Overview

Dataset statistics

Number of variables12
Number of observations35
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory109.7 B

Variable types

Text1
Numeric10
Categorical1

Dataset

Description전북특별자치도 전주시 행정동별 인구 현황입니다. 행정동별, 나이별(18세이상, 65세이상) 세대수, 인구수 등에 대한 항목을 포함합니다.
Author전북특별자치도 전주시
URLhttps://www.data.go.kr/data/15126886/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
세대수 is highly overall correlated with 인구수(계) and 8 other fieldsHigh correlation
인구수(계) is highly overall correlated with 세대수 and 8 other fieldsHigh correlation
인구수(남) is highly overall correlated with 세대수 and 8 other fieldsHigh correlation
인구수(여) is highly overall correlated with 세대수 and 8 other fieldsHigh correlation
18세이상인구수(계) is highly overall correlated with 세대수 and 8 other fieldsHigh correlation
18세이상인구수(남) is highly overall correlated with 세대수 and 8 other fieldsHigh correlation
18세이상인구수(여) is highly overall correlated with 세대수 and 8 other fieldsHigh correlation
65세이상인구수(계) is highly overall correlated with 세대수 and 8 other fieldsHigh correlation
65세이상인구수(남) is highly overall correlated with 세대수 and 8 other fieldsHigh correlation
65세이상인구수(여) is highly overall correlated with 세대수 and 8 other fieldsHigh correlation
구분 has unique valuesUnique
세대수 has unique valuesUnique
인구수(계) has unique valuesUnique
인구수(남) has unique valuesUnique
인구수(여) has unique valuesUnique
18세이상인구수(계) has unique valuesUnique
18세이상인구수(남) has unique valuesUnique
18세이상인구수(여) has unique valuesUnique
65세이상인구수(계) has unique valuesUnique
65세이상인구수(남) has unique valuesUnique
65세이상인구수(여) has unique valuesUnique

Reproduction

Analysis started2024-04-21 13:58:14.739210
Analysis finished2024-04-21 13:58:32.565432
Duration17.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size408.0 B
2024-04-21T22:58:33.168500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.7142857
Min length3

Characters and Unicode

Total characters130
Distinct characters41
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

Unique35 ?
Unique (%)100.0%

Sample

1st row중앙동
2nd row풍남동
3rd row노송동
4th row완산동
5th row동서학동
ValueCountFrequency (%)
중앙동 1
 
2.9%
효자5동 1
 
2.9%
인후1동 1
 
2.9%
인후2동 1
 
2.9%
인후3동 1
 
2.9%
덕진동 1
 
2.9%
금암1동 1
 
2.9%
금암2동 1
 
2.9%
진북동 1
 
2.9%
팔복동 1
 
2.9%
Other values (25) 25
71.4%
2024-04-21T22:58:34.107080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36
27.7%
2 8
 
6.2%
1 8
 
6.2%
5
 
3.8%
5
 
3.8%
5
 
3.8%
4
 
3.1%
4
 
3.1%
3
 
2.3%
3
 
2.3%
Other values (31) 49
37.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 109
83.8%
Decimal Number 21
 
16.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
36
33.0%
5
 
4.6%
5
 
4.6%
5
 
4.6%
4
 
3.7%
4
 
3.7%
3
 
2.8%
3
 
2.8%
3
 
2.8%
3
 
2.8%
Other values (26) 38
34.9%
Decimal Number
ValueCountFrequency (%)
2 8
38.1%
1 8
38.1%
3 3
 
14.3%
5 1
 
4.8%
4 1
 
4.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 109
83.8%
Common 21
 
16.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
36
33.0%
5
 
4.6%
5
 
4.6%
5
 
4.6%
4
 
3.7%
4
 
3.7%
3
 
2.8%
3
 
2.8%
3
 
2.8%
3
 
2.8%
Other values (26) 38
34.9%
Common
ValueCountFrequency (%)
2 8
38.1%
1 8
38.1%
3 3
 
14.3%
5 1
 
4.8%
4 1
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 109
83.8%
ASCII 21
 
16.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
36
33.0%
5
 
4.6%
5
 
4.6%
5
 
4.6%
4
 
3.7%
4
 
3.7%
3
 
2.8%
3
 
2.8%
3
 
2.8%
3
 
2.8%
Other values (26) 38
34.9%
ASCII
ValueCountFrequency (%)
2 8
38.1%
1 8
38.1%
3 3
 
14.3%
5 1
 
4.8%
4 1
 
4.8%

세대수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8484.8286
Minimum2281
Maximum24335
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size443.0 B
2024-04-21T22:58:34.547623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2281
5-th percentile2966.9
Q15420.5
median6329
Q39836.5
95-th percentile18473.6
Maximum24335
Range22054
Interquartile range (IQR)4416

Descriptive statistics

Standard deviation5204.0787
Coefficient of variation (CV)0.61333929
Kurtosis1.5671482
Mean8484.8286
Median Absolute Deviation (MAD)1551
Skewness1.4493935
Sum296969
Variance27082435
MonotonicityNot monotonic
2024-04-21T22:58:34.769457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
4913 1
 
2.9%
2281 1
 
2.9%
7438 1
 
2.9%
5708 1
 
2.9%
14464 1
 
2.9%
10970 1
 
2.9%
5687 1
 
2.9%
5189 1
 
2.9%
4599 1
 
2.9%
5951 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
2281 1
2.9%
2605 1
2.9%
3122 1
2.9%
4314 1
2.9%
4474 1
2.9%
4599 1
2.9%
4913 1
2.9%
5189 1
2.9%
5345 1
2.9%
5496 1
2.9%
ValueCountFrequency (%)
24335 1
2.9%
18573 1
2.9%
18431 1
2.9%
17558 1
2.9%
16679 1
2.9%
14464 1
2.9%
13015 1
2.9%
10970 1
2.9%
10416 1
2.9%
9257 1
2.9%

인구수(계)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18307.771
Minimum3621
Maximum64929
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size443.0 B
2024-04-21T22:58:34.981987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3621
5-th percentile5581.7
Q110020
median12982
Q320755.5
95-th percentile40531.3
Maximum64929
Range61308
Interquartile range (IQR)10735.5

Descriptive statistics

Standard deviation13341.305
Coefficient of variation (CV)0.7287236
Kurtosis3.2359719
Mean18307.771
Median Absolute Deviation (MAD)5010
Skewness1.7369306
Sum640772
Variance1.7799042 × 108
MonotonicityNot monotonic
2024-04-21T22:58:35.363435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
9723 1
 
2.9%
3621 1
 
2.9%
17312 1
 
2.9%
10317 1
 
2.9%
30327 1
 
2.9%
20639 1
 
2.9%
7916 1
 
2.9%
9520 1
 
2.9%
7515 1
 
2.9%
12005 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
3621 1
2.9%
4909 1
2.9%
5870 1
2.9%
7515 1
2.9%
7916 1
2.9%
7972 1
2.9%
9195 1
2.9%
9520 1
2.9%
9723 1
2.9%
10317 1
2.9%
ValueCountFrequency (%)
64929 1
2.9%
43465 1
2.9%
39274 1
2.9%
38623 1
2.9%
36166 1
2.9%
35948 1
2.9%
30327 1
2.9%
24922 1
2.9%
20872 1
2.9%
20639 1
2.9%

인구수(남)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8993.0857
Minimum1800
Maximum32133
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size443.0 B
2024-04-21T22:58:35.735677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1800
5-th percentile2722.1
Q14946.5
median6225
Q310238.5
95-th percentile19614
Maximum32133
Range30333
Interquartile range (IQR)5292

Descriptive statistics

Standard deviation6542.2173
Coefficient of variation (CV)0.72747191
Kurtosis3.4234615
Mean8993.0857
Median Absolute Deviation (MAD)2096
Skewness1.7661967
Sum314758
Variance42800607
MonotonicityNot monotonic
2024-04-21T22:58:36.118047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
4830 1
 
2.9%
1800 1
 
2.9%
8321 1
 
2.9%
5063 1
 
2.9%
14990 1
 
2.9%
10446 1
 
2.9%
4329 1
 
2.9%
4634 1
 
2.9%
4086 1
 
2.9%
5999 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
1800 1
2.9%
2412 1
2.9%
2855 1
2.9%
3856 1
2.9%
4086 1
2.9%
4329 1
2.9%
4393 1
2.9%
4634 1
2.9%
4830 1
2.9%
5063 1
2.9%
ValueCountFrequency (%)
32133 1
2.9%
20846 1
2.9%
19086 1
2.9%
18851 1
2.9%
18071 1
2.9%
17686 1
2.9%
14990 1
2.9%
12140 1
2.9%
10446 1
2.9%
10031 1
2.9%

인구수(여)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9314.6857
Minimum1821
Maximum32796
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size443.0 B
2024-04-21T22:58:36.483831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1821
5-th percentile2859.6
Q15073.5
median6684
Q310517
95-th percentile20917.3
Maximum32796
Range30975
Interquartile range (IQR)5443.5

Descriptive statistics

Standard deviation6807.0594
Coefficient of variation (CV)0.73078788
Kurtosis3.0480946
Mean9314.6857
Median Absolute Deviation (MAD)2568
Skewness1.7046471
Sum326014
Variance46336058
MonotonicityNot monotonic
2024-04-21T22:58:36.873920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
4893 1
 
2.9%
1821 1
 
2.9%
8991 1
 
2.9%
5254 1
 
2.9%
15337 1
 
2.9%
10193 1
 
2.9%
3587 1
 
2.9%
4886 1
 
2.9%
3429 1
 
2.9%
6006 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
1821 1
2.9%
2497 1
2.9%
3015 1
2.9%
3429 1
2.9%
3587 1
2.9%
4116 1
2.9%
4802 1
2.9%
4886 1
2.9%
4893 1
2.9%
5254 1
2.9%
ValueCountFrequency (%)
32796 1
2.9%
22619 1
2.9%
20188 1
2.9%
19772 1
2.9%
18262 1
2.9%
18095 1
2.9%
15337 1
2.9%
12782 1
2.9%
10841 1
2.9%
10193 1
2.9%

18세이상인구수(계)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12297.314
Minimum1989
Maximum44149
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size443.0 B
2024-04-21T22:58:37.264196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1989
5-th percentile3193.4
Q16177
median8251
Q314241
95-th percentile28401.4
Maximum44149
Range42160
Interquartile range (IQR)8064

Descriptive statistics

Standard deviation9419.3909
Coefficient of variation (CV)0.76597139
Kurtosis2.6581709
Mean12297.314
Median Absolute Deviation (MAD)2649
Skewness1.646368
Sum430406
Variance88724924
MonotonicityNot monotonic
2024-04-21T22:58:37.656998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
6072 1
 
2.9%
1989 1
 
2.9%
10900 1
 
2.9%
6386 1
 
2.9%
21117 1
 
2.9%
14518 1
 
2.9%
6092 1
 
2.9%
5968 1
 
2.9%
4866 1
 
2.9%
8051 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
1989 1
2.9%
2849 1
2.9%
3341 1
2.9%
4742 1
2.9%
4866 1
2.9%
5942 1
2.9%
5968 1
2.9%
6072 1
2.9%
6092 1
2.9%
6262 1
2.9%
ValueCountFrequency (%)
44149 1
2.9%
28428 1
2.9%
28390 1
2.9%
27506 1
2.9%
26895 1
2.9%
24116 1
2.9%
21117 1
2.9%
16858 1
2.9%
14518 1
2.9%
13964 1
2.9%

18세이상인구수(남)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6166.4857
Minimum1099
Maximum21877
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size443.0 B
2024-04-21T22:58:37.980532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1099
5-th percentile1652.1
Q13252
median4236
Q37220.5
95-th percentile13958.5
Maximum21877
Range20778
Interquartile range (IQR)3968.5

Descriptive statistics

Standard deviation4606.4915
Coefficient of variation (CV)0.74702054
Kurtosis2.7803307
Mean6166.4857
Median Absolute Deviation (MAD)1335
Skewness1.6620252
Sum215827
Variance21219764
MonotonicityNot monotonic
2024-04-21T22:58:38.191759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
3111 1
 
2.9%
1099 1
 
2.9%
5384 1
 
2.9%
3272 1
 
2.9%
10708 1
 
2.9%
7564 1
 
2.9%
3496 1
 
2.9%
3081 1
 
2.9%
2870 1
 
2.9%
4113 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
1099 1
2.9%
1489 1
2.9%
1722 1
2.9%
2458 1
2.9%
2870 1
2.9%
2901 1
2.9%
3081 1
2.9%
3111 1
2.9%
3232 1
2.9%
3272 1
2.9%
ValueCountFrequency (%)
21877 1
2.9%
14242 1
2.9%
13837 1
2.9%
13493 1
2.9%
13114 1
2.9%
11776 1
2.9%
10708 1
2.9%
8358 1
2.9%
7564 1
2.9%
6877 1
2.9%

18세이상인구수(여)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6130.8286
Minimum890
Maximum22272
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size443.0 B
2024-04-21T22:58:38.408873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum890
5-th percentile1541.3
Q13035.5
median4271
Q37048
95-th percentile14296.1
Maximum22272
Range21382
Interquartile range (IQR)4012.5

Descriptive statistics

Standard deviation4820.2442
Coefficient of variation (CV)0.78623046
Kurtosis2.5214501
Mean6130.8286
Median Absolute Deviation (MAD)1675
Skewness1.6240674
Sum214579
Variance23234754
MonotonicityNot monotonic
2024-04-21T22:58:38.623952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
2961 1
 
2.9%
890 1
 
2.9%
5516 1
 
2.9%
3114 1
 
2.9%
10409 1
 
2.9%
6954 1
 
2.9%
2596 1
 
2.9%
2887 1
 
2.9%
1996 1
 
2.9%
3938 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
890 1
2.9%
1360 1
2.9%
1619 1
2.9%
1996 1
2.9%
2284 1
2.9%
2596 1
2.9%
2887 1
2.9%
2961 1
2.9%
3030 1
2.9%
3041 1
2.9%
ValueCountFrequency (%)
22272 1
2.9%
14553 1
2.9%
14186 1
2.9%
14013 1
2.9%
13781 1
2.9%
12340 1
2.9%
10409 1
2.9%
8500 1
2.9%
7142 1
2.9%
6954 1
2.9%

65세이상인구수(계)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3226.1429
Minimum1448
Maximum8244
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size443.0 B
2024-04-21T22:58:38.837900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1448
5-th percentile1544.7
Q12489.5
median2992
Q33828.5
95-th percentile5579.3
Maximum8244
Range6796
Interquartile range (IQR)1339

Descriptive statistics

Standard deviation1340.1587
Coefficient of variation (CV)0.41540588
Kurtosis4.7730383
Mean3226.1429
Median Absolute Deviation (MAD)674
Skewness1.7587847
Sum112915
Variance1796025.4
MonotonicityNot monotonic
2024-04-21T22:58:39.057969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
2497 1
 
2.9%
1448 1
 
2.9%
3693 1
 
2.9%
3074 1
 
2.9%
4405 1
 
2.9%
3122 1
 
2.9%
1467 1
 
2.9%
2738 1
 
2.9%
2122 1
 
2.9%
2334 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
1448 1
2.9%
1467 1
2.9%
1578 1
2.9%
2010 1
2.9%
2054 1
2.9%
2122 1
2.9%
2318 1
2.9%
2334 1
2.9%
2482 1
2.9%
2497 1
2.9%
ValueCountFrequency (%)
8244 1
2.9%
5685 1
2.9%
5534 1
2.9%
4405 1
2.9%
4314 1
2.9%
4311 1
2.9%
4173 1
2.9%
4077 1
2.9%
3872 1
2.9%
3785 1
2.9%

65세이상인구수(남)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1399.1714
Minimum604
Maximum3571
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size443.0 B
2024-04-21T22:58:39.272936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum604
5-th percentile656.7
Q11098
median1305
Q31613
95-th percentile2452.7
Maximum3571
Range2967
Interquartile range (IQR)515

Descriptive statistics

Standard deviation576.54817
Coefficient of variation (CV)0.412064
Kurtosis5.0432229
Mean1399.1714
Median Absolute Deviation (MAD)227
Skewness1.8160855
Sum48971
Variance332407.79
MonotonicityNot monotonic
2024-04-21T22:58:39.495246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
1113 1
 
2.9%
604 1
 
2.9%
1588 1
 
2.9%
1349 1
 
2.9%
1853 1
 
2.9%
1339 1
 
2.9%
649 1
 
2.9%
1137 1
 
2.9%
940 1
 
2.9%
1078 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
604 1
2.9%
649 1
2.9%
660 1
2.9%
886 1
2.9%
930 1
2.9%
940 1
2.9%
1001 1
2.9%
1078 1
2.9%
1096 1
2.9%
1100 1
2.9%
ValueCountFrequency (%)
3571 1
2.9%
2473 1
2.9%
2444 1
2.9%
1897 1
2.9%
1867 1
2.9%
1853 1
2.9%
1780 1
2.9%
1765 1
2.9%
1638 1
2.9%
1588 1
2.9%

65세이상인구수(여)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1826.9714
Minimum818
Maximum4673
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size443.0 B
2024-04-21T22:58:39.726356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum818
5-th percentile895.8
Q11355
median1667
Q32226.5
95-th percentile3126.6
Maximum4673
Range3855
Interquartile range (IQR)871.5

Descriptive statistics

Standard deviation766.97283
Coefficient of variation (CV)0.4198056
Kurtosis4.4632514
Mean1826.9714
Median Absolute Deviation (MAD)411
Skewness1.6974721
Sum63944
Variance588247.32
MonotonicityNot monotonic
2024-04-21T22:58:39.958791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
1384 1
 
2.9%
844 1
 
2.9%
2105 1
 
2.9%
1725 1
 
2.9%
2552 1
 
2.9%
1783 1
 
2.9%
818 1
 
2.9%
1601 1
 
2.9%
1182 1
 
2.9%
1256 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
818 1
2.9%
844 1
2.9%
918 1
2.9%
1080 1
2.9%
1168 1
2.9%
1182 1
2.9%
1256 1
2.9%
1317 1
2.9%
1326 1
2.9%
1384 1
2.9%
ValueCountFrequency (%)
4673 1
2.9%
3212 1
2.9%
3090 1
2.9%
2552 1
2.9%
2531 1
2.9%
2417 1
2.9%
2351 1
2.9%
2312 1
2.9%
2306 1
2.9%
2147 1
2.9%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size408.0 B
2024-04-05
35 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-04-05
2nd row2024-04-05
3rd row2024-04-05
4th row2024-04-05
5th row2024-04-05

Common Values

ValueCountFrequency (%)
2024-04-05 35
100.0%

Length

2024-04-21T22:58:40.186973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T22:58:40.357507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-04-05 35
100.0%

Interactions

2024-04-21T22:58:29.670811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:15.262336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:16.642699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:18.074645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:19.478545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:20.976561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:22.395436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:23.924789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:25.282782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:27.236466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:29.910586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:15.397853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:16.775902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:18.205475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:19.612989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:21.136682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:22.523792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:24.051628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:25.419669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:27.472510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:30.159162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:15.535145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:16.917074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:18.352683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:19.755995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:21.281506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:22.659096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:24.189754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:25.565412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:27.718799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:30.407021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:15.669891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:17.056373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:18.490884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:19.894747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:21.419882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:22.789462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:24.324612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:25.707201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:27.961957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:30.655628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:15.817974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:17.198421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:18.627116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:20.036367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:21.557191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:22.923934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:24.461112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:25.852243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:28.208060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:30.894918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:15.944369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:17.330492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:18.756882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:20.166934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:21.695618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:23.049328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:24.593742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:26.004948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:28.444163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:31.131315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:16.073615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:17.460788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:18.889277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:20.301317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:21.826092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:23.375422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:24.717741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:26.239611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:28.676852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:31.371134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:16.201499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:17.593900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:19.026161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:20.439768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:21.955516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:23.498897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:24.843211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:26.477539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:28.912297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:31.628203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:16.351029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:17.743836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:19.174982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:20.586066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:22.101008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:23.639286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:24.987694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:26.728095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:29.164686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:31.882249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:16.493011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:17.889969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:19.320322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:20.734882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:22.246557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:23.777366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:25.129514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:26.977609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T22:58:29.412416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T22:58:40.475431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분세대수인구수(계)인구수(남)인구수(여)18세이상인구수(계)18세이상인구수(남)18세이상인구수(여)65세이상인구수(계)65세이상인구수(남)65세이상인구수(여)
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
세대수1.0001.0000.9040.9040.9040.9160.9490.9280.6990.7510.794
인구수(계)1.0000.9041.0001.0001.0000.9980.9910.9940.7970.8230.933
인구수(남)1.0000.9041.0001.0001.0000.9980.9910.9940.7970.8230.933
인구수(여)1.0000.9041.0001.0001.0000.9960.9890.9960.8100.8330.937
18세이상인구수(계)1.0000.9160.9980.9980.9961.0000.9920.9970.7180.7470.899
18세이상인구수(남)1.0000.9490.9910.9910.9890.9921.0000.9820.6930.7030.897
18세이상인구수(여)1.0000.9280.9940.9940.9960.9970.9821.0000.7200.7290.899
65세이상인구수(계)1.0000.6990.7970.7970.8100.7180.6930.7201.0000.9950.972
65세이상인구수(남)1.0000.7510.8230.8230.8330.7470.7030.7290.9951.0000.918
65세이상인구수(여)1.0000.7940.9330.9330.9370.8990.8970.8990.9720.9181.000
2024-04-21T22:58:40.709155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대수인구수(계)인구수(남)인구수(여)18세이상인구수(계)18세이상인구수(남)18세이상인구수(여)65세이상인구수(계)65세이상인구수(남)65세이상인구수(여)
세대수1.0000.9720.9770.9620.9720.9770.9580.6530.6940.638
인구수(계)0.9721.0000.9980.9950.9900.9850.9910.6760.7150.659
인구수(남)0.9770.9981.0000.9920.9900.9890.9880.6680.7070.649
인구수(여)0.9620.9950.9921.0000.9830.9730.9870.6960.7290.681
18세이상인구수(계)0.9720.9900.9900.9831.0000.9950.9940.6170.6610.595
18세이상인구수(남)0.9770.9850.9890.9730.9951.0000.9860.5920.6350.569
18세이상인구수(여)0.9580.9910.9880.9870.9940.9861.0000.6300.6720.611
65세이상인구수(계)0.6530.6760.6680.6960.6170.5920.6301.0000.9870.997
65세이상인구수(남)0.6940.7150.7070.7290.6610.6350.6720.9871.0000.977
65세이상인구수(여)0.6380.6590.6490.6810.5950.5690.6110.9970.9771.000

Missing values

2024-04-21T22:58:32.158593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T22:58:32.451440image/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

구분세대수인구수(계)인구수(남)인구수(여)18세이상인구수(계)18세이상인구수(남)18세이상인구수(여)65세이상인구수(계)65세이상인구수(남)65세이상인구수(여)데이터기준일자
0중앙동49139723483048936072311129612497111313842024-04-05
1풍남동22813621180018211989109989014486048442024-04-05
2노송동549610475513953366262323230303244137818662024-04-05
3완산동260549092412249728491489136015786609182024-04-05
4동서학동3122587028553015334117221619205488611682024-04-05
5서서학동43147972385641164742245822842639109615432024-04-05
6중화산1동632913521640771148939426846712992129916932024-04-05
7중화산2동9257190669555951113645687767682482115613262024-04-05
8서신동175583927419086201882750613493140135685247332122024-04-05
9평화1동704211973571662577164368134833872152123512024-04-05
구분세대수인구수(계)인구수(남)인구수(여)18세이상인구수(계)18세이상인구수(남)18세이상인구수(여)65세이상인구수(계)65세이상인구수(남)65세이상인구수(여)데이터기준일자
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26팔복동4599751540863429486628701996212294011822024-04-05
27우아1동595112005599960068051411339382334107812562024-04-05
28우아2동768413975736066159888541444742649121814312024-04-05
29호성동7880187028880982212165586263033785163821472024-04-05
30송천1동243356492932133327964414921877222725534244430902024-04-05
31송천2동1041624922121401278216858835885004314189724172024-04-05
32조촌동625012072616059127931423636952697117415232024-04-05
33여의동7135159718109786210675554851272952130516472024-04-05
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