Overview

Dataset statistics

Number of variables13
Number of observations61
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.9 KiB
Average record size in memory115.2 B

Variable types

Categorical5
Numeric7
Text1

Dataset

Description대전광역시 유성구 동별인구수 대비 공중, 개방화장실 현황에 대한 데이터로 공중화장실수, 개방화장실수, 인구수, 공중화장실비율(퍼센트), 개방화장실비율(퍼센트) 등의 항목을 제공합니다.
Author대전광역시 유성구
URLhttps://www.data.go.kr/data/15111164/fileData.do

Alerts

시도코드 has constant value ""Constant
시도이름 has constant value ""Constant
시군구코드 has constant value ""Constant
시군구이름 has constant value ""Constant
행정동코드 is highly overall correlated with 법정동코드 and 1 other fieldsHigh correlation
법정동코드 is highly overall correlated with 행정동코드 and 1 other fieldsHigh correlation
공중화장실수 is highly overall correlated with 개방화장실수 and 2 other fieldsHigh correlation
개방화장실수 is highly overall correlated with 공중화장실수 and 1 other fieldsHigh correlation
인구수 is highly overall correlated with 공중화장실수High correlation
공중화장실비율(퍼센트) is highly overall correlated with 공중화장실수High correlation
개방화장실비율(퍼센트) is highly overall correlated with 개방화장실수High correlation
행정동이름 is highly overall correlated with 행정동코드 and 1 other fieldsHigh correlation
공중화장실수 has 19 (31.1%) zerosZeros
개방화장실수 has 26 (42.6%) zerosZeros
공중화장실비율(퍼센트) has 19 (31.1%) zerosZeros
개방화장실비율(퍼센트) has 27 (44.3%) zerosZeros

Reproduction

Analysis started2023-12-12 10:33:25.737766
Analysis finished2023-12-12 10:33:32.007950
Duration6.27 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size620.0 B
3000000000
61 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3000000000
2nd row3000000000
3rd row3000000000
4th row3000000000
5th row3000000000

Common Values

ValueCountFrequency (%)
3000000000 61
100.0%

Length

2023-12-12T19:33:32.083970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:33:32.187865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3000000000 61
100.0%

시도이름
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size620.0 B
대전광역시
61 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대전광역시
2nd row대전광역시
3rd row대전광역시
4th row대전광역시
5th row대전광역시

Common Values

ValueCountFrequency (%)
대전광역시 61
100.0%

Length

2023-12-12T19:33:32.309883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:33:32.734256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 61
100.0%

시군구코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size620.0 B
3020000000
61 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3020000000
2nd row3020000000
3rd row3020000000
4th row3020000000
5th row3020000000

Common Values

ValueCountFrequency (%)
3020000000 61
100.0%

Length

2023-12-12T19:33:32.852900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:33:32.963636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3020000000 61
100.0%

시군구이름
Categorical

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size620.0 B
유성구
61 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row유성구
2nd row유성구
3rd row유성구
4th row유성구
5th row유성구

Common Values

ValueCountFrequency (%)
유성구 61
100.0%

Length

2023-12-12T19:33:33.059469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:33:33.156342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
유성구 61
100.0%

행정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0200551 × 109
Minimum3.020052 × 109
Maximum3.020061 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-12T19:33:33.253431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.020052 × 109
5-th percentile3.020052 × 109
Q13.020053 × 109
median3.0200547 × 109
Q33.020057 × 109
95-th percentile3.02006 × 109
Maximum3.020061 × 109
Range9000
Interquartile range (IQR)4000

Descriptive statistics

Standard deviation2416.7381
Coefficient of variation (CV)8.002298 × 10-7
Kurtosis-0.03371248
Mean3.0200551 × 109
Median Absolute Deviation (MAD)1700
Skewness0.77107207
Sum1.8422336 × 1011
Variance5840623
MonotonicityNot monotonic
2023-12-12T19:33:33.381610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3020055000 11
18.0%
3020058000 8
13.1%
3020052000 8
13.1%
3020054700 7
11.5%
3020054000 5
8.2%
3020052600 4
 
6.6%
3020054600 4
 
6.6%
3020060000 3
 
4.9%
3020057000 3
 
4.9%
3020052700 2
 
3.3%
Other values (3) 6
9.8%
ValueCountFrequency (%)
3020052000 8
13.1%
3020052600 4
 
6.6%
3020052700 2
 
3.3%
3020053000 2
 
3.3%
3020054000 5
8.2%
3020054600 4
 
6.6%
3020054700 7
11.5%
3020054800 2
 
3.3%
3020055000 11
18.0%
3020057000 3
 
4.9%
ValueCountFrequency (%)
3020061000 2
 
3.3%
3020060000 3
 
4.9%
3020058000 8
13.1%
3020057000 3
 
4.9%
3020055000 11
18.0%
3020054800 2
 
3.3%
3020054700 7
11.5%
3020054600 4
 
6.6%
3020054000 5
8.2%
3020053000 2
 
3.3%

행정동이름
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)21.3%
Missing0
Missing (%)0.0%
Memory size620.0 B
신성동
11 
구즉동
8 
진잠동
8 
노은2동
7 
온천2동
5 
Other values (8)
22 

Length

Max length4
Median length3
Mean length3.3606557
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row관평동
2nd row관평동
3rd row관평동
4th row구즉동
5th row구즉동

Common Values

ValueCountFrequency (%)
신성동 11
18.0%
구즉동 8
13.1%
진잠동 8
13.1%
노은2동 7
11.5%
온천2동 5
8.2%
학하동 4
 
6.6%
노은1동 4
 
6.6%
관평동 3
 
4.9%
전민동 3
 
4.9%
상대동 2
 
3.3%
Other values (3) 6
9.8%

Length

2023-12-12T19:33:33.543924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
신성동 11
18.0%
구즉동 8
13.1%
진잠동 8
13.1%
노은2동 7
11.5%
온천2동 5
8.2%
학하동 4
 
6.6%
노은1동 4
 
6.6%
관평동 3
 
4.9%
전민동 3
 
4.9%
상대동 2
 
3.3%
Other values (3) 6
9.8%

법정동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0200126 × 109
Minimum3.0200101 × 109
Maximum3.0200153 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-12T19:33:33.710841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0200101 × 109
5-th percentile3.0200104 × 109
Q13.0200115 × 109
median3.0200125 × 109
Q33.0200139 × 109
95-th percentile3.020015 × 109
Maximum3.0200153 × 109
Range5200
Interquartile range (IQR)2400

Descriptive statistics

Standard deviation1477.4276
Coefficient of variation (CV)4.8921239 × 10-7
Kurtosis-1.0890794
Mean3.0200126 × 109
Median Absolute Deviation (MAD)1200
Skewness0.09889468
Sum1.8422077 × 1011
Variance2182792.3
MonotonicityNot monotonic
2023-12-12T19:33:33.881270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3020012100 3
 
4.9%
3020012000 3
 
4.9%
3020013200 2
 
3.3%
3020013900 2
 
3.3%
3020011600 2
 
3.3%
3020011100 2
 
3.3%
3020011400 1
 
1.6%
3020010300 1
 
1.6%
3020010700 1
 
1.6%
3020010900 1
 
1.6%
Other values (43) 43
70.5%
ValueCountFrequency (%)
3020010100 1
1.6%
3020010200 1
1.6%
3020010300 1
1.6%
3020010400 1
1.6%
3020010500 1
1.6%
3020010600 1
1.6%
3020010700 1
1.6%
3020010800 1
1.6%
3020010900 1
1.6%
3020011000 1
1.6%
ValueCountFrequency (%)
3020015300 1
1.6%
3020015200 1
1.6%
3020015100 1
1.6%
3020015000 1
1.6%
3020014900 1
1.6%
3020014800 1
1.6%
3020014700 1
1.6%
3020014600 1
1.6%
3020014500 1
1.6%
3020014400 1
1.6%

법정동이름
Text

Distinct53
Distinct (%)86.9%
Missing0
Missing (%)0.0%
Memory size620.0 B
2023-12-12T19:33:34.246264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.852459
Min length2

Characters and Unicode

Total characters174
Distinct characters66
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

Unique47 ?
Unique (%)77.0%

Sample

1st row관평동
2nd row용산동
3rd row탑립동
4th row대동
5th row신동
ValueCountFrequency (%)
죽동 3
 
4.9%
지족동 3
 
4.9%
하기동 2
 
3.3%
반석동 2
 
3.3%
복용동 2
 
3.3%
봉명동 2
 
3.3%
학하동 1
 
1.6%
관평동 1
 
1.6%
원신흥동 1
 
1.6%
대정동 1
 
1.6%
Other values (43) 43
70.5%
2023-12-12T19:33:34.804874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 61
35.1%
지 4
 
2.3%
산 4
 
2.3%
신 4
 
2.3%
대 4
 
2.3%
용 4
 
2.3%
봉 4
 
2.3%
죽 3
 
1.7%
정 3
 
1.7%
원 3
 
1.7%
Other values (56) 80
46.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 174
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 61
35.1%
지 4
 
2.3%
산 4
 
2.3%
신 4
 
2.3%
대 4
 
2.3%
용 4
 
2.3%
봉 4
 
2.3%
죽 3
 
1.7%
정 3
 
1.7%
원 3
 
1.7%
Other values (56) 80
46.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 174
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 61
35.1%
지 4
 
2.3%
산 4
 
2.3%
신 4
 
2.3%
대 4
 
2.3%
용 4
 
2.3%
봉 4
 
2.3%
죽 3
 
1.7%
정 3
 
1.7%
원 3
 
1.7%
Other values (56) 80
46.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 174
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 61
35.1%
지 4
 
2.3%
산 4
 
2.3%
신 4
 
2.3%
대 4
 
2.3%
용 4
 
2.3%
봉 4
 
2.3%
죽 3
 
1.7%
정 3
 
1.7%
원 3
 
1.7%
Other values (56) 80
46.0%

공중화장실수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)31.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.2622951
Minimum0
Maximum27
Zeros19
Zeros (%)31.1%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-12T19:33:34.976738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q311
95-th percentile24
Maximum27
Range27
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.4875489
Coefficient of variation (CV)1.1956557
Kurtosis0.68609614
Mean6.2622951
Median Absolute Deviation (MAD)3
Skewness1.2672781
Sum382
Variance56.063388
MonotonicityNot monotonic
2023-12-12T19:33:35.140426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 19
31.1%
11 5
 
8.2%
14 4
 
6.6%
4 4
 
6.6%
2 4
 
6.6%
1 4
 
6.6%
3 4
 
6.6%
5 3
 
4.9%
7 2
 
3.3%
6 2
 
3.3%
Other values (9) 10
16.4%
ValueCountFrequency (%)
0 19
31.1%
1 4
 
6.6%
2 4
 
6.6%
3 4
 
6.6%
4 4
 
6.6%
5 3
 
4.9%
6 2
 
3.3%
7 2
 
3.3%
8 1
 
1.6%
9 1
 
1.6%
ValueCountFrequency (%)
27 1
 
1.6%
25 1
 
1.6%
24 2
 
3.3%
21 1
 
1.6%
19 1
 
1.6%
18 1
 
1.6%
15 1
 
1.6%
14 4
6.6%
11 5
8.2%
9 1
 
1.6%

개방화장실수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7704918
Minimum0
Maximum15
Zeros26
Zeros (%)42.6%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-12T19:33:35.321987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile6
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.8424956
Coefficient of variation (CV)1.6054836
Kurtosis8.6204361
Mean1.7704918
Median Absolute Deviation (MAD)1
Skewness2.689421
Sum108
Variance8.0797814
MonotonicityNot monotonic
2023-12-12T19:33:35.482079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 26
42.6%
1 16
26.2%
2 6
 
9.8%
5 4
 
6.6%
4 4
 
6.6%
3 1
 
1.6%
6 1
 
1.6%
9 1
 
1.6%
15 1
 
1.6%
11 1
 
1.6%
ValueCountFrequency (%)
0 26
42.6%
1 16
26.2%
2 6
 
9.8%
3 1
 
1.6%
4 4
 
6.6%
5 4
 
6.6%
6 1
 
1.6%
9 1
 
1.6%
11 1
 
1.6%
15 1
 
1.6%
ValueCountFrequency (%)
15 1
 
1.6%
11 1
 
1.6%
9 1
 
1.6%
6 1
 
1.6%
5 4
 
6.6%
4 4
 
6.6%
3 1
 
1.6%
2 6
 
9.8%
1 16
26.2%
0 26
42.6%

인구수
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5741.1148
Minimum4
Maximum24453
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-12T19:33:35.634097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile31
Q1354
median1547
Q310495
95-th percentile22168
Maximum24453
Range24449
Interquartile range (IQR)10141

Descriptive statistics

Standard deviation6993.7262
Coefficient of variation (CV)1.2181826
Kurtosis0.48357653
Mean5741.1148
Median Absolute Deviation (MAD)1542
Skewness1.1796656
Sum350208
Variance48912206
MonotonicityNot monotonic
2023-12-12T19:33:35.839621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 2
 
3.3%
31 2
 
3.3%
23429 1
 
1.6%
15327 1
 
1.6%
12224 1
 
1.6%
8189 1
 
1.6%
6839 1
 
1.6%
922 1
 
1.6%
1061 1
 
1.6%
12168 1
 
1.6%
Other values (49) 49
80.3%
ValueCountFrequency (%)
4 2
3.3%
5 1
1.6%
31 2
3.3%
42 1
1.6%
110 1
1.6%
118 1
1.6%
124 1
1.6%
128 1
1.6%
138 1
1.6%
150 1
1.6%
ValueCountFrequency (%)
24453 1
1.6%
23429 1
1.6%
23154 1
1.6%
22168 1
1.6%
18367 1
1.6%
15327 1
1.6%
15152 1
1.6%
14883 1
1.6%
12669 1
1.6%
12481 1
1.6%

공중화장실비율(퍼센트)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)44.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.76459016
Minimum0
Maximum10.94
Zeros19
Zeros (%)31.1%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-12T19:33:35.985754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.06
Q30.19
95-th percentile4.62
Maximum10.94
Range10.94
Interquartile range (IQR)0.19

Descriptive statistics

Standard deviation2.070151
Coefficient of variation (CV)2.7075303
Kurtosis15.007813
Mean0.76459016
Median Absolute Deviation (MAD)0.06
Skewness3.8151353
Sum46.64
Variance4.2855252
MonotonicityNot monotonic
2023-12-12T19:33:36.133025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0.0 19
31.1%
0.06 4
 
6.6%
0.13 4
 
6.6%
0.03 3
 
4.9%
0.05 3
 
4.9%
0.11 3
 
4.9%
0.04 2
 
3.3%
0.16 2
 
3.3%
1.72 2
 
3.3%
0.02 2
 
3.3%
Other values (17) 17
27.9%
ValueCountFrequency (%)
0.0 19
31.1%
0.02 2
 
3.3%
0.03 3
 
4.9%
0.04 2
 
3.3%
0.05 3
 
4.9%
0.06 4
 
6.6%
0.08 1
 
1.6%
0.11 3
 
4.9%
0.13 4
 
6.6%
0.14 1
 
1.6%
ValueCountFrequency (%)
10.94 1
1.6%
9.68 1
1.6%
6.13 1
1.6%
4.62 1
1.6%
2.37 1
1.6%
1.86 1
1.6%
1.72 2
3.3%
1.45 1
1.6%
1.2 1
1.6%
0.85 1
1.6%

개방화장실비율(퍼센트)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)32.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.22278689
Minimum0
Maximum3.23
Zeros27
Zeros (%)44.3%
Negative0
Negative (%)0.0%
Memory size681.0 B
2023-12-12T19:33:36.272248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.01
Q30.05
95-th percentile1.27
Maximum3.23
Range3.23
Interquartile range (IQR)0.05

Descriptive statistics

Standard deviation0.61139766
Coefficient of variation (CV)2.7443162
Kurtosis12.665931
Mean0.22278689
Median Absolute Deviation (MAD)0.01
Skewness3.5091435
Sum13.59
Variance0.3738071
MonotonicityNot monotonic
2023-12-12T19:33:36.428591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.0 27
44.3%
0.01 9
 
14.8%
0.02 4
 
6.6%
0.03 3
 
4.9%
0.09 2
 
3.3%
0.04 2
 
3.3%
0.19 1
 
1.6%
3.23 1
 
1.6%
0.12 1
 
1.6%
0.1 1
 
1.6%
Other values (10) 10
 
16.4%
ValueCountFrequency (%)
0.0 27
44.3%
0.01 9
 
14.8%
0.02 4
 
6.6%
0.03 3
 
4.9%
0.04 2
 
3.3%
0.05 1
 
1.6%
0.09 2
 
3.3%
0.1 1
 
1.6%
0.12 1
 
1.6%
0.13 1
 
1.6%
ValueCountFrequency (%)
3.23 1
1.6%
2.38 1
1.6%
2.16 1
1.6%
1.27 1
1.6%
1.23 1
1.6%
0.78 1
1.6%
0.72 1
1.6%
0.56 1
1.6%
0.19 1
1.6%
0.15 1
1.6%

Interactions

2023-12-12T19:33:30.954366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:26.126072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:26.841891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:27.754613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:28.575028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:29.301041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:30.102612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:31.109377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:26.218972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:26.950538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:27.870356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:28.671350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:29.386274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:30.208346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:31.237016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:26.317364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:27.076611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:27.987489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:28.805447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:29.492668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:30.325542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:31.331415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:26.402613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:27.208466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:28.108044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:28.902193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:29.611307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:30.435378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:31.425541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:26.517115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:27.346846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:28.234680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:29.001066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:29.740723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:30.546048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:31.518300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:26.609895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:27.485680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:28.349991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:29.092479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:29.856628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:30.652539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:31.617182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:26.709313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:27.630118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:28.466616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:29.192549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:29.993136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:33:30.773667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:33:36.550030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동코드행정동이름법정동코드법정동이름공중화장실수개방화장실수인구수공중화장실비율(퍼센트)개방화장실비율(퍼센트)
행정동코드1.0001.0000.8610.8990.4820.3440.2630.0000.000
행정동이름1.0001.0000.8760.0000.6180.4250.4790.0000.000
법정동코드0.8610.8761.0001.0000.3310.0000.0000.0000.000
법정동이름0.8990.0001.0001.0000.0000.0000.4190.0000.695
공중화장실수0.4820.6180.3310.0001.0000.7450.6510.6770.000
개방화장실수0.3440.4250.0000.0000.7451.0000.6440.0000.441
인구수0.2630.4790.0000.4190.6510.6441.0000.0000.000
공중화장실비율(퍼센트)0.0000.0000.0000.0000.6770.0000.0001.0000.892
개방화장실비율(퍼센트)0.0000.0000.0000.6950.0000.4410.0000.8921.000
2023-12-12T19:33:36.726610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동코드법정동코드공중화장실수개방화장실수인구수공중화장실비율(퍼센트)개방화장실비율(퍼센트)행정동이름
행정동코드1.0000.810-0.042-0.203-0.107-0.084-0.2060.952
법정동코드0.8101.000-0.136-0.203-0.315-0.069-0.1640.601
공중화장실수-0.042-0.1361.0000.5410.5680.7430.3490.297
개방화장실수-0.203-0.2030.5411.0000.4660.3350.8560.254
인구수-0.107-0.3150.5680.4661.0000.0680.1270.211
공중화장실비율(퍼센트)-0.084-0.0690.7430.3350.0681.0000.3960.000
개방화장실비율(퍼센트)-0.206-0.1640.3490.8560.1270.3961.0000.000
행정동이름0.9520.6010.2970.2540.2110.0000.0001.000

Missing values

2023-12-12T19:33:31.757005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:33:31.935277image/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

시도코드시도이름시군구코드시군구이름행정동코드행정동이름법정동코드법정동이름공중화장실수개방화장실수인구수공중화장실비율(퍼센트)개방화장실비율(퍼센트)
03000000000대전광역시3020000000유성구3020060000관평동3020014600관평동142234290.060.01
13000000000대전광역시3020000000유성구3020060000관평동3020014400용산동0145880.00.02
23000000000대전광역시3020000000유성구3020060000관평동3020014300탑립동604981.20.0
33000000000대전광역시3020000000유성구3020058000구즉동3020014900대동003190.00.0
43000000000대전광역시3020000000유성구3020058000구즉동3020015100신동0040.00.0
53000000000대전광역시3020000000유성구3020058000구즉동3020015300구룡동001240.00.0
63000000000대전광역시3020000000유성구3020058000구즉동3020014800금고동0050.00.0
73000000000대전광역시3020000000유성구3020058000구즉동3020015000금탄동001500.00.0
83000000000대전광역시3020000000유성구3020058000구즉동3020015200둔곡동00310.00.0
93000000000대전광역시3020000000유성구3020058000구즉동3020014500봉산동5180910.060.01
시도코드시도이름시군구코드시군구이름행정동코드행정동이름법정동코드법정동이름공중화장실수개방화장실수인구수공중화장실비율(퍼센트)개방화장실비율(퍼센트)
513000000000대전광역시3020000000유성구3020054700노은2동3020013800외삼동115210.190.19
523000000000대전광역시3020000000유성구3020054700노은2동3020012000지족동44118600.030.03
533000000000대전광역시3020000000유성구3020054700노은2동3020013200하기동51104950.050.01
543000000000대전광역시3020000000유성구3020054000온천2동3020012200궁동8160400.130.02
553000000000대전광역시3020000000유성구3020054000온천2동3020012100죽동41103890.040.01
563000000000대전광역시3020000000유성구3020054000온천2동3020012400구성동2705854.620.0
573000000000대전광역시3020000000유성구3020054000온천2동3020012300어은동111100320.110.01
583000000000대전광역시3020000000유성구3020054000온천2동3020011700장대동1411124810.110.09
593000000000대전광역시3020000000유성구3020054800노은3동3020013900반석동34126690.020.03
603000000000대전광역시3020000000유성구3020054800노은3동3020012000지족동91244530.040.0