천명당시설물개수순위 has unique valuesUnique
시설물 개수 has 8 (26.7%) zerosZeros
인구 1천명당시설물개수 has 8 (26.7%) zerosZeros
유동 인구 생활여건지수 has 8 (26.7%) zerosZeros

Reproduction

Analysis started2023-12-10 14:05:30.381766
Analysis finished2023-12-10 14:05:39.090998
Duration8.71 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준년월
Date

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
Minimum2020-07-01 00:00:00
Maximum2020-07-01 00:00:00
2023-12-10T23:05:39.167770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:39.329968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

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

2023-12-10T23:05:39.525292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:05:39.775810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 30
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
고양시
4 
광주시
4 
남양주시
4 
성남시
4 
광명시
3 
Other values (5)
11 

Length

Max length4
Median length3
Mean length3.2333333
Min length3

Unique

Unique1 ?
Unique (%)3.3%

Sample

1st row고양시
2nd row고양시
3rd row고양시
4th row광명시
5th row고양시

Common Values

ValueCountFrequency (%)
고양시 4
13.3%
광주시 4
13.3%
남양주시 4
13.3%
성남시 4
13.3%
광명시 3
10.0%
동두천시 3
10.0%
부천시 3
10.0%
구리시 2
6.7%
군포시 2
6.7%
김포시 1
 
3.3%

Length

2023-12-10T23:05:40.042974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:05:40.263594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고양시 4
13.3%
광주시 4
13.3%
남양주시 4
13.3%
성남시 4
13.3%
광명시 3
10.0%
동두천시 3
10.0%
부천시 3
10.0%
구리시 2
6.7%
군포시 2
6.7%
김포시 1
 
3.3%

행정동명
Text

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-10T23:05:40.614767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.2
Min length3

Characters and Unicode

Total characters96
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

Unique26 ?
Unique (%)86.7%

Sample

1st row창릉동
2nd row송산동
3rd row행주동
4th row광명5동
5th row흥도동
ValueCountFrequency (%)
퇴촌면 2
 
6.7%
인창동 2
 
6.7%
창릉동 1
 
3.3%
진접읍 1
 
3.3%
단대동 1
 
3.3%
금곡동 1
 
3.3%
역곡2동 1
 
3.3%
고등동 1
 
3.3%
성곡동 1
 
3.3%
상2동 1
 
3.3%
Other values (18) 18
60.0%
2023-12-10T23:05:41.285426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 24
25.0%
면 4
 
4.2%
퇴 3
 
3.1%
곡 3
 
3.1%
창 3
 
3.1%
하 2
 
2.1%
2 2
 
2.1%
상 2
 
2.1%
내 2
 
2.1%
송 2
 
2.1%
Other values (43) 49
51.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 90
93.8%
Decimal Number 6
 
6.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 24
26.7%
면 4
 
4.4%
퇴 3
 
3.3%
곡 3
 
3.3%
창 3
 
3.3%
하 2
 
2.2%
상 2
 
2.2%
내 2
 
2.2%
송 2
 
2.2%
촌 2
 
2.2%
Other values (39) 43
47.8%
Decimal Number
ValueCountFrequency (%)
2 2
33.3%
1 2
33.3%
4 1
16.7%
5 1
16.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 90
93.8%
Common 6
 
6.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 24
26.7%
면 4
 
4.4%
퇴 3
 
3.3%
곡 3
 
3.3%
창 3
 
3.3%
하 2
 
2.2%
상 2
 
2.2%
내 2
 
2.2%
송 2
 
2.2%
촌 2
 
2.2%
Other values (39) 43
47.8%
Common
ValueCountFrequency (%)
2 2
33.3%
1 2
33.3%
4 1
16.7%
5 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 90
93.8%
ASCII 6
 
6.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 24
26.7%
면 4
 
4.4%
퇴 3
 
3.3%
곡 3
 
3.3%
창 3
 
3.3%
하 2
 
2.2%
상 2
 
2.2%
내 2
 
2.2%
송 2
 
2.2%
촌 2
 
2.2%
Other values (39) 43
47.8%
ASCII
ValueCountFrequency (%)
2 2
33.3%
1 2
33.3%
4 1
16.7%
5 1
16.7%

행정동 코드
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1317271 × 109
Minimum4.113159 × 109
Maximum4.161035 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:05:41.492347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.113159 × 109
5-th percentile4.1133448 × 109
Q14.1210578 × 109
median4.1281605 × 109
Q34.1360535 × 109
95-th percentile4.161034 × 109
Maximum4.161035 × 109
Range47876000
Interquartile range (IQR)14995650

Descriptive statistics

Standard deviation15174189
Coefficient of variation (CV)0.0036726019
Kurtosis-0.17249197
Mean4.1317271 × 109
Median Absolute Deviation (MAD)7892500
Skewness0.8808334
Sum1.2395181 × 1011
Variance2.30256 × 1014
MonotonicityNot monotonic
2023-12-10T23:05:41.690539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
4161034000 2
 
6.7%
4131053000 2
 
6.7%
4128158000 1
 
3.3%
4136037000 1
 
3.3%
4113565700 1
 
3.3%
4113159000 1
 
3.3%
4113566200 1
 
3.3%
4119058000 1
 
3.3%
4113164000 1
 
3.3%
4119080000 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
4113159000 1
3.3%
4113164000 1
3.3%
4113565700 1
3.3%
4113566200 1
3.3%
4119058000 1
3.3%
4119069000 1
3.3%
4119080000 1
3.3%
4121056000 1
3.3%
4121063400 1
3.3%
4121064000 1
3.3%
ValueCountFrequency (%)
4161035000 1
3.3%
4161034000 2
6.7%
4161025300 1
3.3%
4157054000 1
3.3%
4141056000 1
3.3%
4141051000 1
3.3%
4136054000 1
3.3%
4136052000 1
3.3%
4136037000 1
3.3%
4136025300 1
3.3%

대분류
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
안전
11 
건강
8 
문화관광
6 
생활
5 

Length

Max length4
Median length2
Mean length2.4
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row건강
2nd row문화관광
3rd row건강
4th row안전
5th row안전

Common Values

ValueCountFrequency (%)
안전 11
36.7%
건강 8
26.7%
문화관광 6
20.0%
생활 5
16.7%

Length

2023-12-10T23:05:41.915049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:05:42.133290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
안전 11
36.7%
건강 8
26.7%
문화관광 6
20.0%
생활 5
16.7%

시설물 개수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.933333
Minimum0
Maximum277
Zeros8
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:05:42.360533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.25
median11
Q333
95-th percentile109.95
Maximum277
Range277
Interquartile range (IQR)32.75

Descriptive statistics

Standard deviation57.09033
Coefficient of variation (CV)1.7877974
Kurtosis11.518949
Mean31.933333
Median Absolute Deviation (MAD)11
Skewness3.096967
Sum958
Variance3259.3057
MonotonicityNot monotonic
2023-12-10T23:05:42.561835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 8
26.7%
12 3
 
10.0%
1 2
 
6.7%
3 2
 
6.7%
27 2
 
6.7%
16 1
 
3.3%
35 1
 
3.3%
84 1
 
3.3%
123 1
 
3.3%
4 1
 
3.3%
Other values (8) 8
26.7%
ValueCountFrequency (%)
0 8
26.7%
1 2
 
6.7%
3 2
 
6.7%
4 1
 
3.3%
6 1
 
3.3%
10 1
 
3.3%
12 3
 
10.0%
16 1
 
3.3%
17 1
 
3.3%
27 2
 
6.7%
ValueCountFrequency (%)
277 1
3.3%
123 1
3.3%
94 1
3.3%
84 1
3.3%
83 1
3.3%
71 1
3.3%
40 1
3.3%
35 1
3.3%
27 2
6.7%
17 1
3.3%

관내 인구
Real number (ℝ)

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45669.199
Minimum4540.25
Maximum231000.42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:05:42.751105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4540.25
5-th percentile7243.602
Q119236.105
median35093.96
Q350288.048
95-th percentile114761.03
Maximum231000.42
Range226460.17
Interquartile range (IQR)31051.943

Descriptive statistics

Standard deviation45588.906
Coefficient of variation (CV)0.99824186
Kurtosis8.969999
Mean45669.199
Median Absolute Deviation (MAD)15755.47
Skewness2.6747804
Sum1370076
Variance2.0783483 × 109
MonotonicityNot monotonic
2023-12-10T23:05:42.941075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
22470.05 2
 
6.7%
28099.08 2
 
6.7%
49237.29 1
 
3.3%
50638.3 1
 
3.3%
70928.8 1
 
3.3%
18987.27 1
 
3.3%
32467.76 1
 
3.3%
19562.34 1
 
3.3%
18456.58 1
 
3.3%
51708.08 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
4540.25 1
3.3%
7076.94 1
3.3%
7447.3 1
3.3%
9314.45 1
3.3%
10339.32 1
3.3%
18456.58 1
3.3%
18987.27 1
3.3%
19127.36 1
3.3%
19562.34 1
3.3%
22470.05 2
6.7%
ValueCountFrequency (%)
231000.42 1
3.3%
123149.68 1
3.3%
104508.24 1
3.3%
104129.09 1
3.3%
70928.8 1
3.3%
54425.6 1
3.3%
51708.08 1
3.3%
50638.3 1
3.3%
49237.29 1
3.3%
47075.13 1
3.3%

인구 1천명당시설물개수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.76233333
Minimum0
Maximum2.64
Zeros8
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:05:43.184666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.005
median0.195
Q31.365
95-th percentile2.574
Maximum2.64
Range2.64
Interquartile range (IQR)1.36

Descriptive statistics

Standard deviation0.93201185
Coefficient of variation (CV)1.2225779
Kurtosis-0.59499412
Mean0.76233333
Median Absolute Deviation (MAD)0.195
Skewness0.94430269
Sum22.87
Variance0.86864609
MonotonicityNot monotonic
2023-12-10T23:05:43.441559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.0 8
26.7%
1.2 2
 
6.7%
0.32 1
 
3.3%
0.18 1
 
3.3%
1.42 1
 
3.3%
1.79 1
 
3.3%
1.62 1
 
3.3%
2.61 1
 
3.3%
0.12 1
 
3.3%
0.11 1
 
3.3%
Other values (12) 12
40.0%
ValueCountFrequency (%)
0.0 8
26.7%
0.02 1
 
3.3%
0.03 1
 
3.3%
0.08 1
 
3.3%
0.1 1
 
3.3%
0.11 1
 
3.3%
0.12 1
 
3.3%
0.18 1
 
3.3%
0.21 1
 
3.3%
0.32 1
 
3.3%
ValueCountFrequency (%)
2.64 1
3.3%
2.61 1
3.3%
2.53 1
3.3%
2.25 1
3.3%
2.16 1
3.3%
1.79 1
3.3%
1.62 1
3.3%
1.42 1
3.3%
1.2 2
6.7%
0.89 1
3.3%

인구 1천명당시설물개수순위
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean306.43333
Minimum20
Maximum553
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:05:43.624686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile62.2
Q1164.5
median326
Q3437.5
95-th percentile506.65
Maximum553
Range533
Interquartile range (IQR)273

Descriptive statistics

Standard deviation161.92967
Coefficient of variation (CV)0.52843362
Kurtosis-1.2472366
Mean306.43333
Median Absolute Deviation (MAD)149.5
Skewness-0.29238041
Sum9193
Variance26221.22
MonotonicityNot monotonic
2023-12-10T23:05:43.811793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
487 1
 
3.3%
322 1
 
3.3%
432 1
 
3.3%
315 1
 
3.3%
433 1
 
3.3%
253 1
 
3.3%
553 1
 
3.3%
164 1
 
3.3%
71 1
 
3.3%
485 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
20 1
3.3%
55 1
3.3%
71 1
3.3%
89 1
3.3%
98 1
3.3%
101 1
3.3%
105 1
3.3%
164 1
3.3%
166 1
3.3%
246 1
3.3%
ValueCountFrequency (%)
553 1
3.3%
517 1
3.3%
494 1
3.3%
487 1
3.3%
485 1
3.3%
478 1
3.3%
473 1
3.3%
439 1
3.3%
433 1
3.3%
432 1
3.3%

인구 1천명당시설물개수백분위
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.333333
Minimum1
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:05:44.007312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6
Q118
median42.5
Q371
95-th percentile89.65
Maximum97
Range96
Interquartile range (IQR)53

Descriptive statistics

Standard deviation29.711641
Coefficient of variation (CV)0.65540384
Kurtosis-1.2693902
Mean45.333333
Median Absolute Deviation (MAD)28
Skewness0.24433192
Sum1360
Variance882.78161
MonotonicityNot monotonic
2023-12-10T23:05:44.184423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
14 2
 
6.7%
27 2
 
6.7%
6 2
 
6.7%
71 2
 
6.7%
83 2
 
6.7%
85 1
 
3.3%
42 1
 
3.3%
45 1
 
3.3%
41 1
 
3.3%
56 1
 
3.3%
Other values (15) 15
50.0%
ValueCountFrequency (%)
1 1
3.3%
6 2
6.7%
13 1
3.3%
14 2
6.7%
15 1
3.3%
17 1
3.3%
21 1
3.3%
23 1
3.3%
27 2
6.7%
39 1
3.3%
ValueCountFrequency (%)
97 1
3.3%
91 1
3.3%
88 1
3.3%
85 1
3.3%
83 2
6.7%
82 1
3.3%
71 2
6.7%
57 1
3.3%
56 1
3.3%
53 1
3.3%

인구 1천명당시설물개수 표준편차
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
0.06
11 
1.33
8 
1.13
6 
2.41
5 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1.33
2nd row1.13
3rd row1.33
4th row0.06
5th row0.06

Common Values

ValueCountFrequency (%)
0.06 11
36.7%
1.33 8
26.7%
1.13 6
20.0%
2.41 5
16.7%

Length

2023-12-10T23:05:44.399202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T23:05:44.560950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.06 11
36.7%
1.33 8
26.7%
1.13 6
20.0%
2.41 5
16.7%

유동 인구 생활여건지수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)76.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.723667
Minimum0
Maximum233.17
Zeros8
Zeros (%)26.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-10T23:05:44.723001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.15
median39.26
Q3101.285
95-th percentile186.2065
Maximum233.17
Range233.17
Interquartile range (IQR)99.135

Descriptive statistics

Standard deviation68.791561
Coefficient of variation (CV)1.0967401
Kurtosis-0.045127011
Mean62.723667
Median Absolute Deviation (MAD)39.26
Skewness1.0228606
Sum1881.71
Variance4732.2788
MonotonicityNot monotonic
2023-12-10T23:05:44.942749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.0 8
26.7%
24.4 1
 
3.3%
16.21 1
 
3.3%
59.07 1
 
3.3%
74.33 1
 
3.3%
121.99 1
 
3.3%
196.21 1
 
3.3%
10.18 1
 
3.3%
173.98 1
 
3.3%
162.1 1
 
3.3%
Other values (13) 13
43.3%
ValueCountFrequency (%)
0.0 8
26.7%
8.6 1
 
3.3%
10.18 1
 
3.3%
16.21 1
 
3.3%
18.84 1
 
3.3%
24.4 1
 
3.3%
31.43 1
 
3.3%
32.0 1
 
3.3%
46.52 1
 
3.3%
49.82 1
 
3.3%
ValueCountFrequency (%)
233.17 1
3.3%
196.21 1
3.3%
173.98 1
3.3%
168.85 1
3.3%
162.1 1
3.3%
136.91 1
3.3%
121.99 1
3.3%
104.97 1
3.3%
90.23 1
3.3%
74.33 1
3.3%

Interactions

2023-12-10T23:05:37.481351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:31.163947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:32.184963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:32.983629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:33.884625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:34.909435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:36.086059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:37.609337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:31.353607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:32.311531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:33.076260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:34.026488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:35.036477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:36.291787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:37.731877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:31.486488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:32.426414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:33.187163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:34.185728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:35.175966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:36.545655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:37.847332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:31.635780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:32.538379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:33.303422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:34.307722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:35.348191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:36.835396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:37.972919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:31.764524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:32.673416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:33.445316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:34.431824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:35.501628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:37.048356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:38.095378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:31.936340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:32.779680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:33.589242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:34.553054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:35.717392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:37.227199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:38.212955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:32.080122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:32.882124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:33.739357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:34.776934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:35.909094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T23:05:37.349593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T23:05:45.187613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명행정동명행정동 코드대분류시설물 개수관내 인구인구 1천명당시설물개수인구 1천명당시설물개수순위인구 1천명당시설물개수백분위인구 1천명당시설물개수 표준편차유동 인구 생활여건지수
시군구명1.0001.0001.0000.0000.5120.0000.4910.4010.5870.0000.520
행정동명1.0001.0001.0000.0000.8981.0000.7110.8200.7940.0000.000
행정동 코드1.0001.0001.0000.0000.0000.0000.0000.3580.6160.0000.000
대분류0.0000.0000.0001.0000.4720.2770.6140.0000.0001.0000.418
시설물 개수0.5120.8980.0000.4721.0000.7220.7990.7480.7480.4720.846
관내 인구0.0001.0000.0000.2770.7221.0000.0000.6020.6370.2770.000
인구 1천명당시설물개수0.4910.7110.0000.6140.7990.0001.0000.7320.7410.6140.730
인구 1천명당시설물개수순위0.4010.8200.3580.0000.7480.6020.7321.0000.9970.0000.679
인구 1천명당시설물개수백분위0.5870.7940.6160.0000.7480.6370.7410.9971.0000.0000.683
인구 1천명당시설물개수 표준편차0.0000.0000.0001.0000.4720.2770.6140.0000.0001.0000.418
유동 인구 생활여건지수0.5200.0000.0000.4180.8460.0000.7300.6790.6830.4181.000
2023-12-10T23:05:45.436312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인구 1천명당시설물개수 표준편차대분류시군구명
인구 1천명당시설물개수 표준편차1.0001.0000.000
대분류1.0001.0000.000
시군구명0.0000.0001.000
2023-12-10T23:05:45.616634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동 코드시설물 개수관내 인구인구 1천명당시설물개수인구 1천명당시설물개수순위인구 1천명당시설물개수백분위유동 인구 생활여건지수시군구명대분류인구 1천명당시설물개수 표준편차
행정동 코드1.0000.2590.1310.302-0.2710.3840.2860.9330.0000.000
시설물 개수0.2591.0000.2410.924-0.5730.5980.7460.2520.3010.301
관내 인구0.1310.2411.000-0.0050.110-0.085-0.0510.0000.1130.113
인구 1천명당시설물개수0.3020.924-0.0051.000-0.7200.7400.8480.2130.3890.389
인구 1천명당시설물개수순위-0.271-0.5730.110-0.7201.000-0.957-0.9140.1500.0000.000
인구 1천명당시설물개수백분위0.3840.598-0.0850.740-0.9571.0000.9270.2850.0000.000
유동 인구 생활여건지수0.2860.746-0.0510.848-0.9140.9271.0000.1370.2050.205
시군구명0.9330.2520.0000.2130.1500.2850.1371.0000.0000.000
대분류0.0000.3010.1130.3890.0000.0000.2050.0001.0001.000
인구 1천명당시설물개수 표준편차0.0000.3010.1130.3890.0000.0000.2050.0001.0001.000

Missing values

2023-12-10T23:05:38.383793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T23:05:38.969024image/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 코드대분류시설물 개수관내 인구인구 1천명당시설물개수인구 1천명당시설물개수순위인구 1천명당시설물개수백분위인구 1천명당시설물개수 표준편차유동 인구 생활여건지수
02020-07경기도고양시창릉동4128158000건강1649237.290.32487141.3324.4
12020-07경기도고양시송산동4128759000문화관광12123149.680.1439231.138.6
22020-07경기도고양시행주동4128163000건강4045136.140.89333411.3366.55
32020-07경기도광명시광명5동4121056000안전07447.30.0473130.060.0
42020-07경기도고양시흥도동4128153000안전0104129.090.0494170.060.0
52020-07경기도광명시소하1동4121064000안전335509.490.0889850.06136.91
62020-07경기도광명시하안4동4121063400안전010339.320.0478140.060.0
72020-07경기도광주시남종면4161035000문화관광124540.252.6420971.13233.17
82020-07경기도광주시초월읍4161025300안전3104508.240.03267530.0646.52
92020-07경기도광주시퇴촌면4161034000건강2722470.051.2246571.3390.23
기준년월시도명시군구명행정동명행정동 코드대분류시설물 개수관내 인구인구 1천명당시설물개수인구 1천명당시설물개수순위인구 1천명당시설물개수백분위인구 1천명당시설물개수 표준편차유동 인구 생활여건지수
202020-07경기도동두천시상패동4125060000안전19314.450.1155910.06173.98
212020-07경기도동두천시송내동4125056600문화관광434678.430.12412271.1310.18
222020-07경기도동두천시중앙동4125053500안전07076.940.0485150.060.0
232020-07경기도부천시상2동4119069000건강12347075.132.6171881.33196.21
242020-07경기도부천시성곡동4119080000건강8451708.081.62164711.33121.99
252020-07경기도성남시고등동4113164000건강018456.580.055311.330.0
262020-07경기도부천시역곡2동4119058000생활3519562.341.79253562.4174.33
272020-07경기도성남시금곡동4113566200안전032467.760.043360.060.0
282020-07경기도성남시단대동4113159000생활2718987.271.42315452.4159.07
292020-07경기도성남시백현동4113565700안전070928.80.043260.060.0