Overview

Dataset statistics

Number of variables12
Number of observations23
Missing cells44
Missing cells (%)15.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory111.6 B

Variable types

Text1
Categorical4
Numeric6
DateTime1

Dataset

Description경상북도 김천시 행정구역 현황 : 읍면동 및 세부 리, 동 수 현황에 대한 데이터를 담은 엑셀 자료로 이와 관련한 정보를 붙임과 같이 제공합니다.
Author경상북도 김천시
URLhttps://www.data.go.kr/data/15126900/fileData.do

Alerts

데이터기준일 has constant value ""Constant
읍 is highly overall correlated with 계(통_리) and 4 other fieldsHigh correlation
계(읍_면_동) is highly overall correlated with (법정동) and 3 other fieldsHigh correlation
(법정동) is highly overall correlated with 계(통_리) and 4 other fieldsHigh correlation
계(통_리) is highly overall correlated with (법정동) and 5 other fieldsHigh correlation
통 is highly overall correlated with (법정동) and 4 other fieldsHigh correlation
리 is highly overall correlated with 계(통_리) and 3 other fieldsHigh correlation
(법정) is highly overall correlated with 계(통_리) and 2 other fieldsHigh correlation
반수 is highly overall correlated with (법정동) and 6 other fieldsHigh correlation
동 is highly overall correlated with (법정동) and 2 other fieldsHigh correlation
계(읍_면_동) is highly imbalanced (74.2%)Imbalance
읍 is highly imbalanced (57.4%)Imbalance
(법정동) has 15 (65.2%) missing valuesMissing
통 has 15 (65.2%) missing valuesMissing
리 has 7 (30.4%) missing valuesMissing
(법정) has 7 (30.4%) missing valuesMissing
읍면동 has unique valuesUnique
반수 has unique valuesUnique

Reproduction

Analysis started2024-03-14 23:52:38.153894
Analysis finished2024-03-14 23:52:47.629088
Duration9.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

읍면동
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size312.0 B
2024-03-15T08:52:48.267832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.173913
Min length3

Characters and Unicode

Total characters73
Distinct characters40
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

Unique23 ?
Unique (%)100.0%

Sample

1st row김천시
2nd row아포읍
3rd row농소면
4th row남 면
5th row개령면
ValueCountFrequency (%)
김천시 1
 
4.2%
아포읍 1
 
4.2%
지좌동 1
 
4.2%
대곡동 1
 
4.2%
대신동 1
 
4.2%
양금동 1
 
4.2%
평화남산동 1
 
4.2%
자산동 1
 
4.2%
증산면 1
 
4.2%
대덕면 1
 
4.2%
Other values (14) 14
58.3%
2024-03-15T08:52:49.450748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
면 14
19.2%
동 7
 
9.6%
대 4
 
5.5%
산 4
 
5.5%
3
 
4.1%
항 2
 
2.7%
남 2
 
2.7%
천 2
 
2.7%
감 2
 
2.7%
지 2
 
2.7%
Other values (30) 31
42.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 70
95.9%
Space Separator 3
 
4.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
면 14
20.0%
동 7
 
10.0%
대 4
 
5.7%
산 4
 
5.7%
항 2
 
2.9%
남 2
 
2.9%
천 2
 
2.9%
감 2
 
2.9%
지 2
 
2.9%
곡 2
 
2.9%
Other values (29) 29
41.4%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 70
95.9%
Common 3
 
4.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
면 14
20.0%
동 7
 
10.0%
대 4
 
5.7%
산 4
 
5.7%
항 2
 
2.9%
남 2
 
2.9%
천 2
 
2.9%
감 2
 
2.9%
지 2
 
2.9%
곡 2
 
2.9%
Other values (29) 29
41.4%
Common
ValueCountFrequency (%)
3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 70
95.9%
ASCII 3
 
4.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
면 14
20.0%
동 7
 
10.0%
대 4
 
5.7%
산 4
 
5.7%
항 2
 
2.9%
남 2
 
2.9%
천 2
 
2.9%
감 2
 
2.9%
지 2
 
2.9%
곡 2
 
2.9%
Other values (29) 29
41.4%
ASCII
ValueCountFrequency (%)
3
100.0%

계(읍_면_동)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
1
22 
22
 
1

Length

Max length2
Median length1
Mean length1.0434783
Min length1

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st row22
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 22
95.7%
22 1
 
4.3%

Length

2024-03-15T08:52:49.861224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T08:52:50.171454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 22
95.7%
22 1
 
4.3%

읍
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Memory size312.0 B
<NA>
21 
1
 
2

Length

Max length4
Median length4
Mean length3.7391304
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 21
91.3%
1 2
 
8.7%

Length

2024-03-15T08:52:50.517331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T08:52:50.829942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 21
91.3%
1 2
 
8.7%

면
Categorical

Distinct3
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size312.0 B
1
14 
<NA>
8 
14
 
1

Length

Max length4
Median length1
Mean length2.0869565
Min length1

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st row14
2nd row<NA>
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 14
60.9%
<NA> 8
34.8%
14 1
 
4.3%

Length

2024-03-15T08:52:51.223428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T08:52:51.561980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 14
60.9%
na 8
34.8%
14 1
 
4.3%

동
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size312.0 B
<NA>
15 
1
7 
7
 
1

Length

Max length4
Median length4
Mean length2.9565217
Min length1

Unique

Unique1 ?
Unique (%)4.3%

Sample

1st row7
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 15
65.2%
1 7
30.4%
7 1
 
4.3%

Length

2024-03-15T08:52:51.930444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T08:52:52.266687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 15
65.2%
1 7
30.4%
7 1
 
4.3%

(법정동)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)75.0%
Missing15
Missing (%)65.2%
Infinite0
Infinite (%)0.0%
Mean5
Minimum1
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-03-15T08:52:52.473297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.35
Q12
median2.5
Q34.5
95-th percentile15.1
Maximum20
Range19
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation6.2564253
Coefficient of variation (CV)1.2512851
Kurtosis6.5442112
Mean5
Median Absolute Deviation (MAD)1
Skewness2.5060368
Sum40
Variance39.142857
MonotonicityNot monotonic
2024-03-15T08:52:52.787430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2 3
 
13.0%
20 1
 
4.3%
4 1
 
4.3%
6 1
 
4.3%
3 1
 
4.3%
1 1
 
4.3%
(Missing) 15
65.2%
ValueCountFrequency (%)
1 1
 
4.3%
2 3
13.0%
3 1
 
4.3%
4 1
 
4.3%
6 1
 
4.3%
20 1
 
4.3%
ValueCountFrequency (%)
20 1
 
4.3%
6 1
 
4.3%
4 1
 
4.3%
3 1
 
4.3%
2 3
13.0%
1 1
 
4.3%

계(통_리)
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)78.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.26087
Minimum11
Maximum578
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-03-15T08:52:53.128339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile12.1
Q116
median23
Q333.5
95-th percentile61.7
Maximum578
Range567
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation115.92757
Coefficient of variation (CV)2.3065174
Kurtosis22.205446
Mean50.26087
Median Absolute Deviation (MAD)9
Skewness4.6791253
Sum1156
Variance13439.202
MonotonicityNot monotonic
2024-03-15T08:52:53.343416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
16 3
 
13.0%
13 2
 
8.7%
23 2
 
8.7%
19 2
 
8.7%
578 1
 
4.3%
50 1
 
4.3%
34 1
 
4.3%
32 1
 
4.3%
59 1
 
4.3%
62 1
 
4.3%
Other values (8) 8
34.8%
ValueCountFrequency (%)
11 1
 
4.3%
12 1
 
4.3%
13 2
8.7%
16 3
13.0%
18 1
 
4.3%
19 2
8.7%
21 1
 
4.3%
23 2
8.7%
25 1
 
4.3%
26 1
 
4.3%
ValueCountFrequency (%)
578 1
4.3%
62 1
4.3%
59 1
4.3%
50 1
4.3%
37 1
4.3%
34 1
4.3%
33 1
4.3%
32 1
4.3%
26 1
4.3%
25 1
4.3%

통
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct8
Distinct (%)100.0%
Missing15
Missing (%)65.2%
Infinite0
Infinite (%)0.0%
Mean75
Minimum26
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-03-15T08:52:53.540645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum26
5-th percentile28.1
Q133.5
median43.5
Q359.75
95-th percentile216.7
Maximum300
Range274
Interquartile range (IQR)26.25

Descriptive statistics

Standard deviation91.846145
Coefficient of variation (CV)1.2246153
Kurtosis7.5183264
Mean75
Median Absolute Deviation (MAD)13.5
Skewness2.7160143
Sum600
Variance8435.7143
MonotonicityNot monotonic
2024-03-15T08:52:53.719771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
300 1
 
4.3%
37 1
 
4.3%
50 1
 
4.3%
26 1
 
4.3%
62 1
 
4.3%
59 1
 
4.3%
32 1
 
4.3%
34 1
 
4.3%
(Missing) 15
65.2%
ValueCountFrequency (%)
26 1
4.3%
32 1
4.3%
34 1
4.3%
37 1
4.3%
50 1
4.3%
59 1
4.3%
62 1
4.3%
300 1
4.3%
ValueCountFrequency (%)
300 1
4.3%
62 1
4.3%
59 1
4.3%
50 1
4.3%
37 1
4.3%
34 1
4.3%
32 1
4.3%
26 1
4.3%

리
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)68.8%
Missing7
Missing (%)30.4%
Infinite0
Infinite (%)0.0%
Mean34.75
Minimum11
Maximum278
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-03-15T08:52:53.995217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11.75
Q115.25
median18.5
Q323
95-th percentile94.25
Maximum278
Range267
Interquartile range (IQR)7.75

Descriptive statistics

Standard deviation65.111699
Coefficient of variation (CV)1.873718
Kurtosis15.708606
Mean34.75
Median Absolute Deviation (MAD)4.5
Skewness3.9492574
Sum556
Variance4239.5333
MonotonicityNot monotonic
2024-03-15T08:52:54.429123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
16 3
13.0%
13 2
 
8.7%
23 2
 
8.7%
19 2
 
8.7%
278 1
 
4.3%
33 1
 
4.3%
12 1
 
4.3%
25 1
 
4.3%
18 1
 
4.3%
21 1
 
4.3%
(Missing) 7
30.4%
ValueCountFrequency (%)
11 1
 
4.3%
12 1
 
4.3%
13 2
8.7%
16 3
13.0%
18 1
 
4.3%
19 2
8.7%
21 1
 
4.3%
23 2
8.7%
25 1
 
4.3%
33 1
 
4.3%
ValueCountFrequency (%)
278 1
 
4.3%
33 1
 
4.3%
25 1
 
4.3%
23 2
8.7%
21 1
 
4.3%
19 2
8.7%
18 1
 
4.3%
16 3
13.0%
13 2
8.7%
12 1
 
4.3%

(법정)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)62.5%
Missing7
Missing (%)30.4%
Infinite0
Infinite (%)0.0%
Mean18.75
Minimum5
Maximum150
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-03-15T08:52:54.921951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile6.5
Q17.75
median10
Q312.25
95-th percentile50.25
Maximum150
Range145
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation35.144464
Coefficient of variation (CV)1.8743714
Kurtosis15.682101
Mean18.75
Median Absolute Deviation (MAD)2.5
Skewness3.9443778
Sum300
Variance1235.1333
MonotonicityNot monotonic
2024-03-15T08:52:55.380134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
10 3
13.0%
7 3
13.0%
8 2
 
8.7%
12 2
 
8.7%
150 1
 
4.3%
9 1
 
4.3%
15 1
 
4.3%
5 1
 
4.3%
17 1
 
4.3%
13 1
 
4.3%
(Missing) 7
30.4%
ValueCountFrequency (%)
5 1
 
4.3%
7 3
13.0%
8 2
8.7%
9 1
 
4.3%
10 3
13.0%
12 2
8.7%
13 1
 
4.3%
15 1
 
4.3%
17 1
 
4.3%
150 1
 
4.3%
ValueCountFrequency (%)
150 1
 
4.3%
17 1
 
4.3%
15 1
 
4.3%
13 1
 
4.3%
12 2
8.7%
10 3
13.0%
9 1
 
4.3%
8 2
8.7%
7 3
13.0%
5 1
 
4.3%

반수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean228.26087
Minimum42
Maximum2625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size335.0 B
2024-03-15T08:52:55.952127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum42
5-th percentile43.1
Q155
median95
Q3176
95-th percentile357.5
Maximum2625
Range2583
Interquartile range (IQR)121

Descriptive statistics

Standard deviation529.99067
Coefficient of variation (CV)2.3218639
Kurtosis21.53655
Mean228.26087
Median Absolute Deviation (MAD)46
Skewness4.5826987
Sum5250
Variance280890.11
MonotonicityNot monotonic
2024-03-15T08:52:56.589771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2625 1
 
4.3%
141 1
 
4.3%
211 1
 
4.3%
167 1
 
4.3%
317 1
 
4.3%
362 1
 
4.3%
113 1
 
4.3%
231 1
 
4.3%
185 1
 
4.3%
43 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
42 1
4.3%
43 1
4.3%
44 1
4.3%
49 1
4.3%
50 1
4.3%
53 1
4.3%
57 1
4.3%
58 1
4.3%
65 1
4.3%
71 1
4.3%
ValueCountFrequency (%)
2625 1
4.3%
362 1
4.3%
317 1
4.3%
231 1
4.3%
211 1
4.3%
185 1
4.3%
167 1
4.3%
141 1
4.3%
113 1
4.3%
100 1
4.3%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size312.0 B
Minimum2024-03-04 00:00:00
Maximum2024-03-04 00:00:00
2024-03-15T08:52:56.977711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:57.408643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-15T08:52:45.098355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:38.815089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:39.803021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:40.887347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:41.949254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:43.514349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:45.255088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:39.020573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:40.050322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:41.122508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:42.208000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:43.787122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:45.404387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:39.178229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:40.295819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:41.364271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:42.591183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:44.046672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:45.654700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:39.325130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:40.446531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:41.503494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:42.787003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:44.310277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:45.899675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:39.480722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:40.587208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:41.647448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:43.023682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:44.627515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:46.157582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:39.654831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:40.745785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:41.811073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:43.275161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T08:52:44.884824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T08:52:57.984464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
읍면동계(읍_면_동)면동(법정동)계(통_리)통리(법정)반수
읍면동1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
계(읍_면_동)1.0001.0000.5870.3961.0000.6361.0000.5970.5971.000
면1.0000.5871.000NaNNaN0.587NaN0.5870.5870.587
동1.0000.396NaN1.0001.0000.3961.000NaNNaN1.000
(법정동)1.0001.000NaN1.0001.0001.0000.789NaNNaN0.789
계(통_리)1.0000.6360.5870.3961.0001.0001.0000.5970.5971.000
통1.0001.000NaN1.0000.7891.0001.000NaNNaN1.000
리1.0000.5970.587NaNNaN0.597NaN1.0000.5970.597
(법정)1.0000.5970.587NaNNaN0.597NaN0.5971.0000.597
반수1.0001.0000.5871.0000.7891.0001.0000.5970.5971.000
2024-03-15T08:52:58.350531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면읍동계(읍_면_동)
면1.000NaNNaN0.394
읍NaN1.000NaN1.000
동NaNNaN1.0000.218
계(읍_면_동)0.3941.0000.2181.000
2024-03-15T08:52:58.646375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
(법정동)계(통_리)통리(법정)반수계(읍_면_동)읍면동
(법정동)1.0000.7810.781NaNNaN0.6590.816NaNNaN0.816
계(통_리)0.7811.0001.0001.0000.6380.9180.4371.0000.3940.218
통0.7811.0001.000NaNNaN0.9760.913NaNNaN0.913
리NaN1.000NaN1.0000.6380.7610.4021.0000.394NaN
(법정)NaN0.638NaN0.6381.0000.3600.4021.0000.394NaN
반수0.6590.9180.9760.7610.3601.0000.9761.0000.3940.913
계(읍_면_동)0.8160.4370.9130.4020.4020.9761.0001.0000.3940.218
읍NaN1.000NaN1.0001.0001.0001.0001.000NaNNaN
면NaN0.394NaN0.3940.3940.3940.394NaN1.000NaN
동0.8160.2180.913NaNNaN0.9130.218NaNNaN1.000

Missing values

2024-03-15T08:52:46.507572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T08:52:47.035945image/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.
2024-03-15T08:52:47.410879image/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

읍면동계(읍_면_동)읍면동(법정동)계(통_리)통리(법정)반수데이터기준일
0김천시2211472057830027815026252024-03-04
1아포읍11<NA><NA><NA>33<NA>33101412024-03-04
2농소면1<NA>1<NA><NA>13<NA>137722024-03-04
3남 면1<NA>1<NA><NA>16<NA>169652024-03-04
4개령면1<NA>1<NA><NA>12<NA>128532024-03-04
5감문면1<NA>1<NA><NA>23<NA>23151002024-03-04
6어모면1<NA>1<NA><NA>23<NA>2312992024-03-04
7봉산면1<NA>1<NA><NA>16<NA>168502024-03-04
8대항면1<NA>1<NA><NA>19<NA>197712024-03-04
9감천면1<NA>1<NA><NA>13<NA>135442024-03-04
읍면동계(읍_면_동)읍면동(법정동)계(통_리)통리(법정)반수데이터기준일
13부항면1<NA>1<NA><NA>18<NA>1813422024-03-04
14대덕면1<NA>1<NA><NA>21<NA>2112572024-03-04
15증산면1<NA>1<NA><NA>11<NA>1110432024-03-04
16자산동1<NA><NA>143737<NA><NA>1852024-03-04
17평화남산동1<NA><NA>125050<NA><NA>2312024-03-04
18양금동1<NA><NA>122626<NA><NA>1132024-03-04
19대신동1<NA><NA>166262<NA><NA>3622024-03-04
20대곡동1<NA><NA>135959<NA><NA>3172024-03-04
21지좌동1<NA><NA>123232<NA><NA>1672024-03-04
22율곡동1<NA><NA>113434<NA><NA>2112024-03-04