Overview

Dataset statistics

Number of variables12
Number of observations2072
Missing cells24677
Missing cells (%)99.2%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory214.6 KiB
Average record size in memory106.1 B

Variable types

Text2
Numeric9
Unsupported1

Dataset

Description부산광역시남구인구현황(2017년3월말)
Author부산광역시 남구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=3080514

Alerts

Dataset has 1 (< 0.1%) duplicate rowsDuplicates
세대수 is highly overall correlated with 인구수(계) and 7 other fieldsHigh correlation
인구수(계) is highly overall correlated with 세대수 and 7 other fieldsHigh correlation
인구수(남) is highly overall correlated with 세대수 and 7 other fieldsHigh correlation
인구수(여) is highly overall correlated with 세대수 and 7 other fieldsHigh correlation
19세이상인구수(계) is highly overall correlated with 세대수 and 7 other fieldsHigh correlation
19세이상인구수(남) is highly overall correlated with 세대수 and 7 other fieldsHigh correlation
19세이상인구수(여) is highly overall correlated with 세대수 and 7 other fieldsHigh correlation
통 is highly overall correlated with 세대수 and 7 other fieldsHigh correlation
반 is highly overall correlated with 세대수 and 7 other fieldsHigh correlation
구분 has 2055 (99.2%) missing valuesMissing
세대수 has 2055 (99.2%) missing valuesMissing
인구수(계) has 2055 (99.2%) missing valuesMissing
인구수(남) has 2055 (99.2%) missing valuesMissing
인구수(여) has 2055 (99.2%) missing valuesMissing
19세이상인구수(계) has 2055 (99.2%) missing valuesMissing
19세이상인구수(남) has 2055 (99.2%) missing valuesMissing
19세이상인구수(여) has 2055 (99.2%) missing valuesMissing
통 has 2055 (99.2%) missing valuesMissing
반 has 2055 (99.2%) missing valuesMissing
면적(㎢) (구성비,%) has 2055 (99.2%) missing valuesMissing
Unnamed: 11 has 2072 (100.0%) missing valuesMissing
Unnamed: 11 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-19 05:33:14.446225
Analysis finished2024-04-19 05:33:23.390649
Duration8.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

MISSING 

Distinct17
Distinct (%)100.0%
Missing2055
Missing (%)99.2%
Memory size16.3 KiB
2024-04-19T14:33:23.507252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length6
Min length5

Characters and Unicode

Total characters102
Distinct characters19
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)100.0%

Sample

1st row 대연1동
2nd row 대연3동
3rd row 대연4동
4th row 대연5동
5th row 대연6동
ValueCountFrequency (%)
대연3동 1
 
5.3%
감만2동 1
 
5.3%
당 1
 
5.3%
용 1
 
5.3%
문현4동 1
 
5.3%
문현3동 1
 
5.3%
문현2동 1
 
5.3%
문현1동 1
 
5.3%
우암동 1
 
5.3%
감만1동 1
 
5.3%
Other values (9) 9
47.4%
2024-04-19T14:33:23.831034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36
35.3%
동 17
16.7%
용 5
 
4.9%
연 5
 
4.9%
대 5
 
4.9%
현 4
 
3.9%
문 4
 
3.9%
1 4
 
3.9%
호 4
 
3.9%
2 3
 
2.9%
Other values (9) 15
14.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 51
50.0%
Space Separator 36
35.3%
Decimal Number 15
 
14.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 17
33.3%
용 5
 
9.8%
연 5
 
9.8%
대 5
 
9.8%
현 4
 
7.8%
문 4
 
7.8%
호 4
 
7.8%
감 2
 
3.9%
만 2
 
3.9%
우 1
 
2.0%
Other values (2) 2
 
3.9%
Decimal Number
ValueCountFrequency (%)
1 4
26.7%
2 3
20.0%
4 3
20.0%
3 3
20.0%
6 1
 
6.7%
5 1
 
6.7%
Space Separator
ValueCountFrequency (%)
36
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 51
50.0%
Hangul 51
50.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 17
33.3%
용 5
 
9.8%
연 5
 
9.8%
대 5
 
9.8%
현 4
 
7.8%
문 4
 
7.8%
호 4
 
7.8%
감 2
 
3.9%
만 2
 
3.9%
우 1
 
2.0%
Other values (2) 2
 
3.9%
Common
ValueCountFrequency (%)
36
70.6%
1 4
 
7.8%
2 3
 
5.9%
4 3
 
5.9%
3 3
 
5.9%
6 1
 
2.0%
5 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51
50.0%
Hangul 51
50.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
36
70.6%
1 4
 
7.8%
2 3
 
5.9%
4 3
 
5.9%
3 3
 
5.9%
6 1
 
2.0%
5 1
 
2.0%
Hangul
ValueCountFrequency (%)
동 17
33.3%
용 5
 
9.8%
연 5
 
9.8%
대 5
 
9.8%
현 4
 
7.8%
문 4
 
7.8%
호 4
 
7.8%
감 2
 
3.9%
만 2
 
3.9%
우 1
 
2.0%
Other values (2) 2
 
3.9%

세대수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing2055
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean6693.2353
Minimum3019
Maximum15631
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2024-04-19T14:33:23.954358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3019
5-th percentile3310.2
Q14362
median5741
Q37502
95-th percentile14987
Maximum15631
Range12612
Interquartile range (IQR)3140

Descriptive statistics

Standard deviation3709.5833
Coefficient of variation (CV)0.55422873
Kurtosis1.8632496
Mean6693.2353
Median Absolute Deviation (MAD)1761
Skewness1.5721547
Sum113785
Variance13761008
MonotonicityNot monotonic
2024-04-19T14:33:24.411011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
14826 1
 
< 0.1%
4782 1
 
< 0.1%
5270 1
 
< 0.1%
4362 1
 
< 0.1%
6346 1
 
< 0.1%
7601 1
 
< 0.1%
3383 1
 
< 0.1%
6669 1
 
< 0.1%
10412 1
 
< 0.1%
3849 1
 
< 0.1%
Other values (7) 7
 
0.3%
(Missing) 2055
99.2%
ValueCountFrequency (%)
3019 1
< 0.1%
3383 1
< 0.1%
3535 1
< 0.1%
3849 1
< 0.1%
4362 1
< 0.1%
4728 1
< 0.1%
4782 1
< 0.1%
5270 1
< 0.1%
5741 1
< 0.1%
6129 1
< 0.1%
ValueCountFrequency (%)
15631 1
< 0.1%
14826 1
< 0.1%
10412 1
< 0.1%
7601 1
< 0.1%
7502 1
< 0.1%
6669 1
< 0.1%
6346 1
< 0.1%
6129 1
< 0.1%
5741 1
< 0.1%
5270 1
< 0.1%

인구수(계)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing2055
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean16328.588
Minimum7808
Maximum43817
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2024-04-19T14:33:24.531406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7808
5-th percentile7925.6
Q19714
median13862
Q318155
95-th percentile36799.4
Maximum43817
Range36009
Interquartile range (IQR)8441

Descriptive statistics

Standard deviation9747.705
Coefficient of variation (CV)0.59697169
Kurtosis3.5517513
Mean16328.588
Median Absolute Deviation (MAD)4293
Skewness1.897653
Sum277586
Variance95017753
MonotonicityNot monotonic
2024-04-19T14:33:24.666936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
35045 1
 
< 0.1%
11008 1
 
< 0.1%
13527 1
 
< 0.1%
8578 1
 
< 0.1%
16137 1
 
< 0.1%
18155 1
 
< 0.1%
7808 1
 
< 0.1%
16062 1
 
< 0.1%
21318 1
 
< 0.1%
9714 1
 
< 0.1%
Other values (7) 7
 
0.3%
(Missing) 2055
99.2%
ValueCountFrequency (%)
7808 1
< 0.1%
7955 1
< 0.1%
8578 1
< 0.1%
9144 1
< 0.1%
9714 1
< 0.1%
10829 1
< 0.1%
11008 1
< 0.1%
13527 1
< 0.1%
13862 1
< 0.1%
14721 1
< 0.1%
ValueCountFrequency (%)
43817 1
< 0.1%
35045 1
< 0.1%
21318 1
< 0.1%
19906 1
< 0.1%
18155 1
< 0.1%
16137 1
< 0.1%
16062 1
< 0.1%
14721 1
< 0.1%
13862 1
< 0.1%
13527 1
< 0.1%

인구수(남)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing2055
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean8026.2353
Minimum3875
Maximum21125
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2024-04-19T14:33:24.780536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3875
5-th percentile3895
Q14741
median6799
Q39103
95-th percentile17924.2
Maximum21125
Range17250
Interquartile range (IQR)4362

Descriptive statistics

Standard deviation4703.9977
Coefficient of variation (CV)0.58607772
Kurtosis3.314342
Mean8026.2353
Median Absolute Deviation (MAD)2161
Skewness1.8450174
Sum136446
Variance22127594
MonotonicityNot monotonic
2024-04-19T14:33:24.892896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
17124 1
 
< 0.1%
5429 1
 
< 0.1%
6565 1
 
< 0.1%
4270 1
 
< 0.1%
7935 1
 
< 0.1%
9103 1
 
< 0.1%
3900 1
 
< 0.1%
8123 1
 
< 0.1%
10544 1
 
< 0.1%
4741 1
 
< 0.1%
Other values (7) 7
 
0.3%
(Missing) 2055
99.2%
ValueCountFrequency (%)
3875 1
< 0.1%
3900 1
< 0.1%
4270 1
< 0.1%
4638 1
< 0.1%
4741 1
< 0.1%
5327 1
< 0.1%
5429 1
< 0.1%
6565 1
< 0.1%
6799 1
< 0.1%
7158 1
< 0.1%
ValueCountFrequency (%)
21125 1
< 0.1%
17124 1
< 0.1%
10544 1
< 0.1%
9790 1
< 0.1%
9103 1
< 0.1%
8123 1
< 0.1%
7935 1
< 0.1%
7158 1
< 0.1%
6799 1
< 0.1%
6565 1
< 0.1%

인구수(여)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing2055
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean8302.3529
Minimum3908
Maximum22692
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2024-04-19T14:33:25.005585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3908
5-th percentile4045.6
Q14973
median7063
Q39052
95-th percentile18875.2
Maximum22692
Range18784
Interquartile range (IQR)4079

Descriptive statistics

Standard deviation5046.1055
Coefficient of variation (CV)0.60779222
Kurtosis3.7705988
Mean8302.3529
Median Absolute Deviation (MAD)2090
Skewness1.9451929
Sum141140
Variance25463181
MonotonicityNot monotonic
2024-04-19T14:33:25.114938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
17921 1
 
< 0.1%
5579 1
 
< 0.1%
6962 1
 
< 0.1%
4308 1
 
< 0.1%
8202 1
 
< 0.1%
9052 1
 
< 0.1%
3908 1
 
< 0.1%
7939 1
 
< 0.1%
10774 1
 
< 0.1%
4973 1
 
< 0.1%
Other values (7) 7
 
0.3%
(Missing) 2055
99.2%
ValueCountFrequency (%)
3908 1
< 0.1%
4080 1
< 0.1%
4308 1
< 0.1%
4506 1
< 0.1%
4973 1
< 0.1%
5502 1
< 0.1%
5579 1
< 0.1%
6962 1
< 0.1%
7063 1
< 0.1%
7563 1
< 0.1%
ValueCountFrequency (%)
22692 1
< 0.1%
17921 1
< 0.1%
10774 1
< 0.1%
10116 1
< 0.1%
9052 1
< 0.1%
8202 1
< 0.1%
7939 1
< 0.1%
7563 1
< 0.1%
7063 1
< 0.1%
6962 1
< 0.1%

19세이상인구수(계)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing2055
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean13754.176
Minimum6597
Maximum34929
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2024-04-19T14:33:25.220421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6597
5-th percentile6733.8
Q18123
median12025
Q315903
95-th percentile29914.6
Maximum34929
Range28332
Interquartile range (IQR)7780

Descriptive statistics

Standard deviation7736.0765
Coefficient of variation (CV)0.5624529
Kurtosis2.9676016
Mean13754.176
Median Absolute Deviation (MAD)3902
Skewness1.7558206
Sum233821
Variance59846879
MonotonicityNot monotonic
2024-04-19T14:33:25.325596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
28661 1
 
< 0.1%
9631 1
 
< 0.1%
11422 1
 
< 0.1%
7781 1
 
< 0.1%
13542 1
 
< 0.1%
15903 1
 
< 0.1%
6768 1
 
< 0.1%
13706 1
 
< 0.1%
18939 1
 
< 0.1%
8123 1
 
< 0.1%
Other values (7) 7
 
0.3%
(Missing) 2055
99.2%
ValueCountFrequency (%)
6597 1
< 0.1%
6768 1
< 0.1%
7601 1
< 0.1%
7781 1
< 0.1%
8123 1
< 0.1%
9614 1
< 0.1%
9631 1
< 0.1%
11422 1
< 0.1%
12025 1
< 0.1%
12420 1
< 0.1%
ValueCountFrequency (%)
34929 1
< 0.1%
28661 1
< 0.1%
18939 1
< 0.1%
16159 1
< 0.1%
15903 1
< 0.1%
13706 1
< 0.1%
13542 1
< 0.1%
12420 1
< 0.1%
12025 1
< 0.1%
11422 1
< 0.1%

19세이상인구수(남)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing2055
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean6695.7059
Minimum3173
Maximum16587
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2024-04-19T14:33:25.430774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3173
5-th percentile3305.8
Q13934
median5837
Q37852
95-th percentile14418.2
Maximum16587
Range13414
Interquartile range (IQR)3918

Descriptive statistics

Standard deviation3685.1332
Coefficient of variation (CV)0.55037263
Kurtosis2.6566985
Mean6695.7059
Median Absolute Deviation (MAD)1982
Skewness1.6870761
Sum113827
Variance13580207
MonotonicityNot monotonic
2024-04-19T14:33:25.541499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
13876 1
 
< 0.1%
4693 1
 
< 0.1%
5484 1
 
< 0.1%
3853 1
 
< 0.1%
6588 1
 
< 0.1%
7908 1
 
< 0.1%
3339 1
 
< 0.1%
6869 1
 
< 0.1%
9314 1
 
< 0.1%
3934 1
 
< 0.1%
Other values (7) 7
 
0.3%
(Missing) 2055
99.2%
ValueCountFrequency (%)
3173 1
< 0.1%
3339 1
< 0.1%
3853 1
< 0.1%
3855 1
< 0.1%
3934 1
< 0.1%
4693 1
< 0.1%
4701 1
< 0.1%
5484 1
< 0.1%
5837 1
< 0.1%
5964 1
< 0.1%
ValueCountFrequency (%)
16587 1
< 0.1%
13876 1
< 0.1%
9314 1
< 0.1%
7908 1
< 0.1%
7852 1
< 0.1%
6869 1
< 0.1%
6588 1
< 0.1%
5964 1
< 0.1%
5837 1
< 0.1%
5484 1
< 0.1%

19세이상인구수(여)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing2055
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean7058.4706
Minimum3424
Maximum18342
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2024-04-19T14:33:25.642623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3424
5-th percentile3428
Q14189
median6188
Q37995
95-th percentile15496.4
Maximum18342
Range14918
Interquartile range (IQR)3806

Descriptive statistics

Standard deviation4054.0445
Coefficient of variation (CV)0.57435169
Kurtosis3.2532997
Mean7058.4706
Median Absolute Deviation (MAD)1999
Skewness1.8172125
Sum119994
Variance16435277
MonotonicityNot monotonic
2024-04-19T14:33:25.753842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
14785 1
 
< 0.1%
4938 1
 
< 0.1%
5938 1
 
< 0.1%
3928 1
 
< 0.1%
6954 1
 
< 0.1%
7995 1
 
< 0.1%
3429 1
 
< 0.1%
6837 1
 
< 0.1%
9625 1
 
< 0.1%
4189 1
 
< 0.1%
Other values (7) 7
 
0.3%
(Missing) 2055
99.2%
ValueCountFrequency (%)
3424 1
< 0.1%
3429 1
< 0.1%
3746 1
< 0.1%
3928 1
< 0.1%
4189 1
< 0.1%
4913 1
< 0.1%
4938 1
< 0.1%
5938 1
< 0.1%
6188 1
< 0.1%
6456 1
< 0.1%
ValueCountFrequency (%)
18342 1
< 0.1%
14785 1
< 0.1%
9625 1
< 0.1%
8307 1
< 0.1%
7995 1
< 0.1%
6954 1
< 0.1%
6837 1
< 0.1%
6456 1
< 0.1%
6188 1
< 0.1%
5938 1
< 0.1%

통
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)70.6%
Missing2055
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean21.176471
Minimum13
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2024-04-19T14:33:25.865954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile13.8
Q116
median18
Q325
95-th percentile34.4
Maximum40
Range27
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.8917306
Coefficient of variation (CV)0.37266506
Kurtosis0.40907598
Mean21.176471
Median Absolute Deviation (MAD)4
Skewness1.1047237
Sum360
Variance62.279412
MonotonicityNot monotonic
2024-04-19T14:33:25.969196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
16 3
 
0.1%
14 3
 
0.1%
18 2
 
0.1%
31 1
 
< 0.1%
33 1
 
< 0.1%
40 1
 
< 0.1%
23 1
 
< 0.1%
21 1
 
< 0.1%
13 1
 
< 0.1%
25 1
 
< 0.1%
Other values (2) 2
 
0.1%
(Missing) 2055
99.2%
ValueCountFrequency (%)
13 1
 
< 0.1%
14 3
0.1%
16 3
0.1%
18 2
0.1%
19 1
 
< 0.1%
21 1
 
< 0.1%
23 1
 
< 0.1%
25 1
 
< 0.1%
29 1
 
< 0.1%
31 1
 
< 0.1%
ValueCountFrequency (%)
40 1
 
< 0.1%
33 1
 
< 0.1%
31 1
 
< 0.1%
29 1
 
< 0.1%
25 1
 
< 0.1%
23 1
 
< 0.1%
21 1
 
< 0.1%
19 1
 
< 0.1%
18 2
0.1%
16 3
0.1%

반
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct15
Distinct (%)88.2%
Missing2055
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean150.76471
Minimum84
Maximum345
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size18.3 KiB
2024-04-19T14:33:26.081353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum84
5-th percentile84.8
Q194
median129
Q3173
95-th percentile307.4
Maximum345
Range261
Interquartile range (IQR)79

Descriptive statistics

Standard deviation74.756713
Coefficient of variation (CV)0.49585022
Kurtosis2.1558371
Mean150.76471
Median Absolute Deviation (MAD)39
Skewness1.5999781
Sum2563
Variance5588.5662
MonotonicityNot monotonic
2024-04-19T14:33:26.185475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
129 2
 
0.1%
173 2
 
0.1%
174 1
 
< 0.1%
298 1
 
< 0.1%
103 1
 
< 0.1%
94 1
 
< 0.1%
345 1
 
< 0.1%
135 1
 
< 0.1%
90 1
 
< 0.1%
84 1
 
< 0.1%
Other values (5) 5
 
0.2%
(Missing) 2055
99.2%
ValueCountFrequency (%)
84 1
< 0.1%
85 1
< 0.1%
90 1
< 0.1%
91 1
< 0.1%
94 1
< 0.1%
103 1
< 0.1%
120 1
< 0.1%
125 1
< 0.1%
129 2
0.1%
135 1
< 0.1%
ValueCountFrequency (%)
345 1
< 0.1%
298 1
< 0.1%
215 1
< 0.1%
174 1
< 0.1%
173 2
0.1%
135 1
< 0.1%
129 2
0.1%
125 1
< 0.1%
120 1
< 0.1%
103 1
< 0.1%

면적(㎢) (구성비,%)
Text

MISSING 

Distinct16
Distinct (%)94.1%
Missing2055
Missing (%)99.2%
Memory size16.3 KiB
2024-04-19T14:33:26.348233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length11.529412
Min length11

Characters and Unicode

Total characters196
Distinct characters14
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)88.2%

Sample

1st row0.97 (3.62)
2nd row3.84 (14.32)
3rd row1.00 (3.73)
4th row1.11 (4.14)
5th row1.19 (4.44)
ValueCountFrequency (%)
0.83 2
 
5.9%
3.10 2
 
5.9%
1.52 1
 
2.9%
13.54 1
 
2.9%
3.63 1
 
2.9%
2.57 1
 
2.9%
0.69 1
 
2.9%
2.31 1
 
2.9%
0.62 1
 
2.9%
5.37 1
 
2.9%
Other values (22) 22
64.7%
2024-04-19T14:33:26.655733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 34
17.3%
24
12.2%
3 18
9.2%
( 17
8.7%
1 17
8.7%
) 17
8.7%
0 12
 
6.1%
4 10
 
5.1%
2 10
 
5.1%
7 9
 
4.6%
Other values (4) 28
14.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 104
53.1%
Other Punctuation 34
 
17.3%
Space Separator 24
 
12.2%
Open Punctuation 17
 
8.7%
Close Punctuation 17
 
8.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 18
17.3%
1 17
16.3%
0 12
11.5%
4 10
9.6%
2 10
9.6%
7 9
8.7%
6 9
8.7%
9 8
7.7%
5 7
 
6.7%
8 4
 
3.8%
Other Punctuation
ValueCountFrequency (%)
. 34
100.0%
Space Separator
ValueCountFrequency (%)
24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 196
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 34
17.3%
24
12.2%
3 18
9.2%
( 17
8.7%
1 17
8.7%
) 17
8.7%
0 12
 
6.1%
4 10
 
5.1%
2 10
 
5.1%
7 9
 
4.6%
Other values (4) 28
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 196
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 34
17.3%
24
12.2%
3 18
9.2%
( 17
8.7%
1 17
8.7%
) 17
8.7%
0 12
 
6.1%
4 10
 
5.1%
2 10
 
5.1%
7 9
 
4.6%
Other values (4) 28
14.3%

Unnamed: 11
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2072
Missing (%)100.0%
Memory size18.3 KiB

Interactions

2024-04-19T14:33:22.011294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:14.801649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:15.675368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:16.911331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:17.827911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:18.930510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:19.631305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:20.413909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:21.205469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:22.095412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:14.887680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:15.797074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:17.026007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:17.911589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:19.009989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:19.714593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:20.498321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:21.285593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:22.221433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:14.986638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:15.920815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:17.159985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:17.999023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:19.088916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:19.810735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:20.588559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:21.375066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:22.327925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:15.091972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:16.058122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:17.260516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:18.082510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:19.170655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:19.901099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:20.677039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:21.457840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:22.456705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:15.184052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:16.182143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:17.353832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:18.177317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:19.249244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:19.980952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:20.769624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:21.545980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:22.569636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:15.268256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:16.309789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:17.459899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:18.276445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:19.321303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:20.067966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:20.855922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:21.621792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:22.657093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:15.350954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:16.442658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:17.547905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:18.369102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:19.399515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:20.145967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:20.946678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:21.707749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:22.741164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:15.444102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:16.562436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:17.637686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:18.461148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:19.482260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:20.231262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:21.032342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:21.797903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:22.831330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:15.550441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:16.692156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:17.740597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:18.850587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:19.554226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:20.310989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:21.117257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:33:21.910675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-19T14:33:26.783500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분세대수인구수(계)인구수(남)인구수(여)19세이상인구수(계)19세이상인구수(남)19세이상인구수(여)통반면적(㎢) (구성비,%)
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
세대수1.0001.0000.8730.9220.8730.9320.9320.9110.9550.8720.936
인구수(계)1.0000.8731.0000.9971.0000.8640.8640.8860.9410.9540.894
인구수(남)1.0000.9220.9971.0000.9970.9090.9090.9250.9430.9450.833
인구수(여)1.0000.8731.0000.9971.0000.8640.8640.8860.9410.9540.894
19세이상인구수(계)1.0000.9320.8640.9090.8641.0001.0000.9990.9430.8990.936
19세이상인구수(남)1.0000.9320.8640.9090.8641.0001.0000.9990.9430.8990.936
19세이상인구수(여)1.0000.9110.8860.9250.8860.9990.9991.0000.9150.8610.922
통1.0000.9550.9410.9430.9410.9430.9430.9151.0000.9890.946
반1.0000.8720.9540.9450.9540.8990.8990.8610.9891.0001.000
면적(㎢) (구성비,%)1.0000.9360.8940.8330.8940.9360.9360.9220.9461.0001.000
2024-04-19T14:33:26.916747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대수인구수(계)인구수(남)인구수(여)19세이상인구수(계)19세이상인구수(남)19세이상인구수(여)통반
세대수1.0000.9850.9900.9850.9950.9900.9930.9190.928
인구수(계)0.9851.0000.9951.0000.9930.9900.9950.9090.912
인구수(남)0.9900.9951.0000.9950.9980.9950.9950.9090.912
인구수(여)0.9851.0000.9951.0000.9930.9900.9950.9090.912
19세이상인구수(계)0.9950.9930.9980.9931.0000.9930.9980.9140.919
19세이상인구수(남)0.9900.9900.9950.9900.9931.0000.9900.9210.924
19세이상인구수(여)0.9930.9950.9950.9950.9980.9901.0000.9070.912
통0.9190.9090.9090.9090.9140.9210.9071.0000.990
반0.9280.9120.9120.9120.9190.9240.9120.9901.000

Missing values

2024-04-19T14:33:22.947326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-19T14:33:23.109770image/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.
2024-04-19T14:33:23.272547image/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

구분세대수인구수(계)인구수(남)인구수(여)19세이상인구수(계)19세이상인구수(남)19세이상인구수(여)통반면적(㎢) (구성비,%)Unnamed: 11
0대연1동104122131810544107741893993149625311740.97 (3.62)<NA>
1대연3동14826350451712417921286611387614785332983.84 (14.32)<NA>
2대연4동47281082953275502961447014913181291.00 (3.73)<NA>
3대연5동612914721715875631242059646456161031.11 (4.14)<NA>
4대연6동301979553875408065973173342416941.19 (4.44)<NA>
5용호1동15631438172112522692349291658718342403451.60 (5.97)<NA>
6용호2동7502199069790101161615978528307231732.03 (7.57)<NA>
7용호3동574113862679970631202558376188211351.96 (7.31)<NA>
8용호4동384997144741497381233934418914901.52 (5.67)<NA>
9용 당 동353591444638450676013855374613843.63 (13.54)<NA>
구분세대수인구수(계)인구수(남)인구수(여)19세이상인구수(계)19세이상인구수(남)19세이상인구수(여)통반면적(㎢) (구성비,%)Unnamed: 11
2062<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2063<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2064<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2065<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2066<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2067<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2068<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2069<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2070<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2071<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

구분세대수인구수(계)인구수(남)인구수(여)19세이상인구수(계)19세이상인구수(남)19세이상인구수(여)통반면적(㎢) (구성비,%)# duplicates
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>2055