Overview

Dataset statistics

Number of variables8
Number of observations97
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.7 KiB
Average record size in memory70.3 B

Variable types

Numeric5
Categorical2
Text1

Dataset

Description광주광역시 행정동별 현황에 대한 데이터입니다. 구, 동, 인구수, 면적, 세대수, 통, 리, 반, 공무원수 등의 정보를 제공합니다.
Author광주광역시
URLhttps://www.data.go.kr/data/15054803/fileData.do

Alerts

시 has constant value ""Constant
순위 is highly overall correlated with 인구(명) and 2 other fieldsHigh correlation
인구(명) is highly overall correlated with 순위 and 2 other fieldsHigh correlation
세대수 is highly overall correlated with 순위 and 2 other fieldsHigh correlation
공무원(명) is highly overall correlated with 순위 and 2 other fieldsHigh correlation
순위 has unique valuesUnique
인구(명) has unique valuesUnique
세대수 has unique valuesUnique
면적(제곱킬로미터) has 2 (2.1%) zerosZeros

Reproduction

Analysis started2024-03-14 13:24:55.664579
Analysis finished2024-03-14 13:25:02.684558
Duration7.02 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순위
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct97
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean49
Minimum1
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1001.0 B
2024-03-14T22:25:02.835989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.8
Q125
median49
Q373
95-th percentile92.2
Maximum97
Range96
Interquartile range (IQR)48

Descriptive statistics

Standard deviation28.145456
Coefficient of variation (CV)0.57439705
Kurtosis-1.2
Mean49
Median Absolute Deviation (MAD)24
Skewness0
Sum4753
Variance792.16667
MonotonicityStrictly increasing
2024-03-14T22:25:03.088423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
74 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
68 1
 
1.0%
67 1
 
1.0%
66 1
 
1.0%
65 1
 
1.0%
Other values (87) 87
89.7%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%
92 1
1.0%
91 1
1.0%
90 1
1.0%
89 1
1.0%
88 1
1.0%

시
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size904.0 B
광주(光州)
97 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row광주(光州)
2nd row광주(光州)
3rd row광주(光州)
4th row광주(光州)
5th row광주(光州)

Common Values

ValueCountFrequency (%)
광주(光州) 97
100.0%

Length

2024-03-14T22:25:03.406972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T22:25:03.562880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광주(光州 97
100.0%

구
Categorical

Distinct5
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size904.0 B
북(北)
28 
광산(光山)
21 
서(西)
18 
남(南)
17 
동(東)
13 

Length

Max length6
Median length4
Mean length4.4329897
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row광산(光山)
2nd row광산(光山)
3rd row북(北)
4th row북(北)
5th row서(西)

Common Values

ValueCountFrequency (%)
북(北) 28
28.9%
광산(光山) 21
21.6%
서(西) 18
18.6%
남(南) 17
17.5%
동(東) 13
13.4%

Length

2024-03-14T22:25:03.750989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T22:25:03.957831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
북(北 28
28.9%
광산(光山 21
21.6%
서(西 18
18.6%
남(南 17
17.5%
동(東 13
13.4%

동
Text

Distinct96
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size904.0 B
2024-03-14T22:25:04.707845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length8.8865979
Min length6

Characters and Unicode

Total characters862
Distinct characters171
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique95 ?
Unique (%)97.9%

Sample

1st row수완동(水莞洞)
2nd row첨단2동(尖端2洞)
3rd row용봉동(龍鳳洞)
4th row양산동(陽山洞)
5th row풍암동(楓岩洞)
ValueCountFrequency (%)
우산동(牛山洞 2
 
2.1%
수완동(水莞洞 1
 
1.0%
중흥2동(中興2洞 1
 
1.0%
양림동(楊林洞 1
 
1.0%
두암1동(斗岩1洞 1
 
1.0%
학동(鶴洞 1
 
1.0%
비아동(飛鴉洞 1
 
1.0%
광천동(光川洞 1
 
1.0%
주월2동(珠月2洞 1
 
1.0%
대촌동(大村洞 1
 
1.0%
Other values (86) 86
88.7%
2024-03-14T22:25:05.708934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 101
 
11.7%
( 97
 
11.3%
洞 97
 
11.3%
) 97
 
11.3%
1 40
 
4.6%
2 40
 
4.6%
山 11
 
1.3%
산 11
 
1.3%
3 10
 
1.2%
암 8
 
0.9%
Other values (161) 350
40.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 572
66.4%
Open Punctuation 97
 
11.3%
Close Punctuation 97
 
11.3%
Decimal Number 96
 
11.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 101
 
17.7%
洞 97
 
17.0%
山 11
 
1.9%
산 11
 
1.9%
암 8
 
1.4%
岩 8
 
1.4%
월 8
 
1.4%
月 8
 
1.4%
谷 7
 
1.2%
雲 7
 
1.2%
Other values (154) 306
53.5%
Decimal Number
ValueCountFrequency (%)
1 40
41.7%
2 40
41.7%
3 10
 
10.4%
4 4
 
4.2%
5 2
 
2.1%
Open Punctuation
ValueCountFrequency (%)
( 97
100.0%
Close Punctuation
ValueCountFrequency (%)
) 97
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 290
33.6%
Hangul 286
33.2%
Han 286
33.2%

Most frequent character per script

Han
ValueCountFrequency (%)
洞 97
33.9%
山 11
 
3.8%
岩 8
 
2.8%
月 8
 
2.8%
谷 7
 
2.4%
雲 7
 
2.4%
林 6
 
2.1%
興 6
 
2.1%
花 4
 
1.4%
亭 4
 
1.4%
Other values (77) 128
44.8%
Hangul
ValueCountFrequency (%)
동 101
35.3%
산 11
 
3.8%
암 8
 
2.8%
월 8
 
2.8%
운 7
 
2.4%
곡 7
 
2.4%
흥 6
 
2.1%
림 6
 
2.1%
정 6
 
2.1%
화 5
 
1.7%
Other values (67) 121
42.3%
Common
ValueCountFrequency (%)
( 97
33.4%
) 97
33.4%
1 40
13.8%
2 40
13.8%
3 10
 
3.4%
4 4
 
1.4%
5 2
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 290
33.6%
Hangul 286
33.2%
CJK 280
32.5%
CJK Compat Ideographs 6
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 101
35.3%
산 11
 
3.8%
암 8
 
2.8%
월 8
 
2.8%
운 7
 
2.4%
곡 7
 
2.4%
흥 6
 
2.1%
림 6
 
2.1%
정 6
 
2.1%
화 5
 
1.7%
Other values (67) 121
42.3%
ASCII
ValueCountFrequency (%)
( 97
33.4%
) 97
33.4%
1 40
13.8%
2 40
13.8%
3 10
 
3.4%
4 4
 
1.4%
5 2
 
0.7%
CJK
ValueCountFrequency (%)
洞 97
34.6%
山 11
 
3.9%
岩 8
 
2.9%
月 8
 
2.9%
谷 7
 
2.5%
雲 7
 
2.5%
林 6
 
2.1%
興 6
 
2.1%
花 4
 
1.4%
亭 4
 
1.4%
Other values (73) 122
43.6%
CJK Compat Ideographs
ValueCountFrequency (%)
良 2
33.3%
林 2
33.3%
龍 1
16.7%
柳 1
16.7%

인구(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct97
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14631.309
Minimum1708
Maximum75204
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1001.0 B
2024-03-14T22:25:05.947643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1708
5-th percentile2874.6
Q16745
median11564
Q319841
95-th percentile34073.6
Maximum75204
Range73496
Interquartile range (IQR)13096

Descriptive statistics

Standard deviation11412.456
Coefficient of variation (CV)0.78000241
Kurtosis7.1423579
Mean14631.309
Median Absolute Deviation (MAD)6206
Skewness2.0115129
Sum1419237
Variance1.3024416 × 108
MonotonicityNot monotonic
2024-03-14T22:25:06.203411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75204 1
 
1.0%
6565 1
 
1.0%
6626 1
 
1.0%
7248 1
 
1.0%
7320 1
 
1.0%
7447 1
 
1.0%
6975 1
 
1.0%
7562 1
 
1.0%
8220 1
 
1.0%
7980 1
 
1.0%
Other values (87) 87
89.7%
ValueCountFrequency (%)
1708 1
1.0%
1887 1
1.0%
1979 1
1.0%
2129 1
1.0%
2329 1
1.0%
3011 1
1.0%
3237 1
1.0%
3653 1
1.0%
3817 1
1.0%
4120 1
1.0%
ValueCountFrequency (%)
75204 1
1.0%
42053 1
1.0%
37161 1
1.0%
36101 1
1.0%
34864 1
1.0%
33876 1
1.0%
33287 1
1.0%
29463 1
1.0%
29439 1
1.0%
29158 1
1.0%

면적(제곱킬로미터)
Real number (ℝ)

ZEROS 

Distinct80
Distinct (%)82.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1613402
Minimum0
Maximum48.39
Zeros2
Zeros (%)2.1%
Negative0
Negative (%)0.0%
Memory size1001.0 B
2024-03-14T22:25:06.464839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.48
Q10.85
median1.27
Q33.45
95-th percentile29.872
Maximum48.39
Range48.39
Interquartile range (IQR)2.6

Descriptive statistics

Standard deviation9.5187799
Coefficient of variation (CV)1.8442458
Kurtosis7.15504
Mean5.1613402
Median Absolute Deviation (MAD)0.68
Skewness2.7599574
Sum500.65
Variance90.60717
MonotonicityNot monotonic
2024-03-14T22:25:06.793422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 9
 
9.3%
1.2 3
 
3.1%
0.53 2
 
2.1%
0.0 2
 
2.1%
1.04 2
 
2.1%
0.44 2
 
2.1%
1.86 2
 
2.1%
1.18 2
 
2.1%
0.51 2
 
2.1%
1.3 1
 
1.0%
Other values (70) 70
72.2%
ValueCountFrequency (%)
0.0 2
2.1%
0.29 1
1.0%
0.44 2
2.1%
0.49 1
1.0%
0.51 2
2.1%
0.53 2
2.1%
0.54 1
1.0%
0.57 1
1.0%
0.59 1
1.0%
0.61 1
1.0%
ValueCountFrequency (%)
48.39 1
1.0%
38.73 1
1.0%
35.38 1
1.0%
33.41 1
1.0%
29.88 1
1.0%
29.87 1
1.0%
28.0 1
1.0%
26.98 1
1.0%
19.65 1
1.0%
17.63 1
1.0%

세대수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct97
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6757.0412
Minimum968
Maximum28314
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1001.0 B
2024-03-14T22:25:07.225618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum968
5-th percentile1779.2
Q13543
median5502
Q38657
95-th percentile14999
Maximum28314
Range27346
Interquartile range (IQR)5114

Descriptive statistics

Standard deviation4603.9223
Coefficient of variation (CV)0.68135182
Kurtosis4.2278238
Mean6757.0412
Median Absolute Deviation (MAD)2417
Skewness1.6432344
Sum655433
Variance21196101
MonotonicityNot monotonic
2024-03-14T22:25:07.459645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28314 1
 
1.0%
3699 1
 
1.0%
3065 1
 
1.0%
3925 1
 
1.0%
3487 1
 
1.0%
3543 1
 
1.0%
3636 1
 
1.0%
3870 1
 
1.0%
4078 1
 
1.0%
4326 1
 
1.0%
Other values (87) 87
89.7%
ValueCountFrequency (%)
968 1
1.0%
1152 1
1.0%
1206 1
1.0%
1307 1
1.0%
1320 1
1.0%
1894 1
1.0%
1959 1
1.0%
2125 1
1.0%
2208 1
1.0%
2289 1
1.0%
ValueCountFrequency (%)
28314 1
1.0%
18860 1
1.0%
17956 1
1.0%
16126 1
1.0%
15087 1
1.0%
14977 1
1.0%
14376 1
1.0%
13873 1
1.0%
13830 1
1.0%
13034 1
1.0%

공무원(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.865979
Minimum11
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1001.0 B
2024-03-14T22:25:07.661833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11.8
Q113
median14
Q316
95-th percentile20
Maximum27
Range16
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.7824931
Coefficient of variation (CV)0.18717187
Kurtosis3.453657
Mean14.865979
Median Absolute Deviation (MAD)1
Skewness1.5157331
Sum1442
Variance7.742268
MonotonicityNot monotonic
2024-03-14T22:25:08.028714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
14 22
22.7%
13 17
17.5%
15 14
14.4%
16 10
10.3%
12 10
10.3%
19 5
 
5.2%
11 5
 
5.2%
17 4
 
4.1%
18 4
 
4.1%
20 2
 
2.1%
Other values (4) 4
 
4.1%
ValueCountFrequency (%)
11 5
 
5.2%
12 10
10.3%
13 17
17.5%
14 22
22.7%
15 14
14.4%
16 10
10.3%
17 4
 
4.1%
18 4
 
4.1%
19 5
 
5.2%
20 2
 
2.1%
ValueCountFrequency (%)
27 1
 
1.0%
23 1
 
1.0%
22 1
 
1.0%
21 1
 
1.0%
20 2
 
2.1%
19 5
 
5.2%
18 4
 
4.1%
17 4
 
4.1%
16 10
10.3%
15 14
14.4%

Interactions

2024-03-14T22:25:00.728906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:24:56.034420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:24:57.462576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:24:58.731370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:24:59.967553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:25:00.963299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:24:56.474455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:24:57.711485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:24:58.969799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:25:00.145290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:25:01.225651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:24:56.728870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:24:57.968012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:24:59.227536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:25:00.297803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:25:01.476090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:24:56.967179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:24:58.217462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:24:59.466164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:25:00.437340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:25:01.730958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:24:57.205614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:24:58.463009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:24:59.711221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T22:25:00.575735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T22:25:08.283251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순위구동인구(명)면적(제곱킬로미터)세대수공무원(명)
순위1.0000.5010.9430.8640.0000.8310.600
구0.5011.0000.9240.0000.4570.1590.447
동0.9430.9241.0000.9371.0000.0000.000
인구(명)0.8640.0000.9371.0000.0000.8860.817
면적(제곱킬로미터)0.0000.4571.0000.0001.0000.0000.000
세대수0.8310.1590.0000.8860.0001.0000.813
공무원(명)0.6000.4470.0000.8170.0000.8131.000
2024-03-14T22:25:08.571012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순위인구(명)면적(제곱킬로미터)세대수공무원(명)구
순위1.000-0.999-0.342-0.985-0.7700.222
인구(명)-0.9991.0000.3410.9860.7710.000
면적(제곱킬로미터)-0.3420.3411.0000.3210.2720.199
세대수-0.9850.9860.3211.0000.7910.097
공무원(명)-0.7700.7710.2720.7911.0000.265
구0.2220.0000.1990.0970.2651.000

Missing values

2024-03-14T22:25:02.112670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T22:25:02.519707image/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

순위시구동인구(명)면적(제곱킬로미터)세대수공무원(명)
01광주(光州)광산(光山)수완동(水莞洞)752044.612831427
12광주(光州)광산(光山)첨단2동(尖端2洞)420533.451886022
23광주(光州)북(北)용봉동(龍鳳洞)371613.141795619
34광주(光州)북(北)양산동(陽山洞)361017.161612617
45광주(光州)서(西)풍암동(楓岩洞)348644.761508719
56광주(光州)광산(光山)신창동(新倉洞)338764.021387320
67광주(光州)광산(光山)어룡동(魚龍洞)3328717.631437619
78광주(光州)광산(光山)운남동(雲南洞)294632.851224919
89광주(光州)광산(光山)우산동(牛山洞)291584.561497723
910광주(光州)서(西)치평동(治平洞)294393.271383020
순위시구동인구(명)면적(제곱킬로미터)세대수공무원(명)
8788광주(光州)동(東)지산1동(芝山1洞)41200.0244915
8889광주(光州)북(北)중앙동(中央洞)38170.63220811
8990광주(光州)동(東)동명동(東明洞)36530.0241513
9091광주(光州)서(西)양동(良洞)32370.54189414
9192광주(光州)동(東)서남동(瑞南洞)30111.0228913
9293광주(光州)북(北)석곡동(石谷洞)232948.39130711
9394광주(光州)광산(光山)삼도동(三道洞)212938.73132012
9495광주(光州)광산(光山)임곡동(林谷洞)197929.88120612
9596광주(光州)광산(光山)본량동(本良洞)188733.41115212
9697광주(光州)광산(光山)동곡동(東谷洞)170815.4996812