Overview

Dataset statistics

Number of variables13
Number of observations2178
Missing cells28127
Missing cells (%)99.3%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory244.7 KiB
Average record size in memory115.1 B

Variable types

Text2
Numeric9
Unsupported2

Dataset

Description부산광역시남구인구현황(2017년5월말)
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 2161 (99.2%) missing valuesMissing
세대수 has 2161 (99.2%) missing valuesMissing
인구수(계) has 2161 (99.2%) missing valuesMissing
인구수(남) has 2161 (99.2%) missing valuesMissing
인구수(여) has 2161 (99.2%) missing valuesMissing
19세이상인구수(계) has 2161 (99.2%) missing valuesMissing
19세이상인구수(남) has 2161 (99.2%) missing valuesMissing
19세이상인구수(여) has 2161 (99.2%) missing valuesMissing
통 has 2161 (99.2%) missing valuesMissing
반 has 2161 (99.2%) missing valuesMissing
면적(㎢) (구성비,%) has 2161 (99.2%) missing valuesMissing
Unnamed: 11 has 2178 (100.0%) missing valuesMissing
Unnamed: 12 has 2178 (100.0%) missing valuesMissing
Unnamed: 11 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 12 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-19 05:32:32.827180
Analysis finished2024-04-19 05:32:41.908404
Duration9.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

MISSING 

Distinct17
Distinct (%)100.0%
Missing2161
Missing (%)99.2%
Memory size17.1 KiB
2024-04-19T14:32:42.026812image/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%
2024-04-19T14:32:42.334156image/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%
Missing2161
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean6690.1765
Minimum3030
Maximum15619
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2024-04-19T14:32:42.444849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3030
5-th percentile3318
Q14377
median5724
Q37501
95-th percentile15015
Maximum15619
Range12589
Interquartile range (IQR)3124

Descriptive statistics

Standard deviation3714.1293
Coefficient of variation (CV)0.55516164
Kurtosis1.8657071
Mean6690.1765
Median Absolute Deviation (MAD)1777
Skewness1.5753977
Sum113733
Variance13794757
MonotonicityNot monotonic
2024-04-19T14:32:42.560649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
14864 1
 
< 0.1%
4780 1
 
< 0.1%
5252 1
 
< 0.1%
4377 1
 
< 0.1%
6343 1
 
< 0.1%
7590 1
 
< 0.1%
3390 1
 
< 0.1%
6674 1
 
< 0.1%
10412 1
 
< 0.1%
3822 1
 
< 0.1%
Other values (7) 7
 
0.3%
(Missing) 2161
99.2%
ValueCountFrequency (%)
3030 1
< 0.1%
3390 1
< 0.1%
3537 1
< 0.1%
3822 1
< 0.1%
4377 1
< 0.1%
4702 1
< 0.1%
4780 1
< 0.1%
5252 1
< 0.1%
5724 1
< 0.1%
6116 1
< 0.1%
ValueCountFrequency (%)
15619 1
< 0.1%
14864 1
< 0.1%
10412 1
< 0.1%
7590 1
< 0.1%
7501 1
< 0.1%
6674 1
< 0.1%
6343 1
< 0.1%
6116 1
< 0.1%
5724 1
< 0.1%
5252 1
< 0.1%

인구수(계)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing2161
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean16269.647
Minimum7794
Maximum43674
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2024-04-19T14:32:42.707736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7794
5-th percentile7922.8
Q19671
median13764
Q318065
95-th percentile36741.2
Maximum43674
Range35880
Interquartile range (IQR)8394

Descriptive statistics

Standard deviation9725.6102
Coefficient of variation (CV)0.59777635
Kurtosis3.5483638
Mean16269.647
Median Absolute Deviation (MAD)4301
Skewness1.9003919
Sum276584
Variance94587493
MonotonicityNot monotonic
2024-04-19T14:32:42.829102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
35008 1
 
< 0.1%
10959 1
 
< 0.1%
13417 1
 
< 0.1%
8561 1
 
< 0.1%
16081 1
 
< 0.1%
18065 1
 
< 0.1%
7794 1
 
< 0.1%
16010 1
 
< 0.1%
21230 1
 
< 0.1%
9671 1
 
< 0.1%
Other values (7) 7
 
0.3%
(Missing) 2161
99.2%
ValueCountFrequency (%)
7794 1
< 0.1%
7955 1
< 0.1%
8561 1
< 0.1%
9131 1
< 0.1%
9671 1
< 0.1%
10757 1
< 0.1%
10959 1
< 0.1%
13417 1
< 0.1%
13764 1
< 0.1%
14655 1
< 0.1%
ValueCountFrequency (%)
43674 1
< 0.1%
35008 1
< 0.1%
21230 1
< 0.1%
19852 1
< 0.1%
18065 1
< 0.1%
16081 1
< 0.1%
16010 1
< 0.1%
14655 1
< 0.1%
13764 1
< 0.1%
13417 1
< 0.1%

인구수(남)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing2161
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean7997.3529
Minimum3877
Maximum21054
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2024-04-19T14:32:42.939010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3877
5-th percentile3898.6
Q14728
median6745
Q39039
95-th percentile17909.2
Maximum21054
Range17177
Interquartile range (IQR)4311

Descriptive statistics

Standard deviation4694.3789
Coefficient of variation (CV)0.58699159
Kurtosis3.3118985
Mean7997.3529
Median Absolute Deviation (MAD)2123
Skewness1.8492391
Sum135955
Variance22037193
MonotonicityNot monotonic
2024-04-19T14:32:43.049349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
17123 1
 
< 0.1%
5401 1
 
< 0.1%
6529 1
 
< 0.1%
4258 1
 
< 0.1%
7903 1
 
< 0.1%
9039 1
 
< 0.1%
3904 1
 
< 0.1%
8105 1
 
< 0.1%
10498 1
 
< 0.1%
4728 1
 
< 0.1%
Other values (7) 7
 
0.3%
(Missing) 2161
99.2%
ValueCountFrequency (%)
3877 1
< 0.1%
3904 1
< 0.1%
4258 1
< 0.1%
4622 1
< 0.1%
4728 1
< 0.1%
5294 1
< 0.1%
5401 1
< 0.1%
6529 1
< 0.1%
6745 1
< 0.1%
7110 1
< 0.1%
ValueCountFrequency (%)
21054 1
< 0.1%
17123 1
< 0.1%
10498 1
< 0.1%
9765 1
< 0.1%
9039 1
< 0.1%
8105 1
< 0.1%
7903 1
< 0.1%
7110 1
< 0.1%
6745 1
< 0.1%
6529 1
< 0.1%

인구수(여)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing2161
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean8272.2941
Minimum3890
Maximum22620
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2024-04-19T14:32:43.154557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3890
5-th percentile4040.4
Q14943
median7019
Q39026
95-th percentile18832
Maximum22620
Range18730
Interquartile range (IQR)4083

Descriptive statistics

Standard deviation5033.5744
Coefficient of variation (CV)0.60848591
Kurtosis3.7675503
Mean8272.2941
Median Absolute Deviation (MAD)2076
Skewness1.9467996
Sum140629
Variance25336871
MonotonicityNot monotonic
2024-04-19T14:32:43.255812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
17885 1
 
< 0.1%
5558 1
 
< 0.1%
6888 1
 
< 0.1%
4303 1
 
< 0.1%
8178 1
 
< 0.1%
9026 1
 
< 0.1%
3890 1
 
< 0.1%
7905 1
 
< 0.1%
10732 1
 
< 0.1%
4943 1
 
< 0.1%
Other values (7) 7
 
0.3%
(Missing) 2161
99.2%
ValueCountFrequency (%)
3890 1
< 0.1%
4078 1
< 0.1%
4303 1
< 0.1%
4509 1
< 0.1%
4943 1
< 0.1%
5463 1
< 0.1%
5558 1
< 0.1%
6888 1
< 0.1%
7019 1
< 0.1%
7545 1
< 0.1%
ValueCountFrequency (%)
22620 1
< 0.1%
17885 1
< 0.1%
10732 1
< 0.1%
10087 1
< 0.1%
9026 1
< 0.1%
8178 1
< 0.1%
7905 1
< 0.1%
7545 1
< 0.1%
7019 1
< 0.1%
6888 1
< 0.1%

19세이상인구수(계)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing2161
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean13720.412
Minimum6594
Maximum34854
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2024-04-19T14:32:43.359208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6594
5-th percentile6730
Q18087
median11960
Q315828
95-th percentile29917.2
Maximum34854
Range28260
Interquartile range (IQR)7741

Descriptive statistics

Standard deviation7732.9744
Coefficient of variation (CV)0.56361096
Kurtosis2.9548424
Mean13720.412
Median Absolute Deviation (MAD)3873
Skewness1.7566338
Sum233247
Variance59798893
MonotonicityNot monotonic
2024-04-19T14:32:43.453119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
28683 1
 
< 0.1%
9597 1
 
< 0.1%
11344 1
 
< 0.1%
7764 1
 
< 0.1%
13526 1
 
< 0.1%
15828 1
 
< 0.1%
6764 1
 
< 0.1%
13693 1
 
< 0.1%
18906 1
 
< 0.1%
8087 1
 
< 0.1%
Other values (7) 7
 
0.3%
(Missing) 2161
99.2%
ValueCountFrequency (%)
6594 1
< 0.1%
6764 1
< 0.1%
7582 1
< 0.1%
7764 1
< 0.1%
8087 1
< 0.1%
9552 1
< 0.1%
9597 1
< 0.1%
11344 1
< 0.1%
11960 1
< 0.1%
12368 1
< 0.1%
ValueCountFrequency (%)
34854 1
< 0.1%
28683 1
< 0.1%
18906 1
< 0.1%
16145 1
< 0.1%
15828 1
< 0.1%
13693 1
< 0.1%
13526 1
< 0.1%
12368 1
< 0.1%
11960 1
< 0.1%
11344 1
< 0.1%

19세이상인구수(남)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing2161
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean6680.1765
Minimum3171
Maximum16551
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2024-04-19T14:32:43.554852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3171
5-th percentile3307.8
Q13923
median5803
Q37840
95-th percentile14439.8
Maximum16551
Range13380
Interquartile range (IQR)3917

Descriptive statistics

Standard deviation3686.1456
Coefficient of variation (CV)0.55180362
Kurtosis2.6460925
Mean6680.1765
Median Absolute Deviation (MAD)1962
Skewness1.6898257
Sum113563
Variance13587669
MonotonicityNot monotonic
2024-04-19T14:32:43.665094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
13912 1
 
< 0.1%
4681 1
 
< 0.1%
5460 1
 
< 0.1%
3841 1
 
< 0.1%
6580 1
 
< 0.1%
7853 1
 
< 0.1%
3342 1
 
< 0.1%
6870 1
 
< 0.1%
9298 1
 
< 0.1%
3923 1
 
< 0.1%
Other values (7) 7
 
0.3%
(Missing) 2161
99.2%
ValueCountFrequency (%)
3171 1
< 0.1%
3342 1
< 0.1%
3839 1
< 0.1%
3841 1
< 0.1%
3923 1
< 0.1%
4671 1
< 0.1%
4681 1
< 0.1%
5460 1
< 0.1%
5803 1
< 0.1%
5928 1
< 0.1%
ValueCountFrequency (%)
16551 1
< 0.1%
13912 1
< 0.1%
9298 1
< 0.1%
7853 1
< 0.1%
7840 1
< 0.1%
6870 1
< 0.1%
6580 1
< 0.1%
5928 1
< 0.1%
5803 1
< 0.1%
5460 1
< 0.1%

19세이상인구수(여)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)100.0%
Missing2161
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean7040.2353
Minimum3422
Maximum18303
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2024-04-19T14:32:43.758111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3422
5-th percentile3422.8
Q14164
median6157
Q37975
95-th percentile15477.4
Maximum18303
Range14881
Interquartile range (IQR)3811

Descriptive statistics

Standard deviation4049.9004
Coefficient of variation (CV)0.57525072
Kurtosis3.2414683
Mean7040.2353
Median Absolute Deviation (MAD)1993
Skewness1.8167965
Sum119684
Variance16401694
MonotonicityNot monotonic
2024-04-19T14:32:43.858766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
14771 1
 
< 0.1%
4916 1
 
< 0.1%
5884 1
 
< 0.1%
3923 1
 
< 0.1%
6946 1
 
< 0.1%
7975 1
 
< 0.1%
3422 1
 
< 0.1%
6823 1
 
< 0.1%
9608 1
 
< 0.1%
4164 1
 
< 0.1%
Other values (7) 7
 
0.3%
(Missing) 2161
99.2%
ValueCountFrequency (%)
3422 1
< 0.1%
3423 1
< 0.1%
3743 1
< 0.1%
3923 1
< 0.1%
4164 1
< 0.1%
4881 1
< 0.1%
4916 1
< 0.1%
5884 1
< 0.1%
6157 1
< 0.1%
6440 1
< 0.1%
ValueCountFrequency (%)
18303 1
< 0.1%
14771 1
< 0.1%
9608 1
< 0.1%
8305 1
< 0.1%
7975 1
< 0.1%
6946 1
< 0.1%
6823 1
< 0.1%
6440 1
< 0.1%
6157 1
< 0.1%
5884 1
< 0.1%

통
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)70.6%
Missing2161
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean21.176471
Minimum13
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2024-04-19T14:32:43.966503image/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
2024-04-19T14:32:44.076672image/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) 2161
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%
Missing2161
Missing (%)99.2%
Infinite0
Infinite (%)0.0%
Mean150.82353
Minimum84
Maximum345
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size19.3 KiB
2024-04-19T14:32:44.179354image/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 deviation74.707124
Coefficient of variation (CV)0.49532805
Kurtosis2.1623583
Mean150.82353
Median Absolute Deviation (MAD)39
Skewness1.6022793
Sum2564
Variance5581.1544
MonotonicityNot monotonic
2024-04-19T14:32:44.617114image/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) 2161
99.2%
ValueCountFrequency (%)
84 1
< 0.1%
85 1
< 0.1%
90 1
< 0.1%
92 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%
Missing2161
Missing (%)99.2%
Memory size17.1 KiB
2024-04-19T14:32:44.792436image/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%
2024-04-19T14:32:45.109180image/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 

Missing2178
Missing (%)100.0%
Memory size19.3 KiB

Unnamed: 12
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2178
Missing (%)100.0%
Memory size19.3 KiB

Interactions

2024-04-19T14:32:40.720587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:34.343459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:35.144560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:35.889039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:36.656869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:37.399415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:38.141891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:39.247984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:40.005187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:40.808637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:34.480085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:35.227194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:35.977400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:36.733289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:37.477220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:38.238086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:39.328057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:40.098300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:40.888307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:34.568302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:35.309739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:36.066850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:36.818948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:37.558463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:38.334857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:39.416852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:40.181635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:40.964927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:34.657618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:35.391054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:36.162752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:36.904122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:37.649727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:38.418464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:39.497842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:40.264091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:41.041790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:34.750513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:35.475610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:36.249270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:36.987005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:37.741692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:38.514536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:39.581343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:40.342927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:41.116590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:34.830926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:35.556872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:36.335000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:37.062450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:37.815051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:38.597660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:39.659349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:40.417288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:41.193131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:34.913589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:35.642004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:36.415843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:37.149814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:37.893032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:38.676967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:39.742704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:40.494389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:41.274933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:34.992710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:35.727767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:36.503981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:37.237495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:37.991785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:38.761367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:39.831736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:40.575408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:41.346635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:35.068626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:35.805726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:36.578798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:37.314113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:38.064686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:38.854849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:39.918395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-19T14:32:40.644150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-19T14:32:45.203493image/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
세대수1.0001.0000.8730.9220.8730.9320.9320.9020.9550.8720.936
인구수(계)1.0000.8731.0000.9971.0000.8640.8640.8940.9410.9540.894
인구수(남)1.0000.9220.9971.0000.9970.9090.9090.9190.9430.9450.833
인구수(여)1.0000.8731.0000.9971.0000.8640.8640.8940.9410.9540.894
19세이상인구수(계)1.0000.9320.8640.9090.8641.0001.0000.9980.9430.8990.936
19세이상인구수(남)1.0000.9320.8640.9090.8641.0001.0000.9980.9430.8990.936
19세이상인구수(여)1.0000.9020.8940.9190.8940.9980.9981.0000.8980.8610.922
통1.0000.9550.9410.9430.9410.9430.9430.8981.0000.9890.946
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
2024-04-19T14:32:45.333625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대수인구수(계)인구수(남)인구수(여)19세이상인구수(계)19세이상인구수(남)19세이상인구수(여)통반
세대수1.0000.9850.9900.9850.9950.9980.9900.9190.928
인구수(계)0.9851.0000.9951.0000.9930.9900.9980.9090.912
인구수(남)0.9900.9951.0000.9950.9980.9950.9930.9090.912
인구수(여)0.9851.0000.9951.0000.9930.9900.9980.9090.912
19세이상인구수(계)0.9950.9930.9980.9931.0000.9980.9950.9140.919
19세이상인구수(남)0.9980.9900.9950.9900.9981.0000.9930.9190.925
19세이상인구수(여)0.9900.9980.9930.9980.9950.9931.0000.9140.919
통0.9190.9090.9090.9090.9140.9190.9141.0000.990
반0.9280.9120.9120.9120.9190.9250.9190.9901.000

Missing values

2024-04-19T14:32:41.462976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-19T14:32:41.633362image/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.
2024-04-19T14:32:41.790283image/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: 11Unnamed: 12
0대연1동104122123010498107321890692989608311740.97 (3.62)<NA><NA>
1대연3동14864350081712317885286831391214771332983.84 (14.32)<NA><NA>
2대연4동47021075752945463955246714881181291.00 (3.73)<NA><NA>
3대연5동611614655711075451236859286440161031.11 (4.14)<NA><NA>
4대연6동303079553877407865943171342316941.19 (4.44)<NA><NA>
5용호1동15619436742105422620348541655118303403451.60 (5.97)<NA><NA>
6용호2동7501198529765100871614578408305231732.03 (7.57)<NA><NA>
7용호3동572413764674570191196058036157211351.96 (7.31)<NA><NA>
8용호4동382296714728494380873923416414901.52 (5.67)<NA><NA>
9용 당 동353791314622450975823839374313843.63 (13.54)<NA><NA>
구분세대수인구수(계)인구수(남)인구수(여)19세이상인구수(계)19세이상인구수(남)19세이상인구수(여)통반면적(㎢) (구성비,%)Unnamed: 11Unnamed: 12
2168<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2169<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2170<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2171<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2172<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2173<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2174<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2175<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2176<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2177<NA><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>2161